
MUMmer is an open source software package for the rapid alignment of very large DNA and amino acid sequences. The latest version, release 3.0, includes a new suffix tree algorithm that has further improved the efficiency of the package and has been integral to making MUMmer an open source product. If you are familiar with the previous versions of MUMmer, you will find the new version is very similar because most of the changes have been to the implementation and not the interface, however this document assumes no previous experience with MUMmer, so past users may find it desirable to skip or skim through some of the sections.
MUMmer is a modular and versatile package that relies on a suffix tree data 
  structure for efficient pattern matching. Suffix trees are suited for large 
  data sets because they can be constructed and searched in linear time and space. 
  This allows mummer to find all 20 base pair maximal exact matches 
  between two ~5 million base pair bacterial genomes in 20 seconds, using 90 MB 
  of RAM, on a typical 1.7 GHz Linux desktop computer. Using a seed and extend 
  strategy, other parts of the MUMmer pipeline use these exact matches as alignment 
  anchors to generate pair-wise alignments similar to BLAST output. Also included 
  are some utilities to handle the alignment output and 
  a primitive plotting tool (mummerplot) that allows the user to 
  convert MUMmer output to gnuplot 
  files for dot and percent identity plots. Another graphical 
  utility called MapView is included with the MUMmer distribution and displays 
  sequence alignments to a annotated reference sequence for exon refinement and 
  investigation.
This modular design has an important side effect, it allows for the easy reuse of MUMmer modules in other software. For instance, one can imagine primer design, repeat masking and even comparative annotation tools based on the efficient matching algorithm MUMmer provides. Another advantage of MUMmer is its speed. Its low runtime and memory requirements allow it to be used on most any computer. MUMmer's efficiency also makes it ideal for aligning huge sequences such as completed and draft eukarotic genomes. MUMmer has been successfully used to align the mouse and human genomes, showing it can handle most any input available. In addition, its ability to handle multiple sequences facilitate many vs. many searches, and make the comparison of unfinished draft sequence quite simple. However, because of it's many abilities, inexperienced users may find it difficult to determine the best methods for their application, so please refer to the Running MUMmer and Use cases sections for brief descriptions, use case examples, and tips on making the most of the MUMmer package, or if you want to understand more about a specific utility, refer to Program descriptions section for more detailed information and output formats.
The MUMmer package provides efficient means for comparing an entire genome against another. However, until 1999 there were no two genomes of sufficient similarity to compare. With the publication of the second strain of Helicobacter pylori in 1999, following the publication of the first strain in 1997, the scientific world had its first chance to look at two complete bacterial genomes whose DNA sequences were highly similar. The number of pairs of closely-related genomes has exploded in recent years, facilitating many comparative studies. For instance, the published databases include the following genomes for which multiple strains and/or multiple species have been sequenced:
| multiple strains of...
 | multiple species of...
 | 
Most of these genomes can be obtained from the NCBI ftp site: ftp://ftp.ncbi.nlm.nih.gov/genomes/
With the capability to align the entire human genome to itself, there is no genome too large for MUMmer. The following table gives run times and space requirements for a cross comparison of all human chromosomes. The 1st column indicates the chromosome number, with "Un" referring to unmapped contigs. Column 2 shows chromosome length and column 4 shows the length of the total genomic DNA searched against the chromosome in column 1. Column 3 shows the time to construct the suffix tree, and column 5 the time to stream the query sequence through it. Column 6 shows the maximum amount of computer memory occupied by the program and data, and column 7 shows memory usage for the suffix tree in bytes per base pair. Each human chromosome was used as a reference, and the rest of the genome was used as a query and streamed against it. To avoid duplication, we only included chromosomes in the query if they had not already been compared; thus we first used chromosome 1 as a reference, and streamed the other 23 chromosomes against it. Then we used chromosome 2 as a reference, and streamed chromosomes 3–22, X, and Y against that, and so on.
| Chr | Ref length (Mbp) | Suffix time (min) | Qry length (Mbp) | Query time (min) | Total space (Mb) | Suffix space (bytes/bp) | 
| 1 | 221.8 | 24.6 | 2617.1 | 679.5 | 3702 | 15.43 | 
| 2 | 237.6 | 27.4 | 2379.5 | 625.8 | 3908 | 15.43 | 
| 3 | 194.8 | 21.2 | 2184.7 | 565.0 | 3232 | 15.43 | 
| 4 | 188.4 | 22.4 | 1996.3 | 518.0 | 3121 | 15.43 | 
| 5 | 177.7 | 18.6 | 1818.6 | 461.4 | 2952 | 15.43 | 
| 6 | 175.8 | 17.9 | 1642.8 | 407.6 | 2900 | 15.43 | 
| 7 | 153.8 | 15.7 | 1489.0 | 360.1 | 2550 | 15.43 | 
| 8 | 142.8 | 14.4 | 1346.2 | 322.3 | 2378 | 15.43 | 
| 9 | 117.0 | 10.7 | 1229.2 | 303.7 | 1974 | 15.43 | 
| 10 | 131.1 | 13.2 | 1098.1 | 263.3 | 2195 | 15.43 | 
| 11 | 133.2 | 13.1 | 964.9 | 225.6 | 2228 | 15.43 | 
| 12 | 129.4 | 12.5 | 835.5 | 195.9 | 2168 | 15.43 | 
| 13 | 95.2 | 8.6 | 740.3 | 163.6 | 1633 | 15.44 | 
| 14 | 88.2 | 7.5 | 652.1 | 141.0 | 1523 | 15.44 | 
| 15 | 83.6 | 6.8 | 568.5 | 122.1 | 1451 | 15.44 | 
| 16 | 80.9 | 6.4 | 487.6 | 106.3 | 1409 | 15.44 | 
| 17 | 80.7 | 6.6 | 406.9 | 91.8 | 1406 | 15.44 | 
| 18 | 74.6 | 6.3 | 332.3 | 78.8 | 1311 | 15.44 | 
| 19 | 56.4 | 3.7 | 275.8 | 56.1 | 1026 | 15.45 | 
| 20 | 59.4 | 4.6 | 216.4 | 45.8 | 1073 | 15.45 | 
| 21 | 33.9 | 2.1 | 182.5 | 33.7 | 673 | 15.48 | 
| 22 | 33.8 | 2.0 | 148.6 | 26.4 | 672 | 15.48 | 
| Un | 1.4 | 0.03 | 147.3 | 10.0 | 164 | 16.96 | 
| X | 147.3 | 14.6 | 4.8 | 2327 | 15.57 | 
The Human Chromosomes can be obtained from the NCBI ftp site: ftp://ftp.ncbi.nih.gov/genomes/H_sapiens/
| The key difference between version 3.0 and previous versions of MUMmer, 
        is its qualification as an open source project. Previous versions of MUMmer 
        were always free for non-profit, but now MUMmer is free for all organizations, 
        both for- and non-profit. Please refer to the  To receive software update notices, please join the MUMmer mailing list. This list will only be used to announce major version releases and help us keep track of MUMmer users. |  | 
MUMmer comes as a source distribution only, and needs to be compiled before 
  use. This sections describes the steps and requirements necessary to compile 
  the package. Installation problems are usually caused by incompatible versions 
  of one or more OS utilities, so if installation fails please check that you 
  have the needed system requirements before alerting us of your problem. The 
  INSTALL file included in the source distribution also contains 
  much of the same information provided in this section.
MUMmer is mostly written in C and C++. With some technical expertise it could be ported to any system with a C++ compiler, but our distribution was specifically designed to be compiled with the GNU GCC compiler and has been successfully tested on the following three platforms:
Redhat Linux 6.2 and 7.3 (Pentium 4)Compaq Tru64 UNIX 5.1 (alpha)SunOS UNIX 5.8 (sparc)Mac OS X 10.2.8 (PowerPC G4)MUMmer also requires some third party software to run successfully. In the absence of one or more of the below utilities, certain MUMmer programs may fail to run correctly. Listed in parenthesis are the versions used to test the MUMmer package. These versions, or subsequent versions should assure the proper execution of the various MUMmer programs. These utilities must be accessible via the system path:
make (GNU make 3.79.1)perl (PERL 5.6.0)sh   (GNU sh 1.14.7)csh  (tcsh 6.10.00)g++  (GNU gcc 2.95.3)sed  (GNU sed 3.02)awk  (GNU awk 3.0.4)ar   (GNU ar 2.9.5)For running the MUMmer display programs, these additional system utilities are required:
fig2dev (fig2dev 3.2.3)gnuplot (gnuplot 4.0)xfig    (xfig 3.2)Sufficient memory and disk space are also necessary, but required sizes vary 
  considerably with input size, so please be aware of your disk and memory usage, 
  as insufficient capacities will result in incorrect or missing output. In general, 
  512 MB of RAM and 1 GB of disk space is sufficient for most mid-sized comparisons. 
  For Mac OSX, the Mac development kit must be downloaded and installed. This 
  kit will include gcc, ar, and make which 
  are necessary for building MUMmer. MUMmer is not supported for any Mac operating 
  system other than OSX.
The current MUMmer release can be downloaded from our SourceForge.net project page.
For explanation purposes, let's suppose you just downloaded the MUMmer3.0.tar.gz 
  distribution from the SourceForge site. The first step would be to move this 
  file to the desired installation directory and type:
tar -xvzf MUMmer3.0.tar.gz
 to extract the MUMmer source into a MUMmer3.0 subdirectory. Switch 
  to this newly created subdirectory and execute:
make check
to assure the makefile can identify the necessary utilities. If no error messages 
  appear, the diagnostics were successful and you may continue. However, if error 
  messages are displayed, the listed programs are not accessible via your system 
  path. Install the utilities if necessary, add them to your system PATH 
  variable, and continue with the MUMmer installation by typing:
make install
This will attempt to compile the MUMmer scripts and executables. If the make 
  command issues no errors, the compilation was successful and you are ready to 
  begin using MUMmer. If the command fails, it is likely that make 
  was confused by the existence of more than one copy of the same utility, such 
  as two versions of gcc. When this happens, it is important to arrange 
  you system PATH variable so that the more recent versions are listed 
  first, or to hard code the location of your utility location in the makefile. 
  The same advice goes for your LD_LIBRARY_PATH variable if your system 
  is having a difficult time locating the appropriate C or C++ libraries at runtime.
It is important to note that the make command dynamically builds 
  the MUMmer scripts to reference the install directory, therefore if the install 
  directory is moved after the make command is issued the MUMmer 
  scripts will fail. If you need certain MUMmer executables in a directory other 
  than the install directory, it is recommend to leave the install directory untouched 
  and link the needed executables to the desired destination. An alternative would 
  be to move the install directory and reissue the make command at 
  the new location.
The five most commonly used programs in the MUMmer package are mummer, 
  nucmer, promer, run-mummer1 and run-mummer3, 
  so this section covers the basics of executing these tools and what each of 
  them specializes in. To better understand how to view the outputs of these programs, 
  please refer to the use cases section or the MUMmer 
  examples webpage for a brief walk-through of each major module with full 
  input data and expected outputs. For further information, please refer to the 
  Program descriptions section for a detailed explanation 
  of each program and its output.
mummer efficiently locates maximal unique matches between 
  two sequences using a suffix tree data structure. This makes mummer 
  most suited for generating lists of exact matches that can be displayed as a 
  dot plot, or used as anchors in generating pair-wise 
  alignments.
mummer [options] <reference file> <query file1> . . . [query 
  file32]
There must be exactly one reference file and at least one query file. Both 
  the reference and query files should be in multi-FastA format and may contain 
  any set of upper and lowercase characters, thus DNA and protein sequences are 
  both allowed and matching is case insensitive. The maximum number of query files 
  is 32, but there is no limit on how many sequences each reference or query file 
  may contain. Output is to stdout. Refer to the mummer 
  section for a list of options and output descriptions.
NUCmer is a Perl script pipeline for the alignment of multiple closely related nucleotide sequences. It begins by finding maximal exact matches of a given length, it then clusters these matches to form larger inexact alignment regions, and finally, it extends alignments outward from each of the matches to join the clusters into a single high scoring pair-wise alignment. This makes NUCmer most suited for locating and displaying highly conserved regions of DNA sequence. To increase NUCmer's accuracy, it may be desirable to mask the input sequences to avoid the alignment of uninteresting sequence, or to change the uniqueness constraints (see the NUCmer section) to reduce the number of repeat induced alignments.
nucmer [options] <reference file> <query file>
Both the reference and query files should be in multi-FastA format and may 
  contain any set of upper and lowercase characters, however only the 
  DNA characters a, c, t and g will be aligned 
  (case insensitive). There is no limit on how many sequences the reference or 
  query files may contain. Output is written to the file out.delta 
  This is an ASCII file, but not formatted for human 
  consumption, so it is necessary to run a utility program to parse the output. 
  The two primary utility programs for viewing the contents of a .delta 
  file are show-aligns, and show-coords. show-aligns 
  displays all of the pair-wise alignments between two sequences, while show-coords 
  displays a summary of the coordinates, percent identity, etc. of the alignment 
  regions. Refer to the NUCmer section for a list of options 
  and output descriptions.
PROmer is a Perl script pipeline for the alignment of multiple somewhat divergent nucleotide sequences. It works exactly like NUCmer, but with a small twist. Before any of the exact matching takes place, the input sequences are translated in all six amino acid reading frames. This allows PROmer to identify regions of conserved protein sequences that may not be conserved on the DNA level and thus gives it a higher sensitivity than NUCmer. Note however, this increase in sensitivity will result in huge amounts of output for highly similar sequences, therefore it is recommended that PROmer only be used when the input sequences are too divergent to produce a reasonable amount of NUCmer output. As with NUCmer, it is recommended to mask the input sequences to avoid the alignment of uninteresting sequence, or to change the uniqueness constraints (see the PROmer section) to reduce the number of repeat induced alignments.
promer [options] <reference file> <query file>
Both the reference and query files should be in multi-FastA format and may contain any set of upper and lowercase characters, however only valid DNA characters will result in correctly translated sequence, all other characters will be translated into masking characters and therefore will not be matched by the BLOSUM scoring matrix. There is no limit on how many sequences the reference or query files may contain. Output is written to the same files as NUCmer and can also be viewed with the same utility programs (see above). Refer to the PROmer section for a list of options and output descriptions.
run-mummer1 and run-mummer3 are cshell script pipelines 
  for the general alignment of two sequences. They follow the same three steps 
  of NUCmer and PROmer, in that they match, cluster and extend, however they handle 
  any input sequence, not just nucleotide. This non-discrimination can be useful, 
  however the program interface is not very user friendly and the output can be 
  difficult to parse. In their favor, the run-mummer* programs are 
  good at aligning very similar DNA sequences and identifying their differences, 
  this makes them well suited for SNP and error detection. run-mummer1 
  is recommended for one vs. one comparisons with no rearrangements, while run-mummer3 
  is recommended for one vs. many comparisons that may involved rearrangements. 
  Sequence masking is only recommended if a different character is used to mask 
  the reference and query sequences so that they are not aligned.
run-mummer1 <reference file> <query file> <prefix> 
  [-r]
or
run-mummer3 <reference file> <query file> <prefix>
The reference and query files should both be in FastA format and may contain 
  any set of upper and lowercase characters. The reference file may only contain 
  a single sequence, and run-mummer1 only allows a single query 
  sequence, but run-mummer3 has no limit on the number of query sequences 
  . The -r option for run-mummer1 reverses the query 
  sequence, while run-mummer3 automatically finds both forward and 
  reverse matches. Output is written to the files <prefix>.out, 
  <prefix>.gaps, <prefix>.errorsgaps and 
  <prefix>.align. There are no utilities included to parse 
  these files, so they must be viewed as raw text files. Refer to the run-mummer1 
  and run-mummer3 sections for info on changing the program 
  parameters and output descriptions.
Because of its breadth, MUMmer can be overwhelming at first, and sometimes the hardest part of using MUMmer is deciding which alignment program to run for a particular application. This section attempts to overview some of the basic MUMmer use cases and propose the best MUMmer alignment routine for each case. This section only gives a set of command line calls to generate alignments for each use case. For further information, please refer to the Program descriptions section for a detailed explanation of each program and its output, and the MUMmer examples webpage for a brief walk-through of each major module with full input data and expected outputs.
The most basic use case is the alignment of two contiguous sequences. For all 
  of the one vs. one use cases the mummer program alone, when coupled 
  with mummerplot, may be all that is necessary to visualize a global 
  alignment of the two sequences. This process alone can be very helpful in determining 
  the large scale differences between the two sequences. For a single reference 
  sequence ref.fasta and a single query sequence qry.fasta 
  in FastA format, type:
mummer -mum -b -c ref.fasta qry.fasta > ref_qry.mums
mummerplot --postscript --prefix=ref_qry ref_qry.mums
gnuplot ref_qry.gp
Then view or print the postscript plot ref_qry.ps in whatever 
  manner you wish.
When comparing two near identical sequences, the object of the alignment is 
  usually SNP and small indel identification. The original MUMmer1.0 pipeline 
  still proves to be a handy tool for this type of analysis, although run-mummer3 
  with combineMUMs -D can prove to be even handier. Its LIS clustering 
  algorithm and reliance on unique matches give it some reliability advantages 
  over the newer pipelines. For a single reference sequence ref.fasta 
  and a single query sequence qry.fasta in FastA format, type:
run-mummer1 ref.fasta qry.fasta ref_qry
or for sequences that match on the reverse strand
run-mummer1 ref.fasta qry.fasta ref_qry -r
SNP detection and one-to-one global alignment can also be performed by nucmer 
  as described in the SNP detection walkthrough. The 
  NUCmer pipeline provides a more user-friendly method for SNP detection while 
  sacrificing a small degree of sensitivity.
Often two sequences are highly similar, but large chunks of the sequence are 
  rearranged, inverted and inserted. In order to align these and produce an output 
  that is similar to the MUMmer1.0 pipeline, use run-mummer3. It 
  uses a clustering method that allows for these types of large scale mutations, 
  but retains many of the other features of run-mummer1. To hunt 
  for SNPs more accurately, you can edit the script and add the -D 
  option to the combineMUMs command line, thus producing a concise 
  file of only the difference positions between the two sequences. For a single 
  reference sequence ref.fasta and a single query sequence qry.fasta 
  in FastA format, type:
run-mummer3 ref.fasta qry.fasta ref_qry
SNP detection and one-to-one local alignment can also be performed by nucmer 
  as described in the SNP detection walkthrough. The 
  NUCmer pipeline provides a more user-friendly method for SNP detection while 
  sacrificing a small degree of sensitivity.
While run-mummer1 and run-mummer3 focus more on what 
  is different between two sequences, nucmer focuses on what is the 
  same. It has very few restrictions on what it will align, so rearrangements, 
  inversions and repeats will all be identified by nucmer. For a 
  single reference sequence ref.fasta and a single query sequence 
  qry.fasta in FastA format, type:
nucmer --maxgap=500 --mincluster=100 --prefix=ref_qry ref.fasta qry.fasta
show-coords -r ref_qry.delta > ref_qry.coords
show-aligns ref_qry.delta refname qryname > ref_qry.aligns
Where refname and qryname are the FastA IDs of the 
  two sequences. The output of NUCmer can often be voluminous and is best visualized 
  with mummerplot. In addition, its output can be filtered in a varity 
  of ways with the delta-filter program. For example, to select and 
  display a one-to-one local mapping of reference to query sequences, use:
delta-filter -q -r ref_qry.delta > ref_qry.filter
mummerplot ref_qry.filter -R ref.fasta -Q qry.fasta
This will first filter the delta file, selecting only those alignments which comprise the one-to-one mapping between reference and query, and then display a dotplot of the selected alignments. Note that NUCmer allows for multiple reference and query sequences, so the above methods will also work for such and input. See the delta-filter and mummerplot sections for more details.
Sometimes two sequences exhibit poor similarity on the DNA level, but their 
  protein sequences are conserved. In this case, promer will be the 
  most useful MUMmer tool, since it translates the DNA input sequences into amino 
  acids before proceeding with the alignment. For a single DNA reference sequence 
  ref.fasta and a single DNA query sequence qry.fasta 
  in FastA format, type:
promer --prefix=ref_qry ref.fasta qry.fasta
show-coords -r ref_qry.delta > ref_qry.coords
show-aligns -r ref_qry.delta refname qryname > ref_qry.aligns
Where refname and qryname are the FastA IDs of the 
  two sequences. Note that the -k option can be added to show-coords 
  to reduce the amount of output by only displaying the best frame in situations 
  where the same hit is represented in multiple, overlapping frames. The output 
  of PROmer can often be voluminous and is best visualized with mummerplot. 
  In addition, its output can be filtered in a varity of ways with the delta-filter 
  program. For example, to select and display a one-to-one local mapping of reference 
  to query sequences, use:
delta-filter -q -r ref_qry.delta > ref_qry.filter
mummerplot ref_qry.filter -R ref.fasta -Q qry.fasta
This will first filter the delta file, selecting only those alignments which comprise the one-to-one mapping between reference and query, and then display a dotplot of the selected alignments. Note that PROmer allows for multiple reference and query sequences, so the above methods will also work for such an input. See the delta-filter and mummerplot sections for more details.
Many times it is necessary to align two genomes that have not yet been completed, 
  or two genomes with multiple chromosomes. This can make things a little more 
  complicated, since a separate alignment would have to be generated for each 
  possible pairing of the sequences. However, both NUCmer and PROmer automate 
  this process and accept multi-FastA inputs, thus simplifying the process of 
  aligning two sets of contigs, scaffolds or chromosomes. Since NUCmer and PROmer 
  have an almost identical user interface, this use case will only be explained 
  using nucmer. If the two inputs are too divergent for nucmer 
  to align, simply use promer instead. For two sets of contigs, ref.fasta 
  and qry.fasta, type:
nucmer --prefix=ref_qry ref.fasta qry.fasta
show-coords -rcl ref_qry.delta > ref_qry.coords
show-aligns ref_qry.delta refname qryname > ref_qry.aligns
Where refname and qryname are the FastA IDs of two 
  contigs. The show-aligns step will have to be repeated for every 
  combination of contigs that the user wishes to analyze. Because the output of 
  the all-vs-all comparison described above can be immense, it is often essential 
  to filter the resulting alignment data with the delta-filter program. 
  To map each reference to a position in the query, use delta-filter -r. 
  To map each query to a position in the reference, use delta-filter -q. 
  To determine a one-to-one mapping of each reference and query, combine the options 
  and use delta-filter -r -q. Also, the mummerplot utility 
  provides a very handy visualization method for viewing contig mappings, type:
mummerplot ref_qry.delta -R ref.fasta -Q qry.fasta --filter --layout
This will generate a plot displaying the one-to-one mapping between the two contig sets. When plotted to an X11 terminal, the plot is zoom-able and browse-able via the mouse and keyboard commands provided by gnuplot 4.0. See the delta-filter and mummerplot sections for more details.
There are many benefits of mapping a draft sequence to the finished sequence 
  of a related organism. Determining the location and orientation of each query 
  contig as it maps to the finished reference sequence can significantly speed 
  up the closure process of the draft sequence, and by examining the areas of 
  conservation, the annotation of the draft sequence can be improved and refined. 
  Since NUCmer and PROmer have an almost identical user interface, this use case 
  will only be explained using nucmer. If the two inputs are to divergent 
  for nucmer, simply use promer instead. For a finished 
  reference chromosome(s) ref.fasta and a set of near identical contigs 
  qry.fasta, type:
nucmer --prefix=ref_qry ref.fasta qry.fasta
show-coords -rcl ref_qry.delta > ref_qry.coords
show-aligns ref_qry.delta refname qryname > ref_qry.aligns
show-tiling ref_qry.delta > ref_qry.tiling
Where refname and qryname are the FastA IDs of two 
  sequences. The  show-aligns step will have to be repeated for every 
  combination of sequences that the user wishes to analyze. If mapping the draft 
  sequences to each of their repeat locations is not required, the delta-filter 
  program can quickly select the optimal placement of each draft sequence to the 
  reference using the following:
delta-filter -q ref_qry.delta > ref_qry.filter
The newly created delta file ref_qry.filter can then be substituted 
  for the original in the above procedures in order to generate slimmed down versions 
  of the output.
Joining a couple of the MUMmer components together can form a quite reliable 
  SNP detection pipeline. MUMmer can perform all steps of this pipeline from aligning 
  the sequences, to selecting the one-to-one mapping, and finally calling the 
  SNP positions. The user can then process these SNP positions to assign quality 
  scores based on the underlying traces and surrounding context. Such methods 
  have been successfully applied to various SNP studies for organisms including 
  Bacillus anthracis and Yersinia pestis. Of important note, 
  a SNP pipeline built with nucmer allows for the identification 
  of SNPs between two genomes with many rearrangements. The Yersinia pestis 
  strains, for example, demonstrate significant genome "shuffling", 
  and make SNP detection difficult with global alignment programs such as run-mummer1. 
  However, a pipeline built with nucmer (like shown below) is capable 
  of finding all of the SNPs between two genomes, regardless of their structural 
  similarity.
To find a reliable set of SNPs between to highly similar multi-FastA sequence 
  sets ref.fasta and qry.fasta, type:
nucmer --prefix=ref_qry ref.fasta qry.fasta
show-snps -Clr ref_qry.delta > ref_qry.snps
The -C option in show-snps assures that only SNPs 
  found in uniquely aligned sequence will be reported, thus excluding SNPs contained 
  in repeats. An alternative method which first attempts to determine the "correct" 
  repeat copy is:
nucmer --prefix=ref_qry ref.fasta qry.fasta
delta-filter -r -q ref_qry.delta > ref_qry.filter
show-snps -Clr ref_qry.filter > ref_qry.snps
Now, conflicting repeat copies will first be eliminated with delta-filter 
  and the SNPs will be re-called in hopes of finding some that were previously 
  masked by another repeat copy.
Although MUMmer was not specifically designed to identify repeats, it does 
  has a few methods of identifying exact and exact tandem repeats. In addition 
  to these methods, the nucmer alignment script can be used to align a 
  sequence (or set of sequences) to itself. By ignoring all of the hits that have 
  the same coordinates in both inputs, one can generate a list of inexact repeats. 
  When using this method of repeat detection, be sure to set the --maxmatch
  and --nosimplify options to ensure the correct results.
To find large inexact repeats in a set of sequences seq.fasta, 
  type the following and ignore all hits with the same start
  coordinate in each copy of the sequence:
nucmer --maxmatch --nosimplify --prefix=seq_seq seq.fasta 
  seq.fasta
show-coords -r seq_seq.delta > seq_seq.coords
To find exact repeats of length 50 or greater in a single sequence seq.fasta, 
  type:
repeat-match -n 50 seq.fasta > seq.repeats
To find exact tandem repeats of length 50 or greater in a single sequence seq.fasta, 
  type:
exact-tandems seq.fasta 50 > seq.tandems
The most commonly used MUMmer pipelines (nucmer, promer, 
  run-mummer1 and run-mummer3) are comprised of three 
  main sections. The first section identifies a certain subset of maximal exact 
  matches between the two inputs, the second section clusters these matches into 
  groups that will likely make good alignment anchors, and the third and final 
  section extends alignments between these clustered matches to produce the final 
  gapped alignment. These three sections also outline the primary types of programs 
  included in the MUMmer package - the Maximal exact matching 
  section describes the programs that compute different types maximal exact matches, 
  the Clustering section describes the two different 
  types of clustering algorithms, and Alignment generators 
  describes the scripts that combine matching, clustering and extending in order 
  to produce high scoring pair-wise alignments. Finally, the Utilities 
  section reviews a few of the tools that have been developed for interpreting 
  and displaying the output of the MUMmer alignment routines.
It is noteworthy to point out the simplicity of improving the current MUMmer 
  pipeline. For instance, if a different and/or better clustering algorithm was 
  needed for a certain application, a program could be written in any language 
  and inserted into the pipeline. So long as the program was able to read the 
  appropriate input and produce output that mimics the existing module, it could 
  be swapped for the existing module with a single edit to the calling script. 
  NUCmer for example is a Perl script that invokes various MUMmer routines. If 
  you were to develop a new clustering algorithm called mygaps you 
  could edit the line in NUCmer that defines the location of mgaps 
  to instead define the location of mygaps. It's that easy, as long 
  as mygaps had the same input and output mgaps the 
  transition would be seamless.
The heart of the MUMmer package is its suffix tree based maximal matching routines. 
  These can be used for repeat detection within a single sequence as is done by 
  repeat-match and exact-tandems, or can be used for 
  the alignment of two or more sequences as is done by mummer. Most 
  every other program in the MUMmer packages builds off of the output of the mummer 
  maximal exact matcher, so it is of great importance to first understand the 
  workings of this program.
mummer is a suffix tree algorithm designed to find maximal exact 
  matches of some minimum length between two input sequences. MUMmer's namesake 
  program originally stood for Maximal  Unique Matcher, 
  however in subsequent versions the meaning of unique has been skewed. 
  The original version (1.0) required all maximal matches to be unique in both 
  the reference and the query sequence (MUMs); the second version (2.0) required 
  uniqueness only in the reference sequence (MUM-candidates); and the current 
  version (3.0) can ignore uniqueness completely, however it defaults to finding 
  MUM-candidates and can be switched on the command line. To restate, by default 
  mummer will only find maximal matches that are unique in the entire 
  set of reference sequences. The match lists produced by mummer 
  can be used alone to generate alignment dot plots, or 
  can be passed on to the clustering algorithms for the identification of longer 
  non-exact regions of conservation. These match lists have great versatility 
  because they contain huge amounts of information and can be passed forward to 
  other interpretation programs for clustering, analysis, searching, etc.
mummer achieves its high performance by using a very efficient 
  data structure known as a suffix tree. This data structure can be both constructed 
  and searched in linear time, making it ideal for large scale pattern matching. 
  To save memory, only the reference sequence(s) is used to construct the suffix 
  tree and the query sequences are then streamed through the data structure while 
  all of the maximal exact matches are extracted and displayed to the user. Because 
  only the reference sequence is loaded into memory, the space requirement for 
  any particular mummer run is only dependent on the size of the 
  reference sequence. Therefore, if you have a reasonably sized sequence set that 
  you want to match against an enormous set of sequences, it is wise to make the 
  smaller file the reference to assure the process will not exhaust your computer's 
  memory resources. The query files are loaded into memory one at a time, so for 
  an enormous query that will require a significant amount of memory just to load 
  the character string, it is helpful to partition the query into multiple smaller 
  files using the syntax described below.
mummer [options] <reference file> <query file1> . . . [query 
  file32]
There must be exactly one reference file and at least one query file. Both the reference and query files should be in multi-FastA format and may contain any set of upper and lowercase characters, thus DNA and protein sequences are both allowed and matching is case insensitive. The maximum number of query files is 32, but there is no limit on how many sequences each reference or query file may contain.
| -mum | Compute MUMs, i.e. matches that are unique in both the reference 
      and query | 
| -mumreference | Compute MUM-candidates, i.e. matches that are unique in the reference 
      but not necessarily in the query | 
| -maxmatch | Compute all maximal matches regardless of their uniqueness | 
| -n | Only match the characters a, c, g, or 
      t (case insensitive) | 
| -l int | Minimum match length (default 20) | 
| -b | Compute both forward and reverse complement matches | 
| -r | Only compute reverse complement matches | 
| -s | Show the matching substring in the output | 
| -c | Report the query position of a reverse complement match relative 
      to the forward strand of the query sequence | 
| -F | Force 4 column output format that prepends every match line with 
      the reference sequence identifier | 
| -L | Show the length of the query sequence on the header line | 
| -help | Show the possible options and exit | 
Option grouping is not allowed, therefore each option should be separated by 
  a space. The options -mum, -mumreference, and -maxmatch 
  cannot be combined, and if neither is used, then the program will default to 
  -mumreference. For a string to be unique in the reference, it must 
  occur only once in the concatenation of all the reference superstrings, 
  but for string to be unique in the query it need only be unique in its own superstring. 
  Setting either the -mum or -mumreference option can 
  significantly cut down on the number of repeat induced matches as opposed to 
  -maxmatch, and is recommended for most all applications. Also, 
  setting the -l option any lower than around 15 can significantly 
  increase the number of spurious matches and therefore balloon the runtime. When 
  dealing with masked DNA sequence, use the -n option to avoid matching 
  the masking characters. Options -b and -r exclude 
  each other, and if neither is used then only forward matches will be reported. 
  All reverse complementing will affect only the query sequences. Option -c 
  can only be used in combination with -b or -r, as 
  it would have no relevance without these options. The -F option 
  is useful for forcing mummer to output a consistent format regardless 
  of the number of input sequences.
For those familiar with the previous versions of MUMmer, the -mum 
  option mimics the functionality of MUMmer1.0; the -mumreference 
  option mimics the functionality of MUMmer2.0; and the -maxmatch 
  option mimics the functionality of the max-match program included 
  with MUMmer2.0. The default behavior of the current version is -mumreference 
  because it is a good balance between finding all matches and only unique matches.
Output formatting varies depending on the command line parameters used. Program 
  diagnostic information is always output to stderr while the match 
  lists are output to stdout. This allows for the match output to 
  be redirected into a file, which is quite useful since the output is generally 
  quite large. The standard output format that results from running mummer 
  on a single reference sequence with the -b option is as follows:
> ID1
 4655667         1        31
 4655699        33       319
 4656019       353       520
 4656540       874        20
> ID1 Reverse
  741743        22       872
> ID2
 4655520         1       498
 4656019       500       274
 4656317       798        39
 4656376       855        29
> ID2 Reverse
> ID3
> ID3 Reverse
 4655178        27       840
 4656019       868       171
(output continues ...)For each query sequence, the corresponding ID tag is reported on each line 
  beginning with a '>' symbol, even if there are no matches corresponding 
  to this sequence. Reverse complemented matches follow a query header that has 
  the keyword Reverse following the sequence tag, thus creating two 
  headers for each query sequence and alternating forward and reverse match lists. 
  For each match, the three columns list the position in the reference sequence, 
  the position in the query sequence, and the length of the match respectively. 
  Reverse complemented query positions are reported relative to the reverse 
  of the query sequence unless the -c option was used. As was stated 
  above the -L option adds the sequence lengths to the header line 
  and the -s option adds the match strings to the output, if these 
  options were used the format would be as follows:
> ID1  Len = 893
 4655667         1        31
ctgacgacaaccatgcaccacctgtcactct
 4655699        33       319
ctcccgaaggagaagccctatctctagggttgtcagaggatgtcaagacctgg . . .
 4656019       353       520
gttcctccatatctctacgcatttcaccgctacacatggaattccactttcct . . .
 4656540       874        20
tttcgaaccatgcggttcaa
> ID1 Reverse  Len = 893
  741743        22       872
tgaaaggcggcttcggctgtcacttatggatggacccgcgtcgcattagctag . . .
> ID2  Len = 884
 4655520         1       498
tcataaggggcatgatgatttgacgtcatccccaccttcctccggtttgtcac . . .
 4656019       500       274
gttcctccatatctctacgcatttcaccgctacacatggaattccactttcct . . .
 4656317       798        39
aagccttcatcactcacgcggcgttgctccgtcagactt
 4656376       855        29
cctactgctgcctcccgtaggagtctggg
> ID2 Reverse  Len = 884
> ID3  Len = 1039
> ID3 Reverse  Len = 1039
 4655178        27       840
atcaattctccatagaaaggaggtgatccagccgcaccttccgatacggctac . . .
 4656019       868       171
gttcctccatatctctacgcatttcaccgctacacatggaattccactttcct . . .
(output continues ...)Where the length of each query is noted after the Len keyword 
  and the match string is listed on the line after its match coordinates. Note 
  that the ellipsis marks are not part of the actual output, but added to fit 
  the output into the webpage. Finally, when dealing with multiple reference sequences 
  (or the -F option), it is necessary to output the ID of the reference 
  sequence. This is placed at the beginning of each match line, creating an four 
  column output format as follows:
> ID1
  220594       479         1       728
> ID1 Reverse
  220716      3527         1        20
  220716      3548        22       840
> ID2
> ID2 Reverse
  219093        13       401       484
  220716      3682         2        29
  220716      3731        49        39
  220716      3794       112       693
> ID3
  219093        13       188       721
  220716      3897         2       590
  220716      4488       593       423
> ID3 Reverse
  220594         1        38       509
(output continues ...)
repeat-match is a suffix tree algorithm designed to find maximal 
  exact repeats within a single input sequence. It uses a similar algorithm to 
  mummer, but altered slightly to find maximal exact matches within 
  a single sequence.
repeat-match [options] <sequence file>
The sequence file should contain only one sequence in FastA format, however if multiple sequences exist the first one will be used. The sequence may contain any set of upper and lowercase characters, thus DNA and protein sequences are both allowed and matching is case insensitive.
| -f | Use the forward strand only | 
| -n int | Minimum match length (default 20) | 
| -t | Only output tandem repeats | 
The program will report both forward and reverse complement repeats by default 
  unless the -f option is used. While the -t option 
  identifies tandem repeats, the exact-tandems script is a wrapper 
  for repeat-match and does a more graceful job of reporting the 
  tandem repeats.
Output formatting varies depending on the command line parameters. Program 
  diagnostic information is always output to stderr while the match 
  lists are output to stdout. This allows for the match output to 
  be redirected into a file, which is quite useful since the output can be quite 
  large. The standard output format that results from running repeat-match 
  with default parameters is as follows:
Long Exact Matches:
   Start1     Start2    Length
  4919485    4919506r       22
  4997298    4997319r       22
  4919485    4997298        22
  3461866    3751066        53
   537897    4650529r       76
(output continues ...)
The three columns are the first position of the repeat, the second position 
  of the repeat, and the length of the repeat respectively. Reverse complement 
  repeat positions are denoted by an 'r' following the Start2 position, 
  and are relative to the forward strand of the sequence.
exact-tandems is a wrapper cshell script for the repeat-match 
  program. It provides a list of exact tandem repeats within a single input sequence.
exact-tandems <sequence file> <min length>
As with repeat-match the sequence file should contain only one 
  sequence in FastA format, however if multiple sequences exist the first one 
  will be used. The sequence may contain any set of upper and lowercase characters, 
  thus DNA and protein sequence are both allowed and matching is case insensitive. 
  The minimum match length parameter should be a positive integer, this value 
  will be passed to the repeat-match program via the -n option.
Program diagnostic information is always output to stderr while 
  the match lists are output to stdout. This allows for the match 
  output to be redirected into a file, which is quite useful since the output 
  can be quite large. The output format of exact-tandems is as follows:
Finding matches
Tandem repeats
   Start   Extent  UnitLen     Copies
  416173      150       45        3.3
  554810      102       42        2.4
  554943      109       42        2.6
  880346      191       63        3.0
  880370       62       21        3.0
(output continues ...)
The four columns are the first position of the tandem, the extent of the repeat region, the length of each tandem repeat unit, and the number of repeat units respectively.
MUMmer's clustering algorithms attempt to order small individual matches into 
  larger match clusters in order to make the output of mummer more 
  intelligible. A dot plot makes it easy to spot alignment 
  regions from a match list, however when examining the data without graphic aids, 
  it is very difficult to draw any reasonable conclusions from the simple flat 
  file list of matches. Clustering the matches together into larger groups of 
  neighboring matches makes this process much easier by ordering the data and 
  removing spurious matches.
gaps is the primary clustering algorithm for run-mummer1, 
  and although classified as a "clustering" step, gaps 
  is more of a sorting routine. It implements the LIS (longest increasing subset) 
  algorithm to extract the longest consistent set of matches between two sequences, 
  and generates a single cluster that represents the best "straight-line" 
  arrangement of matches between the sequences. By straight-line, we mean no rearrangements 
  or inversions, just a simple path of agreeing matches between the two sequences. 
  This limits the usability of this program to the alignment of genomes that are 
  very similar and with no large scale mutations. To further illustrate the purpose 
  of this program, consider the following set of MUMs (illustrated as line connecting 
  two rectangles) between two sequences:
 
 The rectangles connected by lines are maximal exact matches between two sequences, 
  however only the red rectangles would be included in the LIS because they form 
  the longest increasing subset of matches, i.e. the longest subset of matches 
  that are consistently ordered in both genomes. Note that the empty rectangles 
  will be discarded, even though they probably represent a major rearrangement 
  between the two sequences. Because of this limitation gaps is best 
  suited for the comparison of near identical sequences with the goal of finding 
  minor mutations like SNPs and small indels.
mummer [params] | tail +2 | gaps <reference file> [-r]
or
gaps <reference file> [-r] < <match list>
Because gaps receives its input from stdin, the input 
  can either be piped directly from filtered mummer output, or redirected 
  as input from a file. The strange syntax is a result of a legacy issue described 
  in the Known problems section, and requires the header 
  be stripped from the mummer output. In addition, gaps 
  is only designed to handle a single reference and a single query sequence, thus 
  the preceding mummer run must also follow this constraint. The 
  -r is optional and designates the incoming matches as reverse complement 
  matches which must reference the reverse complement of the sequence, therefore 
  forcing mummer to be run without the -c option. 
  Please refer to the run-mummer1 script for an example of how to 
  use this program in an alignment pipeline. A rewrite of this algorithm to handle 
  multiple reference and/or query sequences may eventually appear, but is not 
  currently in development.
The stdout output of gaps shares much in common with 
  the standard three column match output, with the addition of three extra columns:
> /home/aphillip/data/GHP.1con  Consistent matches
     183       17     22    none      -      -
     238       72    108    none     33     33
     347      181     92    none      1      1
     458      292     50    none     19     19
     705      539     44    none      1      1
     750      584     38    none      1      1
     807      641     23     -16      0      4
(output continues ...)
> Wrap around
  334398   329917     47    none      -    225
  334446   329965     62    none      1      1
  334539   330058     20    none     31     31
  334560   330079     92    none      1      1
  334653   330172     77    none      1      1
  334740   330259     41    none     10     10
(output continues ...)
> /home/aphillip/data/GHP.1con  Other matches
 1317231     4891     21    none      -      -
 1317275     4927     21    none      -      -
 1317804     5399     25    none    508    451
  947580     5436     36    none      -      -
   23406     5518     34    none      -      -
  333079     6592     32    none      -      -
(output continues ...)
Where the first line is the location of the reference file, and the first three 
  columns are the same as the three column match format described in the mummer 
  section. The final three columns are the overlap between this match and the 
  previous match, the gap between the start of this match and the end of the previous 
  match in the reference, and the gap between the start of this match and the 
  end of the previous match in the query respectively. A couple suggestions on 
  how to visually scan through this output: a gap size == 1 means a single mismatch 
  between the two sequences, e.g. a SNP, an overlap like seen in the last line 
  of the Consistent matches indicates the existence of a tandem repeat, 
  and a '-' character means that the gap size could not be calculated. 
  The Wrap around list is for circular genomes where the consistent 
  set of matches wraps around the origin of the reference, and the Other 
  matches list shows the matches that were not included in the LIS (like 
  the white boxes in the above image). Finally, if the -r was passed 
  on the command line the Consistent matches and Other matches 
  headers would contain the reverse keyword after the reference file.
mgaps was introduced into the MUMmer pipeline in an effort to 
  better handle large-scale rearrangements and duplications. Unlike gaps, 
  mgaps is a full clustering algorithm that is capable of generating 
  multiple groups of consistently ordered matches. Clustering is controlled by 
  a set of command-line parameters that adjust the minimum cluster size, maximum 
  gap between matches, etc. Only matches that were included in clusters will appear 
  in the output, so by adjusting the command-line parameters it is possible to 
  filter out many of the spurious matches, thus leaving only the larger areas 
  of conservation between the input sequences. The major advantage of mgaps is 
  its ability to identify these "islands" of conservation. This frees 
  the user from the single LIS restraints of the gaps program and 
  allows for the identification of large-scale rearrangements, duplications, gene 
  families and so on. To further illustrate the purpose of this program, consider 
  once again the following set of MUMs (illustrated as line connecting two rectangles) 
  between two sequences:
 
 Just like before the rectangles connected by lines are maximal exact matches 
  between two sequences, with each distinct cluster having its own unique color. 
  In the previous demonstration using this MUM set, gaps failed to 
  identify the blue cluster because it was not consistent with the LIS. However, 
  by using mgaps, all regions of conservation have now been identified. 
  The only fallback being the increased complexity of the output, where you once 
  had only one cluster for the whole comparison, you now have four. Because of 
  this, it can sometimes be difficult separating the repetitive clusters from 
  "correct" clusters, making mgaps more suited for global 
  alignments instead of localized error detection.
mummer [params] | mgaps [options]
or
mgaps < <match list>
Because gaps receives its input from stdin, the input 
  can either be piped directly from raw mummer output, or redirected 
  as input from a mummer output file. mgaps is only 
  designed to handle a single reference and one or more query sequences, thus 
  the preceding mummer run must also follow this constraint. Please 
  refer to the run-mummer3 script for an example of how to use this 
  program in an alignment pipeline. Note that in order to cluster reverse complement 
  matches, the reverse complement matches must reference the reverse complement 
  strand of the query sequence, therefore forcing mummer to be run 
  without the -c option. A rewrite of this algorithm to 
  handle multiple reference sequences and a better coordinate system (forward 
  coordinates for reverse complement matches) is doubtful but may eventually appear.
| -C | Check that input header labels alternately have the "Reverse" 
      keyword  | 
| -d int | Maximum fixed diagonal difference (default 5) | 
| -e | Use extent of cluster (end - start) rather than the sum of the match 
      lengths to determine cluster length | 
| -f float | Maximum fraction of separation for diagonal difference (default 
      0.05)  | 
| -l int | Minimum cluster length (default 200) | 
| -s int | Maximum separation between adjacent matches in a cluster (default 
      1000)  | 
The -d option can be interpreted as the number of insertions allowed 
  between two matches in the same cluster, while the -f option is 
  a fraction equal to (diagonal difference / match separation) where a higher 
  value will increase the indel tolerance. Minimum cluster length is the sum of 
  the contained matches unless the -e option is used. The best way 
  to get a feel for what each parameter controls is to cluster the same data set 
  numerous times with different values and observe the resulting differences. 
  It can also be helpful to set these parameters to the size of the element you 
  wish to capture, i.e. set the minimum cluster size to say the smallest exon 
  you expect and set the max gap to the smallest intron you expect to obtain clusters 
  that could represent single exons (depending of course of the similarity of 
  the two sequences).
The stdout output of mgaps shares much in common 
  with the output of mummer and gaps, with a slightly 
  different header formatting than gaps to allow for multiple query 
  sequences and multiple clusters. The output of mgaps run on both 
  forward and reverse complement matches is as follows:
> ID41
> ID41 Reverse
 5177399        1    232    none      -      -
 5177632      234   6794    none      1      1
 5184433     7035     24    none      7      7
 5184468     7069     23    none     11     10
> ID42
   10181       43   1521    none      -      -
> ID42 Reverse
 4654536       17     36    none      -      -
 4654578       57    298    none      6      4
 4654877      356    226    none      1      1
#
 4655139      845     28    none      -      -
 4655178      884    694    none     11     11
 4655873     1579     20    none      1      1
#
 4850044       17   1492    none      -      -
 4851537     1510    711    none      1      1
 4852249     2222     42    none      1      1
(output continues ...)
'>' 
characters, and a following Reverse keyword identifies the reverse 
matches for that query sequence. Individual clusters for each sequence are separated 
by a '#' character, and the six columns are exactly the same as the 
gaps output (see the gaps section for more details). 
The alignment scripts described in this section build upon the data generated by the previous two sections, maximal exact matching and clustering. Each of these scripts independently runs the matching and clustering steps, and then generates pair-wise alignments for each of the clusters. This translates to a basic seed and extend method of alignment. The individual matches within each cluster are used as alignment anchors and only the mismatching sequence between the matches is processed by the Smith-Waterman dynamic programming routine. This reduces both the time and memory necessary to align large sequences, while still producing accurate alignments.
NUCmer (NUCleotide MUMmer) is the most user-friendly alignment 
  script for standard DNA sequence alignment. It is a robust pipeline that allows 
  for multiple reference and multiple query sequences to be aligned in a many 
  vs. many fashion. For instance, a very common use for nucmer is 
  to determine the position and orientation of a set of sequence contigs in relation 
  to a finished sequence, however it can be just as effective in comparing two 
  finished sequences to one another. Like all of the other alignment scripts, 
  it is a three step process - maximal exact matching, match clustering, and alignment 
  extension. It begins by using mummer to find all of the maximal 
  unique matches of a given length between the two input sequences. Following 
  the matching phase, individual matches are clustered into closely grouped sets 
  with mgaps. Finally, the non-exact sequence between matches is 
  aligned via a modified Smith-Waterman algorithm, and the clusters themselves 
  are extended outwards in order to increase the overall coverage of the alignments. 
  nucmer uses the mgaps clustering routine which allows 
  for rearrangements, duplications and inversions; as a consequence, nucmer 
  is best suited for large-scale global alignments, as is shown in the following 
  plot:
 
 This dot plot represents a nucmer alignment of two different strains 
  of Helicobacter pylori (26695 on the x-axis and J99 on the y-axis). 
  Forward matches are shown in red, while reverse matches are shown in green. 
  This alignment, which took only 12 seconds to compute, clearly shows a major 
  inversion event centered around the origin of replication, and demonstrates 
  NUCmer's ability to handle large scale rearrangements between sequences of high 
  nucleotide similarity.
nucmer [options] <reference file> <query file>
The reference and query files should both be in multi-FastA format and have 
  no limit on the number of sequences they man contain. However, because nucmer 
  uses mummer for its maximal exact matching, the memory usage will 
  be dependent on the size of the reference file, so it may be advisable to make 
  the smaller of the input files the reference to assure the program does not 
  exhaust your computer's memory resources. In addition, masking the uninteresting 
  regions of the input with any character other than a, c, g, 
  or t will both speed up nucmer by reducing the number 
  of possible matches and also cut down on the number of alignments induced by 
  repetitive sequence.
| --mum | Use anchor matches that are unique in both the reference and query | 
| --mumreference | Use anchor matches that are unique in the reference but not necessarily 
      unique in the query (default behavior) | 
| --maxmatch | Use all anchor matches regardless of their uniqueness | 
| -b int | Distance an alignment extension will attempt to extend poor scoring 
      regions before giving up (default 200) | 
| -c int | Minimum cluster length (default 65) | 
| --[no]delta | Toggle the creation of the delta file. Setting --nodelta prevents 
      the alignment extension step and only outputs the match clusters (default 
      --delta) | 
| --depend | Print the dependency information and exit | 
| -d float | Maximum diagonal difference factor for clustering, i.e. diagonal 
      difference / match separation (default 0.12) | 
| --[no]extend | Toggle the outward extension of alignments from their anchoring 
      clusters. Setting --noextend will prevent alignment extensions but still 
      align the DNA between clustered matches and create the .delta file (default 
      --extend) | 
| -f | Align only the forward strands of each sequence | 
| -g int | Maximum gap between two adjacent matches in a cluster (default 90) | 
| -h | Print the help information and exit | 
| -l int | Minimum length of an maximal exact match (default 20) | 
| -o | Automatically generate the <prefix>.coords file using the 
      'show-coords' program with the -r option | 
| --[no]optimize | Toggle alignment score optimization. Setting --nooptimize will prevent 
      alignment score optimization and result in sometimes longer, but lower scoring 
      alignments (default --optimize) | 
| -p string | Set the output file prefix (default out) | 
| -r | Align only the reverse strand of the query sequence to the forward 
      strand of the reference | 
| --[no]simplify | Simplify alignments by removing shadowed clusters. Turn this option off
      if aligning a sequence to itself to look for repeats (default --simplify) | 
| -V | Print the version information and exit | 
All values are measured in DNA bases unless otherwise noted. Using either the 
  -mum or -mumreference options (along with masking 
  the input sequences) can help reduce the number of repeat induced alignments, 
  and is suggested for most applications. If no uniqueness options are set, the 
  program will default to -mumreference. Decreasing the values of 
  the -mincluster and --minmatch options will increase 
  the sensitivity of the alignment but may produce less reliable alignments. In 
  addition, significantly raising the value of the --maxgap value 
  (say to 1000) can be crucial in producing alignments for more divergent genomes. 
  Setting --noextend speeds up the process by preventing alignment 
  extensions outward from each cluster, while --nodelta takes this 
  a step further and doesn't even align the sequence between the matches in a 
  cluster, however both of these reduce the amount of information contained in 
  the output. See mgaps description for hints on setting the clustering 
  parameters --mincluster, --diagdiff and --maxgap. 
  The --coords option exists only for NUCmer1.0 compatibility; instead, 
  it is recommended to run show-coords afterwards with more specific 
  options. The --nooptimize option will force alignments within --breaklen 
  bases of the sequence end to extend all the way to the sequence end, regardless 
  of the resulting alignment score. The --prefix string should be 
  unique in the output directory to prevent overwriting pre-existing data. Finally, 
  by default nucmer matches the forward and reverse strands of the 
  query sequences to the forward strand of the reference sequence unless the --forward 
  or --reverse options were used, and all output coordinates always 
  reference the forward strand of their respective sequence. Only use
  the --nosimplify option when aligning a sequence to
  itself in order to find inexact repeats.
Because nucmer and promer produce the same output 
  files, this section will serve to explain the <prefix>.delta 
  format for both programs. The delta file contains an encoded representation 
  of all the alignments generated in the "extend" phase of the pipeline, 
  and is a unique format for concise, machine representation 
  of the pair-wise alignments. Several tools described in the Utilities 
  section were designed to interpret these files and extract useful, human-readable 
  information from them, however the full format description the 
  delta file is described below to aid developers.
The "delta" file is an encoded representation of the all-vs-all alignment 
  between the input sequences to either the NUCmer or PROmer pipeline. It is the 
  primary output of these alignment scripts and there are various utilities described 
  in section 5.4. that are designed to take the delta 
  file as input, and output some human-readable information to the user. Also, 
  the delta-filter utility is designed to manipulate these 
  files and select desired alignments. The primary function of the delta file 
  is to catalog the coordinates of each alignment and note the distance between 
  insertions and deletions contained in these alignments. By only storing the 
  location of each indel as an offset, disk space is efficiently utilized, and 
  a potentially enormous alignment can be stored in a relatively small space. 
  The first line lists the two original input files separated by a space, while the second 
  line specifies the alignment data type, either "NUCMER" 
  or "PROMER". Every grouping of alignments have a unique 
  header specifying the two aligning sequences. Only sequences with shared alignments 
  will have a header; therefore, there can be no empty 
  headers (i.e. those that have no alignments following them). An example header 
  might look like
>tagA1 tagB1 500 20000000
"0"). An example header might look like: 
2631 3401 2464 3234 15 15 2
Notice that the start coordinate points to the first base in the first codon, 
  and the end coordinate points to the last base in the last codon. Therefore 
  making (end - start + 1) % 3 = 0. This makes determining the frame 
  of the amino acid alignment a simple matter of determining the reading frame 
  of the start coordinate for the reference and query. Obviously, these calculations 
  are not necessary when dealing with vanilla DNA alignments.
Each of these alignment headers is followed by a string of signed digits, one 
  per line, with the final line before the next header equaling 0 (zero). Each 
  digit represents the distance to the next insertion in the reference (positive 
  int) or deletion in the reference (negative int), as measured in DNA bases OR 
  amino acids depending on the alignment data type. For example, with the PROMER 
  data type, the delta sequence (1, -3, 4, 0) would represent an 
  insertion at positions 1 and 7 in the translated reference sequence and an insertion 
  at position 3 in the translated query sequence. Or with letters:
A = ABCDACBDCAC$
B = BCCDACDCAC$
Delta = (1, -3, 4, 0)
A = ABC.DACBDCAC$
B = .BCCDAC.DCAC$
Using this delta information, it is possible to re-generate the alignments 
  calculated by nucmer or promer as is done in the show-coords 
  program. This allows various utilities to be crafted to process and analyze 
  the alignment data using a universal format. This also means the delta only 
  needs to be created once, yet it can be analyzed numerous times without ever 
  having to rerun the costly alignment algorithm. Below is an example of what 
  a delta file might look like:
/home/username/reference.fasta /home/username/query.fasta
PROMER
>tagA1 tagB1 3000000 2000000
1667803 1667078 1641506 1640769 14 7 2
-145
-3
-1
-40
0
1667804 1667079 1641507 1640770 10 5 3
-146
-1
-1
-34
0
>tagA2 tagB4 4000 3000
2631 3401 2464 3234 4 0 0
0
2608 3402 2456 3235 10 5 0
7
1
1
1
1
0
(output continues ...)
PROmer (PROtein MUMmer) is a close relative to the NUCmer script. 
  It follows the exact same steps as NUCmer and even uses most of the same programs 
  in its pipeline, with one exception - all matching and alignment routines are 
  performed on the six frame amino acid translation of the DNA input sequence. 
  This provides promer with a much higher sensitivity than nucmer 
  because protein sequences tends to diverge much slower than their underlying 
  DNA sequence. Therefore, on the same input sequences, promer may 
  find many conserved regions that nucmer will not, simply because 
  the DNA sequence is not as highly conserved as the amino acid translation.
All of this is performed behind the scenes, as the input is still the raw DNA 
  sequence and output coordinates are still reported in reference to the DNA, 
  so the two programs (nucmer and promer) exhibit little 
  difference in their interfaces and usability. Because of its greatly increased 
  sensitivity, it is usually best to use promer on those sequences 
  that cannot be adequately compared by nucmer, because if run on 
  very similar sequences the promer output can be quite voluminous. 
  This is because promer makes no effort to distinguish between proteins 
  and junk amino acid translations, therefore a single highly conserved gene may 
  have up to six alignments in promer output, one for each 
  of the six amino acid reading frames, when only the correct reading frame would 
  be sufficient. This makes promer ideally suited for highly divergent 
  sequences that show little DNA sequence conservation, as is shown in the following 
  two plots:
|  |  | 
These dot plots represent two comparisons of Streptococcus pyogenes 
  (x-axis) and Streptococcus mutans (y-axis), with forward matches colored 
  red and reverse matches colored green. The graph generated with nucmer 
  output is on the left, while the graph generated with promer output 
  is on the right (both run with default parameters). It is clearly visible that 
  promer has aligned the two genomes with a much greater sensitivity, 
  thus demonstrating the effectiveness of comparing two divergent genomes on the 
  amino acid level.
promer [options] <reference file> <query file>
The reference and query files should both be in multi-FastA format and have 
  no limit on the number of sequences they man contain. However, because promer 
  uses mummer for its maximal exact matching, the memory usage will 
  be dependent on the size of the reference file, so it may be advisable to make 
  the smaller of the input files the reference to assure the program does not 
  exhaust your computer's memory resources. In addition, masking the uninteresting 
  regions of the input with n or x will both speed up promer 
  by reducing the number of possible matches and also cut down on the number of 
  alignments induced by repetitive sequence.
| --mum | Use anchor matches that are unique in both the reference and query | 
| --mumreference | Use anchor matches that are unique in the reference but not necessarily 
      unique in the query (default behavior) | 
| --maxmatch | Use all anchor matches regardless of their uniqueness | 
| -b int | Distance an alignment extension will attempt to extend poor scoring 
      regions before giving up (default 60) | 
| -c int | Minimum cluster length (default 20) | 
| --[no]delta | Toggle the creation of the delta file. Setting --nodelta prevents 
      the alignment extension step and only outputs the match clusters (default 
      --delta) | 
| --depend | Print the dependency information and exit | 
| -d float | Maximum diagonal difference factor for clustering, i.e. diagonal 
      difference / match separation (default 0.11) | 
| --[no]extend | Toggle the outward extension of alignments from their anchoring 
      clusters. Setting --noextend will prevent alignment extensions but still 
      align the DNA between clustered matches and create the .delta file (default 
      --extend) | 
| -g int | Maximum gap between two adjacent matches in a cluster (default 30) | 
| -h | Print the help information and exit | 
| -l int | Minimum length of an maximal exact match (default 6) | 
| -m int | Maximum stop codon bookend masking length (default 8) | 
| -o | Automatically generate the <prefix>.coords file using the 
      'show-coords' program with the -r option | 
| --[no]optimize | Toggle alignment score optimization. Setting --nooptimize will prevent 
      alignment score optimization and result in sometimes longer, but lower scoring 
      alignments (default --optimize) | 
| -p string | Set the output file prefix (default out) | 
| -V | Print the version information and exit | 
| -x type | The alignment matrix type, 1 [BLOSUM 45], 2 [BLOSUM 62] or 3 [BLOSUM 
      80] (default 2) | 
All values are measured in amino acids unless otherwise noted. Refer to the 
  NUCmer Program options section for more information 
  regarding their shared options. The --masklen value determines 
  the number of amino acids between stop codons that will be automatically masked 
  by promer, e.g. if an amino acid sequence were ...AAA*AAAA*AAA... 
  and the --masklen value were greater than or equal to 4, the sequence 
  would be masked to read ...AAA*XXXX*AAA... for the duration of 
  the script. The --matrix option sets the BLOSUM matrix for scoring 
  mismatches in the amino acid sequence, where options 1 assumes 
  greater diversity between the two sequences and 3 assumes greater 
  similarity between the two sequences.
Output files follow the same format as described in the NUCmer Output format section.
run-mummer1 is a legacy script from the original MUMmer1.0 release. 
  It has been updated to utilize the new suffix tree code of version 3.0, however 
  all other programs called from this script are identical to the original MUMmer 
  release back in 1999. Even though it is an outdated program, it still has some 
  advantages over the newer alignment scripts (nucmer, promer, 
  run-mummer3). Like all of the alignment scripts, run-mummer1 
  is a three step process - matching, clustering and extension. However, unlike 
  the newer alignment scripts, run-mummer1 uses the gaps 
  program for its clustering step. The gaps program does not allow 
  for rearrangements like mgaps, instead if finds the single longest 
  increasing subset of matches across the full length of both sequences. This 
  makes it well suited for SNP and small indel identification between small (< 
  10 Mbp), very similar sequences with few to no rearrangements.
run-mummer1 <reference file> <query file> <prefix> 
  [-r]
The reference and query files must both be in FastA format and contain only 
  one sequence. Memory usage will be dependent on the size of the reference sequence, 
  so it may be advisable to make the smaller of the input files the reference 
  to assure the program does not exhaust your computer's memory resources. run-mummer1 
  uses a simplified scoring function that does not recognize masking characters, 
  so it is not recommended to perform any masking on the input sequences. The 
  <prefix> value will be prefixed to the names of the resulting 
  output files. The -r is optional and tells the script to reverse 
  complement the query input sequence, thus all output coordinates will reference 
  the reverse complement of the query. If the -r option is omitted, 
  all matching will be limited to the forward strand of each sequence; if it is 
  included, all matching will be limited to the forward strand of the reference 
  and the reverse strand of the query.
There are no available command line options for run-mummer1. Instead, 
  the user must directly edit the csh script to alter the command 
  line values passed to the individual pipeline programs. The only available tweak 
  is changing the minimum match length value for mummer, set with 
  the -l option within the script. Decreasing this value may increase 
  the sensitivity of the script, but may drastically increase the resulting runtime.
There are four output files generated with each call of run-mummer1, 
  and each of these files is prefixed with the <prefix> value 
  set on the command line. Each of these files will be referred to by its file 
  extension (out, gaps, errorsgaps, align), and are described below.
The standard output of the mummer program with it's header information 
  stripped, see the mummer output section for more 
  information. Just a simple three column list, noting the position and length 
  of every maximal exact match. Note that for reverse complement matches (produced 
  with the -r option), the query start positions will reference the 
  reverse complement of the query input sequence.
The standard output of the gaps program, see the gaps 
  output section for more information.
An annotated version of the gaps format, with an extra column listing the number 
  of errors counted in each gap. This is perhaps the most useful output file produced 
  by run-mummer1 as it is easy to parse and identify SNPs, which 
  appear as a '1' in the final column. A '-' character 
  in the final column means the alignment was too large to compute. Example slice 
  from an errorsgaps file:
  403382   356512     77    none      1      1       -
  403466   356595     56    none      7      6       4
  403542   356670     81    none     20     19       2
  403626   356756     75    none      3      5       4
The align file is difficult to parse, but contains some useful visual information. 
  It intersperses the gaps output file with the actual pair-wise alignment of 
  each gap. Each alignment follows the listing of the two involved matches and 
  uses a '^' character to identify the non-identities. If an alignment 
  was too large to process in memory a tag reading "*** Too long ***" 
  will be listed in its place. Example align file:
> /home/aphillip/data/mgen.seq reverse Consistent matches
  170273   729167    158    none      8      8
  170433   729327     34    none      2      2
    Errors = 2
T:  gaaggtctttttgattgtaaag
S:  gaaggtctttaagattgtaaag
              ^^          
  170501   729395    155    none     34     34
    Errors = 4
T:  aagaatgactctagcaggcaatggctggagtttgactgtaccactttgaataag
S:  aagaatgactttagcaggtaatggctagagtttgactgtaccattttgaataag
              ^       ^       ^                ^          
  170659   729553    187    none      3      3
    Errors = 2
T:  tggaaactatcagtctagagtgt
S:  tggaaactattaatctagagtgt
              ^ ^          
  170856   729750    281    none     10     10
    Errors = 2
T:  tagctgtcggagcgatcccttcggtagtga
S:  tagctgtcggggcgatcccctcggtagtga
              ^        ^          
(output continues ...)
Each alignment region is padded with 10bp of the exact match surrounding it on either side.
run-mummer3 is the simplest pipeline of the latest MUMmer3.0 programs. 
  It runs the same matching and clustering algorithm as nucmer and 
  promer, however it uses a different extension technique and does 
  not perform the important pre- and post-processing steps of NUC/PROmer. Because 
  of its simplistic form, run-mummer3 can only handle a single reference 
  sequence, but like run-mummer1 its error-focused output makes it 
  a handy tool for detecting SNPs and other small errors. The only major difference 
  between run-mummer3 and run-mummer1 is the new version's 
  ability to handle multiple query sequences and its tolerance of large rearrangements. 
  This makes run-mummer3 well suited for error detection between 
  highly similar sequences that may have large rearrangements, inversions etc. 
  Edit the script by adding the -D option to the combineMUMs 
  command line to output a format designed for SNP identification. Still, run-mummer3 
  provides few advantages of the more user friendly nucmer program, 
  and should be avoided where possible.
run-mummer3 <reference file> <query file> <prefix>
The reference and query files should both be FastA format. The reference file 
  may only have a single sequence, but there is no limit on the number 
  of sequences the query file may contain. It is very important that 
  the reference file only contain one sequence, because the script will give you 
  no indication something went wrong and there will just be empty output files. 
  run-mummer3 uses a simplified scoring function that does not recognize 
  masking characters, so it is not recommended to perform any masking on the input 
  sequences. The <prefix> value will be prefixed to the names 
  of the resulting output files. Both forward and reverse complement matches will 
  be found by default; to change this behavior or change any parameters, requires 
  requires hand editing the script.
There are no available command line options for run-mummer3. Instead, 
  the user must directly edit the csh script to alter the command 
  line values passed to the individual pipeline programs. Altering these parameters 
  is suggested for most applications, as the default values may not always produce 
  the best output. Parameter values may be added or changed for mummer, 
  mgaps and combineMUMs. Run these programs with the 
  -help option for a list of available options, or refer to this 
  manual for more information on mummer or mgaps. Note 
  that the -c option cannot be used for mummer in this 
  script, or mgaps will fail to cluster the reverse complement matches.
Like run-mummer1, run-mummer3 produces four output 
  files prefixed with the value set on the command line. Each of these files will 
  be referred to by its file extension (out, gaps, errorsgaps, align), and are 
  described below.
Pure, unadulterated mummer output. See the mummer 
  output section for more information. Just a simple three column list, noting 
  the position and length of every maximal exact match. Note that for reverse 
  complement matches, the query start positions will reference the reverse complement 
  of the query input sequence.
The standard output of the mgaps program, see the mgaps 
  output section for more information.
An annotated version of the gaps format, with an extra column listing the number 
  of errors counted in each gap. This is perhaps the most useful output file produced 
  by run-mummer1 as it is easy to parse and identify SNPs, which 
  appear as a '1' in the final column. A '-' character 
  in the final column means the alignment was too large to compute. Example slice 
  from an errorsgaps file:
  403382   356512     77    none      1      1       -
  403466   356595     56    none      7      6       4
  403542   356670     81    none     20     19       2
  403626   356756     75    none      3      5       4The align file is difficult to parse, but contains some useful visual information. 
  It intersperses the mgaps output file with the actual pair-wise 
  alignment of each gap. Each alignment follows the listing of the two involved 
  matches and uses a '^' character to identify the non-identities 
  and a '=' character to identify the MUM portion. The gap alignment 
  is also padded with 10bp of the exact match surrounding it on either side. Example 
  align file:
(... output continues)
> ID21
 3944620       24    983    none      -      -
 3945604     1008     22    none      1      1
     Errors = 1
A: agactctttctttggttgatt
B: agactctttccttggttgatt
   ==========^==========
 3945655     1059     26    none     29     29
     Errors = 3
A: cttgcgattgtctttgcatttgtctttgtttctttttcttcatgctgct
B: cttgcgattggctttgcatttggctttgtttctttttcctcatgctgct
   ==========^           ^               ^==========
 3945684     1088     29    none      3      3
     Errors = 2
A: ttacttttttctc-cattatagta
B: ttactttttt-tctcattatagta
   ==========^  ^==========
Region:    3944620 .. 3945743           24 .. 1146             8 / 1124        0.71%
> ID21 Reverse
> ID22
> ID22 Reverse
 5183942        8     31    none      -      -
 5183980       47   4221    none      7      8
     Errors = 3
A: cccagaaaac-accacctccggccagta
B: cccagaaaaccaccactcccggccagta
   ==========^     ^^==========
 5188202     4269    314    none      1      1
     Errors = 1
A: tgcaccagaacgtaataatcc
B: tgcaccagaaagtaataatcc
   ==========^==========
Region:    5183942 .. 5188515         4578 .. 4                4 / 4575        0.09%
(output continues ...)
After each cluster, the align file prints a line beginning with the Region 
  keyword that shows the start and stop of the alignment in the reference and 
  the start and stop of the alignment in the query respectively. The query coordinates 
  in the region line will reference the forward strand of the query, while the 
  lines taken from the gaps file will still reference the reverse strand of the 
  query. The region line also shows and error ratio and the error percentage.
MUMmer includes a few utility programs intended to parse the delta encoded 
  alignment files and output their contents to the user. The majority of these 
  programs will only operate on the delta file output of NUCmer or PROmer, however 
  the generalized visualization tool, mummerplot, will function on 
  a variety of input.
delta-filter is a utility program for the manipulation of the 
  delta encoded alignment files output by the NUCmer and PROmer pipelines. It 
  takes a delta file as input and filters the information based on the various 
  command line switches, outputting only the desired alignments to stdout. Options 
  to filter by alignment length, identity, uniqueness and consistency are provided. 
  Certain combinations of these options can greatly reduce the number of unwanted 
  alignments in the delta file, thus making the output of programs such as show-coords 
  more comprehendible.
delta-filter [options] <delta file> > <filtered delta file>
The <delta file> may represent either NUCmer of PROmer data. 
  The <filtered delta file> will be the filtered down version 
  of the input. Output will be to stdout. delta-filter run with no 
  options is the identity function.
| -g | Global alignment using length*identity weighted LIS (longest increasing 
      subset). For every reference-query pair, leave only the alignments which 
      form the longest mutually consistent set | 
| -h | Print the help information and exit | 
| -i float | Set the minimum alignment identity [0, 100], (default 0) | 
| -l int | Set the minimum alignment length (default 0) | 
| -q | Query alignment using length*identity weighted LIS. For each query, 
      leave only the alignments which form the longest consistent set for the 
      query | 
| -r | Reference alignment using length*identity weighted LIS. For each 
      reference, leave only the alignments which form the longest consistent set 
      for the reference. | 
| -u float | Set the minimum alignment uniqueness, i.e. percent of the alignment 
      matching to unique reference AND query sequence [0, 100], (default 0) | 
| -o float | Set the maximum alignment overlap for -r and -q options as a percent 
      of the alignment length [0, 100], (default 75) | 
The -g option simulates the behavior of MUMmer1 by performing 
  a similar algorithm to determine the longest mutually consistent set of matches, 
  while the -r and -q option only require the match 
  set to be consistent with respect to either the reference or query respectively. 
  The difference being, the -g option does not allow for inversions, 
  translocations, etc. while the -r and -q options do. 
  However, none of these options (-g -r -q) allow for the inclusion 
  of multiple repeat copies. Use -g when aligning two sequences which 
  are globally consistent, use -r for determining the best mapping 
  of a reference to a query (one-to-many), use -q for determining 
  the best mapping of a query to a reference (many-to-one), and use -r 
  and -q in conjunction for a one-to-one mapping of reference to 
  query. The -u option is handy for keeping only those alignments 
  which are anchored in unique sequence. The -o option sets the alignment 
  overlap tolerance for the -r and -q options, i.e. 
  the amount two adjacent alignments included by -r or -q 
  are allowed to overlap.
Output format is the same as the input format. See the NUCmer Output format section for more details.
mapview is a utility script for displaying sequence alignments 
  as provided by NUCmer or PROmer. It takes the output from show-coords 
  or mgaps and converts it to a FIG, PDF or PS image file. By default, 
  it produces FIG files which can be viewed with the common system utility xfig 
  or converted to PDF or PS with the fig2dev utility (neither programs 
  are included with MUMmer). mapview is useful for mapping multiple 
  query contigs (e.g. from a draft sequencing project) against an annotated reference 
  sequence. Exons and other features can also be plotted with the NUCmer or PROmer 
  alignments, aiding in exon refinement and analysis. Individual MUMmer hits are 
  plotted according to their percent identity, making regions of high or low similarity 
  easily distinguishable.
mapview [options] <coords file> [UTR coords] [CDS coords]
The <coords file> must be produced with the show-coords 
  program run with the -r -l options (see show-coords 
  section), or the mgaps program. This coords file may represent 
  either NUCmer or PROmer data, and it is recommended that it be generated with 
  the -k option (or run on a filtered delta file) 
  to reduce redundancy in the PROmer output, however this option does not always 
  select the proper reading frame. The optional UTR and CDS coordinate files which 
  refer to the reference sequence, should be in GFF 
  format. These contain the coordinates of coding sequences and untranslated 
  regions for genes on the reference genome and will be displayed graphically 
  if provided.
| -d int | Set the maximum distance, in base-pairs, between graphically linked 
      matches (default 50000) | 
| -f string | Set the output file format to 'fig', 'pdf' or 'ps' (default 'fig') | 
| -h | Print help information and exit | 
| -m float | Set the magnification at which the figure is rendered, this option 
      will be used when generating PDF or PS files (default 1.0) | 
| -n int | Set the number of output files used to partition the output, this 
      is to avoid generating files that are too large to display (default 10) | 
| -p string | Set the output file prefix (default PROMER_graph or NUCMER_graph) | 
| -v | Verbose logging of the processed files | 
| -V | Display the version information and exit | 
| -x1 int | Set the lower coordinate bound of the display window | 
| -x2 int | Set the upper coordinate bound of the display window | 
| -g|ref | 
 | 
| -I | Display the name of the query sequences | 
| -Ir | Display the name of the reference genes | 
All matches from the same contig are linked by drawing lines between each successive 
  pair of matches, if the matches occur too far apart, then this can get a little 
  messy. The -d option can help clean up the plots by limiting the 
  distance a link can span. The -n value can be increased or decreased 
  if the resulting FIG files are either too big or too small respectively.
The mapview script produces FIG output files (or PDF or PS if 
  requested) that graphically represent the alignment described in the input coords 
  file. An example of the resulting figures can be seen below.
 
 
The above MapView FIG shows a 220 kbp slice of D. melanogaster chromosome 2L and its alignment to D. pseudoobscura. The alignment, generated by PROmer, shows all regions of conserved amino acid sequence. The blue rectangle spanning the figure represents the reference (D. melanogaster), with annotated genes shown above it and the PROmer alignments shown below it. Alternative splice variants of the same gene are stacked vertically. Exons are shown as boxes, with intervening introns connecting them. The 5' and 3' UTRs are colored pink and blue to indicate the gene's direction of translation. PROmer matches are shown twice, once just below the reference genome, where all matches are collapsed into red boxes, and in a larger display showing the separate matches within each contig, where the contigs are colored differently to indicate contig boundaries. The vertical position of the matches indicates their percent identity, ranging from 50% at the bottom of the display to 100% just below the red rectangles. Percent identity is of the amino acid translations used by PROmer. Matches from the same query sequence are connected by lines of the same color.
mummerplot is a script utility that takes output from mummer, 
  nucmer, promer or show-tiling, and converts 
  it to a format suitable for plotting with gnuplot. The primary 
  plot type is an alignment dotplot where a sequence is laid out on each axis 
  and a point is plotted at every position where the two sequences show similarity. 
  As an extension to this plot style, mummerplot is also able to 
  offset multiple 1-vs-1 dotplots to form a multiplot where multiple sequences 
  can be laid out on each axis. This plot style is especially handy for browsing 
  an alignment of two contig sets. Identity plots are also possible by coloring 
  each data point with a color gradient representing identity, or by collapsing 
  the y-axis data onto a single line and then vertically offsetting the data points 
  by their identities. In addition to producing the plot data, mummerplot 
  also generates a gnuplot script that will be evaluated in order 
  to generate the graph. Since mummerplot simply generates gnuplot 
  input, gnuplot must also be installed and accessible from the system 
  path. Information about the free gnuplot software is currently 
  available at www.gnuplot.info.
mummerplot [options] <match file>
The <match file> can either be a three column match list 
  from mummer (either 3 or 4 column format), the delta file from 
  nucmer or promer, or the default output from show-tiling. 
  mummerplot will automatically detect the type of input file it 
  is given, regardless of its file extension, or it will fail if the input file 
  is of an unrecognized type. If the X11 terminal is selected for output (default 
  behavior), an X11 window will be spawned and the plot will be drawn to the screen. 
  If a terminal other than X11 is selected, an extra file will be output containing 
  the plot graphic. The leftover <prefix>.gp script contains 
  the commands necessary for generating the plot, and may be edited afterwards 
  and rerun with gnuplot to change line thickness, labels, colors, etc.
| -b int | Highlight alignments with a breakpoint further than the given distance 
      from the nearest sequence end | 
| --[no]color | Color plot lines with a percent similarity gradient or turn off 
      all color (default color by match direction) | 
| -c | Generate a reference coverage plot, also known as a percent identity 
      plot (default behavior for show-tiling input) | 
| --depend | Print dependency information and exit | 
| -f | Only display alignments which represent the "best" one-to-one 
      mapping of reference and query subsequences (requires delta formatted input) | 
| -h | Print help information and exit | 
| -l | Layout a multiplot by ordering and orienting sequences such that 
      the largest hits cluster near the main diagonal (requires delta formatted 
      input)  | 
| -p string | Set the output file prefix (default 'out') | 
| --rv | Reverse video, swap the foreground and background colors for x11 
      plots (requires x11 terminal) | 
| -r string | Select a specific reference sequence for the x-axis | 
| -q string | Select a specific query sequence for the y-axis | 
| -R string | Generate a multiplot by using the order and length information contained 
      in this file, either a FastA file of the desired reference sequences or 
      a tab-delimited list of sequence IDs, lengths and orientations [ +-] | 
| -Q string | Generate a multiplot by using the order and length information contained 
      in this file, either a FastA file of the desired query sequences or a tab-delimited 
      list of sequence IDs, lengths and orientations [ +-] | 
| 
 | Set the output size to small, medium or large | 
| 
 | Highlight SNP locations in the alignment | 
| 
 | Set the output terminal to x11, postscript or png | 
| 
 | Set the x-range for the plot in the form "[min,max]" | 
| 
 | Set the y-range for the plot in the form "[min,max]" | 
| -V | Display version information and exit | 
The --breaklen option is only useful for highlighting discrepancies 
  between two near identical sequence sets. The --color option looks 
  best when plotted to a postscript terminal and looks worst when plotted to a 
  png terminal. If the alignment is very sparse, many of the alignments will "disappear" 
  because they are too small to be rendered. If this happens, try editing the 
  gnuplot script to plot with "linespoints" instead of "lines". 
  The --coverage option is sometimes the only sensible way to plot 
  one vs. many comparisons if "many" is very large, and it is also a 
  useful plot for finding gaps in the reference (e.g. physical gaps in a contig 
  set). The --filter option will throw away sometimes valuable repeat 
  information, but is nonetheless very helpful in cleaning up an otherwise noisy 
  plot. The --layout feature is only meant to be used for multiplots 
  where the two sequence sets are near identical, and even when this is true, 
  the layout algorithm isn't perfect. The -R -Q options are necessary 
  for any multiplot, otherwise the script won't know how long the sequences are. 
  The sequences will be laid out in the order found in these files and every sequence 
  in --Rfile and --Qfile will be plotted even if no 
  alignments exist. The --SNP or --breaklen options 
  will change the plot colors so that green is normal and red is highlighted.
The mummerplot script outputs three files, <prefix>.gp 
  <prefix>.fplot <prefix>.rplot, when run with standard parameters. 
  The first of which is the gnuplot script. This script contains the commands 
  necessary to generate the plot, and refers to the two data files which contain 
  the forward and reverse matches respectively. If the --filter or 
  --layout option are specified, an additional <prefix>.filter 
  file will be generated containing the filtered delta information. If the --breaklen 
  or --SNP are included, an additional data file <prefix>.hplot 
  will be created containing the highlight information. Finally, if a terminal 
  other than X11 is specified, the plot graphic will saved to the file <prefix>.ps 
  or <prefix>.png if the terminal is postscript of PNG respectively. 
  Line thickness, color, and many other options can be added or removed from the 
  plot by hand editing the gnuplot script. Examples of the two types of plots 
  are displayed below, the dot plot first, followed by the coverage plot, and 
  finnaly a couple multiplots.
 
 
For a dot plot, the reference sequence is laid across the x-axis, while the 
  query sequence is on the y-axis. Wherever the two sequences agree, a colored 
  line or dot is plotted. The forward matches are displayed in red, while the 
  reverse matches are displayed in green. If the two sequences were perfectly 
  identical, a single red line would go from the bottom left to the top right. 
  However, two sequences rarely exhibit this behavior, and in the above plot, 
  multiple gaps and inversions can be identified between these two strains of 
  Helicobacter pylori. This plot was generated from nucmer 
  output, however running mummerplot on a simple match list from 
  mummer would produce similar results, but with more "noise". 
  In the newer versions, mummerplot plots points at the beginning 
  and end of each line to avoid pixel resolution issues and also uses different 
  plotting colors. Therefore, the output may look slightly different than displayed 
  on these pages.
 
 
When there are many query sequences mapping to a single reference sequence, 
  it is often helpful to use a coverage or percent identity plot. This type of 
  plot lays out each of the alignment regions (or for show-tiling, 
  the full contigs) according to their percent similarity and mapping location 
  to the reference. For easier visualization of gaps, all of the alignments are 
  also re-plotted at 10% similarity to normalize the y coordinates and produce 
  a secondary 1D plot. Note that since mummer produces nothing but 
  exact matches, only the normalized 1D plot will appear in the figure.
|  |  | 
A multiplot is a plot for multiple reference and query sequences where each 
  reference/query pair is given its own grid box and their dotplot is drawn within 
  the constraints of that box. Thus, every grid line represents the end of one 
  sequence and the beginning of the next. This allows us to draw every dotplot 
  for the two sequence sets at once, as displayed by the two contig sets in the 
  above left image. With a little shuffling of the order and orientation of the 
  sequences, a more pleasing layout can be obtained as show in the above right 
  image. This is the same contig set as on the left, however the contigs have 
  been reordered and oriented so that the major alignments cluster around the 
  main diagonal of the plot. This allows for easier browsing of the plot by centralizing 
  the important information, and also highlights contigs that have disagreeing 
  sequences by breaking the diagonal. Currently a greedy approach is used to perform 
  the layout, and while good at bringing alignments to the diagonal, it does not 
  always produce the optimal ordering. Therefore, a break in the diagonal does 
  not always signal a disagreement between the two sequence sets (see the mummerplot 
  --breaklen option for an easy way to highlight assembly discrepancies).
A quick reference guide for interpretting the dot plot is available here.
show-aligns parses the delta encoded alignment output of NUCmer 
  and PROmer, and displays the pair-wise alignments from the two sequences specified 
  on the command line. It is handy for identifying the exact location of errors 
  and looking for SNPs between two sequences.
show-aligns [options] <delta file> <IdR> <IdQ>
The <delta file> is the delta output file of either nucmer 
  or promer. <IdR> is the FastA header tag of 
  the desired reference sequence, and <IdQ> is the FastA header 
  tag of the desired query sequence. All alignments between these two sequences 
  will be displayed. Output will be to stdout.
| -h | Print help information and exit | 
| -q | Sort alignments by the query start coordinate | 
| -r | Sort alignments by the reference start coordinate | 
| -w int | Set the screen width of the output (default 60) | 
| -x int | The alignment matrix type, 1 [BLOSUM 45], 2 [BLOSUM 62] or 3 [BLOSUM 
      80] (default 2) | 
The -x option applies to amino acid alignments (promer 
  output) and will only affect the error notations, not the alignment.
Output is to stdout and is slightly different depending on the 
  type of alignment, i.e. nucleotide or amino acid. Each alignment is preceded 
  with a header containing the BEGIN keyword, the frame/direction 
  information and the start and end in the reference and query respectively. Each 
  individual line of the alignment is prefixed with the position of the first 
  base on that line, these positions reference the forward strand of the DNA sequence 
  regardless of alignment type. Errors in nucleotide alignments are marked with 
  a '^' character below the two mismatching sequence bases. Errors 
  in protein alignments are noted with a whitespace in between the two mismatching 
  acids, while similarities (positive alignment scores) are marked with a '+' 
  and identities are noted with a copy of the matching acid. Each alignment is 
  followed by a footer containing the END keyword, the frame/direction 
  information and the start and end in the reference and query respectively. Perhaps 
  the best way to explain this format is by example, so snippets of the two types 
  of alignments are given below.
/home/aphillip/data/GHP.1con /home/aphillip/data/GHPJ9.1con
============================================================
-- Alignments between Helicobacter_pylori_26695 and Helicobacter_pylori_strain_J99
-- BEGIN alignment [ +1 4262 - 4316 | +1 4469 - 4522 ]
4262       gatttgaacttccgtttccaccgtgaaagggtggtatccttggccacta
4469       gatttgaacccctgtaaccaccgtgaaagggtggtatcc.taaccacta
                    ^^ ^  ^^                      ^ ^^      
4311       gatgaa
4517       gatgaa
                 
--   END alignment [ +1 4262 - 4316 | +1 4469 - 4522 ]
-- BEGIN alignment [ +1 5198 - 22885 | +1 5389 - 23089 ]
(output continues ...)
/home/aphillip/data/mgen.seq /home/aphillip/data/ecoliO157.seq
============================================================
-- Alignments between mgen.seq and Escherichia_coli_O157:H7
-- BEGIN alignment [ +1 31690 - 31995 | +3 3336375 - 3336680 ]
31690      VSFSFYLVPNKRSPASPRPGIMYLLSFNFSSIAARNIST*GCIFSTLLI
           + F  Y VP   SPASPRPGIMY  SF+  SI A   ST GC FS+  I
3336375    IIFILYFVPKILSPASPRPGIMYPCSFSP*SIDAVYSSTSGCAFSSAAI
31837      PSGAATIAITLILIGLSSLIDLIAVNNVVPVASIGSRIITCESEMFSGI
           PSGAAT   TL+L+  +     +      PVASIGS I    S M    
3336522    PSGAATSTRTLMLLQPAFFSRSMVAITEPPVASIGSTISAIRSSMLETS
31984      FL*Y
           F  Y
3336669    FWKY
--   END alignment [ +1 31690 - 31995 | +3 3336375 - 3336680 ]
-- BEGIN alignment [ +2 50819 - 51220 | -1 3263900 - 3263499 ]
(output continues ...)
show-coords parses the delta alignment output of NUCmer and PROmer, 
  and displays summary information such as position, percent identity and so on, 
  of each alignment. It is the most commonly used tool for analyzing the delta 
  files.
show-coords [options] <delta file>
The <delta file> is the delta output file of either nucmer 
  or promer.
| -b | Brief output that only displays the non-redundant locations of aligning 
      regions | 
| -B | Switch output to btab format | 
| -c | Include percent coverage columns in the output | 
| -d | Include the alignment direction/reading frame in the output (default 
      for promer) | 
| -g | Only display alignments included in the Longest Ascending Subset, 
      i.e. the global alignment. Recommened to be used in conjunction with the 
      -r or -q options. Does not support circular sequences | 
| -h | Print help information and exit | 
| -H | Omit the output header | 
| -I float | Set minimum percent identity to display | 
| -k | *PROMER ONLY* Knockout (do not display) alignments that overlap 
      another alignment in a better reading frame | 
| -l | Include sequence length columns in the output | 
| -L int | Set minimum alignment length to display | 
| -o | Annotate maximal alignments between two sequences, i.e. overlaps 
      between reference and query sequences | 
| -q | Sort output lines by query | 
| -r | Sort output lines by reference | 
| -T | Switch output to tab-delimited format | 
The -b option alters the output table to only display the location 
  of the aligning regions, not their identity, direction, frame, etc. Also, for 
  protein data, the -b option will collapse all overlapping frames, 
  and list a single encompassing region. -B switches the output format 
  to "btab" (Blast tablature) which is a tab-delimited table with a 
  different layout than the standard show-coords format. The coverage 
  information added with the -c option is equal to the length of 
  the alignment divided by the length of the sequence. The -k option 
  will select the "best" reading frame by choosing the alignment that 
  is longest, or has the highest percent identity and is within 75% of the length 
  of the longest alignment; only alignments that overlap each other by greater 
  than 50% of their length will be considered for knockout. The -T 
  option is different than the -B option because it retain the normal 
  ordering of output columns. The output of the -d option for NUCmer 
  data will appear under the [FRM] column, just like the reading 
  frame info from PROmer data. The -o annotations will appear in 
  the final column of the output. The descriptions reference the reference sequence, 
  e.g. [END] means the overlap is on the end of the reference 
  sequence and [CONTAINED] means the reference sequence is contained 
  by the query sequence.
The -c and -l options are useful when comparing two 
  sets of assembly contigs, in that these options help determine if an alignment 
  spans an entire contig, or is just a partial hit to a different sequence. The 
  -b option is useful when the user wishes to identify syntenic regions 
  between two genomes, but is not particularly interested in the actual alignment 
  similarity or appearance. This option also disregards match orientation, so 
  should not be used if this information is needed. The -g option 
  comes in handy when comparing sequences that share a linear alignment relationship, 
  that is there are no rearrangements. Large nsertions, deletions and gaps can 
  then be identified by the break between two adjacent alignments in the output. 
  If there are more than one global alignment that share the same score, then 
  one of them is picked at random to display. This is useful when mapping repetitive 
  reads to a finished sequence.
Output is to stdout and is slightly different depending on the 
  type of alignment, i.e. nucleotide or amino acid. Some of the described columns, 
  such as percent similarity, will not appear for nucleotide comparisons. When 
  run without the -H or -B options, show-coords 
  prints a header tag for each column; the descriptions of each tag follows. [S1] 
  start of the alignment region in the reference sequence [E1] end 
  of the alignment region in the reference sequence [S2] start of 
  the alignment region in the query sequence [E2] end of the alignment 
  region in the query sequence [LEN 1] length of the alignment region 
  in the reference sequence [LEN 2] length of the alignment region 
  in the query sequence [% IDY] percent identity of the alignment 
  [% SIM] percent similarity of the alignment (as determined by the 
  BLOSUM scoring matrix) [% STP] percent of stop codons in the alignment 
  [LEN R] length of the reference sequence [LEN Q] length 
  of the query sequence [COV R] percent alignment coverage in the 
  reference sequence [COV Q] percent alignment coverage in the query 
  sequence [FRM] reading frame for the reference and query sequence 
  alignments respectively [TAGS] the reference and query FastA IDs 
  respectively. All output coordinates and lengths are relative to the forward 
  strand of the reference DNA sequence.
When run with the -B option, output format will consist of 21 
  tab-delimited columns. These are as follows: [1] query sequence 
  ID [2] date of alignment [3] length of query sequence 
  [4] alignment type [5] reference file [6] 
  reference sequence ID [7] start of alignment in the query [8] 
  end of alignment in the query [9] start of alignment in the reference 
  [10] end of alignment in the reference [11] percent 
  identity [12] percent similarity [13] length of alignment 
  in the query [14] 0 for compatibility [15] 0 for compatibility 
  [16] NULL for compatibility [17] 0 for compatibility 
  [18] strand of the query [19] length of the reference 
  sequence [20] 0 for compatibility [21] and 0 for compatibility.
show-snps is a utility program for reporting polymorphisms contained 
  in a delta encoded alignment file output by NUCmer or PROmer. It catalogs all 
  of the single nucleotide polymorphisms (SNPs) and insertions/deletions within 
  the delta file alignments. Polymorphisms are reported one per line, in a delimited 
  fashion similar to show-coords. Pairing this program with the appropriate 
  MUMmer tools can create an easy to use SNP pipeline for the rapid identification 
  of putative SNPs between any two sequence sets, as demonstrated in SNP 
  detection section. 
show-snps [options] <delta file>
The <delta file> is the delta output of either nucmer 
  or promer. Output will be to stdout.
| -C | Do not report SNPs from alignments with an ambiguous mapping, i.e. 
      only report SNPs where the [R] and [Q] columns equal 0 and do not output 
      these columns | 
| -h | Print help information and exit | 
| -H | Do not print the output header | 
| -I | Do not report indels | 
| -l | Include sequence length information in the output | 
| -q | Sort output lines by query IDs and SNP positions | 
| -r | Sort output lines by reference IDs and SNP positions | 
| -S | Specify which alignments to report by passing 'show-coords' lines 
      to stdin | 
| -T | Switch to tab-delimited format | 
| -x int | Include x characters of surrounding SNP context in the output (default 
      0)  | 
The -C option is a little confusing, but in simple terms it avoids 
  calling SNPs from repetitive regions. "ambiguous mapping" refers to 
  a position on the reference or query that is covered by more than one alignment. 
  This can be caused by simple repeats, or overlapping alignments caused by tandem 
  repeats that exist in different copy numbers. Either way, calling SNPs from 
  these regions is questionable, and therefore the -C option should 
  be invoked in most instances. To generate output suitable for further parsing, 
  use the -H -T options. The [BUFF] output column will 
  refer to the sequence positions requested by the -r -q options, 
  so these options affect more than the order of the output. The -S 
  option will accept all forms of show-coords output, so output can 
  be piped into show-snps or a simple cut/paste from one xterm to 
  another should get the job done. This option is helpful when the user has a 
  specific alignment they would like to see SNPs from. -x does nothing 
  other than print out the characters on either side of the listed position for 
  both the reference and query. The '.' character is used to represent 
  indels, while '-' represents end-of-sequence.
Output is to stdout and is slightly different depending on which command switches 
  are set. For instance, by default the output is arranged in a table style, however 
  if the -T option is active, the output will be tab-delimited. Also, 
  the sequence files, alignment type and column headers are output by default, 
  however if the -H option is active, the headers will be stripped 
  from the output. Other options like -l -C -x will add or remove 
  columns from the output. So, for description purposes, all possible column headers 
  will be given and it is up to the user to pair the column header with the column 
  number. The descriptions for each header tag follows. [P1] position 
  of the SNP in the reference sequence. For indels, this position refers to the 
  1-based position of the first character before the indel, e.g. for an indel 
  at the very beginning of a sequence this would report 0. For indels on the reverse 
  strand, this position refers to the forward-strand position of the first character 
  before indel on the reverse-strand, e.g. for an indel at the very end of a reverse 
  complemented sequence this would report 1. [SUB] character or gap 
  at this position in the reference [SUB] character or gap at this 
  position in the query [P2] position of the SNP in the query sequence 
  [BUFF] distance from this SNP to the nearest mismatch (end of alignment, 
  indel, SNP, etc) in the same alignment [DIST] distance from this 
  SNP to the nearest sequence end [R] number of repeat alignments 
  which cover this reference position [Q] number of repeat alignments 
  which cover this query position [LEN R] length of the reference 
  sequence [LEN Q] length of the query sequence [CTX R] 
  surrounding reference context [CTX Q] surrounding query context 
  [FRM] sequence direction (NUCmer) or reading frame (PROmer) [TAGS] 
  the reference and query FastA IDs respectively. All positions are relative to 
  the forward strand of the DNA input sequence, while the [BUFF] 
  distance is relative to the sorted sequence.
show-tiling attempts to construct a tiling path out of the query 
  contigs as mapped to the reference sequences. Given the delta alignment information 
  of a few long reference sequences and many small query contigs, show-tiling 
  will determine the best mapped location of each query contig. Note that each 
  contig may only be tiled once, so repetitive regions may cause this program 
  some difficulty. This program is useful for aiding in the scaffolding and closure 
  of an unfinished set of contigs, if a suitable, high similarity reference genome 
  is available. Or, if using PROmer, show-tiling will help in the 
  identification of syntenic regions and their contig's mapping to the references.
This program is not suitable for "many vs. many" assembly comparisons, 
  however a new tool based on the concepts of show-tiling should 
  be available in the near future that will facilitate the mapping of assembly 
  contigs.
show-tiling [options] <delta file>
The <delta file> is the delta output file of either nucmer 
  or promer. Primary output will be to stdout.
| -a | Describe the tiling path by printing the tab-delimited alignment 
      regions | 
| -c | Assume the reference sequences are circular, and allow tiled contigs 
      to span the origin | 
| -h | Print help information and exit | 
| -g int | Maximum gap between clustered alignments, where -1 will represent 
      infinity (nucmer default 1000, promer default -1) | 
| -i float | Minimum percent identity (nucmer default 90.0, promer default 55.0) | 
| -l int | Minimum contig length (default 1) | 
| -p filename | Output a pseudo molecule of the query contigs to file | 
| -R | Deal with repetitive contigs by randomly placing them in one of 
      their copy locations (implies -V 0) | 
| -t filename | Output a TIGR assembler style contig list of EVERY mapping contig 
      to file | 
| -u filename | Output the tab-delimited alignment regions of the unusable contigs 
      to file | 
| -v float | Minimum contig alignment coverage (nucmer default 95.0, promer default 
      50.0) | 
| -V float | Minimum contig coverage difference (nucmer default 10.0, promer 
      default 30.0) | 
| -x | Describe the tiling path by printing the XML contig linking information | 
The -i and -l options filter out all contigs below 
  these cutoffs. The -p option creates a pseudo molecule from the 
  query sequence, and arranges them as the map to the reference. The -v 
  option sets the minimum percent of the query contig that must be covered by 
  aligning bases, while the -V option sets the difference in percent 
  coverage to determine one mapping is better than another. To include the most 
  possible contigs in the tiling, set the -V option to zero and lower 
  the -i and -v options to reasonable values. For NUCmer 
  data, percent coverage is the non-redundant number of aligning bases divided 
  by the length of the query sequence, while for PROmer data, percent coverage 
  is the extent of the syntenic region divided by the length of the query sequence. 
  The difference being, show-tiling does not penalize a PROmer mapping 
  for having big gaps and small alignments. The -x option output 
  can be used as input to the TIGR scaffolder "Bambus", for use as contig 
  linking information. With the exception of the output generated by the -t 
  option, all tiling paths include the minimal number of contigs needed to generate 
  the maximum reference coverage. This means that there may be other, smaller 
  contigs that map to the reference, but because they are shadowed by larger contigs, 
  they are not reported. The -R option is very useful for maintaining 
  uniform, 'random' coverage of reads when mapping to a reference.
Output is to stdout and differs depending on the command line 
  options. Standard output has an 8 column list per mapped contig, separated by 
  the FastA headers of each reference sequence. These columns are as follows: 
  [1] start in the reference [2] end in the reference 
  [3] gap between this contig and the next [4] length 
  of this contig [5] alignment coverage of this contig [6] 
  average percent identity of this contig [7] contig orientation 
  [8] contig ID. Output of the -a and -u 
  options have the same columns as show-coords run with the -THcl 
  options. Output of the -x option follows standard XML format. An 
  example of the standard output of show-tiling follows:
>gba:6615 5227293 bases
-10807  20017   105     30825   100.00  99.99   +       253
20123   21388   42      1266    100.00  100.00  -       121
21431   93545   37      72115   100.00  100.00  +       272
93583   96184   -15     2602    100.00  100.00  +       51
96170   98575   161     2406    100.00  99.96   -       93
98737   100543  1072    1807    100.00  99.83   -       94
101616  103405  3121    1790    100.00  99.89   +       107
5215716 5216412 73      697     100.00  100.00  -       92
(output continues ...)
>gbx:17223 181677 bases
-12269  43162   -258    55432   100.00  100.00  -       9
42905   49553   -106    6649    100.00  100.00  +       7
49448   112332  -659    62885   100.00  100.00  -       21
111674  112935  -519    1262    100.00  100.00  +       22
112417  116940  -201    4524    100.00  100.00  +       23
116740  160401  -27     43662   100.00  100.00  +       10
160375  167673  1734    7299    100.00  100.00  -       159
>gbx:17224 94829 bases
-89937  5606    54601   95544   100.00  99.99   -       168
60208   61126   -56235  919     100.00  99.24   -       43
The negative start positions indicate contigs that are wrapping around the 
  origin, since this output was generated with the -c option.
MUMmer's modular design is very beneficial, however it has created a small 
  set of inconveniences. Some modules like mummer have been updated 
  in the recent 3.0 release, while others like mgaps have not. Since 
  it is not always possible to update all modules at once, some legacy issues 
  appear. For example, because mgaps was originally written to cluster 
  the output of a matching algorithm that could only handle one reference sequence, 
  its input and output is constrained to handle only a single reference sequence. 
  When mummer was updated in the 3.0 release, it was modified to 
  handle multiple reference sequences, but this causes a slight incompatibility 
  as its output can no longer be fed into mgaps when it contains 
  multiple reference sequences. The same type of annoyance occurs between mummer 
  and gaps, as gaps was originally designed to handle 
  only one reference and only one query sequence. Such incompatibilities 
  can be inconvenient, but workarounds with stream editors and conversion scripts 
  are common practice by those familiar with MUMmer. Learning more about the output 
  of each program can lead to a better understanding of how the modules communicate 
  with one another and make it possible to format the output of one module so 
  that it can be understood by a legacy module.
nucmer, promer and run-mummer3 all have 
  a difficult time with tandem repeats. If the two sequences contain a different 
  number of copies of the same tandem repeat, these alignment routines will sometimes 
  generate a cluster on either side of the tandem and extend alignments past one 
  another, failing to join them into a single alignment region. This generates 
  two overlapping alignments and makes it difficult to determine what caused this 
  erratic behavior. In addition, the %identity for this region may appear artificially 
  low as the alignment extension attempted to align sequence that was offset by 
  the difference in length of the tandem repeats, instead of identifying the single 
  large insertion. Any difference in the tandem between the reference and query 
  can be calculated as the difference of the alignment overlap in each sequence. 
  This bug is more of a nuisance than a critical problem, so a fix is being considered 
  but no timeline has been set for its implementation.
The MUMmer programs do not perform validity checking on their inputs. If any part of the package appears to malfunction, please check that the input files are within the constraints of each program (i.e. number of sequences allowed, FastA format, memory usage, etc.).
This document will be under constant edit, so if you notice any errors please contact us.
The development of MUMmer is supported in part by the National Science Foundation under grants IIS-9902923 and IIS-9820497, and by the National Institutes of Health under grants R01-LM06845 and N01-AI-15447.
MUMmer3.0 is a joint development effort by Stefan Kurtz of the University of Hamburg and Adam Phillippy, Art Delcher and Steven Salzberg at TIGR. Stefan's contribution of the new suffix tree code was essential to making MUMmer3.0 an open source project. Please see the ACKNOWLEDGEMENTS file in the distribution for an updated list of contributors.
Please address questions and bug reports via Email to:
VERSION 3.17 - May 2005