MatGAT: An application that generates similarity/identity matrices using protein or DNA sequences
© Campanella et al; licensee BioMed Central Ltd. 2003
Received: 09 May 2003
Accepted: 10 July 2003
Published: 10 July 2003
The rapid increase in the amount of protein and DNA sequence information available has become almost overwhelming to researchers. So much information is now accessible that high-quality, functional gene analysis and categorization has become a major goal for many laboratories. To aid in this categorization, there is a need for non-commercial software that is able to both align sequences and also calculate pairwise levels of similarity/identity.
We have developed MatGAT (Matrix Global Alignment Tool), a simple, easy to use computer application that generates similarity/identity matrices for DNA or protein sequences without needing pre-alignment of the data.
The advantages of this program over other software are that it is open-source freeware, can analyze a large number of sequences simultaneously, can visualize both sequence alignment and similarity/identity values concurrently, employs global alignment in calculations, and has been formatted to run under both the Unix and the Microsoft Windows Operating Systems. We are presently completing the Macintosh-based version of the program.
Keywordsnucleic acid protein sequence alignment pairwise analysis similarity matrix
The application of phylogenetics in the examination of a genome has been dubbed "phylogenomics" [1–3]. The analytic process of phylogenomics is taking on more importance as additional DNA and protein sequences from a multitude of species become available.
GenBank has approximately 28 million DNA sequences in its database http://www.ncbi.nlm.nih.gov/Genbank/genbankstats.html. The number of sequences in GenBank has increased by five orders of magnitude since its founding in 1982. The Institute for Genomic Research (TIGR), by the end of 1998, had completed sequencing seven microbial genomes, half of the world total at the time. Today, TIGR is in the process of sequencing and characterizing the genomes of many major organisms of the world, including 20 animal, 19 plant, 14 protist, 8 fungal and over 100 bacterial species http://www.tigr.org.
All this new information is obviously a great asset to scientists, since there is constantly new supplementary data to be employed in genomic, physiologic and genetic research. The drawback with all of this new information is that the sheer amount of it has become overwhelming. So much information is now becoming available that high-quality, functional gene analysis and categorization is becoming a paramount goal.
One of the most important analyses that can be employed in phylogenomics or phylogenetics is the pairwise determination of similarity or identity between DNA or protein sequences. The percent identity is the calculated percentage of how two sequences compare at a base-to-base or residue-to-residue level. The percent similarity is a more strict calculation where sequence gaps and mismatches are included in the evaluation and scored using a more complex formula and a comparison look-up table [4–6].
We have noted that there is a lack of non-commercial software available that is able to both align a series of DNA or protein sequences and also calculate pairwise levels of similarity/identity. Timothy Carver's DISTMAT program http://bioinfo.pbi.nrc.ca:8090/cgi-bin/emboss.pl?_action=input%26;_app=distmat calculates pairwise divergence, but not similarity, and it only functions if the sequences have already been aligned by some other computer program. Pairwise BLAST http://www.ncbi.nlm.nih.gov/blast/bl2seq/bl2.html may also be used to calculate similarity, but its limitations are that only two sequences may be analyzed at one time and percent similarity/identity are based on local alignment – not global alignment . MegAlign, which comes with the DNASTAR package (DNASTAR, Inc.), also generates similarity matrices, but it is quite expensive and not available as a stand-alone product.
MatGAT (Matrix Global Alignment Tool) is a simple, easy to use similarity/identity matrix generator that calculates the similarity and identity between every pair of sequences in a given data set without requiring pre-alignment of the data. The program performs a series of pairwise alignments using the Myers and Miller global alignment algorithm , calculates similarity and identity, and then places the results in a distance matrix. In order to increase alignment speed, they are computed in the C++ language while the "front-end" of the MatGAT program is encoded in Java.
Data files of up to 200 DNA or protein sequences have been analyzed successfully using MatGAT. The DNA sequences analyzed were 1000–2000 basepairs in size and took ~90 min to finish a run using a Pentium 3 Processor on a standard PC. The protein sequences ranged from 300 to 600 amino acids in length and took 12 min to complete an analysis using the same machine.
The output for MatGAT may be viewed on the computer screen or printed directly. The results may also be saved as a text file, or Microsoft Excel delimited file, to be used for further statistical and phylogenetic analyses. Moreover, when first booted up MatGAT searches for the presence of Excel on the user's hard drive. If detected, this information is saved and output matrices may then be directly transferred to Excel by the click of a single screen button.
Error detection routines include detection and stripping of numbers in DNA and protein data; detection of inappropriate DNA bases other than G, T, C, A, N, and * for wildcards; discrimination between DNA and amino acid sequences and indication of the appropriate type of analysis; automatic stripping of spaces from pasted datasets and prohibition of spaces in data during manual entry; and, finally evaluation of the number of sequence entries and error flagging if this value is not greater than one.
MatGAT v2.0 can be obtained as a compacted Zip-file from the following World Wide Web sites: http://www.angelfire.com/nj2/arabidopsis/MatGAT.html or http://www.bitincka.com/ledion/matgat. Additionally, the software has been submitted for public distribution to the Indiana University Biology Archive (IUBIO Archive) http://iubio.bio.indiana.edu/soft/molbio/evolve/. The PC version of the program requires the presence of a JAVA run-time environment under the following MS Windows interfaces: Windows 98, 2000, NT, or XP. The Unix version of the program must also run on a JAVA-enabled machine. Additionally, the PC version of the program will run effectively under Windows emulation on Macintosh Computers running under OS X.
The Java runtime environment is available on all PC computers installed with Windows 98, or later, and Netscape. If the user does not have Java installed on their PC, then they may obtain it from http://java.sun.com/j2se/1.3/download.html. Macintosh users may download Java from http://devworld.apple.com/java/download.html. Users of the Unix Operating system may download Java from http://www.sco.com/developers/java/download/index.html.
A link for downloading the MatGAT v2.0 program for Windows is included with this article [see Additional file: 1 1]. The archive is formatted as a Microsoft Zip file, entitled "MatGAT 2.0.zip", and can be opened by any Windows unpacking program such as WinZip. Included in the archive are all files that are needed to run MatGAT 2.0, including two test data files entitled "Test Data DNA.txt" and "Test Data Protein.txt". Additionally in the archive, there is a README.txt file that acts as a help and bug repair update file. Once the program files are unzipped from their archive and into their own folder, the user starts the program by double-clicking on the MS-DOS batch "Run" file in the directory.
We thank Vinela Bakllamaja for her interface suggestions, and Lisa Campanella for her help in editing this article.
- Eisen JA: Phylogenomics: Improving functional predictions for uncharacterized genes by evolutionary analysis. Genome Research 1998, 8: 163–167.View ArticlePubMedGoogle Scholar
- Eisen JA, Hanawalt PC: A phylogenomic study of DNA repair genes, proteins, and processes. Mutat Res 1999, 435: 171–213. 10.1016/S0921-8777(99)00050-6PubMed CentralView ArticlePubMedGoogle Scholar
- Eisen JA, Wu M: Phylogenetic analysis and gene functional predictions: phylogenomics in action. Theoretical Population Biology 2002, 61: 481–487. 10.1006/tpbi.2002.1594View ArticlePubMedGoogle Scholar
- Needleman SB, Wunsch CD: A general method applicable to the search for similarities in the amino acid sequence of two proteins. J Mol Biol 1970, 48: 443–453.View ArticlePubMedGoogle Scholar
- Pearson WR, Lipman DJ: Improved Tools for Biological Sequence Comparison. Proc Natl Acad Sci 1988, 85: 2444–2448.PubMed CentralView ArticlePubMedGoogle Scholar
- Shpaer EG, Robinson M, Yee D, Candlin JD, Mines R, Hunkapiller T: Sensitivity and Selectivity in Protein Similarity Searches. Genomics 1996, 38: 179–191. 10.1006/geno.1996.0614View ArticlePubMedGoogle Scholar
- Tatusova TA, Madden TL: Blast 2 sequences – a new tool for comparing protein and nucleotide sequences. FEMS Microbiol Lett 1999, 174: 247–250. 10.1016/S0378-1097(99)00149-4View ArticlePubMedGoogle Scholar
- Myers EW, Miller W: Optimal alignments in linear space. Comp Applic Biosci 1988, 4: 11–17.Google Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL.