- Open Access
GenomeBlast: a web tool for small genome comparison
© Lu et al; licensee BioMed Central Ltd 2006
- Published: 12 December 2006
Comparative genomics has become an essential approach for identifying homologous gene candidates and their functions, and for studying genome evolution. There are many tools available for genome comparisons. Unfortunately, most of them are not applicable for the identification of unique genes and the inference of phylogenetic relationships in a given set of genomes.
GenomeBlast is a Web tool developed for comparative analysis of multiple small genomes. A new parameter called "coverage" was introduced and used along with sequence identity to evaluate global similarity between genes. With GenomeBlast, the following results can be obtained: (1) unique genes in each genome; (2) homologous gene candidates among compared genomes; (3) 2D plots of homologous gene candidates along the all pairwise genome comparisons; and (4) a table of gene presence/absence information and a genome phylogeny. We demonstrated the functions in GenomeBlast with an example of multiple herpesviral genome analysis and illustrated how GenomeBlast is useful for small genome comparison.
We developed a Web tool for comparative analysis of small genomes, which allows the user not only to identify unique genes and homologous gene candidates among multiple genomes, but also to view their graphical distributions on genomes, and to reconstruct genome phylogeny. GenomeBlast runs on a Linux server with 4 CPUs and 4 GB memory. The online version of GenomeBlast is available to public by using a Web browser with the URL http://bioinfo-srv1.awh.unomaha.edu/genomeblast/.
- Default Threshold
- Tree Search Algorithm
- Genome Phylogeny
- Alignment Coverage
- Herpesviral Genome
With the rapidly increasing availability of complete genome sequences, genome-wide sequence comparison has become an essential approach for finding homologous gene candidates, for identifying gene functions, and for studying genome evolution [1, 2]. Genome comparison can be used to find genes that characterize unique features in a given organism such as specific phenotypic variation or particular pathogenicity . Meanwhile, genome phylogenies based on gene content or gene order shed new light on the construction of the Tree of Life [4, 5].
Currently many tools such as MUMmer and Artemis are available for comparative genomic analysis [2, 6–8]. These tools can be used for pairwise genome alignment (e.g., [3, 9, 10]) as well as multiple genome alignment e.g., [11, 12]). Unfortunately, most of them are not applicable for the identification of unique genes in a given set of genomes, since the tools were developed for homologous gene detection in most cases. Additionally, only a few tools can be used for the study of phylogeny from the genomic point of view .
The BLAST (Basic Local Alignment Search Tool) algorithm as well as other anchor-based algorithms are commonly used for the identification of homologous gene candidates across diverse genomes [2, 14]. Although the BLAST algorithm has its pros such as fast computation and accurate results in detecting local highly-similar sequences regions, it sustains two cons when used to identify global sequence similarity: (1) genes that reside in local highly-similar regions can be erroneously identified as homologue candidates; and (2) multiple local hits that happen against the same subjective sequence need to be combined to obtain the overall aligned region between the query and subject sequences.
In order to solve these problems, we developed a Web tool, GenomeBlast. It performs multiple genome comparisons, identifies unique genes as well as shared (possibly homologous) genes among the genomes, and reconstructs the genome phylogeny. Identification of homologous gene candidates is done by detecting global sequence similarity using alignment coverage information. This paper describes its architecture, algorithms, and implementation. We demonstrate the practical use of GenomeBlast with an example using herpesviral genomes, and discuss its future improvement plan.
The inputs to GenomeBlast are genome sequences in the GenBank format, each in a single file. Each genome sequence record needs to include the FEATURE table with coding sequence (CDS) annotations. Such data can be downloaded from public databases such as the National Center for Biotechnology Information (NCBI). Protein sequences are extracted from translation records in the CDS annotations. The formatdb program is used to generate protein database files from the protein dataset for each genome. These protein database files can be used with the blastp program. The all-against-all blasting strategy is used for genome comparison. Each of the protein sequences from one genome is compared against protein sequences from all other genomes. The BLAST results are then filtered and presented in various outputs.
Three-level outputs generated from GenomeBlast include: (1) candidates for unique genes and homologous genes; (2) 2D plots of homologous gene candidates for pairwise genome comparisons; (3) a table of gene presence/absence information; (4) genome phylogeny; and (5) a summary table for multiple genome comparison.
where L i , Li,j, and L query represent the alignment length for the ith hit, the overlap length between the hits i and j, and the query length, respectively; and k is the total number of hits to the same subject sequence for a given query sequence.
Identification of homologous gene candidates
In order to identify homologous gene candidates and to exclude related genes that share similarities only with limited regions, GenomeBlast can use a combination of following thresholds:
i) Coverage. The coverage is the length of aligned regions calculated as above. The default threshold is 50%.
ii) Identity. The identity is the proportion (%) of identical amino acid pairs in the aligned region. The default threshold is 30%.
iii) E-value. The E-value, expectation value, is the number of different alignments with scores equivalent to or better than the scores that are expected to occur in a database search by chance. The default threshold is 10. In the default setting, GenomeBlast uses only the coverage and identity, but not the E-value threshold.
Genome phylogeny reconstruction
Based on the results of multiple genome comparison, the presence and absence of each CDS is tabulated with 1s (for presence) and 0s (for absence) for each genome. Using this binary character matrix, the maximum parsimony method  with the branch-and-bound tree search algorithm is used to infer genome phylogeny. The branch-and-bound algorithm effectively searches the possible tree topologies and guarantees finding the most parsimonious phylogeny .
Backend programs and the Web server
The blastp program in the BLAST stand-alone package ftp://ftp.ncbi.nih.gov/blast/ was used for protein sequence comparison. The PENNY program of the PHYLIP package implements the maximum parsimony phylogenetic method using the branch-and-bound tree search algorithm and a binary character data matrix . The data processing/analysis and integration of the blastp and PENNY programs into GenomeBlast were implemented with the PERL programming language. The Web applications were developed using PHP. GenomeBlast runs on a Linux server, which has four processors (2.0 GHz each), 4 GB memory, and 400 GB disk space.
The first step is to set up blastp options. We did not choose the filter option to mask off low compositional complexity or mask for the lookup table. We used the default values provided in GenomeBlast (E-value: 10, word size: 3, gap existence cost: 11, gap extension cost: 1, and scoring matrix: BLOSUM62).
The next step is to upload genome sequence files. We set up the number of genomes to compare as 13 and clicked the OK button. We then uploaded the 13 herpesviral genome sequence files, which were originally downloaded from NCBI in the GenBank format. The average size of these genomes was approximately 150 kb. Formatting databases and performing all-against-all blastp comparison took 5 minutes 16 seconds on our server.
GenomeBlast has several unique features compared with other comparative genomics tools [2, 3, 9–12, 20, 21]. Instead of focusing on generating alignments, GenomeBlast identifies unique and shared, possibly homologous, CDS sets among multiple genomes and presents the information in a summary table. It generates 2D plots depicting the distribution of homologous CDS between given pairs of genomes. In order to identify possible homologous CDS, GenomeBlast uses the blastp sequence similarity search program. Combining the length of alignment coverage with % identity of the aligned region, it evaluates gene-wide similarity. This combination of coverage and identity can better identify homologous CDS candidates. GenomeBlast also provides flexibility in choosing different combinations of parameters and their threshold values. Once the blast search is done, there is no need for redoing the blast search and the user can return to the parameter-setting page to reset thresholds for identifying homologous gene candidates.
GenomeBlast reconstructs genome phylogeny based on gene content using the maximum parsimony method. In this context, GenomeBlast overlap with the Web server, SHOT . SHOT also includes a gene-order phylogeny method. Whereas SHOT can be used for only a certain set of genomes, GenomeBlast offers more flexibility.
Montague and Hutchison  reconstructed whole-genome phylogenies for 13 herpesviral genomes based on the Clusters of Orthologous Groups (COGs) data . They used several computer programs/packages before reconstructing the genome phylogenies including the Wisconsin Package (GCG) , BLAST programs, and PAUP (Phylogenetic Analysis Using Parsimony) . We performed the same analysis using GenomeBlast alone and our genome phylogeny agreed with their result . It demonstrates that GenomeBlast is a very useful application for small genome comparison. Our plan to extend functions in GenomeBlast includes automatic CDS extraction/translation, use of FASTA sequence format, DNA-level analysis using blastn, and gene-order based genome phylogeny.
GenomeBlast is suitable for small genome comparison. We do not expect it to compare large genomes, such as human and mouse genomes, because such computation with large genomes is extremely expensive, which will take several days or even weeks to complete. For larger genomes, standalone programs such as MUMmer and Artemis can be used. Or for the model organisms, some homologous gene databases such as HomoloGene  and Inparanoid are available for use [26–28].
We have developed a Web tool for comparative analysis of small genomes. With GenomeBlast, we can identify unique genes and homologous gene candidates among multiple genomes, view their graphical distributions on genomes, and reconstruct genome phylogeny. An example with 13 herpesviral genomes demonstrated that GenomeBlast is a useful tool for genome comparison.
Project name: GenomeBlast project
Project home page: http://bioinfo-srv1.awh.unomaha.edu/genomeblast/index.php
Operating system(s): Linux
Programming language: PERL and PHP
Other requirements: Any standard Web browsers (e.g., Microsoft Internet Explorer 6.0 or later)
Any restrictions to use by non-academics: yes, contact the author GL for details
This work was funded by the University of Nebraska – Lincoln Biomedical Research Enhancement Funds. G.L. acknowledges the Pre-tenure Award from University of Nebraska at Omaha.
This article has been published as part of BMC Bioinformatics Volume 7, Supplement 4, 2006: Symposium of Computations in Bioinformatics and Bioscience (SCBB06). The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2105/7?issue=S4.
- Alba MM, Das R, Orengo CA, Kellam P: Genomewide function conservation and phylogeny in the Herpesviridae. Genome Res 2001, 11(1):43–54. 10.1101/gr.149801PubMed CentralView ArticlePubMedGoogle Scholar
- Kurtz S, Phillippy A, Delcher AL, Smoot M, Shumway M, Antonescu C, Salzberg SL: Versatile and open software for comparing large genomes. Genome Biol 2004, 5(2):R12. 10.1186/gb-2004-5-2-r12PubMed CentralView ArticlePubMedGoogle Scholar
- Florea L, Riemer C, Schwartz S, Zhang Z, Stojanovic N, Miller W, McClelland M: Web-based visualization tools for bacterial genome alignments. Nucleic Acids Res 2000, 28(18):3486–3496. 10.1093/nar/28.18.3486PubMed CentralView ArticlePubMedGoogle Scholar
- Montague MG, Hutchison CA 3rd: Gene content phylogeny of herpesviruses. Proc Natl Acad Sci U S A 2000, 97(10):5334–5339. 10.1073/pnas.97.10.5334PubMed CentralView ArticlePubMedGoogle Scholar
- Yang S, Doolittle RF, Bourne PE: Phylogeny determined by protein domain content. Proc Natl Acad Sci U S A 2005, 102(2):373–378. 10.1073/pnas.0408810102PubMed CentralView ArticlePubMedGoogle Scholar
- The MUMmer package[http://www.tigr.org.]
- The Artemis software[http://www.sanger.ac.uk/Software/Artemis/.]
- Berriman M, Rutherford K: Viewing and annotating sequence data with Artemis. Brief Bioinform 2003, 4(2):124–132. 10.1093/bib/4.2.124View ArticlePubMedGoogle Scholar
- Xie T, Hood L: ACGT-a comparative genomics tool. Bioinformatics 2003, 19(8):1039–1040. 10.1093/bioinformatics/btg121View ArticlePubMedGoogle Scholar
- Frazer KA, Pachter L, Poliakov A, Rubin EM, Dubchak I: VISTA: computational tools for comparative genomics. Nucleic Acids Res 2004, 32(Web Server):W273–279.PubMed CentralView ArticlePubMedGoogle Scholar
- Carver TJ, Rutherford KM, Berriman M, Rajandream MA, Barrell BG, Parkhill J: ACT: the Artemis Comparison Tool. Bioinformatics 2005, 21(16):3422–3423. 10.1093/bioinformatics/bti553View ArticlePubMedGoogle Scholar
- Treangen T, Messeguer X: M-GCAT: Multiple Genome Comparison and Alignment Tool. 5th Annual Spanish Bioinformatics Conference (JBI 2004) 2004, 30–33.Google Scholar
- Korbel JO, Snel B, Huynen MA, Bork P: SHOT: a web server for the construction of genome phylogenies. Trends Genet 2002, 18(3):158–162. 10.1016/S0168-9525(01)02597-5View ArticlePubMedGoogle Scholar
- Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 1997, 25(17):3389–3402. 10.1093/nar/25.17.3389PubMed CentralView ArticlePubMedGoogle Scholar
- National Center for Biotechnology Information(NCBI)[http://www.ncbi.nlm.nih.gov.]
- Hendy MD, Penny D: Branch and bound algorithms to determine minimal evolutionary trees. Mathematical Biosciences 1982, 59: 277–290. 10.1016/0025-5564(82)90027-XView ArticleGoogle Scholar
- Swofford DL, Olsen GJ: Phylogeny reconstruction. In Molecular Systematics. Volume 11. Edited by: Hillis DM, Moritz C. Sunderland, Massachusetts: Sinauer Associates; 1990:411–501.Google Scholar
- Felsenstein J: PHYLIP (Phylogeny Inference Package) version 3.6. Distributed by the author. Department of Genome Sciences, University of Washington, Seattle. 2005.Google Scholar
- Page RD: TreeView: an application to display phylogenetic trees on personal computers. Comput Appl Biosci 1996, 12(4):357–358.PubMedGoogle Scholar
- Delcher AL, Kasif S, Fleischmann RD, Peterson J, White O, Salzberg SL: Alignment of whole genomes. Nucleic Acids Res 1999, 27(11):2369–2376. 10.1093/nar/27.11.2369PubMed CentralView ArticlePubMedGoogle Scholar
- Schwartz S, Zhang Z, Frazer KA, Smit A, Riemer C, Bouck J, Gibbs R, Hardison R, Miller W: PipMaker – a web server for aligning two genomic DNA sequences. Genome Res 2000, 10(4):577–586. 10.1101/gr.10.4.577PubMed CentralView ArticlePubMedGoogle Scholar
- Tatusov RL, Koonin EV, Lipman DJ: A genomic perspective on protein families. Science 1997, 278(5338):631–637. 10.1126/science.278.5338.631View ArticlePubMedGoogle Scholar
- Womble DD: GCG: The Wisconsin Package of sequence analysis programs. Methods Mol Biol 2000, 132: 3–22.PubMedGoogle Scholar
- PAUP package[http://paup.csit.fsu.edu/index.html]
- O'Brien KP, Remm M, Sonnhammer EL: Inparanoid: a comprehensive database of eukaryotic orthologs. Nucleic Acids Res 2005, 33(Database):D476–480. 10.1093/nar/gki107PubMed CentralPubMedGoogle Scholar
- Remm M, Storm CE, Sonnhammer EL: Automatic clustering of orthologs and in-paralogs from pairwise species comparisons. J Mol Biol 2001, 314(5):1041–1052. 10.1006/jmbi.2000.5197View ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.