|Year : 2018 | Volume
| Issue : 3 | Page : 163-167
Bioinformatics tools for genomic and evolutionary analysis of infectious agents
Vivek Dhar Dwivedi1, Shiv Bharadwaj2, Partha Sarathi Mohanty1, Umesh Datta Gupta3
1 Department of Epidemiology, ICMR-National JALMA Institute for Leprosy and Other Mycobacterial Diseases, Agra, Uttar Pradesh, India
2 Nanotechnology Research and Application Center, Sabanci University, Istanbul, Turkey
3 Department of Animal Experimentation, ICMR-National JALMA Institute for Leprosy and Other Mycobacterial Diseases, Agra, Uttar Pradesh, India
|Date of Web Publication||6-Sep-2018|
Dr. Umesh Datta Gupta
Department of Animal Experimentation, ICMR-National Jalma Institute for Leprosy and Other Mycobacterial Diseases, Agra, Uttar Pradesh
Source of Support: None, Conflict of Interest: None
Genome sequence analysis of infectious agents (IAs) reveals many secrets about their life processes and evolutionary history. Increasing the huge amount of genomic sequence data of various IAs in different biological sequence databases, which are being produced through different sequencing projects, is continuously motivating the genome researchers to unlock the mysteries related to the life of IAs. Furthermore, that information may be helpful for treating the serious illness problem caused by IAs. However, all the genome analysis work requires a good knowledge of bioinformatics tools that may be useful for genome researchers to extract the meaningful and accurate information from the genome sequence data of IAs. In this article, the most recent bioinformatics tools for the genomic and evolutionary analysis of infectious agents have been discussed and compared in detail which will help the genome researchers to select the most appropriate tool for genomic and evolutionary analysis of IAs.
Keywords: Bioinformatics tools, evolution, genome, infectious agents
|How to cite this article:|
Dwivedi VD, Bharadwaj S, Mohanty PS, Gupta UD. Bioinformatics tools for genomic and evolutionary analysis of infectious agents. Biomed Biotechnol Res J 2018;2:163-7
|How to cite this URL:|
Dwivedi VD, Bharadwaj S, Mohanty PS, Gupta UD. Bioinformatics tools for genomic and evolutionary analysis of infectious agents. Biomed Biotechnol Res J [serial online] 2018 [cited 2020 Feb 24];2:163-7. Available from: http://www.bmbtrj.org/text.asp?2018/2/3/163/240709
| Introduction|| |
Infectious agents (IAs) such as bacteria, fungi, protozoans, helminths, and viruses cause very serious health issues in human beings. Genome of all the IAs contains DNA\RNA as their genetic material which possesses a specific order of nucleotides. The specific order of nucleotides in the genome of each IA differentiates their identity from one another. The mystery of the origin, growth, survival, virulence, and evolution of IAs is hidden in the specific order of nucleotides of their genomes., Hence, it is very important to analyze the genome of IAs for understating of their identity, molecular mechanism of infection, and development of new effective drugs for treating their bad effects. Genome sequence data of IAs, which are produced through different sequencing projects around the world and are deposited in various nucleotide sequence databases, require various in silico tools for unlocking the mystery of their life. The huge amount of the nucleotide sequence data experimentally produced is collected, organized, and distributed by the International Nucleotide Sequence Database Collaboration, which is a joint effort of the nucleotide sequence databases such as EMBL-EBI (European Bioinformatics Institute, http://www.ebi.ac.uk), DDBJ (DNA Data Bank of Japan, http://www.ddbj.nig.ac.jp), and GenBank (National Center for Biotechnology Information, http://www.ncbi.nlm.nih.gov).,,In silico tools have been the integral part of the biological research which are designed to reveal the meaningful information from biological data within a very short time. Although in silico tools cannot reveal results as reliable asin vitro orin vivo investigations, which is very costly and time taking, however, the bioinformatics analyses can still facilitate to reach an informed decision for conducting expensive research., But, in so many cases, only in silico tools are capable of answering the questions of biological research. Developments of these tools are the important part of bioinformatics and computational biology field. A large number of in silico tools have been developed for genomic and evolutionary analysis of IAs, but the selection of appropriate tool for the analysis of genomic data requires a strong knowledge of statistics and computational algorithms. Hence, it is very it is very difficult for the researchers of noncomputational biology background to choose the appropriate one.
In light of the above facts, it is very necessary to explore the importance and accuracy of various bioinformatics tools for different types of genomic and evolutionary analysis of IAs. In this review, various bioinformatics tools for the genomic and evolutionary analysis of IAs have been discussed which will be helpful to the IA researchers of nonbioinformatics background for selecting the appropriate tool for their work.
| Tools for Various Types of Genome Analysis|| |
Sequence identification or similarity searching
DNA sequence identification or similarity searching tools (SSTs) are the first most important programs of biological research which helps the scientists for taking the correct decision about the species identity and classification by providing the information about their closely related organisms as a result. These tools search the similar DNA sequences in the databases for a given query DNA sequence. Each nucleotide database contains its own unique SST for performing sequence similarity search. BLAST, FASTA, and ENA search are the most popular sequence SSTs., Among these three tools, BLAST is a very efficient program which contains many options for the sequence similarity searching. Here, only nucleotide sequence similarity searching programs of BLAST have been discussed. BLAST stands for basic local alignment search tool which is group of tools for nucleotide and protein sequence similarity searching. Nucleotide BLAST (BLASTn) is one among those tools which take a nucleotide sequence (genome sequence) as a query sequence and search for the similar DNA sequences in the NCBI database. Researchers have options to choose the type of optimization program such as megablast, discontiguous megablast, and BLASTn. Megablast searches for highly similar sequences which are very useful for the species identification and intraspecies comparison. The selection of discontiguous megablast option searches for more dissimilar sequences and can be used for cross-species comparison. The BLASTn option is used for searching of somewhat similar sequences in the NCBI database. The BLASTx program of BLAST package is used for identifying the potential protein products encoded by a nucleotide query. The tBLASTx program of nucleotide sequence analysis can be used for identifying nucleotide sequences similar to the query based on their coding potential.
Open reading frames
Identification of genes in the genomes of IAs is the main goal of their sequencing projects. Authentic prediction of genes and their positions can be useful for understanding the molecular mechanism of IAs growth, survival, and virulence. Furthermore, those information can be utilized to develop the molecular diagnostic kits and potential drugs for thein vitro identification and treating the infections of IAs, respectively. Open reading frame (ORF) is a best hypothesis for the prediction of a protein-coding region in the genome sequence data of an organism. It is the region of genome sequence between a start codon and the next stop codon. Various tools for the prediction of ORF have been developed, but according to the Wikipedia, ORF finder, ORF investigator, and ORF predictor are the most powerful tools for the efficient prediction of ORFs.,, The Open Reading Frame Finder (ORF Finder) predicts all possible ORFs in given nucleotide sequence. The ORF investigator is a graphical user interface program that finds all ORFs in a given DNA sequence and converts them into their corresponding protein sequence by declaring their respective positions in the sequence.
Alignment of genomes or gene sequences of IAs provide an interesting knowledge about their percentage of relatedness and variations between two or among more than two species. The alignment between two sequences (pairwise sequence alignment) predicts the conserved and variable regions and also provides the percentage similarity. While the alignment between more than two sequences (multiple sequence alignment [MSA]) provides not only information about the conserved and variable regions but also generates data for phylogenetic analysis. Emboss is a most powerful program for the pairwise sequence alignment (global and local) small DNA sequences. Emboss is available at http://www.ebi.ac.uk/Tools/emboss/. wgVISTA is a software package used for comparing the genome data (up to 10 megabases long) of two microbial organisms , and is available at http://genome.lbl.gov/cgi-bin/WGVistaInput. Similarly another software package mVISTA is used for the comparison of two or more nucleotide sequences from two or more organisms and is available at http://genome.lbl.gov/cgi-bin/GenomeVista., mVISTA is an online program which provides the significant and clean results of genome sequences alignment, allowing the representation of alignment results at different levels of resolution. It offers the access to global pairwise, multiple, and glocal (global with rearrangements) alignment tools. AVID (for global alignment of DNA sequences of arbitrary length), LAGAN (for pairwise and MSA), and Shuffle-LAGAN (to find rearrangements in a global alignment framework) program have been incorporated into the mVISTA for the better results. DNASTAR (https://www.dnastar.com/t-sub-solutions-molecular-biology-sequence-alignment.aspx) is a software which aligns DNA sequences through different alignment algorithms including MUSCLE, Mauve, MAFFT, Clustal Omega, and many other programs for generating best results. The European Bioinformatics Institute (EBI) offers a number of programs such as Clustal Omega, Kalign, MAFFT, MUSCLE, MView, T-Coffee, and WebPRANK for MSA, available at http://www.ebi.ac.uk/Tools/msa/. Clustal Omega is a tool of MSA which perform medium-large alignments of up to 4000 sequences or a 4 MB of sequence data file. Kalign MSA tool is a very fast tool which can perform alignment of up to 2000 sequences or a 2 MB of sequence data file. MAFT tool for medium-large alignments that have the ability to align up to 500 sequences or a maximum file size of 1 MB. Muscle MSA tool is suitable for medium alignments and align up to 4000 sequences or a 4 MB of sequence data file. Muscle is best for protein sequence alignments. MView tool transforms a sequence similarity search result into an MSA or reformat an MSA. It can align up to 4000 sequences or a 4 MB of sequence data file. For small alignments, T-Coffee program is very suitable that can align up to 500 sequences or a maximum file size of 1 MB. WebPRANK is a new phylogeny-aware MSA tool which makes use of evolutionary information to help place insertions and deletions. All above-described MSA tools are the most popular tools which can be used as per their requirements.
DNA motif discovery and analysis
DNA sequence motifs are the short segment of DNA which contains many prestigious information about the functional attributes of IAs which have been preserved during the time of evolution. Identification of DNA sequence motifs of IAs may contribute the significant information to the scientist to design and develop new effective drugs for various types of IAs infections.,,, Multiple EM for Motif Elicitation (MEME) suits web portal (available at: http://meme-suite.org/) is the collection of motif identification and analysis tools.,, MEME, Gapped Local Alignment of Motifs (GLAM2), Discriminative Regular Expression Motif Elicitation (DREME), and MEME-ChIP are the tools for motif discovery.,,,, MEME is a very powerful tool for the identification of novel, ungapped motifs in a set of interconnected DNA sequences. By default, it searches for minimum three motifs of about 6–50, while the user can define their own parameters for motif discovery. GLAM2 finds out the gapped motifs in a group of input DNA data. GLAM2 attempts to discover the best potential motif several times by replicates analysis. Hence, GLAM2 is better than MEME. DREME searches motifs on large DNA sequence data sets derived from ChIP-seq experimentation. MEME-ChIP tool discovers and analyze motifs in large nucleotide datasets derived from ChIP-seq and CLIP-seq experiments. FIMO, GLAM2SCAN, and MAST (Motif Alignment and Search Tool) are the tools for finding the possible occurrences of motif in a sequence database, and hence these are called the motif searching tools.,, SpaMo and CentriMo are the tools for the motif enrichment analysis.,, MCAST (Motif Cluster Alignment and Search Tool) is a motif cluster analysis tool that searches a sequence database for statistically significant clusters of nonoverlapping occurrences of a set of motifs. TOMTOM tool is used for the comparison of DNA motif in the database of known DNA sequence motifs data.,, GOMO (Gene Ontology for Motifs) program had been designed for the functional analysis of the DNA-binding motifs.
Central dogma is directly related with three different molecular processes of the cell transcription (DNA to RNA), translation (RNA to Protein), and reverse transcription (RNA to DNA). Hence, the computational tools which are able to convert DNA into RNA, RNA into protein, and RNA into DNA can be called as central dogma tools. Many programs have been designed for this purpose biological data analysis program is one among them which can perform the central dogma-related calculations.
Mutation and recombination analysis
Mutational analysis of IAs provides an idea to check the possible changes in their genome which are very useful to know their origin, virulence, and evolution. It also helps to find the genetic diversity among a group of IAs. Among the mutation and recombination analysis tools, Molecular Evolutionary Genetics Analysis (MEGA5) and DNASP are the very popular tools for mutation- and recombination-related calculations, respectively.,,,,,,,
Genomic sequences of IAs contain rich information about their origin and the functional constraints on macromolecules such as proteins/enzymes. Evolution in the genomic sequences of IAs can originate new strains/species, which may be more virulent than its parent strains/species.,,, Hence, the phylogenetic analysis of IAs is important to understanding their origin and evolutionary history. For this purpose, a good knowledge of phylogenetic analysis tools are required therefore the most popular tools and their advantages and disadvantages have been discussed. Phylogeny Inference Package (PHYLIP) is the most popular used software package for evolutionary analysis developed by the scientists of Department of Genome Sciences and the Department of Biology, University of Washington, Seattle. It is a freely available software package which analyses molecular sequences using different methods including parsimony, distance matrix, and likelihood methods, including bootstrapping and consensus trees., Hypothesis testing using phylogenies (HyPhy) is a freely distributed software package for phylogenetic analysis of biological sequences, in particular for inferring the strength of selection from sequence data. In addition, HyPhy features a flexible batch language for implementing and customizing discrete state Markov models in a phylogenetic framework. MEGA is a very popular software package for evolutionary analysis of organisms at molecular level. Different versions of this package are freely available for academicians. It implements several methods and programs for the purpose of evolutionary analysis which are most algorithms in the area of evolutionary biology.
| Conclusion|| |
Genome and evolutionary analysis of infectious agents reveal many meaningful information for understanding their origin, growth, survival, and virulence nature. It also provides important knowledge for choosing the potential therapeutic targets and also for discovery of novel drugs for treating their infections. The most recent bioinformatics tools, which have been discussed in this article for the genomic and evolutionary analysis of infectious agents, will be helpful for the genome researchers to select the most appropriate tool of genomic and evolutionary analysis of IAs for unlocking their life mysteries.
The authors of this article acknowledge National JALMA Institute for Leprosy and Other Mycobacterial Diseases (ICMR), Agra, India.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Dwivedi VD, Tripathi IP, Tripathi RC, Bharadwaj S, Mishra SK. Genomics, proteomics and evolution of dengue virus. Brief Funct Genomics 2017;16:217-27.
Marks DS, Hopf TA, Sander C. Protein structure prediction from sequence variation. Nat Biotechnol 2012;30:1072-80.
Cochrane G, Karsch-Mizrachi I, Takagi T, International Nucleotide Sequence Database Collaboration. The international nucleotide sequence database collaboration. Nucleic Acids Res 2016;44:D48-50.
Benson DA, Cavanaugh M, Clark K, Karsch-Mizrachi I, Lipman DJ, Ostell J, et al.
GenBank. Nucleic Acids Res 2017;45:D37-42.
Tateno Y, Imanishi T, Miyazaki S, Fukami-Kobayashi K, Saitou N, Sugawara H, et al.
DNA data bank of japan (DDBJ) for genome-scale research in life science. Nucleic Acids Res 2002;30:27-30.
Kulikova T, Akhtar R, Aldebert P, Althorpe N, Andersson M, Baldwin A, et al.
EMBL nucleotide sequence database in 2006. Nucleic Acids Res 2007;35:D16-20.
Pruess M, Apweiler R. Bioinformatics resources for in silico
proteome analysis. J Biomed Biotechnol 2003;2003:231-6.
Mehmood MA, Sehar U, Ahmad N. Use of bioinformatics tools in different spheres of life sciences. J Data Min Genomics Proteomics 2014;5:1-13.
Pearson W. Finding protein and nucleotide similarities with FASTA. Curr Protoc Bioinformatics 2016;53:3-9. doi: 10.1002/0471250953.bi0309s53.
Leinonen R, Akhtar R, Birney E, Bower L, Cerdeno-Tárraga A, Cheng Y, et al.
The European nucleotide archive. Nucleic Acids Res 2011;39:D28-31.
Johnson M, Zaretskaya I, Raytselis Y, Merezhuk Y, McGinnis S, Madden TL, et al.
NCBI BLAST: A better web interface. Nucleic Acids Res 2008;36:W5-9.
Rombel IT, Sykes KF, Rayner S, Johnston SA. ORF-FINDER: A vector for high-throughput gene identification. Gene 2002;282:33-41.
Dwivedi VD, Mishra SK. ORF investigator: A new ORF finding tool combining pairwise global gene alignment. Res J Recent Sci 2012:1:32-5.
Min XJ, Butler G, Storms R, Tsang A. OrfPredictor: Predicting protein-coding regions in EST-derived sequences. Nucleic Acids Res 2005;33:W677-80.
Hung CL, Lin CY. Open reading frame phylogenetic analysis on the cloud. Int J Genomics 2013;2013:614923.
Frazer KA, Pachter L, Poliakov A, Rubin EM, Dubchak I. VISTA: Computational tools for comparative genomics. Nucleic Acids Res 2004;32:W273-9.
Mayor C, Brudno M, Schwartz JR, Poliakov A, Rubin EM, Frazer KA, et al.
VISTA: Visualizing global DNA sequence alignments of arbitrary length. Bioinformatics 2000;16:1046-7.
Brudno M, Malde S, Poliakov A, Do CB, Couronne O, Dubchak I, et al.
Glocal alignment: Finding rearrangements during alignment. Bioinformatics 2003;19 Suppl 1:i54-62.
Bray N, Dubchak I, Pachter L. AVID: A global alignment program. Genome Res 2003;13:97-102.
Brudno M, Do CB, Cooper GM, Kim MF, Davydov E, Green ED, Sidow A, Batzoglou S. NISC Comparative Sequencing Program. LAGAN and Multi-LAGAN: Efficient tools for large-scale multiple alignment of genomic DNA. Genome Res 2003;13:721-31.
Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, Li W, et al.
Fast, scalable generation of high-quality protein multiple sequence alignments using clustal omega. Mol Syst Biol 2011;7:539.
Lassmann T, Sonnhammer EL. Kalign – An accurate and fast multiple sequence alignment algorithm. BMC Bioinformatics 2005;6:298.
Katoh K, Misawa K, Kuma K, Miyata T. MAFFT: A novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res 2002;30:3059-66.
Edgar RC. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 2004;32:1792-7.
Brown NP, Leroy C, Sander C. MView: A web-compatible database search or multiple alignment viewer. Bioinformatics 1998;14:380-1.
Notredame C, Higgins DG, Heringa J. T-coffee: A novel method for fast and accurate multiple sequence alignment. J Mol Biol 2000;302:205-17.
Löytynoja A, Goldman N. WebPRANK: A phylogeny-aware multiple sequence aligner with interactive alignment browser. BMC Bioinformatics 2010;11:579.
Kumar A, Dwivedi VD. Evolutionary analysis and motif discovery in rhodopsin from vertebrates. Int Res J Biol Sci 2013;2:6-11.
Bose R, Arora S, Dwivedi VD, Pandey A. Amino acid sequence based in silico
analysis of β-galactosidases. Int J Bioinform Biosci 2013;3:37-44.
Kumar S, Bhagabati P, Sachan R, Kaushik AC, Dwivedi VD. In silico
analysis of sequence-structure-function relationship of the Escherichia coli
methionine synthase. Interdiscip Sci 2015;7:382-90.
Dwivedi VD, Arora S, Kumar A, Mishra SK. Computational analysis of xanthine dehydrogenase enzyme from different source organisms. Netw Model Anal Health Inform Bioinform 2013;2:185-9.
Bailey TL, Johnson J, Grant CE, Noble WS. The MEME suite. Nucleic Acids Res 2015;43:W39-49.
Bailey TL, Boden M, Buske FA, Frith M, Grant CE, Clementi L, et al.
MEME SUITE: Tools for motif discovery and searching. Nucleic Acids Res 2009;37:W202-8.
Dwivedi VD, Mishra SK. In silico
analysis of L-asparaginase from different source organisms. Interdiscip Sci 2014;6:93-9.
Bailey TL, Williams N, Misleh C, Li WW. MEME: Discovering and analyzing DNA and protein sequence motifs. Nucleic Acids Res 2006;34:W369-73.
Bailey TL, Elkan C. Fitting a mixture model by expectation maximization to discover motifs in biopolymers. Proc Int Conf Intell Syst Mol Biol 1994;2:28-36.
Machanick P, Bailey TL. MEME-ChIP: Motif analysis of large DNA datasets. Bioinformatics 2011;27:1696-7.
Bailey TL. DREME: Motif discovery in transcription factor ChIP-seq data. Bioinformatics 2011;27:1653-9.
Frith MC, Saunders NF, Kobe B, Bailey TL. Discovering sequence motifs with arbitrary insertions and deletions. PLoS Comput Biol 2008;4:e1000071.
Ma W, Noble WS, Bailey TL. Motif-based analysis of large nucleotide data sets using MEME-ChIP. Nat Protoc 2014;9:1428-50.
Grant CE, Bailey TL, Noble WS. FIMO: Scanning for occurrences of a given motif. Bioinformatics 2011;27:1017-8.
Bailey TL, Gribskov M. Combining evidence using P
values: Application to sequence homology searches. Bioinformatics 1998;14:48-54.
Whitington T, Frith MC, Johnson J, Bailey TL. Inferring transcription factor complexes from chIP-seq data. Nucleic Acids Res 2011;39:e98.
Bailey TL, Machanick P. Inferring direct DNA binding from ChIP-seq. Nucleic Acids Res 2012;40:e128.
Lesluyes T, Johnson J, Machanick P, Bailey TL. Differential motif enrichment analysis of paired chIP-seq experiments. BMC Genomics 2014;15:752.
Bailey TL, Noble WS. Searching for statistically significant regulatory modules. Bioinformatics 2003;19 Suppl 2:ii16-25.
Gupta S, Stamatoyannopoulos JA, Bailey TL, Noble WS. Quantifying similarity between motifs. Genome Biol 2007;8:R24.
Bodén M, Bailey TL. Associating transcription factor-binding site motifs with target GO terms and target genes. Nucleic Acids Res 2008;36:4108-17.
Dwivedi VD, Tripathi IP, Kaushik AC, Bharadwaj S, Mishra SK. Biological data analysis program (BDAP): A multitasking biological sequence analysis program. Neural Comput Appl 2016;17:1-9. doi: 10.1007/s00521-016-2772-z.
Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S, et al.
MEGA5: Molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Biol Evol 2011;28:2731-9.
Rozas J, Rozas R. DnaSP, DNA sequence polymorphism: An interactive program for estimating population genetics parameters from DNA sequence data. Comput Appl Biosci 1995;11:621-5.
Rozas J, Rozas R. DnaSP version 2.0: A novel software package for extensive molecular population genetics analysis. Comput Appl Biosci 1997;13:307-11.
Rozas J, Rozas R. DnaSP version 3: An integrated program for molecular population genetics and molecular evolution analysis. Bioinformatics 1999;15:174-5.
Rozas J, Sánchez-DelBarrio JC, Messeguer X, Rozas R. DnaSP, DNA polymorphism analyses by the coalescent and other methods. Bioinformatics 2003;19:2496-7.
Librado P, Rozas J. DnaSP v5: A software for comprehensive analysis of DNA polymorphism data. Bioinformatics 2009;25:1451-2.
Rozas, J. DNA sequence polymorphism analysis using DnaSP. In: Posada D, editor. Bioinformatics for DNA Sequence Analysis, Methods in Molecular Biology. New York: Humana Press 2009. p. 337-50.
Kumar S, Stecher G, Tamura K. MEGA7: Molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol Biol Evol 2016;33:1870-4.
Kaushik AC, Pal A, Kumar A, Dwivedi VD, Bharadwaj S, Pandey A, et al
. Internal transcribed spacer sequence database of plant fungal pathogens: PFP-ITSS database. Inform Med Unlocked 2017;7:34-8.
Dwivedi VD, Tripathi IP, Mishra SK. In silico
evaluation of inhibitory potential of triterpenoids from Azadirachta indica
against therapeutic target of dengue virus, NS2B-NS3 protease. J Vector Borne Dis 2016;53:156-61.
] [Full text]
Dwivedi VD, Tripathi IP, Bharadwaj S, Kaushik AC, Mishra SK. Identification of new potent inhibitors of dengue virus NS3 protease from traditional Chinese medicine database. Virusdisease 2016;27:220-5.
Felsenstein J. Evolutionary trees from DNA sequences: A maximum likelihood approach. J Mol Evol 1981;17:368-76.
PHYLIP (phylogeny inference package), version 3.6a2. Washington, Seattle: Department of Genetics University; 1993.
Pond SL, Muse SV. HyPhy: Hypothesis testing using phylogenies. In: Statistical Methods in Molecular Evolution. New York: Springer; 2005.