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 Table of Contents  
Year : 2018  |  Volume : 2  |  Issue : 3  |  Page : 163-167

Bioinformatics tools for genomic and evolutionary analysis of infectious agents

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 Publication6-Sep-2018

Correspondence Address:
Dr. Umesh Datta Gupta
Department of Animal Experimentation, ICMR-National Jalma Institute for Leprosy and Other Mycobacterial Diseases, Agra, Uttar Pradesh
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/bbrj.bbrj_74_18

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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 2023 Mar 27];2:163-7. Available from: https://www.bmbtrj.org/text.asp?2018/2/3/163/240709

  Introduction Top

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.[1],[2] 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,[3] 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).[4],[5],[6]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.[7],[8] 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 Top

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.[9],[10] 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.[11] 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.[12] The tBLASTx program of nucleotide sequence analysis can be used for identifying nucleotide sequences similar to the query based on their coding potential.[12]

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.[13] 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.[14],[15],[16] The Open Reading Frame Finder (ORF Finder) predicts all possible ORFs in given nucleotide sequence.[17] 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.[17]

Comparative genomics

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 [18],[19] 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.[18],[19] 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),[21] LAGAN (for pairwise and MSA),[22] and Shuffle-LAGAN (to find rearrangements in a global alignment framework) program have been incorporated into the mVISTA for the better results.[20] 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.[23] 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.[24] MAFT tool for medium-large alignments that have the ability to align up to 500 sequences or a maximum file size of 1 MB.[25] Muscle MSA tool is suitable for medium alignments and align up to 4000 sequences or a 4 MB of sequence data file.[26] Muscle is best for protein sequence alignments. MView tool transforms a sequence similarity search result into an MSA or reformat an MSA.[27] 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.[28] WebPRANK is a new phylogeny-aware MSA tool which makes use of evolutionary information to help place insertions and deletions.[29] 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.[30],[31],[32],[33] Multiple EM for Motif Elicitation (MEME) suits web portal (available at: http://meme-suite.org/) is the collection of motif identification and analysis tools.[34],[35],[36] MEME, Gapped Local Alignment of Motifs (GLAM2), Discriminative Regular Expression Motif Elicitation (DREME), and MEME-ChIP are the tools for motif discovery.[37],[38],[39],[40],[41] 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.[42] 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.[41],[43],[44] SpaMo and CentriMo are the tools for the motif enrichment analysis.[45],[46],[47] 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.[48] TOMTOM tool is used for the comparison of DNA motif in the database of known DNA sequence motifs data.[34],[35],[49] GOMO (Gene Ontology for Motifs) program had been designed for the functional analysis of the DNA-binding motifs.[50]

Central dogma

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.[51]

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.[52],[53],[54],[55],[56],[57],[58],[59]

Evolutionary analysis

Genomic sequences of IAs contain rich information about their origin and the functional constraints on macromolecules such as proteins/enzymes.[2] Evolution in the genomic sequences of IAs can originate new strains/species, which may be more virulent than its parent strains/species.[1],[60],[61],[62] 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.[63],[64] 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.[65] 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.[59]

  Conclusion Top

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.

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