Identification of Gene Families Using Genomics and/or Transcriptomics Data

Abstract

Thousands of putative open reading frames (ORFs) are identified via annotation of sequenced plant genomes. Classification of these ORFs into gene families has a crucial importance to understand the evolution, function, and structure of the encoded proteins such as transcription factors, and the non-coding RNAs such as microRNAs (miRNAs). Thus, molecular mechanisms underlying the metabolic processes in plants are uncovered as well. Some members of the gene families are species-specific being more dynamic during evolution whereas others are more conserved, phylogenetically sharing common features. The latter are especially important for the annotation of putative ORFs by revealing known counterparts with high sequence identity via sequence alignment to discover conserved motifs. Various bioinformatic tools are available to find out gene families in plants. The BLAST tool (http://blast.ncbi.nlm.nih.gov/Blast.cgi) is widely used for identification of homologous sequences. Phytozome (http://www.phytozome.net) or GreenPhyl (http://www.greenphyl.org) are the web resources utilized for the functional and comparative genomics in plants to analyze gene families. TRAPID (http://bioinformatics.psb.ugent.be/webtools/trapid) offers a free of charge web source for functional and comparative analyses of transcriptome data sets for identification of gene families, alignment of multiple sequences and phylogenetic tree construction. Some of the databases store specific type of gene families such as plant transcription factor databases PlantTFDB (http://planttfdb.cbi.pku.edu.cn) and PlnTFDB (http://plntfdb.bio.uni-potsdam.de/v3.0), or miRBase (http://www.mirbase.org) for miRNAs. Molecular Evolutionary Genetics Analysis (MEGA) software is an integrated tool for the analyses such as alignment of sequence, construction of phylogenetic trees, and access to online databases. In this chapter, the bioinformatic tools for analyses of genomics and/or transcriptomics data sets to discover gene families as well as sample researches are discussed.