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Bioinformatics Resources

  • Neetu JabaliaEmail author
Chapter

Abstract

Bioinformatics is an interdisciplinary research area at the interface between computer sciences and biological sciences. One of the goals of this chapter is to give a predominant perception of living cell and its functions at the molecular level using bioinformatics approaches including databases, tools, visualization, and data analysis. These approaches are implied at various levels such as metabolites, transcripts, and proteins. Therefore, the major focus of the present chapter will include many applications of bioinformatics in the area of genomics, proteomics, transcriptome, and metabolomics. Automated data-gathering tools are used for clustering and analysis of experimentally derived genomic data. Different in silico tools are used with implications both in structural and functional genomics. The chapter gives a detailed overview of the significant tools used for structural genomics such as TIGR assembler, VecScreen, EULER, Phred, and Phrap. Glimpses of comparative genomics approaches, namely, MAVID, LAGAN, BLASTZ, PipMaker, CoreGenes, and GeneOrder, are elaborated with a focus on gene functions at the whole genome level. A snapshot of high-throughput approaches using ESTs includes UniGene, TIGR Gene Indices, and SAGE (SAGEmap, SAGE Geneie, SAGExProfiler) and microarray-based approaches (SOTA, TIGR Tm4, Array Designer 2, Array mining) facilitates in understanding the interaction of genes and their regulations. The central dogma of life is incomplete without an understanding of each level spanning from genomics to proteomics. Thus, an exhaustive proteome analysis will immensely help in the elucidation of cellular functions. The latter dimension is covered by protein expression analysis tools such as Melanie, SWISS-2DPAGE, Comp 2D gel, protein identification through database searching (Mascot, ProFound, PepIdent), posttranslational modifications (AutoMotif, FindMod), protein sorting (TargetP, SignalO, PSORT), and protein–protein interactions (STRING, APID, InterPreTS). The last section describes the databases and mining software used for data integration, data interpretation, and metabolomics data in system biology. A brief explanation about commercial software, namely, ChromaTOF, GeneSpring MS, MarkerView, Mass Frontier, MarkerLynx, and complex LC/MS data analysis (BLSOM, Chrompare, MathDAMP), will help the readers to effectively use the information for their research endeavors.

Keywords

Genomics Proteomics Transcriptomics Metabolomics Bioinformatics 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Amity Institute of BiotechnologyAmity UniversityNoidaIndia

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