Bioinformatics Resources

  • Neetu JabaliaEmail author


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.


Genomics Proteomics Transcriptomics Metabolomics Bioinformatics 


  1. Barrett, T., Troup, D. B., Wilhite, S. E., Ledoux, P., Evangelista, C., Kim, I. F., Tomashevsky, M., Marshall, K. A., Phillippy, K. H., Sherman, P. M., & Muertter, R. N. (2010). NCBI GEO: Archive for functional genomics data sets—10 years on. Nucleic Acids Research, 20:39(suppl_1), D1005–D1010.PubMedGoogle Scholar
  2. Brazma, A. (2009). Minimum information about a microarray experiment (MIAME)–successes, failures, challenges. The Scientific World Journal, 9, 420–423.CrossRefGoogle Scholar
  3. Bunnik, E. M., & Le Roch, K. G. (2013). An introduction to functional genomics and systems biology. Advances in Wound Care, 1;2(9), 490–498.CrossRefGoogle Scholar
  4. Commisso, M., Strazzer, P., Toffali, K., Stocchero, M., & Guzzo, F. (2013). Untargeted metabolomics: An emerging approach to determine the composition of herbal products. Computational and Structural Biotechnology Journal, 4(5), e201301007.CrossRefGoogle Scholar
  5. Fahrmann, J., Grapov, D., Yang, J., Hammock, B., Fiehn, O., Bell, G. I., & Hara, M. (2015). Systemic alterations in the metabolome of diabetic nod mice delineate increased oxidative stress accompanied by reduced inflammation and hypertriglyceridemia. American Journal of Physiology. Endocrinology and Metabolism, 308(11), E978–E989.CrossRefGoogle Scholar
  6. Friedrich, N. (2012). Metabolomics in diabetes research. The Journal of Endocrinology, 215(1), 29–42.CrossRefGoogle Scholar
  7. Gracie, S., Pennell, C., Ekman-Ordeberg, G., et al. (2011). An integrated systems biology approach to the study of preterm birth using -omic technology – A guideline for research. BMC Pregnancy and Childbirth, 11, 71.CrossRefGoogle Scholar
  8. Harrow, J., Frankish, A., Gonzalez, J. M., et al. (2012). GENCODE: The reference human genome annotation for the ENCODE project. Genome Research, 22, 1760–1774.CrossRefGoogle Scholar
  9. Kanehisa, M., & Goto. (2000). KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Research, 28(1), 27–30.CrossRefGoogle Scholar
  10. Kuhn, R. M., Karolchik, D., Zweig, A. S., Wang, T., Smith, K. E., Rosenbloom, K. R., Rhead, B., Raney, B. J., Pohl, A., Pheasant, M., Meyer, L., Hsu, F., Hinrichs, A. S., Harte, R. A., Giardine, B., Fujita, P., Diekhans, M., Dreszer, T., Clawson, H., Barber, G. P., Haussler, D., & Kent, W. J. (2009). The UCSC genome browser database: Update 2009. Nucleic Acids Research, 37, D755–D761.CrossRefGoogle Scholar
  11. Lee, D. Y., Bowen, B. P., & Northen, T. R. (2010). Mass spectrometry-based metabolomics, analysis of metabolite-protein interactions, and imaging. BioTechniques, 49(2), 557–565.CrossRefGoogle Scholar
  12. Markley, J. L., Brüschweiler, R., Edison, A. S., Eghbalnia, H. R., Powers, R., Raftery, D., & Wishart, D. S. (2017, February 1). The future of NMR-based metabolomics. Current Opinion in Biotechnology, 43, 34–40.CrossRefGoogle Scholar
  13. Ono, Y., Asai, K., & Hamada, M. (2013). PBSIM: PacBio reads simulator--toward accurate genome assembly. Bioinformatics, 29, 119–121.CrossRefGoogle Scholar
  14. Psychogios, N., Hau, D. D., Peng, J., Guo, A. C., Mandal, R., Bouatra, S., Sinelnikov, I., Krishnamurthy, R., Eisner, R., Gautam, B., Young, N., Xia, J., Knox, C., Dong, E., Huang, P., Hollander, Z., Pedersen, T. L., Smith, S. R., Bamforth, F., Greiner, R., McManus, B., Newman, J. W., Goodfriend, T., & Wishart, D. S. (2011). The human serum metabolome. PLoS One, 6(2), e16957.CrossRefGoogle Scholar
  15. Roberts, L. D., Souza, A. L., Gerszten, R. E., & Clish, C. B. (2012). Targeted metabolomics. Current Protocols in Molecular Biology, CHAPTER, Unit 30.2. Scholar
  16. Rung, J., & Brazma, A. (2013). Reuse of public genome-wide gene expression data. Nature Reviews. Genetics, 14(2), 89–99.CrossRefGoogle Scholar
  17. Simpson, J. T., & Pop, M. (2015). The theory and practice of genome sequence assembly. Annual Review of Genomics and Human Genetics, 16, 153–172.CrossRefGoogle Scholar
  18. Venter, J. C., Smith, H. O., & Adams, M. D. (2015). The sequence of the human genome. Clinical Chemistry, 61, 1207–1208.CrossRefGoogle Scholar
  19. Wikoff, W. R., Grapov, D., Fahrmann, J. F., DeFelice, B., Rom, W., Pass, H., Kim, K., Nguyen, U., Taylor, S. L., Kelly, K., & Fiehn, O. (2015). Metabolomic markers of altered nucleotide metabolism in early stage adenocarcinoma. Cancer Prevention Research (Philadelphia, Pa.), 8(5), 410–418.CrossRefGoogle Scholar
  20. Zhou, X., Peris, D., Kominek, J., Kurtzman, C. P., Hittinger, C. T., & Rokas, A. (2016, November 1). In silico whole genome sequencer and analyzer (iWGS): A computational pipeline to guide the design and analysis of de novo genome sequencing studies. G3: Genes, Genomes, Genetics, 6(11), 3655–62.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Amity Institute of BiotechnologyAmity UniversityNoidaIndia

Personalised recommendations