The growth of bioinformatics

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Abstract

Briefings in Bioinformatics, or BiB for short, will celebrate its 7th anniversary this year. The journal’s mission has remained unchanged throughout this period: we are committed to disseminating knowledge on databases and computational tools for life sciences through review articles. During its history, some 250 articles have been published and they have been cited more than 2700 times, making BiB the premier bioinformatics journal using the perarticle citation impact measure. The common theme for this issue is the cross-disciplinary nature of bioinformatics and the proliferation of bioinformatics into new areas of life sciences. This issue brings six outstanding reviews that collectively demonstrate the broad outreach of bioinformatics. Perez-Iratxeta, Andrade-Navarro and Wren performed a meta-analysis of abstracts published in MEDLINE and abstracts of NIH-funded project grants to determine the growth and spread of computational approaches across the various subfields of biomedicine during the past 30 years. They explore three major bioinformatics concepts: computation, the Internet and databases. Their analysis of MeSH terms indicate the major areas of focus within bioinformatics are protein, gene and nucleic acid databases, computational biology, computing methodologies and programming languages. Software and software design, database management systems and principal component analysis, are found to be of high importance, followed by several other subareas of biocomputing. The areas with highest growth during the period of 2000–03 include bioinformatics-dependent areas of genomics, genetic databases, gene expression profiling and oligonucleotide array sequence analysis. Computational biology alone showed a 3-fold increase during this period, while bioinformatics showed a 15-fold increase. Bioinformatics has spawned into sub-disciplines such as cheminformatics, neuroinformatics and immunoinformatics, and the boundaries between bioinformatics and biomedical disciplines are increasingly blurred. Tong, Tan and Ranganathan have summarized the latest developments of methods and protocols for predicting immunogenic epitopes, a major topic within the rapidly growing field of immunoinformatics. They present a clear case of how bioinformatics-driven methods for the selection of key experiments resulted in a significant increase in the speed and economy of mapping of vaccine targets. These methods enable the formulation of new testable hypotheses through the in-depth analysis of complex immunological data that could not have been developed by traditional experimental approaches alone. Bioinformatics keeps proliferating into diverse biomedical disciplines. Law enforcement increasingly uses biomolecular data and databases. Forensic DNA databases, for example, have been established in a large number of countries. Bianchi and Liò discuss the state of the art in forensic DNA science and the potential of bioinformatics in developing this field. They point out that the bioinformatics analysis of forensic DNA has important implications for the organization of forensic evidence and the integration of crime databases with public health and population genetics databases. Any information of such nature has potential for misuse. The authors also discuss privacy rights and the role of bioinformatics in protection of these rights. Genomics is the fastest growing area at the intersection of biomedicine and bioinformatics. A large number of statistical methods and software solutions are appearing in support of genomic studies. Gold and co-authors address the issues of statistical testing and of the use of a priori knowledge for the assignment of biological properties to genes and have a number of recommendations for the users. Their results support the assumption of gene independence for the analysis of genomic data, thus allowing the use of a range of statistical techniques. They also offer insights into the practical use of software packages for these tasks. Their final words of wisdom encourage the bioinformatics community to remain wary of the implicit assumptions present in software packages. Bayesian statistics is used as inference engine and for information extraction. It is particularly BRIEFINGS IN BIOINFORMATICS. VOL 8. NO 2. 69^70 doi:10.1093/bib/bbm008 Advance Access publication March 24, 2007