Several other university libraries have also embraced this type of bioinformatics-focused specialized service as a means to connect with their own biomedical research communities a 2006 special issue of the Journal of Medical Library Association describes a few of these programs in detail. Program activities recorded from implementation to 2006 were previously described. MBIS has been well-received by the Pitt research community and is now in its second decade of service. HSLS emulated the UW program in 2002 by hiring a PhD molecular biologist and developing a four-facet service with the following goals: (1) identify, procure, and implement commercially-licensed bioinformatics software, (2) teach hands-on workshops using bioinformatics tools to solve research questions, (3) provide in-person and email consultations on software/databases, and (4) maintain a web portal providing overall guidance on the access and use of bioinformatics resources and MBIS-created webtools. The UW service was initiated in 1995 and directed by a PhD scientist. The first library to offer a bioinformatics service was the University of Washington Health Sciences Library (UW). HSLS was an early pioneer in the implementation of a health sciences library-based bioinformatics support service. As shared-use facilities, libraries can leverage their operational infrastructure to provide a bioinformatics-focused information service by incorporating molecular databases and software into their collections and offer training on the use of these resources. Medical libraries traditionally support biomedical research by providing access to journals and books, procuring licenses for electronic resources, providing instructional workshops, and efficiently delivering digital content. In most biomedical research-intensive institutions, bioinformatics support is typically delivered through departments such as computational biology or biomedical informatics, or facilities such as the sequencing core. To help such researchers at the University of Pittsburgh (Pitt), the Health Sciences Library System (HSLS) established the Molecular Biology Information Service (MBIS) in 2002 as an innovative bioinformatics support service. It is very challenging for biomedical researchers to self-train and stay updated with this moving target. Additionally, the bioinformatics resources landscape changes at a rapid pace – the most sought-after resources can quickly become obsolete. Undergraduate and even graduate curricula do not routinely include mandatory bioinformatics classes. The opportunities for experimental biologists to receive bioinformatics training is often limited. Thriving in the current big data-intensive life sciences research environment requires proficiency in bioinformatics tools, which assist with the formulation of new hypotheses, the design of studies to test these hypotheses, and the analysis, interpretation, and validation of experimental results. In response to this data deluge, bioinformatics software and databases utilizing computational and statistical methods rapidly evolve. Following the successful completion of the Human Genome Project, initiatives such as the Human Microbiome Project, the ENCyclopedia of DNA Elements, The Cancer Genome Atlas, and the 1000 Genome Project continue to generate a massive catalog of biological datasets. Error probabilities.Recent advancement in molecular technologies such as massively parallel DNA sequencing, microarray platforms, and other high-throughput methodologies, generate substantial amounts of scientific data. Ewing B, Green P (1998) Base-calling of automated sequencer traces using phred.Koch CM, Chiu SF, Akbarpour M, Bharat A, Ridge KM, Bartom ET, Winter DR (2018) A Beginner’s guide to analysis of RNA sequencing data.Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B (2008) Mapping and quantifying mammalian transcriptomes by RNA-seq.Byron SA, Van Keuren-Jensen KR, Engelthaler DM, Carpten JD, Craig DW (2016) Translating RNA sequencing into clinical diagnostics: opportunities and challenges.Ozsolak F, Milos PM (2011) RNA sequencing: advances, challenges and opportunities.Royce TE, Rozowsky JS, Gerstein MB (2007) Toward a universal microarray: prediction of gene expression through nearest-neighbor probe sequence identification.Okoniewski MJ, Miller CJ (2006) Hybridization interactions between probesets in short oligo microarrays lead to spurious correlations. van Hal NL, Vorst O, van Houwelingen AM, Kok EJ, Peijnenburg A, Aharoni A, van Tunen AJ, Keijer J (2000) The application of DNA microarrays in gene expression analysis.Wang Z, Gerstein M, Snyder M (2009) RNA-seq: a revolutionary tool for transcriptomics.
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