Abstract
BackgroundHigh-throughput technologies are rapidly generating large amounts of diverse omics data. Although this offers a great opportunity, it also poses great challenges as data analysis becomes more complex. The purpose of this study was to identify the main challenges researchers face in analyzing data, and how academic libraries can support them in this endeavor.MethodsA multimodal needs assessment analysis combined an online survey sent to 860 Yale-affiliated researchers (176 responded) and 15 in-depth one-on-one semi-structured interviews. Interviews were recorded, transcribed, and analyzed using NVivo 10 software according to the thematic analysis approach.ResultsThe survey response rate was 20%. Most respondents (78%) identified lack of adequate data analysis training (e.g., R, Python) as a main challenge, in addition to not having the proper database or software (54%) to expedite analysis. Two main themes emerged from the interviews: personnel and training needs. Researchers feel they could improve data analyses practices by having better access to the appropriate bioinformatics expertise, and/or training in data analyses tools. They also reported lack of time to acquire expertise in using bioinformatics tools and poor understanding of the resources available to facilitate analysis.ConclusionsThe main challenges identified by our study are: lack of adequate training for data analysis (including need to learn scripting language), need for more personnel at the University to provide data analysis and training, and inadequate communication between bioinformaticians and researchers. The authors identified the positive impact of medical and/or science libraries by establishing bioinformatics support to researchers.
Highlights
High-throughput technologies are rapidly generating large amounts of diverse omics data
Data analysis became a major bottleneck for researchers, since it requires very specialized training and the use of dedicated bioinformatics software tools (Carvalho & Rustici, 2013; Geskin et al, 2015)
In order to ensure the safety and anonymity of the participants in this study, both the survey and the subsequent interviews were approved by the Yale University Review Board (HSC# 1511016778)
Summary
High-throughput technologies are rapidly generating large amounts of diverse omics data This offers a great opportunity, it poses great challenges as data analysis becomes more complex. Two main themes emerged from the interviews: personnel and training needs Researchers feel they could improve data analyses practices by having better access to the appropriate bioinformatics expertise, and/or training in data analyses tools. Kenny (2011), a holistic view or universal detection of the molecules (e.g., genes, genomics; mRNA, transcriptomics; proteins, proteomics) in a specific biological sample (Horgan & Kenny, 2011) While this advancement allows researchers faster access to data, it created a gap between data production and analysis that tremendously slows publishing study results. A delay in omics and high-throughput data (high-throughput data defined as those obtained from the use of high-throughput methodologies such as generation sequencing, microarray, mass spectrometry, etc.) interpretation results on more time needed to finalize projects based on -omics methodology (Alyass, Turcotte & Meyre, 2015; Gligorijevic, Malod-Dognin & Przulj, 2016; Merelli et al, 2014)
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