Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
, 6, e5553
eCollection

Data Challenges of Biomedical Researchers in the Age of Omics

Affiliations

Data Challenges of Biomedical Researchers in the Age of Omics

Rolando Garcia-Milian et al. PeerJ.

Abstract

Background: High-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.

Methods: A 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.

Results: The 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.

Conclusions: The 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.

Keywords: Computational biology; Data interpretation; Genomics; Information seeking behavior; Software; Statistical; Survey.

Conflict of interest statement

The authors declare there are no competing interests.

Figures

Figure 1
Figure 1. Response to the question: which of the following best describes your role? Total responses: 157.
Figure 2
Figure 2. Top departments by the number of respondents. Total responses: 146.
Figure 3
Figure 3. Response to the question: (A) What are the main challenges with data analysis? (B) Responses to this question by position. Total responses: 130.

Similar articles

See all similar articles

Cited by 1 article

References

    1. Alyass A, Turcotte M, Meyre D. From big data analysis to personalized medicine for all: challenges and opportunities. BMC Medical Genomics. 2015;8:33. doi: 10.1186/s12920-015-0108-y. - DOI - PMC - PubMed
    1. Barriball KL, While A. Collecting data using a semi-structured interview: a discussion paper. Journal of Advanced Nursing. 1994;19(2):328–335. doi: 10.1111/j.1365-2648.1994.tb01088.x. - DOI - PubMed
    1. Beck JB, Tieder JS. Electronic resources preferred by pediatric hospitalists for clinical care. Journal of the Medical Library Association. 2015;103(4):177–183. doi: 10.3163/1536-5050.103.4.003. - DOI - PMC - PubMed
    1. Braun V, Clarke V. Using thematic analysis in psychology. Qualitative Research in Psychology. 2006;3(2):77–101. doi: 10.1191/1478088706qp063oa. - DOI
    1. Carvalho BS, Rustici G. The challenges of delivering bioinformatics training in the analysis of high-throughput data. Briefings in Bioinformatics. 2013;14(5):538–547. doi: 10.1093/bib/bbt018. - DOI - PMC - PubMed

Grant support

The authors received no funding for this work.

LinkOut - more resources

Feedback