Literature DB >> 32266827

The State of Data Science in Genomic Nursing.

Caitlin Dreisbach1,2, Theresa A Koleck3.   

Abstract

Nurse scientists are generating, acquiring, distributing, processing, storing, and analyzing greater volumes of complex omics data than ever before. To take full advantage of big omics data, to address core biological questions, and to enhance patient care, however, genomic nurse scientists must embrace data science. Intended for readership with limited but expanding data science knowledge and skills, this article aims to provide a brief overview of the state of data science in genomic nursing. Our goal is to introduce key data science concepts to genomic nurses who participate at any stage of the data science lifecycle, from research patient recruitment to data wrangling, preprocessing, and analysis to implementation in clinical practice to policy creation. We address three major components in this review: (1) fundamental terminology for the field of genomic nursing data science, (2) current genomic nursing data science research exemplars, and (3) the spectrum of genomic nursing data science roles as well as education pathways and training opportunities. Links to helpful resources are included throughout the article.

Entities:  

Keywords:  data science; genomics; machine learning; research; team science

Mesh:

Year:  2020        PMID: 32266827      PMCID: PMC7492779          DOI: 10.1177/1099800420915991

Source DB:  PubMed          Journal:  Biol Res Nurs        ISSN: 1099-8004            Impact factor:   2.522


  17 in total

1.  NuRsing Research in the 21st Century: R You Ready?

Authors:  Michelle L Wright; Melinda Higgins; Jacquelyn Y Taylor; Vicki Stover Hertzberg
Journal:  Biol Res Nurs       Date:  2018-11-01       Impact factor: 2.522

2.  Using Genetic Burden Scores for Gene-by-Methylation Interaction Analysis on Metabolic Syndrome in African Americans.

Authors:  Jacquelyn Y Taylor; Erin B Ware; Michelle L Wright; Jennifer A Smith; Sharon L R Kardia
Journal:  Biol Res Nurs       Date:  2019-02-19       Impact factor: 2.522

3.  Nursing Needs Big Data and Big Data Needs Nursing.

Authors:  Patricia Flatley Brennan; Suzanne Bakken
Journal:  J Nurs Scholarsh       Date:  2015-08-19       Impact factor: 3.176

4.  A cross-package Bioconductor workflow for analysing methylation array data.

Authors:  Jovana Maksimovic; Belinda Phipson; Alicia Oshlack
Journal:  F1000Res       Date:  2016-06-08

5.  RNA-Seq workflow: gene-level exploratory analysis and differential expression.

Authors:  Michael I Love; Simon Anders; Vladislav Kim; Wolfgang Huber
Journal:  F1000Res       Date:  2015-10-14

6.  QIIME allows analysis of high-throughput community sequencing data.

Authors:  J Gregory Caporaso; Justin Kuczynski; Jesse Stombaugh; Kyle Bittinger; Frederic D Bushman; Elizabeth K Costello; Noah Fierer; Antonio Gonzalez Peña; Julia K Goodrich; Jeffrey I Gordon; Gavin A Huttley; Scott T Kelley; Dan Knights; Jeremy E Koenig; Ruth E Ley; Catherine A Lozupone; Daniel McDonald; Brian D Muegge; Meg Pirrung; Jens Reeder; Joel R Sevinsky; Peter J Turnbaugh; William A Walters; Jeremy Widmann; Tanya Yatsunenko; Jesse Zaneveld; Rob Knight
Journal:  Nat Methods       Date:  2010-04-11       Impact factor: 28.547

Review 7.  Establishing an analytic pipeline for genome-wide DNA methylation.

Authors:  Michelle L Wright; Mikhail G Dozmorov; Aaron R Wolen; Colleen Jackson-Cook; Angela R Starkweather; Debra E Lyon; Timothy P York
Journal:  Clin Epigenetics       Date:  2016-04-27       Impact factor: 6.551

8.  Bioconductor workflow for microbiome data analysis: from raw reads to community analyses.

Authors:  Ben J Callahan; Kris Sankaran; Julia A Fukuyama; Paul J McMurdie; Susan P Holmes
Journal:  F1000Res       Date:  2016-06-24

Review 9.  Genomic sequencing in clinical practice: applications, challenges, and opportunities.

Authors:  Joel B Krier; Sarah S Kalia; Robert C Green
Journal:  Dialogues Clin Neurosci       Date:  2016-09       Impact factor: 5.986

10.  A tutorial on conducting genome-wide association studies: Quality control and statistical analysis.

Authors:  Andries T Marees; Hilde de Kluiver; Sven Stringer; Florence Vorspan; Emmanuel Curis; Cynthia Marie-Claire; Eske M Derks
Journal:  Int J Methods Psychiatr Res       Date:  2018-02-27       Impact factor: 4.035

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  1 in total

1.  Multi-Staged Data-Integrated Multi-Omics Analysis for Symptom Science Research.

Authors:  Carolyn S Harris; Christine A Miaskowski; Anand A Dhruva; Janine Cataldo; Kord M Kober
Journal:  Biol Res Nurs       Date:  2021-04-08       Impact factor: 2.318

  1 in total

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