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      Transcriptomic and bioinformatics analysis of the early time-course of the response to prostaglandin F2 alpha in the bovine corpus luteum ☆☆

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          Abstract

          RNA expression analysis was performed on the corpus luteum tissue at five time points after prostaglandin F2 alpha treatment of midcycle cows using an Affymetrix Bovine Gene v1 Array. The normalized linear microarray data was uploaded to the NCBI GEO repository (GSE94069). Subsequent statistical analysis determined differentially expressed transcripts ± 1.5-fold change from saline control with P ≤ 0.05. Gene ontology of differentially expressed transcripts was annotated by DAVID and Panther. Physiological characteristics of the study animals are presented in a figure. Bioinformatic analysis by Ingenuity Pathway Analysis was curated, compiled, and presented in tables. A dataset comparison with similar microarray analyses was performed and bioinformatics analysis by Ingenuity Pathway Analysis, DAVID, Panther, and String of differentially expressed genes from each dataset as well as the differentially expressed genes common to all three datasets were curated, compiled, and presented in tables. Finally, a table comparing four bioinformatics tools’ predictions of functions associated with genes common to all three datasets is presented. These data have been further analyzed and interpreted in the companion article “Early transcriptome responses of the bovine mid-cycle corpus luteum to prostaglandin F2 alpha includes cytokine signaling” [1].

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          Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

          DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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            Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists

            Functional analysis of large gene lists, derived in most cases from emerging high-throughput genomic, proteomic and bioinformatics scanning approaches, is still a challenging and daunting task. The gene-annotation enrichment analysis is a promising high-throughput strategy that increases the likelihood for investigators to identify biological processes most pertinent to their study. Approximately 68 bioinformatics enrichment tools that are currently available in the community are collected in this survey. Tools are uniquely categorized into three major classes, according to their underlying enrichment algorithms. The comprehensive collections, unique tool classifications and associated questions/issues will provide a more comprehensive and up-to-date view regarding the advantages, pitfalls and recent trends in a simpler tool-class level rather than by a tool-by-tool approach. Thus, the survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.
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              STRING v10: protein–protein interaction networks, integrated over the tree of life

              The many functional partnerships and interactions that occur between proteins are at the core of cellular processing and their systematic characterization helps to provide context in molecular systems biology. However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness. The STRING database (http://string-db.org) aims to provide a critical assessment and integration of protein–protein interactions, including direct (physical) as well as indirect (functional) associations. The new version 10.0 of STRING covers more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms. For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution. Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein–protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks.
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                Author and article information

                Contributors
                Journal
                Data Brief
                Data Brief
                Data in Brief
                Elsevier
                2352-3409
                01 September 2017
                October 2017
                01 September 2017
                : 14
                : 695-706
                Affiliations
                [a ]Olson Center for Women’s Health/Obstetrics and Gynecology Department, University of Nebraska Medical Center, 989450 Nebraska Medical Center, Omaha, NE 68198-9450, USA
                [b ]Biochemistry and Molecular Biology Department, University of Nebraska Medical Center, 985870 Nebraska Medical Center, Omaha, NE 68198-5870, USA
                [c ]Biostatistics Department, University of Nebraska Medical Center, 984375 Nebraska Medical Center, Omaha, NE 68198-4375, USA
                [d ]Bioinformatics and Systems Biology Core, University of Nebraska Medical Center, 985805 Nebraska Medical Center, Omaha, NE 68198-5805, USA
                [e ]Nutrition and Environmental Management Research Unit, Department of Agriculture, P.O. Box 166 (State Spur 18D)/USDA-ARS-PA-USMARC, Clay Center, NE 68933, USA
                [f ]Department of Animal Science, University of Nebraska—Lincoln, P.O. Box 830908, C203 ANSC, Lincoln, NE 68583-0908, USA
                [g ]Omaha Veterans Affairs Medical Center, 4101 Woolworth Ave, Omaha, NE 68105, USA
                Author notes
                [* ]Corresponding author. jsdavis@ 123456unmc.edu
                [1]

                Mention of a trade name, proprietary product, or specific equipment does not constitute a guarantee or warranty by the USDA and does not imply approval to the exclusion of other products that may be suitable.

                [2]

                The USDA is an equal opportunity provider and employer.

                Article
                S2352-3409(17)30403-1
                10.1016/j.dib.2017.08.026
                5596332
                28932774
                0ab8497d-1b92-4a84-a987-e82b34a8bec8

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 19 May 2017
                : 4 August 2017
                : 24 August 2017
                Categories
                Agricultural and Biological Science

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