Dataset Information

Dataset of the frequency patterns of publications annotated to human protein-coding genes, their protein products and genetic relevance.

ABSTRACT: We present data concerning the distribution of scientific publications for human protein-coding genes together with their protein products and genetic relevance. We annotated the gene2pubmed dataset Maglott et al., 2007 provided by the NCBI (National Center for Biotechnology Information) with publication years, genetic metadata corresponding to Online Mendelian Inheritance in Man (OMIM) Hamosh et al., 2005 entries and the frequency of their appearance in Genome-Wide Association Studies (GWAS) Buniello et al., 2019 provided by the European Bioinformatics Institute (EBI) using the KNIME^® Analytics Platform Berthold et al., 2008. The results of this data integration process comprise two datasets: 1) A dataset containing information on all human protein-coding genes that can be used to analyse the number of scientific publications in context of the potential disease relevance of the individual genes. 2) A table with the annual and cumulated number of PubMed entries. For further interpretation of the data presented in this article, please see the research article 'Target 2035 - probing the human proteome' by Carter et al. https://doi.org/10.1016/j.drudis.2019.06.020 Carter et al., 2019.

SUBMITTER: Zwick M

PROVIDER: S-EPMC6702404 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Dataset of the frequency patterns of publications annotated to human protein-coding genes, their protein products and genetic relevance.

Zwick Matthias M Kraemer Oliver O Carter Adrian J AJ

Data in brief 20190718

We present data concerning the distribution of scientific publications for human protein-coding genes together with their protein products and genetic relevance. We annotated the gene2pubmed dataset Maglott et al., 2007 provided by the NCBI (National Center for Biotechnology Information) with publication years, genetic metadata corresponding to Online Mendelian Inheritance in Man (OMIM) Hamosh et al., 2005 entries and the frequency of their appearance in Genome-Wide Association Studies (GWAS) Bu ...[more]

PMID: 31453287

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Project description:BACKGROUND:Analysis of any newly sequenced bacterial genome starts with the identification of protein-coding genes. Despite the accumulation of multiple complete genome sequences, which provide useful comparisons with close relatives among other organisms during the annotation process, accurate gene prediction remains quite difficult. A major reason for this situation is that genes are tightly packed in prokaryotes, resulting in frequent overlap. Thus, detection of translation initiation sites and/or selection of the correct coding regions remain difficult unless appropriate biological knowledge (about the structure of a gene) is imbedded in the approach. RESULTS:We have developed a new program that automatically identifies biologically significant candidate genes in a bacterial genome. Twenty-six complete prokaryotic genomes were analyzed using this tool, and the accuracy of gene finding was assessed by comparison with existing annotations. This analysis revealed that, despite the enormous effort of genome program annotators, a small but not negligible number of genes annotated within the framework of sequencing projects are likely to be partially inaccurate or plainly wrong. Moreover, the analysis of several putative new genes shows that, as expected, many short genes have escaped annotation. In most cases, these new genes revealed frameshifts that could be either artifacts or genuine frameshifts. Some entirely unexpected new genes have also been identified. This allowed us to get a more complete picture of prokaryotic genomes. The results of this procedure are progressively integrated into the SWISS-PROT reference databank. CONCLUSIONS:The results described in the present study show that our procedure is very satisfactory in terms of gene finding accuracy. Except in few cases, discrepancies between our results and annotations provided by individual authors can be accounted for by the nature of each annotation process or by specific characteristics of some genomes. This stresses that close cooperation between scientists, regular update and curation of the findings in databases are clearly required to reduce the level of errors in genome annotation (and hence in reducing the unfortunate spreading of errors through centralized data libraries).

Dataset Information

Dataset of the frequency patterns of publications annotated to human protein-coding genes, their protein products and genetic relevance.

Publications

Dataset of the frequency patterns of publications annotated to human protein-coding genes, their protein products and genetic relevance.

Similar Datasets

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