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. 2019 Jul 18;25:104284. doi: 10.1016/j.dib.2019.104284

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

Matthias Zwick a,∗, Oliver Kraemer b, Adrian J Carter b
PMCID: PMC6702404  PMID: 31453287

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.

Keywords: Human proteome, Human genome, Scientific publications, Data integration, Genome-Wide Association Studies (GWAS), Online Mendelian Inheritance in Man (OMIM)

Specifications Table

Subject Chemical biology
Specific subject area Data integration and mapping
Type of data Tab separated files
How data were acquired Download, integration and filtering of publicly available datasets in a KNIME workflow.
Data format Filtered, Summarised
Parameters for data collection Information about publications on human genes was identified via the gene2pubmed dataset. Relevance for disease phenotype was assessed via GWAS catalog and OMIM.
Description of data collection Publicly available datasets from the NCBI (gene2pubmed, mim2gene_medgen, gene_info) and the EBI (GWAS catalog) were downloaded. Data from the GWAS catalog were filtered for p-value thresholds and after mapping of ENSEMBL gene identifiers and gene symbols to NCBI gene identifiers via Biomart, all datasets were integrated and summarised in KNIME.
Data source location The data were gathered from the National Center for Biotechnology Information (ftp://ftp.ncbi.nlm.nih.gov/) and the European Bioinformatics Institute (https://www.ebi.ac.uk/gwas/).
Data accessibility With the article
Related research article A. J. Carter, O. Kraemer, M. Zwick, A. Mueller-Fahrnow, C. H. Arrowsmith, A. M. Edwards
Target 2035 – probing the human proteome
Drug Discovery Today
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Value of the data
  • •

    The data will be useful to analyse the research activity (as evidenced by scientific publications) on human protein-coding genes and their protein products. The dataset also provides information on the potential disease relevance or phenotype of the individual genes. As shown by Carter et al. [5] the data indicate that researchers tend to focus on a relatively small, already well-studied fraction of the proteins coded by the human genome despite evidence that many understudied proteins are potentially important for human disease phenotypes.

  • •

    The analysis of these data allows the identification of genes that are understudied despite a link to disease phenotypes or an association with specific disease traits. This could stimulate research and promote the development of pharmacological tools to interrogate the understudied proteins encoded by these genes.

  • •

    Dataset entries have been tagged with a number of different ID types. This allows mapping to other datasets as a basis for generation of further insights. Moreover, we are also publishing the KNIME workflow that has been used to compile and integrate the data. This will allow researchers to reproduce an updated dataset at any future point in time.

  • •

    The data also provides access to the frequency of scientific publications on an annual or cumulated basis.

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1. Data

The main dataset that is provided as Supplementary File 1 and contains a list of:

  • (i)

    all human protein-coding genes with year of earliest and year of latest publication,

  • (ii)

    number of publications, number and type of OMIM entries annotated to the genes,

  • (iii)

    number of GWAS that refer to the gene as well as a selection of gene and protein identifiers to allow for easy integration with other analyses.

Table 1 provides a description of the columns in Supplementary File 1.

Table 1.

Description of columns in Supplementary File 1.

Column name Description
Gene_ID NCBI gene identifier
Gene_Type type_of_gene from NCBI gene info (e.g. protein-coding)
PMID_Count Number of publications for this gene in gene2pubmed
OMIM_Type Comma separated list of types of corresponding OMIM entries (gene or phenotype)
OMIM_IDs Comma separated list of corresponding OMIM entries
OMIM_Count Number of corresponding OMIM entries
GWAS_Count_All Number of GWAS studies with an association to this gene
GWAS_Count_Filtered Same as GWAS_Count_All after p-value filtering
Gene_Symbol Gene Symbol from NCBI gene info
Gene_Description Description from NCBI gene info
Year_Min Earliest Publication Year
Year_Max Latest Publication Year
Rank Rank by number of publications and earliest publication year
Uniprot_Swissprot_IDs Comma separated list of Uniprot and Swissprot IDs retrieved from Biomart
Interpro_IDs Comma separated list of Interpro IDs retrieved from Biomart
PFAM_IDs Comma separated list of PFAM IDs retrieved from Biomart
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In addition, we have generated a table (Table 2) with overall PubMed entries per year, PubMed entries related to any genes and PubMed entries related to human genes. The gene-related data were additionally restricted to only protein-coding genes.

Table 2.

Overall Publication counts in PubMed with or without restriction to human and/or protein-coding genes since 1980. All PubMed: Number of all publications in PubMed for a given year. All genes: Number of all publications in gene2pubmed for a given year. All other columns are subsets of this column according to their title and based on entries in gene2pubmed.

Year All PubMed gene2pubmed
All genes All protein-coding genes All human genes All human protein-coding genes All human protein- coding genes cumulated
2019 386983 2078 1963 1234 1143 596891
2018 1328241 35725 33809 20627 19138 595748
2017 1126444 58694 56255 35854 33852 576610
2016 1111578 66224 63898 39811 37903 542758
2015 1089521 69137 66808 41861 40013 504855
2014 1041878 70953 68839 42352 40668 464842
2013 994460 72064 70350 41403 40091 424174
2012 939943 69191 67852 39052 38110 384083
2011 875517 65992 64895 36591 35846 345973
2010 822812 64335 63422 36220 35594 310127
2009 784058 63871 63107 34891 34425 274533
2008 750728 62229 61574 33327 32929 240108
2007 710052 54356 53755 27550 27198 207179
2006 684468 47520 46900 22740 22416 179981
2005 655917 45476 44833 22038 21679 157565
2004 619734 41980 41368 21213 20838 135886
2003 583849 38925 38299 20324 19945 115048
2002 558469 37403 36800 20137 19778 95103
2001 543369 25486 24850 12437 12047 75325
2000 530063 22135 21426 9533 9065 63278
1999 493347 20030 19360 8104 7713 54213
1998 474129 17950 17365 7350 6988 46500
1997 455877 16351 15810 6453 6140 39512
1996 457769 15074 14589 5576 5307 33372
1995 448236 13739 13282 4890 4648 28065
1994 437452 12395 11956 4354 4117 23417
1993 425908 10326 9904 3647 3429 19300
1992 417212 9198 8870 3262 3098 15871
1991 412474 7489 7188 2681 2539 12773
1990 409831 6475 6190 2186 2057 10234
1989 401987 5160 4910 1737 1635 8177
1988 386221 4382 4138 1466 1381 6542
1987 367193 3546 3345 1160 1089 5161
1986 349045 2898 2728 916 856 4072
1985 334454 2605 2407 727 670 3216
1984 317553 2145 1983 549 496 2546
1983 308772 1836 1695 404 379 2050
1982 295103 1583 1423 311 268 1671
1981 283122 1386 1229 241 208 1403
1980 280381 1173 1049 202 180 1195
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2. Experimental design, materials, and methods

We downloaded the gene2pubmed dataset [1], [6] and all other datasets mentioned below on 25 March 2019 to derive information on publications annotated to each gene. All of the data integration steps were carried out using a KNIME [4] workflow (KIME version 3.5.3) that is included as Supplementary File 2 with this publication.

To annotate all PubMed identifiers (PMID) with the corresponding publication year we used an internal PubMed index. Alternatively, this could also be done using the NCBI E-Utilities [7]. The resulting list was used to generate overall counts of PMIDs per year and was joined to the initial gene2pubmed dataset. The resulting dataset was filtered for human (Homo sapiens) genes via taxonomy identifier 9606.

We then joined the human gene2pubmed subset with Homo sapiens gene information [8] via NCBI gene identifiers (IDs) to annotate the list with information on types of genes and descriptive metadata for later interpretation, and used this information to filter the dataset for protein-coding genes only. The dataset was grouped by gene IDs to create a table containing gene IDs, gene symbols, gene description metadata, number of publications, year of earliest publication and year of latest publication.

To obtain information about disease relevance of the individual genes, we downloaded the mim2gene_medgen file [9] from the NCBI and the GWAS [3] catalog from the EBI [10]. The mim2gene_medgen dataset was used to link genes to OMIM [2] entries. Using reported and mapped genes, upstream, downstream and SNP gene IDs we created a list of potentially relevant genes per study, mapped to NCBI gene IDs. The GWAS catalog data were filtered for p-values < 10−6 to select for potentially more reliable hits. Mim2gene_medgen data were mapped directly via NCBI gene IDs, for the GWAS catalog the gene symbols and Ensembl gene identifiers were mapped to NCBI gene IDs using Biomart and Ensembl version 86 [11]. Descriptive metadata (OMIM IDs, number of GWAS studies) from those tables and additional protein identifiers (Uniprot, Swissprot, Interpro and PFAM) obtained via Biomart were also added to the dataset.

Finally, genes were ranked according to their highest number of annotated publications and earliest publication year to generate the dataset that is provided with this publication as Supplementary File 1.

We also merged the global PMID to publication year table with the complete gene2pubmed dataset via PMID and subsequently with the gene information file for all species [12] via gene IDs to obtain the overall yearly publication counts for PubMed with or without restriction to human and/or protein-coding genes (Table 2).

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.dib.2019.104284.

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Supplementary data

The following are the Supplementary data to this article:

Multimedia component 1
mmc1.zip (1,009.2KB, zip)
Multimedia component 2
mmc2.zip (88.9KB, zip)

References

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Multimedia component 1
mmc1.zip (1,009.2KB, zip)
Multimedia component 2
mmc2.zip (88.9KB, zip)

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