Introduction

Research into human genes concentrates on a subset of genes that were already frequently investigated prior to the completion of the Human Genome Project in 20031-5. This concentration stems from historically acquired research patterns rather than present-day experimental possibilities6,7. For most human diseases, these patterns lead to little correlation between the volume of literature published on individual genes and the strength of supporting evidence from genome-wide approaches8-13. For instance, we found that 44% of the genes identified as promising Alzheimer’s disease targets by the U.S. National Institutes of Health (NIH) Accelerating Medicine Partnership for Alzheimer’s Disease (AMP-AD) initiative have never appeared in the title or abstract of any publication on Alzheimer’s disease13. Furthermore, when comparing gene-disease pairs, there is no correlation between the ranks of support by transcriptomics and occurrence in annotation databases9.

Although -omics technologies can provide insights on numerous genes across the genome at a time and thus offer the promise to counter historically acquired research patterns14-17, this discrepancy has persisted9,18-22 even as the popularity of -omics technologies has risen5,23,24. We therefore sought to use bibliometric data to delineate where and why understudied human protein-coding genes are abandoned as research targets following -omics experiments. In the absence of any prior quantitative testing of existing hypotheses, it remains unclear whether policies to promote the exploration of a greater set of disease-related genes should focus on how experiments are conducted, how results are reported, or how these results are subsequently received by other scientists.

Data

We considered 450 genome-wide association studies (GWAS, from studies indexed by the NHGRI-EBI GWAS catalog25), 296 studies using affinity capture mass spectrometry (Aff-MS, indexed by BioGRID26), 148 transcriptomic studies (indexed by the EBI Gene Expression Atlas, EBI-GXA27), and 15 genome-wide screens using CRISPR (indexed by BioGRID Open Repository of CRISPR Screens, BioGRID ORCS26) (see PRISMA diagrams in Figures S1-S4). We denote genes that are found to have statistically significant changes in expression or associations with a phenotype as ‘hit’ genes.

As a surrogate for a given gene having been investigated closer, we consider whether it was reported in the title or abstract of a research article. We determined which genes were mentioned in the title or abstract of articles using annotations from gene2pubmed28 and PubTator29. We used NIH iCite v32 for citations30. For determining which gene properties were associated with selection as research targets, we synthesized quantitative measures from a variety of authoritative sources (see Methods).

Results

Understudied genes are abandoned at synthesis/writing stage

We sought to identify at which point in the scientific process understudied genes are ignored as research targets in investigations using -omics experiments (Figure 1A). To receive scholarly attention, a gene must travel through a pipeline from biological reality to experimental results to write-up of those results. These results must be extended by subsequent research by other scholars. Understudied genes do not progress all the way through the pipeline, but it is unclear where this leak primarily occurs. The first possibility is that seemingly understudied genes are, in fact, not understudied as they would rarely be identified through experiments. Prior studies have, however, shown that understudied genes are frequent hits in high-throughput experiments8,9,31, suggesting that this is not the case. The second possibility is that understudied genes are frequently found as hits in high-throughput experiments but are not investigated further by the authors. The final possibility is that subsequent studies do not continue work on understudied genes revealed by the initial study.

While understudied genes appear often as hits high-throughput -omics experiments, they are seldom highlighted by authors.

a, Conceptual diagram depicting possible points of abandonment for understudied genes in studies using high-throughput -omics experiments. b, Bibliometric data reveals that understudied genes are frequently hits in -omics experiments but are not typically highlighted in the title/abstract of reporting articles, nor in the title/abstract or articles citing reporting articles. We considered articles reporting on genome-wide CRISPR screens (CRISPR, n=15 articles), transcriptomics (T-omics, n=148 articles), affinity capture – mass spectrometry (Aff-MS, n=296 articles), and GWAS (n=450 articles). Numbers to the right of each box plot indicate the percentile (in terms of number of articles about that gene) of all genes exceeded by the median gene in each box plot. ** denotes p < 0.01 and *** denotes p < 0.001 by two-sided Mann-Whitney U test, comparing genes highlighted in title/abstract to genes present in hit lists.

Evaluating the first possibility, we found that understudied genes were frequently found as hits in high-throughput experiments. (Figure 1B, Figure S5, and Figure S6). This demonstrates, in line with earlier studies8-13, that the lack of publications on some genes is not explained by underlying biological experimental evidence.

Evaluating the second possibility, we found that hit genes that are highlighted in the title or abstract are strongly over-represented among the 20% highest-studied genes in all biomedical literature (Figure 1B). These trends are independent of significance threshold (Figure S5) and (except for CRISPR screens) whether we considered the current scientific literature or literature published before 2003, before any of these articles had been published (Figure S6).

Understudied genes are least frequently elevated to the title/abstract in transcriptomics experiments and most frequently elevated to the title/abstract in CRISPR screens. GWAS studies tend to return better-studied genes as hits; the median hit gene in GWAS studies was more popular than 75% of genes. Hit genes promoted to the title/abstract in GWAS studies had a median popularity greater 85% of all protein coding genes. This may explain the prior observation that the total number of articles on individual genes partially correlates with the total number of occurrences as a hit in GWAS studies32.

Evaluating the final possibility, we found that the reception of -omics studies in later scientific literature either reproduced authors’ initial selection of highly studied genes or slightly mitigated it. Jointly, the above findings reinforce that understudied genes become abandoned between the completion of -omics experiments and the reporting of results, rather than being abandoned by later research.

Subsequent reception by other scientists does not penalize studies on understudied genes

The abandonment of understudied genes could be driven by the valid concern of biomedical researchers that focusing on less-investigated genes will yield articles with lower impact17, as observed around the turn of the millenium33. If this were the case, preemptively avoiding understudied hits would be the rational decision for authors of -omics studies.

We thus decided to complement our preceding analysis by an analysis explicitly focused on citation impact. Notably, we found that the concern of publications on understudied genes receiving fewer citations does not hold for present-day research on human genes; in biomedical literature at-large, articles focusing on less-investigated genes accumulate more citations, an effect that has held consistently since 2001 (Figure 2). Important to human health, this also holds when only considering disease-related fields (Figure S7).

Articles focusing on less popular genes tend to accrue more citations.

a, Density plot shows correlation between articles per gene before 2015 and median citations to articles published in 2015. Contours correspond to deciles in density. Solid red line shows locally weighted scatterplot smoothing (LOWESS) regression. ρ is Spearman rank correlation and p the significance values of the Spearman rank correlation as described by Kendall and Stuart73. b, Spearman correlation of previous gene popularity (i.e. number of articles) to median citations per year since 1990. Solid blue line indicates nominal Spearman correlation, shaded region indicates bootstrapped 95% confidence interval (n=1,000). Only articles with a single gene in the title/abstract are considered, excluding the 30.4% of gene-focused studies which feature more than one gene in the title/abstract.

To rule out that these macroscopic observations stem from us having aggregated over different diseases, we separately analyzed 602 disease-related MeSH terms. We found 29 MeSH terms with a statistically significant Spearman correlation using Benjamini-Hochberg FDR < 0.01 (Table S4), of which 27 showed a negative association and only 2 a positive association. This result suggests that is may actually be rational for most scientists to pursue studies focusing on understudied genes, although most scientists specializing in a disease may also not receive more citations when focusing on understudied genes.

Returning to our observation that understudied hits from high-throughput assays are not promoted to the title and abstract of the resulting publication, we next tested if different experimental approaches demonstrated distinct associations between gene popularity and citations (Figure S8). Among 264 technique-related MeSH terms tested, there were 20 MeSH terms with a statistically significant Spearman correlation using Benjamini-Hochberg FDR < 0.01 (Table S5), of which 16 showed a negative association and only 4 a positive association. Notably, MeSH terms representing high-throughput techniques (e.g. D055106:Genome-Wide Association Study and D020869:Gene Expression Profiling) showed no significant association. This finding suggests that authors of high-throughput studies have little to gain or lose citationwise by highlighting understudied genes.

To summarize, our investigations detail the previously described separation between “largescale” and “small-scale” biological research34-36. Authors of high-throughput studies do not highlight understudied genes in the title or abstract of their publications, the sections of the publication most accessible to other scientists. While, overall, understudied genes (and high-throughput assays themselves5) correlate with increased citation impact, for high-throughput studies any potential gain in citations is either absent or too small to be significant. Thus, there may not be any incentive for authors of high-throughput studies to highlight understudied genes.

Identification of biological and experimental factors associated with selection of highlighted genes

To illuminate why understudied genes are abandoned between experimental results and the write-up of results, we performed a literature review to identify factors that have been proposed to limit studies of understudied genes (Table S1). These factors range from evolutionary factors (e.g., whether a gene only has homologs in primates), to chemical factors (e.g., gene length or hydrophobicity of protein product), to historical factors (e.g., whether a gene’s sequence has previously been patented) to materialistic factors affecting experimental design (e.g., whether designed antibodies are robust for immunohistochemistry).

As any of these factors could plausibly affect gene selection within individual domains of biomedical research, we returned to the -omics data described above (Figure 1) and measured how much these factors align with the selective highlighting of hit genes in the title or abstract of GWAS, Aff-MS, transcriptomics, and CRISPR studies.

We identified 45 factors that relate to genes and found 35 (14 out of 23 binary factors and 21 out of 22 continuous factors) associated with selection in at least one assay type at p < 0.001 (Figure 3, Table S2, and Table S3). Across the four assay types, the most informative binary factor describes whether there is a plasmid available for a gene in the AddGene plasmid catalog. This might reflect that many different research groups produce reagents surrounding the genes that they actively study. The most informative continuous factor is the number of research articles about a gene, supporting the conclusion that gene popularity drives whether it is highlighted or not (Figure 1).

We evaluated which gene-related factors are associated with elevation to the title/abstract of an article featuring a high-throughput experiment.

a) Association between factors with binary (True/False) identities and highlighting hits in title/abstract of reporting articles. Values represent the odds ratio between hits in the collected articles and hits mentioned in the title or abstract of collected articles (e.g. hits with a compound known to affect gene activity are 5.114 times as likely to be mentioned in the title/abstract in an article using transcriptomics). Collected articles are described in Figure 1B and Figures S5 and S6. 95% confidence interval of odds ratio is shown in parentheses. * = p < 0.05, ** = p < 0.01, and *** = p < 0.001 by two-sided Fisher exact test. Results are shown numerically in Table S2. For consistency between studies, hits were restricted to protein-coding genes. Thus, status as a protein-coding gene could not be tested. †No genes without a defined HUGO symbol were found as hits in GWAS or transcriptomics studies. b) Association with factors with continuous identities and highlighting hits in title/abstract of reporting articles. Values represent F, the common-language effect size (equivalent to AUROC, where ∼0.5 indicates little effect, >0.5 indicates positive effect and <0.5 indicates negative effect) of being mentioned in the titles/abstracts of the collected articles described in Figure 1B and Figures S5 and S6. * = p < 0.05, ** = p < 0.01, and *** = p < 0.001 by two-sided Mann-Whitney U test. Results are shown numerically in Table S3.

To better understand how all 45 factors are related, we performed a cluster analysis of the collected factors (Figure 4, Figure S9, and Figure S10). This clustering reflects the fact that many suggested factors influencing the abandonment of understudied genes are not independent. For instance, we find that the number of articles about a gene is heavily correlated with the number of annotations for that gene in all surveyed databases. In another case, gene length is heavily correlated with the number of GWAS annotations for a gene, as described before in terms of transcript length and single-nucleotide polymoprhisms37.

Clustermap showing collected factors across all human protein-coding genes.

Factors are shown along the x axis, with genes along the y axis. Eight factors, representing the default factors we selected for FMUG, are shown (all factors are shown in Figure S7 and Figure S8). Binary factors are coded to 0 (purple) and 1 (white), while continuous factors are ranked from 0 to 1 with ties resolved to minimum rank. Clustering was performed with Ward’s method for hierarchical clustering74.

Study limitations

Our study has several limitations. First, all analysis is subject to annotation errors in the various databases we employ. While these should be rare and not affect our overall findings, they may affect users who are interested in genes with discordant annotations. Second, we focus only on human genes. Different patterns of selection may exist for research on genes in other organisms. Third, our literature review also identified further factors that we could not test more directly because of absent access to fitting data. These are: experts’ tendency to deepen their expertise3, a perceived lack of accuracy of -omics studies17,38, -omics serving research purposes beyond target gene identification22, the absence of good protocols for mass spectrometry39, the electronic distribution and reading of research articles40, rates of reproducibility17, career prospects of investigators7,41, and the human tendency to fall back to simplifying heuristics when making decisions under conditions with uncertainty42. Fourth, we cannot resolve further which specific step between the conduct of an experiment and the writing of a research article leads to the abandonment of hit genes. Finally, we interpret the results of high-throughput experiments based on their representation in the NHGRI-EBI GWAS, BioGRID, EBI-GXA and BioGRID ORCS databases. The authors of the original studies may have processed their data differently, obtaining different results.

As our present analysis is correlative, it also is tempting to propose controlled trials where published manuscripts on high-throughput studies randomly report hit genes in the abstract even if not investigated further by the authors.

Discussion

Efforts to address the gaps in detailed knowledge about most genes have crystallized as initiatives promoting the investigation of understudied sets of genes16,43-47, an approach to gene scholarship recently termed ‘unknomics’48. We believe that enabling scientists to consciously engage with bias in research target selection will enable more biomedical researchers to participate in unknomics, to the potential benefit of their own research impact and towards the advancement of our collective understanding of the entire human genome.

To achieve this goal, we combined all the above insights to create a tool we denoted find my understudied genes (FMUG). Our literature review revealed several tools and resources aiming to promote research of understudied genes by publicizing understudied genes49-56 or by providing information about hit genes7,57-61. However, we noted the absence of tools enabling scientists to actively engage with factors that align with gene selection. Although such factors are largely correlated when considering all genes (Figure 4, Figure S9, and Figure S10), some cluster together and the influence of specific factors could vary across laboratories. For instance, scientists could vary in their ability to perform proteomics, or ability to explore orthologous genes in C. elegans, or ability to leverage human population data, or perform standardized mouse assays.

Our tool makes selection bias explicit, while acknowledging that different laboratories vary in their techniques and capabilities for follow-up research. Rather than telling scientists about the existence of biases, FMUG aims to prompt scientists to make bias-aware informed decisions to identify and potentially tackle important gaps in knowledge that they are well-suited to address. For this reason, we believe that FMUG will not be of value only to scientists engaging in high-throughput studies, but also by scientists wishing to mine existing datasets for hit genes that they would be well-positioned to investigate further.

FMUG takes a list of genes from the user (ostensibly a hit list from a high-throughput -omics experiment) and provides the kind of information that will allow a user to select genes for further study.

Users can employ filters that reflect the factors identified in our literature review and supported by our analysis. The default information provided to users consists of factors that are representative of the identified clusters (Figure 4) and strongly associated with gene selection in high-throughput experiments (Figure 3, Table S2, and Table S3). In extended options, users can select any factor that demonstrated a significant association with the selection of genes. For instance, a user may need to decide whether loss-of-function intolerant genes should be considered for further research or not, or whether there should be robust evidence that a gene is protein-coding. Some of these filters are context aware. For instance, a user may select genes that have already been studied in the general biomedical literature but not yet within the literature of their disease of interest.

To provide real-time feedback, users are, in parallel, presented the number of articles about genes in their initial input list and the number of articles about genes that passed their filters. Users can then export their filtered list of genes. In the interest of researcher privacy, FMUG keeps all information local to the user’s machine. Usage of FMUG is illustrated in Figure 5A and demonstrated in Movie S1. FMUG is developed in Flutter and is available for download at fmug.amaral.northwestern.edu as a MacOS/Windows app. For the development of custom software and analytical code, we provide the data underlying FMUG at github.com/amarallab/fmug_analysis.

We created FMUG to help researchers identify understudied genes among their genes of interest and characterize their tractability for future research.

a, Diagram describing use of FMUG. b, An early prototype of FMUG led us to the hypothesis that transcript length negatively correlates with up-regulation during aging. First, we identified genes that strongly associate with age-dependent transcriptional change across multiple cohorts. We then performed a literature review for each of these genes to identify the most direct way the genes (or evolutionally closely related genes or functionally closely related partner proteins) had been studied in aging. 64% had been functionally investigated in aging, 15% shown to change a measure of gene expression, 3% functionally investigated in a biological domain close to aging (such as senescence), and 5% shown to change a measure of gene expression in a biological domain close to aging. For genes reported by others to change expression with age, we identified tissues in which transcripts of the genes change during aging. We computed ‘feasibility scores’ scientific strategies (GEM: G: strong genetic support, E: and experimental potential, M: homolog in invertebrate model organism) as described by Stoeger et al.7 and total number of publications in MEDLINE. Splicing factor, proline- and glutamine-rich (Sfpq) had previously been demonstrated by Takeuchi et al. to be required for the transcriptional elongation of long genes62. When performing a data-driven analysis of factors that could possibly explain age-dependent changes of the entire transcriptome, we thus included gene and transcript lengths, and subsequently found them to be more informative than transcription factors or microRNAs63.

To determine the practical usefulness of FMUG to scientists we used an early prototype of FMUG to identify understudied genes associated with aging. One of these genes was Splicing factor, proline- and glutamine-rich (Sfpq), which had not yet been investigated toward its role in biological aging. We found Sfpq to be transcriptionally downregulated during murine aging. Others had shown Sfpq to be required for the transcriptional elongation of long genes62. This led us to hypothesize that during vertebrate aging, the transcripts of long genes become downregulated in most tissues (Figure 5B). We found this hypothesis to be supported through a multi-species analysis which we published in December 2022 in Nature Aging63, with another group publishing so in January 2023 in Nature Genetics64, and a third group in iScience in March 202365.

Materials and Methods

Genes information

Homo sapiens gene information was downloaded from NCBI Gene on Aug 16, 2022 [ftp.ncbi.nlm.nih.gov/gene/DATA/GENE_INFO/All_Data.gene_info.gz]. Only genes with an unambiguous mapping of Entrez ID to Ensembl ID were used (n = 36,035). Number of gene synonyms, protein-coding status, and official gene symbol were derived from this dataset. A gene symbol was considered undefined if the gene’s entry for HGNC gene symbol was “-”.

Genes in title/abstract of primary research articles

Homo sapiens gene information was downloaded from NCBI Gene on Aug 16, 2022 [ftp.ncbi.nlm.nih.gov/gene/DATA/GENE_INFO/Mammalia/Homo_sapiens.gene_info.gz]. gene2pubmed was download from NCBI Gene on Aug 16, 2022 [ftp.ncbi.nlm.nih.gov/gene/DATA/gene2pubmed.gz]28. PubTator gene annotations were downloaded from NIH-NLM on July 12, 2022 [https://ftp.ncbi.nlm.nih.gov/pub/lu/PubTatorCentral/] 28,66. PubMed was downloaded on Dec 17, 2021 [https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/].

Only using PMIDs annotated as primary research articles, a human gene was considered as mentioned in the title/abstract of the publication if gene was annotated as being in the title/abstract by PubTator and the article appeared in gene2pubmed.

CRISPR articles

BioGRID ORCS26 v1.1.6 was downloaded on April 25, 2022 [https://downloads.thebiogrid.org/BioGRID-ORCS/Release-Archive/BIOGRID-ORCS-1.1.6/]. Any genome-wide CRISPR knockout screens in human with an associated PubMed ID in which hit genes were mentioned in the title or abstract was considered (n = 15). 9,268 unique genes were found as hits. Of these, 18 (0.19%) were elevated to titles/abstracts in the reporting articles and 19 (0.21%) were elevated to titles/abstracts in citing articles. A full list of PubMed IDs is available in Supplementary File 2.

Transcriptomics articles

EBI-GXA27 release 36 was downloaded on Sep 15, 2020 [https://web.archive.org/web/20201022184159/ https://www.ebi.ac.uk/gxa/download]. This is the most recent release of EBI-GXA available as a bulk download. Any transcriptomics comparisons with an associated PubMed ID in which hit genes were mentioned in the title or abstract was considered (n= 148). Analysis was restricted to protein-coding genes (some screens featured non-protein-coding genes, but this was not common to all analyses). DE was called at Benjamini-Hochberg FDR q < 0.05. 18,295 unique genes were found as hits. Of these, 161 (0.88%) were elevated to titles/abstracts in the reporting articles and 692 (3.78%) were elevated to titles/abstracts in citing articles. A full list of PubMed IDs is available in Supplementary File 2.

Affinity capture – mass spectrometry articles

BioGRID26 v3.5.186 was downloaded on April 25, 2022

[https://downloads.thebiogrid.org/BioGRID/Release-Archive/BIOGRID-3.5.186/]. Any interactions involving a human gene as the prey protein with an experimental evidence code of ‘Affinity Capture-MS’ labeled as ‘High-Throughput’ that had an associated PubMed ID in which hit genes were mentioned in the title or abstract was considered (n= 296). Prey proteins in these interactions were considered hits. 7,919 unique genes were found as hits. Of these, 311 (3.93%) were elevated to titles/abstracts in reporting articles and 407 (5.14%) were elevated to titles/abstracts in citing articles. A full list of PubMed IDs is available in Supplementary File 2.

GWAS articles

The NHGRI-EBI GWAS catalog25 (associations and studies) was download on Aug 17, 2022 [https://www.ebi.ac.uk/gwas/docs/file-downloads]. Any GWAS screens with an associated PubMed ID in which hit genes were mentioned in the title or abstract was considered (n= 450). Only SNPs occurring within a gene were considered hits. 1,043 unique genes were found as hits. Of these, 413 (39.6%) were elevated to titles/abstracts in reporting articles and 319 (30.6%) were elevated to titles/abstracts in citing articles. A full list of PubMed IDs is available in Supplementary File 2.

Citing articles

NIH iCite v32 was downloaded on Aug 25, 202230 [https://nih.figshare.com/collections/iCite_Database_Snapshots_NIH_Open_Citation_Collection_/4586573/32].

Functional annotations

Mapping of genes to Gene Ontology / Protein Interaction Database / WikiPathways / Reactome / Kyoto Encyclopedia of Genes and Genomes / Human Phenotype Ontology / BioCarta categories was derived from MSigDB v7.5 Entrez ID .gmt files, downloaded on Apr 12, 2022 [http://www.gsea-msigdb.org/gsea/downloads_archive.jsp].

Between-species homology

Homologene Build 68 was used to determine interspecies homology [ftp.ncbi.nih.gov/pub/HomoloGene/build68/]. Human = taxid:9606, mouse = taxid:10090, rat = taxid:10116, c. elegans = taxid:6239, d. melanogaster = taxid:7227, yeast = taxid:559292, zebrafish = taxid:7955.

Primate specificity

Human genes were considered primate-specific if the only other members of their homology group belonged to primate genomes. Primate taxonomy ids were downloaded from NCBI Taxonomy on Sep 20, 2022 [https://www.ncbi.nlm.nih.gov/taxonomy/?term=txid9443[Subtree]].

Number of publications in model organisms

Gene information was downloaded from NCBI Gene on Aug 16, 2022 [ftp.ncbi.nlm.nih.gov/gene/DATA/GENE_INFO/All_Data.gene_info.gz].

Only using PMIDs annotated as primary research articles, genes was considered as mentioned in the title/abstract of the publication if gene was annotated as being in the title/abstract by PubTator and the article appeared in gene2pubmed.

Genes in model organisms were mapped to human genes and the number of articles on those mapping to human genes were counted. If a model organism’s gene had homology to human but no associated publications, the number of publications was resolved to zero. Otherwise, counts were listed as NA.

Mouse phenotype hits

International Mouse Phenotyping Consortium data release 17.0 was downloaded on Aug 18, 2022 [https://www.mousephenotype.org/data/release]. Mouse genes were matched to human genes with Homologene.

Gene Expression Atlas (EBI-GXA)

EBI-GXA release 36 was downloaded on Sep 15, 2020 [https://web.archive.org/web/20201022184159/ https://www.ebi.ac.uk/gxa/download]. This is the most recent release of EBI-GXA available as a bulk download. For probability of DE, only RNA-seq comparisons were considered and DE was called at Benjamini-Hochberg q < 0.05.

Global RNA expression

RNA consensus tissue gene data from HPA release 21.1 was downloaded on Sep 20, 2022 [https://www.proteinatlas.org/about/download]. Global RNA expression was estimated by taking the median expression (nTPM) across tissues for each gene and the proportion of tissues with detectable (≥1 nTPM) expression for each gene.

Expression in HeLa cells

RNA cell line gene data from HPA release 21.1 was downloaded on Sep 20, 2022 [https://www.proteinatlas.org/about/download]. Expression is in nTPM.

Previous patent activity

Genes with patent activity were defined from Table S1 of Rosenfeld and Mason, 201367. Genes were mapped with their HGNC symbol. This analysis aligned sequences in patents to the human genome to estimate patent coverage of human coding sequences. Although this does not necessarily reflect whether the mapped genes were claimed directly by the patent holder, as noted by others68, this analysis remains the most comprehensive available for determining patent coverage of the human genome.

Druggability

Druggable genes were identified from Table S1 of Finan et al., 201769. Genes were mapped with their Ensembl identifier.

Gene length

GenBank was downloaded in spring 2017 (genome version GRCh38.p10). Gene length is defined here as the span of the longest transcript on the chromosome. This aligns with the model of gene length used in Stoeger et al., 20187.

Solubility

SwissProt protein sequences and mapping tables to Entrez GeneIDs were downloaded from Uniprot in spring 2017. Protein GRAVY score (ignoring Pyrrolysine and Selenocysteine) was estimated with BioPython70.

Loss of function intolerance

Data was obtained from Karczewski et al.71. pLI scores > 0.9 on main transcripts, as flagged by authors, were considered as highly loss-of-function intolerant as described by Lek et al.72.

Number of GWAS hits

EBI GWAS catalog25 (associations and studies) was download on Aug 17, 2022 [https://www.ebi.ac.uk/gwas/docs/file-downloads]. Loci were mapped to the nearest gene.

Status as understudied protein

The Illuminating the Druggable Genome understudied protein list was downloaded on Sep 20, 2022 [https://github.com/druggablegenome/IDGTargets/blob/master/IDG_TargetList_CurrentVersion.json].

Human Protein Atlas

HPA release 21.1 was downloaded on Sep 20, 2022 [https://www.proteinatlas.org/search]. Evidence for a protein’s existence, as determined by NeXtProt, HPA, or UniProt was resolved as True if the respective evidence entry was annotated as “Evidence at protein level”. Status as a membrane protein was determined by whether the ‘Protein class’ column contained the string ‘membrane protein’. Antibodies were considered available for each protein if the protein’s entry in the ‘Antibody’ column was not null.

Availability of plasmids

The AddGene plasmid catalog was downloaded on Aug 12, 2022 [https://www.addgene.org/browse/gene/gene-list-data/?_=1666368044314].

Availability of compounds

The catalog of gene targets was downloaded from ChEMBL on Sep 20, 2022 [https://www.ebi.ac.uk/chembl/g/#browse/targets]. UniProt IDs were converted to Entrez IDs to identify which human genes were affected by any compound.

Mendelian inheritance

Autosomal dominant [https://hpo.jax.org/app/browse/term/HP:0000006] and autosomal recessive [https://hpo.jax.org/app/browse/term/HP:0000007] inherited disease-gene associations were downloaded from the Human Phenotype Ontology on Sep 20, 2022. Genes were considered to have evidence of Mendelian inheritance if they appeared in these lists of associations.

Code

Code for analysis is available at github.com/amarallab/fmug_analysis. Code for FMUG is available at github.com/amarallab/fmug.

Data Availability

All underlying data for figures are available at github.com/amarallab/fmug_analysis.

Acknowledgements

We thank Xiaojing Sui for testing FMUG and Northwestern Information Technology for technical assistance. RAKR was supported in part by the National Institutes of Health Training Grant (T32GM008449) through Northwestern University’s Biotechnology Training Program. RAKR also acknowledges support from the Dr. John N. Nicholson fellowship from Northwestern University and Moderna Inc., “Identifying bias and improving reproducibility in RNA-seq computational pipelines”. LANA was supported by NSF 1956338, NIH U19AI135964 and Simons Foundation DMS-1764421. TS was supported by NIH K99AG068544. We thank Alexander Misharin, Richard Morimoto, and Scott Budinger for feedback on an early prototype of FMUG which we used as part of our shared research into the biology of aging.