ProteomicsDB: a multi-omics and multi-organism resource for life science research

Abstract

ProteomicsDB (https://www.ProteomicsDB.org) started as a protein-centric in-memory database for the exploration of large collections of quantitative mass spectrometry-based proteomics data. The data types and contents grew over time to include RNA-Seq expression data, drug-target interactions and cell line viability data. In this manuscript, we summarize new developments since the previous update that was published in Nucleic Acids Research in 2017. Over the past two years, we have enriched the data content by additional datasets and extended the platform to support protein turnover data. Another important new addition is that ProteomicsDB now supports the storage and visualization of data collected from other organisms, exemplified by Arabidopsis thaliana. Due to the generic design of ProteomicsDB, all analytical features available for the original human resource seamlessly transfer to other organisms. Furthermore, we introduce a new service in ProteomicsDB which allows users to upload their own expression datasets and analyze them alongside with data stored in ProteomicsDB. Initially, users will be able to make use of this feature in the interactive heat map functionality as well as the drug sensitivity prediction, but ultimately will be able to use all analytical features of ProteomicsDB in this way.

Toward Development of the Male Pill: A Decade of Potential Non-hormonal Contraceptive Targets

“…Henceforth, these 45 genes will be listed according to their published expression patterns (Figure 3), which is an important criterium that should be considered in the selection of a safe and/or desirable contraceptive target. It is worth noting that nearly all of the genes mentioned below are male reproductive tractspecific or highly enriched as reported in the literature and with additional confirmation through the published expression data from CITDBase (GTEx Consortium, 2013;Uhlen et al, 2015;Schmidt et al, 2017;Lee, 2019;Samaras et al, 2019) and/or the Human Protein Atlas (Djureinovic et al, 2014;Uhlen et al, 2015). A graphical summary of the RNAseq-based expression data for these 45 genes is depicted in Figure 4.…”

“…There are 1,517 human genes associated with male infertility and/or either azoospermia, globozospermia, or oligospermia as reported by GeneCards and MalaCards (Stelzer et al, 2016;Rappaport et al, 2017). Of these 1,517 genes, 202 genes are reproductive tract-specific in humans as reported through the CITDBase Contraceptive Target Database (GTEx Consortium, 2013;Djureinovic et al, 2014;Uhlen et al, 2015;Schmidt et al, 2017;Lee, 2019;Samaras et al, 2019) and/or Djureinovic et al (2014) and Uhlen et al (2015) (Figure 1). Of these 202 genes, 21 genes do not have a corresponding mouse ortholog and 3 of these genes (KLK2, CDY2A, and RHOXF2) may be of potential interest for further study as they encode an enzyme, an epigenetic protein, and a transcription factor, respectively; all proteins with potential druggable activity (Supplementary Table S1).…”

“…Not discussed in terms of potential drug target specificity, however of additional potential interest are the following genes that are also reproductive tract-specific in humans according to CITDBase (GTEx Consortium, 2013;Uhlen et al, 2015;Schmidt et al, 2017;Lee, 2019;Samaras et al, 2019) with mouse models displaying male infertility phenotype published in peer-reviewed journals in the last 10 years: 3 genes encoding enzymes [ENO4 (Nakamura et al, 2013), PNLDC1 (Nishimura et al, 2018), and SPINK2 (Lee et al, 2011)] and 8 genes encoding proteins of unknown drug target type [M1AP (Arango et al, 2013), MEIG1 (Zhang et al, 2009), MEIKIN (Kim et al, 2015), NXF2 (Pan et al, 2009), ODF1 (Yang et al, 2012), PPP3R2 (Miyata et al, 2015), SMC1B (Revenkova et al, 2010), and SPATA16 (Fujihara et al, 2017)]. Furthermore, the following genes are reproductive tractspecific in humans according to CITDBase (GTEx Consortium, 2013;Uhlen et al, 2015;Schmidt et al, 2017;Lee, 2019;Samaras et al, 2019) with mouse models displaying male infertility phenotypes as reported by the International Mouse Phenotyping Consortium (IMPC) (Munoz-Fuentes et al, 2018): 1 gene encoding an enzyme (ADAD2); 1 gene encoding an epigeneticrelated protein (PHF7); and 11 genes encoding proteins of unknown drug target type (ACTL7B, ADGB, ARRDC5, C11orf94, C16orf92, C3orf20, DNAH17, FBXO47, NUTM1, ODF4, TEX38). The expression pattern of these additional individually published and IMPC-reported genes are listed in Figure 4.…”

A novel predicted ADP-ribosyltransferase family conserved in eukaryotic evolution

“…For analysis of protein and mRNA expression, the Proteomics-db and Protein Atlas databases were used [78,79]. The BioGRID Interaction Database was used to obtain information about possible place of LRRC9 in human protein-protein interaction networks [80].…”

Clinical Knowledge Graph Integrates Proteomics Data into Clinical Decision-Making

“…Over the last decade, the technology of mass spectrometry MS-based proteomics has advanced greatly and now provides a comprehensive view of biological processes, cellular signaling events and protein interplay (Aebersold and Mann, 2016). Moreover, improvements in sample preparation workflows, MS instrumentation, automation and data analysis software have dramatically enhanced the ability of this technology to more rapidly analyze samples of increasing complexity (cell lines, tissues and body fluids), with increased sensitivity and accuracy (Bache et al, 2018;Bian et al, 2020;Bruderer et al, 2015;Demichev et al, 2020;Gessulat et al, 2019;Heusel et al, 2019;Krieger et al, 2019;Kulak et al, 2014;Meier et al, 2015Meier et al, , 2018Post et al, 2017;Samaras et al, 2019;Sinitcyn et al, 2018;Slavov, 2020;Tiwary et al, 2019;Zhou et al, 2017). However, currently used MS-based proteomics workflows were conceptualized more than a decade ago, and rapidly increasing data volumes are posing new challenges for the field.…”