The neXtProt knowledgebase on human proteins: current status

The SIB Swiss Institute of Bioinformatics Semantic Web of data

The SIB Swiss Institute of Bioinformatics (https://www.sib.swiss/) is a federation of bioinformatics research and service groups. The international life science community in academia and industry has been accessing the freely available databases provided by SIB since its inception in 1998. In this paper we present the 11 databases which currently offer semantically enriched data in accordance with the FAIR principles (Findable, Accessible, Interoperable, Reusable), as well as the Swiss Personalized Health Network initiative (SPHN) which also employs this enrichment. The semantic enrichment facilitates the manipulation of large data sets from public databases and private data sets. Examples are provided to illustrate that the data from the SIB databases can not only be queried using precise criteria individually, but also across multiple databases, including a variety of non-SIB databases. Data manipulation, be it exploration, extraction, annotation, combination, and publication, is possible using the SPARQL query language. Providing documentation, tutorials and sample queries makes it easier to navigate this web of semantic data. Through this paper, the reader will discover how the existing SIB knowledge graphs can be leveraged to tackle the complex biological or clinical questions that are being addressed today.

Comprehensive and systematic characterization of multi-functionalized cisplatin nano-conjugate: from the chemistry and proteomic biocompatibility to the animal model

Background

Nowadays, nanoparticles (NPs) have evolved as multifunctional systems combining different custom anchorages which opens a wide range of applications in biomedical research. Thus, their pharmacological involvements require more comprehensive analysis and novel nanodrugs should be characterized by both chemically and biological point of view. Within the wide variety of biocompatible nanosystems, iron oxide nanoparticles (IONPs) present mostly of the required features which make them suitable for multifunctional NPs with many biopharmaceutical applications.

Results

Cisplatin-IONPs and different functionalization stages have been broadly evaluated. The potential application of these nanodrugs in onco-therapies has been assessed by studying in vitro biocompatibility (interactions with environment) by proteomics characterization the determination of protein corona in different proximal fluids (human plasma, rabbit plasma and fetal bovine serum),. Moreover, protein labeling and LC–MS/MS analysis provided more than 4000 proteins de novo synthetized as consequence of the nanodrugs presence defending cell signaling in different tumor cell types (data available via ProteomeXchanges with identified PXD026615). Further in vivo studies have provided a more integrative view of the biopharmaceutical perspectives of IONPs.

Conclusions

Pharmacological proteomic profile different behavior between species and different affinity of protein coating layers (soft and hard corona). Also, intracellular signaling exposed differences between tumor cell lines studied. First approaches in animal model reveal the potential of theses NPs as drug delivery vehicles and confirm cisplatin compounds as strengthened antitumoral agents.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12951-022-01546-y.

Deciphering Biomarkers for Leptomeningeal Metastasis in Malignant Hemopathies (Lymphoma/Leukemia) Patients by Comprehensive Multipronged Proteomics Characterization of Cerebrospinal Fluid

Simple Summary

The early diagnosis of leptomeningeal disease is a challenge because it is asymptomatic in the early stages. Consequently, it is important to identify a panel of biomarkers to help in its diagnosis and/or prognosis. For this purpose, we explored a multipronged proteomics approach in cerebrospinal fluid (CSF) to determine a potential panel of biomarkers. Thus, a systematic and exhaustive characterization of more than 300 CSF samples was performed by an integrated approach by Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) and functional proteomics analysis to establish protein profiles, which were useful for developing a panel of biomarkers validated by in silico approaches.

Abstract

In the present work, leptomeningeal disease, a very destructive form of systemic cancer, was characterized from several proteomics points of view. This pathology involves the invasion of the leptomeninges by malignant tumor cells. The tumor spreads to the central nervous system through the cerebrospinal fluid (CSF) and has a very grim prognosis; the average life expectancy of patients who suffer it does not exceed 3 months. The early diagnosis of leptomeningeal disease is a challenge because, in most of the cases, it is an asymptomatic pathology. When the symptoms are clear, the disease is already in the very advanced stages and life expectancy is low. Consequently, there is a pressing need to determine useful CSF proteins to help in the diagnosis and/or prognosis of this disease. For this purpose, a systematic and exhaustive proteomics characterization of CSF by multipronged proteomics approaches was performed to determine different protein profiles as potential biomarkers. Proteins such as PTPRC, SERPINC1, sCD44, sCD14, ANPEP, SPP1, FCGR1A, C9, sCD19, and sCD34, among others, and their functional analysis, reveals that most of them are linked to the pathology and are not detected on normal CSF. Finally, a panel of biomarkers was verified by a prediction model for leptomeningeal disease, showing new insights into the research for potential biomarkers that are easy to translate into the clinic for the diagnosis of this devastating disease.

Deepening into Intracellular Signaling Landscape through Integrative Spatial Proteomics and Transcriptomics in a Lymphoma Model

Human Proteome Project (HPP) presents a systematic characterization of the protein landscape under different conditions using several complementary-omic techniques (LC-MS/MS proteomics, affinity proteomics, transcriptomics, etc.). In the present study, using a B-cell lymphoma cell line as a model, comprehensive integration of RNA-Seq transcriptomics, MS/MS, and antibody-based affinity proteomics (combined with size-exclusion chromatography) (SEC-MAP) were performed to uncover correlations that could provide insights into protein dynamics at the intracellular level. Here, 5672 unique proteins were systematically identified by MS/MS analysis and subcellular protein extraction strategies (neXtProt release 2020-21, MS/MS data are available via ProteomeXchange with identifier PXD003939). Moreover, RNA deep sequencing analysis of this lymphoma B-cell line identified 19,518 expressed genes and 5707 protein coding genes (mapped to neXtProt). Among these data sets, 162 relevant proteins (targeted by 206 antibodies) were systematically analyzed by the SEC-MAP approach, providing information about PTMs, isoforms, protein complexes, and subcellular localization. Finally, a bioinformatic pipeline has been designed and developed for orthogonal integration of these high-content proteomics and transcriptomics datasets, which might be useful for comprehensive and global characterization of intracellular protein profiles.

VISPR-online: a web-based interactive tool to visualize CRISPR screening experiments

BACKGROUND

VISPR is an interactive visualization and analysis framework for CRISPR screening experiments. However, it only supports the output of MAGeCK, and requires installation and manual configuration. Furthermore, VISPR is designed to run on a single computer, and data sharing between collaborators is challenging.

RESULTS

To make the tool easily accessible to the community, we present VISPR-online, a web-based general application allowing users to visualize, explore, and share CRISPR screening data online with a few simple steps. VISPR-online provides an exploration of screening results and visualization of read count changes. Apart from MAGeCK, VISPR-online supports two more popular CRISPR screening analysis tools: BAGEL and JACKS. It provides an interactive environment for exploring gene essentiality, viewing guide RNA (gRNA) locations, and allowing users to resume and share screening results.

CONCLUSIONS

VISPR-online allows users to visualize, explore and share CRISPR screening data online. It is freely available at http://vispr-online.weililab.org , while the source code is available at https://github.com/lemoncyb/VISPR-online .

Integrative functional genomic analysis of intron retention in human and mouse brain with Alzheimer's disease

Intron retention (IR) has been implicated in the pathogenesis of complex diseases such as cancers; its association with Alzheimer's disease (AD) remains unexplored. We performed genome-wide analysis of IR through integrating genetic, transcriptomic, and proteomic data of AD subjects and mouse models from the Accelerating Medicines Partnership-Alzheimer's Disease project. We identified 4535 and 4086 IR events in 2173 human and 1736 mouse genes, respectively. Quantitation of IR enabled the identification of differentially expressed genes that conventional exon-level approaches did not reveal. There were significant correlations of intron expression within innate immune genes, like HMBOX1, with AD in humans. Peptides with a high probability of translation from intron-retained mRNAs were identified using mass spectrometry. Further, we established AD-specific intron expression Quantitative Trait Loci, and identified splicing-related genes that may regulate IR. Our analysis provides a novel resource for the search for new AD biomarkers and pathological mechanisms.

IDSM ChemWebRDF: SPARQLing small-molecule datasets

The Resource Description Framework (RDF), together with well-defined ontologies, significantly increases data interoperability and usability. The SPARQL query language was introduced to retrieve requested RDF data and to explore links between them. Among other useful features, SPARQL supports federated queries that combine multiple independent data source endpoints. This allows users to obtain insights that are not possible using only a single data source. Owing to all of these useful features, many biological and chemical databases present their data in RDF, and support SPARQL querying. In our project, we primary focused on PubChem, ChEMBL and ChEBI small-molecule datasets. These datasets are already being exported to RDF by their creators. However, none of them has an official and currently supported SPARQL endpoint. This omission makes it difficult to construct complex or federated queries that could access all of the datasets, thus underutilising the main advantage of the availability of RDF data. Our goal is to address this gap by integrating the datasets into one database called the Integrated Database of Small Molecules (IDSM) that will be accessible through a SPARQL endpoint. Beyond that, we will also focus on increasing mutual interoperability of the datasets. To realise the endpoint, we decided to implement an in-house developed SPARQL engine based on the PostgreSQL relational database for data storage. In our approach, data are stored in the traditional relational form, and the SPARQL engine translates incoming SPARQL queries into equivalent SQL queries. An important feature of the engine is that it optimises the resulting SQL queries. Together with optimisations performed by PostgreSQL, this allows efficient evaluations of SPARQL queries. The endpoint provides not only querying in the dataset, but also the compound substructure and similarity search supported by our Sachem project. Although the endpoint is accessible from an internet browser, it is mainly intended to be used for programmatic access by other services, for example as a part of federated queries. For regular users, we offer a rich web application called ChemWebRDF using the endpoint. The application is publicly available at https://idsm.elixir-czech.cz/chemweb/.

Pan-cancer analysis of transcripts encoding novel open-reading frames (nORFs) and their potential biological functions

Uncharacterized and unannotated open-reading frames, which we refer to as novel open reading frames (nORFs), may sometimes encode peptides that remain unexplored for novel therapeutic opportunities. To our knowledge, no systematic identification and characterization of transcripts encoding nORFs or their translation products in cancer, or in any other physiological process has been performed. We use our curated nORFs database (nORFs.org), together with RNA-Seq data from The Cancer Genome Atlas (TCGA) and Genotype-Expression (GTEx) consortiums, to identify transcripts containing nORFs that are expressed frequently in cancer or matched normal tissue across 22 cancer types. We show nORFs are subject to extensive dysregulation at the transcript level in cancer tissue and that a small subset of nORFs are associated with overall patient survival, suggesting that nORFs may have prognostic value. We also show that nORF products can form protein-like structures with post-translational modifications. Finally, we perform in silico screening for inhibitors against nORF-encoded proteins that are disrupted in stomach and esophageal cancer, showing that they can potentially be targeted by inhibitors. We hope this work will guide and motivate future studies that perform in-depth characterization of nORF functions in cancer and other diseases.

Novel functional proteins coded by the human genome discovered in metastases of melanoma patients

In the advanced stages, malignant melanoma (MM) has a very poor prognosis. Due to tremendous efforts in cancer research over the last 10 years, and the introduction of novel therapies such as targeted therapies and immunomodulators, the rather dark horizon of the median survival has dramatically changed from under 1 year to several years. With the advent of proteomics, deep-mining studies can reach low-abundant expression levels. The complexity of the proteome, however, still surpasses the dynamic range capabilities of current analytical techniques. Consequently, many predicted protein products with potential biological functions have not yet been verified in experimental proteomic data. This category of 'missing proteins' (MP) is comprised of all proteins that have been predicted but are currently unverified. As part of the initiative launched in 2016 in the USA, the European Cancer Moonshot Center has performed numerous deep proteomics analyses on samples from MM patients. In this study, nine MPs were clearly identified by mass spectrometry in MM metastases. Some MPs significantly correlated with proteins that possess identical PFAM structural domains; and other MPs were significantly associated with cancer-related proteins. This is the first study to our knowledge, where unknown and novel proteins have been annotated in metastatic melanoma tumour tissue.

Is It Possible to Find Needles in a Haystack? Meta-Analysis of 1000+ MS/MS Files Provided by the Russian Proteomic Consortium for Mining Missing Proteins

Despite direct or indirect efforts of the proteomic community, the fraction of blind spots on the protein map is still significant. Almost 11% of human genes encode missing proteins; the existence of which proteins is still in doubt. Apparently, proteomics has reached a stage when more attention and curiosity need to be exerted in the identification of every novel protein in order to expand the unusual types of biomaterials and/or conditions. It seems that we have exhausted the current conventional approaches to the discovery of missing proteins and may need to investigate alternatives. Here, we present an approach to deciphering missing proteins based on the use of non-standard methodological solutions and encompassing diverse MS/MS data, obtained for rare types of biological samples by members of the Russian Proteomic community in the last five years. These data were re-analyzed in a uniform manner by three search engines, which are part of the SearchGUI package. The study resulted in the identification of two missing and five uncertain proteins detected with two peptides. Moreover, 149 proteins were detected with a single proteotypic peptide. Finally, we analyzed the gene expression levels to suggest feasible targets for further validation of missing and uncertain protein observations, which will fully meet the requirements of the international consortium. The MS data are available on the ProteomeXchange platform (PXD014300).

The jPOST environment: an integrated proteomics data repository and database

Rapid progress is being made in mass spectrometry (MS)-based proteomics, yielding an increasing number of larger datasets with higher quality and higher throughput. To integrate proteomics datasets generated from various projects and institutions, we launched a project named jPOST (Japan ProteOme STandard Repository/Database, https://jpostdb.org/) in 2015. Its proteomics data repository, jPOSTrepo, began operations in 2016 and has accepted more than 10 TB of MS-based proteomics datasets in the past two years. In addition, we have developed a new proteomics database named jPOSTdb in which the published raw datasets in jPOSTrepo are reanalyzed using standardized protocol. jPOSTdb provides viewers showing the frequency of detected post-translational modifications, the co-occurrence of phosphorylation sites on a peptide and peptide sharing among proteoforms. jPOSTdb also provides basic statistical analysis tools to compare proteomics datasets.

Anatomy and evolution of database search engines-a central component of mass spectrometry based proteomic workflows

Sequence database search engines are bioinformatics algorithms that identify peptides from tandem mass spectra using a reference protein sequence database. Two decades of development, notably driven by advances in mass spectrometry, have provided scientists with more than 30 published search engines, each with its own properties. In this review, we present the common paradigm behind the different implementations, and its limitations for modern mass spectrometry datasets. We also detail how the search engines attempt to alleviate these limitations, and provide an overview of the different software frameworks available to the researcher. Finally, we highlight alternative approaches for the identification of proteomic mass spectrometry datasets, either as a replacement for, or as a complement to, sequence database search engines.

Comprehensive Identification and Characterization of Human Secretome Based on Integrative Proteomic and Transcriptomic Data

Secreted proteins (SPs) play important roles in diverse important biological processes; however, a comprehensive and high-quality list of human SPs is still lacking. Here we identified 6,943 high-confidence human SPs (3,522 of them are novel) based on 330,427 human proteins derived from databases of UniProt, Ensembl, AceView, and RefSeq. Notably, 6,267 of 6,943 (90.3%) SPs have the supporting evidences from a large amount of mass spectrometry (MS) and RNA-seq data. We found that the SPs were broadly expressed in diverse tissues as well as human body fluid, and a significant portion of them exhibited tissue-specific expression. Moreover, 14 cancer-specific SPs that their expression levels were significantly associated with the patients’ survival of eight different tumors were identified, which could be potential prognostic biomarkers. Strikingly, 89.21% of 6,943 SPs (2,927 novel SPs) contain known protein domains. Those novel SPs we mainly enriched with the known domains regarding immunity, such as Immunoglobulin V-set and C1-set domain. Specifically, we constructed a user-friendly and freely accessible database, SPRomeDB (www.unimd.org/SPRomeDB), to catalog those SPs. Our comprehensive SP identification and characterization gain insights into human secretome and provide valuable resource for future researches.

Human Proteome Project Mass Spectrometry Data Interpretation Guidelines 3.0

The Human Proteome Organization's (HUPO) Human Proteome Project (HPP) developed Mass Spectrometry (MS) Data Interpretation Guidelines that have been applied since 2016. These guidelines have helped ensure that the emerging draft of the complete human proteome is highly accurate and with low numbers of false-positive protein identifications. Here, we describe an update to these guidelines based on consensus-reaching discussions with the wider HPP community over the past year. The revised 3.0 guidelines address several major and minor identified gaps. We have added guidelines for emerging data independent acquisition (DIA) MS workflows and for use of the new Universal Spectrum Identifier (USI) system being developed by the HUPO Proteomics Standards Initiative (PSI). In addition, we discuss updates to the standard HPP pipeline for collecting MS evidence for all proteins in the HPP, including refinements to minimum evidence. We present a new plan for incorporating MassIVE-KB into the HPP pipeline for the next (HPP 2020) cycle in order to obtain more comprehensive coverage of public MS data sets. The main checklist has been reorganized under headings and subitems, and related guidelines have been grouped. In sum, Version 2.1 of the HPP MS Data Interpretation Guidelines has served well, and this timely update to version 3.0 will aid the HPP as it approaches its goal of collecting and curating MS evidence of translation and expression for all predicted ∼20 000 human proteins encoded by the human genome.

AAgMarker 1.0: a resource of serological autoantigen biomarkers for clinical diagnosis and prognosis of various human diseases

Autoantibodies are produced to target an individual's own antigens (e.g. proteins). They can trigger autoimmune responses and inflammation, and thus, cause many types of diseases. Many high-throughput autoantibody profiling projects have been reported for unbiased identification of serological autoantigen-based biomarkers. However, a lack of centralized data portal for these published assays has been a major obstacle to further data mining and cross-evaluate the quality of these datasets generated from different diseases. Here, we introduce a user-friendly database, AAgMarker 1.0, which collects many published raw datasets obtained from serum profiling assays on the proteome microarrays, and provides a toolbox for mining these data. The current version of AAgMarker 1.0 contains 854 serum samples, involving 136 092 proteins. A total of 7803 (4470 non-redundant) candidate autoantigen biomarkers were identified and collected for 12 diseases, such as Alzheimer's disease, Bechet's disease and Parkinson's disease. Seven statistical parameters are introduced to quantitatively assess these biomarkers. Users can retrieve, analyse and compare the datasets through basic search, advanced search and browse. These biomarkers are also downloadable by disease terms. The AAgMarker 1.0 is now freely accessible at http://bioinfo.wilmer.jhu.edu/AAgMarker/. We believe this database will be a valuable resource for the community of both biomedical and clinical research.

Using deep learning to identify translational research in genomic medicine beyond bench to bedside

Tracking scientific research publications on the evaluation, utility and implementation of genomic applications is critical for the translation of basic research to impact clinical and population health. In this work, we utilize state-of-the-art machine learning approaches to identify translational research in genomics beyond bench to bedside from the biomedical literature. We apply the convolutional neural networks (CNNs) and support vector machines (SVMs) to the bench/bedside article classification on the weekly manual annotation data of the Public Health Genomics Knowledge Base database. Both classifiers employ salient features to determine the probability of curation-eligible publications, which can effectively reduce the workload of manual triage and curation process. We applied the CNNs and SVMs to an independent test set (n = 400), and the models achieved the F-measure of 0.80 and 0.74, respectively. We further tested the CNNs, which perform better results, on the routine annotation pipeline for 2 weeks and significantly reduced the effort and retrieved more appropriate research articles. Our approaches provide direct insight into the automated curation of genomic translational research beyond bench to bedside. The machine learning classifiers are found to be helpful for annotators to enhance the efficiency of manual curation.

200+ Protein Concentrations in Healthy Human Blood Plasma: Targeted Quantitative SRM SIS Screening of Chromosomes 18, 13, Y, and the Mitochondrial Chromosome Encoded Proteome

This work continues the series of the quantitative measurements of the proteins encoded by different chromosomes in the blood plasma of a healthy person. Selected Reaction Monitoring with Stable Isotope-labeled peptide Standards (SRM SIS) and a gene-centric approach, which is the basis for the implementation of the international Chromosome-centric Human Proteome Project (C-HPP), were applied for the quantitative measurement of proteins in human blood plasma. Analyses were carried out in the frame of C-HPP for each protein-coding gene of the four human chromosomes: 18, 13, Y, and mitochondrial. Concentrations of proteins encoded by 667 genes were measured in 54 blood plasma samples of the volunteers, whose health conditions were consistent with requirements for astronauts. The gene list included 276, 329, 47, and 15 genes of chromosomes 18, 13, Y, and the mitochondrial chromosome, respectively. This paper does not make claims about the detection of missing proteins. Only 205 proteins (30.7%) were detected in the samples. Of them, 84, 106, 10, and 5 belonged to chromosomes 18, 13, and Y and the mitochondrial chromosome, respectively. Each detected protein was found in at least one of the samples analyzed. The SRM SIS raw data are available in the ProteomeXchange repository (PXD004374, PASS01192).

Chromosome 17 Missing Proteins: Recent Progress and Future Directions as Part of the neXt-MP50 Challenge

The Chromosome-centric Human Proteome Project (C-HPP), announced in September 2016, is an initiative to accelerate progress on the detection and characterization of neXtProt PE2,3,4 "missing proteins" (MPs) with a mandate to each chromosome team to find about 50 MPs over 2 years. Here we report major progress toward the neXt-MP50 challenge with 43 newly validated Chr 17 PE1 proteins, of which 25 were based on mass spectrometry, 12 on protein-protein interactions, 3 on a combination of MS and PPI, and 3 with other types of data. Notable among these new PE1 proteins were five keratin-associated proteins, a single olfactory receptor, and five additional membrane-embedded proteins. We evaluate the prospects of finding the remaining 105 MPs coded for on Chr 17, focusing on mass spectrometry and protein-protein interaction approaches. We present a list of 35 prioritized MPs with specific approaches that may be used in further MS and PPI experimental studies. Additionally, we demonstrate how in silico studies can be used to capture individual peptides from major data repositories, documenting one MP that appears to be a strong candidate for PE1. We are close to our goal of finding 50 MPs for Chr 17.

Analysis of impact metrics for the Protein Data Bank

Since 1971, the Protein Data Bank (PDB) archive has served as the single, global repository for open access to atomic-level data for biological macromolecules. The archive currently holds >140,000 structures (>1 billion atoms). These structures are the molecules of life found in all organisms. Knowing the 3D structure of a biological macromolecule is essential for understanding the molecule's function, providing insights in health and disease, food and energy production, and other topics of concern to prosperity and sustainability. PDB data are freely and publicly available, without restrictions on usage. Through bibliometric and usage studies, we sought to determine the impact of the PDB across disciplines and demographics. Our analysis shows that even though research areas such as molecular biology and biochemistry account for the most usage, other fields are increasingly using PDB resources. PDB usage is seen across 150 disciplines in applied sciences, humanities, and social sciences. Data are also re-used and integrated with >400 resources. Our study identifies trends in PDB usage and documents its utility across research disciplines.

Flexible and Fast Mapping of Peptides to a Proteome with ProteoMapper

Bottom-up proteomics relies on the proteolytic or chemical cleavage of proteins into peptides, the identification of those peptides via mass spectrometry, and the mapping of the identified peptides back to the reference proteome to infer which possible proteins are identified. Reliable mapping of peptides to proteins still poses substantial challenges when considering similar proteins, protein families, splice isoforms, sequence variation, and possible residue mass modifications, combined with an imperfect and incomplete understanding of the proteome. The ProteoMapper tool enables a comprehensive and rapid mapping of peptides to a reference proteome. The indexer component creates a segmented index for an input proteome from a FASTA or PEFF file. The ProMaST component provides ultrafast mapping of one or more input peptides against the index. ProteoMapper allows searches that take into account known sequence variation encoded in PEFF files. It also enables fuzzy searches to find highly similar peptides with residue order changes or other isobaric or near-isobaric substitutions within a specified mass tolerance. We demonstrate an example of a one-hit-wonder identification in PeptideAtlas that may be better explained by a combination of catalogued and uncatalogued sequence variation in another highly observed protein. ProteoMapper is a free and open source, available for local use after downloading, embedding in other applications, as an online web tool at http://www.peptideatlas.org/map , and as a web service.

Deep Dive on the Proteome of Human Cerebrospinal Fluid: A Valuable Data Resource for Biomarker Discovery and Missing Protein Identification

Cerebrospinal fluid (CSF) is a body fluid of choice for biomarker studies of brain disorders but remains relatively under-studied compared with other biological fluids such as plasma, partly due to the more invasive means of its sample collection. The present study establishes an in-depth CSF proteome through the analysis of a unique CSF sample from a pool of donors. After immunoaffinity depletion, the CSF sample was fractionated using off-gel electrophoresis and analyzed with liquid chromatography tandem mass spectrometry (MS) using the latest generation of hybrid Orbitrap mass spectrometers. The shotgun proteomic analysis allowed the identification of 20 689 peptides mapping on 3379 proteins. To the best of our knowledge, the obtained data set constitutes the largest CSF proteome published so far. Among the CSF proteins identified, 34% correspond to genes whose transcripts are highly expressed in brain according to the Human Protein Atlas. The principal Alzheimer's disease biomarkers (e.g., tau protein, amyloid-β, apolipoprotein E, and neurogranin) were detected. Importantly, our data set significantly contributes to the Chromosome-centric Human Proteome Project (C-HPP), and 12 proteins considered as missing are proposed for validation in accordance with the HPP guidelines. Of these 12 proteins, 8 proteins are based on 2 to 6 uniquely mapping peptides from this CSF analysis, and 4 match a new peptide with a "stranded" single peptide in PeptideAtlas from previous CSF studies. The MS proteomic data are available to the ProteomeXchange Consortium ( http://www.proteomexchange.org/ ) with the data set identifier PXD009646.

Assembling the Community-Scale Discoverable Human Proteome

The increasing throughput and sharing of proteomics mass spectrometry data have now yielded over one-third of a million public mass spectrometry runs. However, these discoveries are not continuously aggregated in an open and error-controlled manner, which limits their utility. To facilitate the reusability of these data, we built the MassIVE Knowledge Base (MassIVE-KB), a community-wide, continuously updating knowledge base that aggregates proteomics mass spectrometry discoveries into an open reusable format with full provenance information for community scrutiny. Reusing >31 TB of public human data stored in a mass spectrometry interactive virtual environment (MassIVE), the MassIVE-KB contains >2.1 million precursors from 19,610 proteins (48% larger than before; 97% of the total) and doubles proteome coverage to 6 million amino acids (54% of the proteome) with strict library-scale false discovery controls, thereby providing evidence for 430 proteins for which sufficient protein-level evidence was previously missing. Furthermore, MassIVE-KB can inform experimental design, helps identify and quantify new data, and provides tools for community construction of specialized spectral libraries.

Wang et al. introduce MassIVE-KB, a program designed to distill the entire community’s mass spectrometry data into reusable spectral library resources. As a result, the statistically-significant discovery of a peptide or protein in a single researcher’s data will thus be made available to the whole community to support its identification (in shotgun experiments) or quantitative detection (in targeted experiments) in all future analyses.

Complex overlapping concepts: An effective auditing methodology for families of similarly structured BioPortal ontologies

In previous research, we have demonstrated for a number of ontologies that structurally complex concepts (for different definitions of "complex") in an ontology are more likely to exhibit errors than other concepts. Thus, such complex concepts often become fertile ground for quality assurance (QA) in ontologies. They should be audited first. One example of complex concepts is given by "overlapping concepts" (to be defined below.) Historically, a different auditing methodology had to be developed for every single ontology. For better scalability and efficiency, it is desirable to identify family-wide QA methodologies. Each such methodology would be applicable to a whole family of similar ontologies. In past research, we had divided the 685 ontologies of BioPortal into families of structurally similar ontologies. We showed for four ontologies of the same large family in BioPortal that "overlapping concepts" are indeed statistically significantly more likely to exhibit errors. In order to make an authoritative statement concerning the success of "overlapping concepts" as a methodology for a whole family of similar ontologies (or of large subhierarchies of ontologies), it is necessary to show that "overlapping concepts" have a higher likelihood of errors for six out of six ontologies of the family. In this paper, we are demonstrating for two more ontologies that "overlapping concepts" can successfully predict groups of concepts with a higher error rate than concepts from a control group. The fifth ontology is the Neoplasm subhierarchy of the National Cancer Institute thesaurus (NCIt). The sixth ontology is the Infectious Disease subhierarchy of SNOMED CT. We demonstrate quality assurance results for both of them. Furthermore, in this paper we observe two novel, important, and useful phenomena during quality assurance of "overlapping concepts." First, an erroneous "overlapping concept" can help with discovering other erroneous "non-overlapping concepts" in its vicinity. Secondly, correcting erroneous "overlapping concepts" may turn them into "non-overlapping concepts." We demonstrate that this may reduce the complexity of parts of the ontology, which in turn makes the ontology more comprehensible, simplifying maintenance and use of the ontology.

The Cellosaurus, a Cell-Line Knowledge Resource

The Cellosaurus is a knowledge resource on cell lines. It aims to describe all cell lines used in biomedical research. Its scope encompasses both vertebrates and invertebrates. Currently, information for >100,000 cell lines is provided. For each cell line, it provides a wealth of information, cross-references, and literature citations. The Cellosaurus is available on the ExPASy server (https://web.expasy.org/cellosaurus/) and can be downloaded in a variety of formats. Among its many uses, the Cellosaurus is a key resource to help researchers identify potentially contaminated/misidentified cell lines, thus contributing to improving the quality of research in the life sciences.

Insights into Islet Differentiation and Maturation through Proteomic Characterization of a Human iPSC-Derived Pancreatic Endocrine Model

PURPOSE

Great progresses have been made for generating in vitro pluripotent stem cell pancreatic β-like cells. However, the maturation stage of the cells still requires in vivo maturation to recreate the environmental niche. A deeper understanding of the factors promoting maturation of the cells is of great interest for clinical applications.

EXPERIMENTAL DESIGN

Label-free mass spectrometry based proteomic analysis is performed on samples from a longitudinal study of differentiation of human induced pluripotent stem cells toward glucose responsive insulin producing cells.

RESULTS

Proteome patterns correlate with specific transcription factor gene expression levels during in vitro differentiation, showing the relevance of the technology for identification of pancreatic-specific markers. The analysis of proteomes of the implanted cells in a longitudinal study shows that the neovascularization process linked to the extracellular matrix environment is time-dependent and conditions the proper maturation of the cells in β-like cells secreting insulin in response to glucose.

CONCLUSIONS AND CLINICAL RELEVANCE

Proteomic profiling is valuable to qualify and better understand in vivo maturation of progenitor cells toward β-cells. This is critical for future clinical trials where in vivo maturation still needs to be improved for robustness and effectiveness of cell therapy. Novel biomarkers for predicting the efficiency of maturation represents noninvasive monitoring tools for following efficiency of the implant.

Silencing of Profilin-1 suppresses cell adhesion and tumor growth via predicted alterations in integrin and Ca2+ signaling in T24M-based bladder cancer models

Bladder cancer (BC) is the second most common malignancy of the genitourinary system, characterized by the highest recurrence rate of all cancers. Treatment options are limited; thus a thorough understanding of the underlying molecular mechanisms is needed to guide the discovery of novel therapeutic targets. Profilins are actin binding proteins with attributed pleiotropic functions to cytoskeletal remodeling, cell adhesion, motility, even transcriptional regulation, not fully characterized yet. Earlier studies from our laboratory revealed that decreased tissue levels of Profilin-1 (PFN1) are correlated with BC progression to muscle invasive disease. Herein, we describe a comprehensive analysis of PFN1 silencing via shRNA, in vitro (by employing T24M cells) and in vivo [(with T24M xenografts in non-obese diabetic severe combined immunodeficient mice (NOD/SCID) mice]. A combination of phenotypic and molecular assays, including migration, proliferation, adhesion assays, flow cytometry and total mRNA sequencing, as well as immunohistochemistry for investigation of selected findings in human specimens were applied. A decrease in BC cell adhesion and tumor growth in vivo following PFN downregulation are observed, likely associated with the concomitant downregulation of Fibronectin receptor, Endothelin-1, and Actin polymerization. A decrease in the levels of multiple key members of the non-canonical Wnt/Ca²⁺ signaling pathway is also detected following PFN1 suppression, providing the groundwork for future studies, addressing the specific role of PFN1 in Ca²⁺ signaling, particularly in the muscle invasive disease.

Overview of the BioCreative VI text-mining services for Kinome Curation Track

The text-mining services for kinome curation track, part of BioCreative VI, proposed a competition to assess the effectiveness of text mining to perform literature triage. The track has exploited an unpublished curated data set from the neXtProt database. This data set contained comprehensive annotations for 300 human protein kinases. For a given protein and a given curation axis [diseases or gene ontology (GO) biological processes], participants’ systems had to identify and rank relevant articles in a collection of 5.2 M MEDLINE citations (task 1) or 530 000 full-text articles (task 2). Explored strategies comprised named-entity recognition and machine-learning frameworks. For that latter approach, participants developed methods to derive a set of negative instances, as the databases typically do not store articles that were judged as irrelevant by curators. The supervised approaches proposed by the participating groups achieved significant improvements compared to the baseline established in a previous study and compared to a basic PubMed search.

Proteomics Standards Initiative: Fifteen Years of Progress and Future Work

The Proteomics Standards Initiative (PSI) of the Human Proteome Organization (HUPO) has now been developing and promoting open community standards and software tools in the field of proteomics for 15 years. Under the guidance of the chair, cochairs, and other leadership positions, the PSI working groups are tasked with the development and maintenance of community standards via special workshops and ongoing work. Among the existing ratified standards, the PSI working groups continue to update PSI-MI XML, MITAB, mzML, mzIdentML, mzQuantML, mzTab, and the MIAPE (Minimum Information About a Proteomics Experiment) guidelines with the advance of new technologies and techniques. Furthermore, new standards are currently either in the final stages of completion (proBed and proBAM for proteogenomics results as well as PEFF) or in early stages of design (a spectral library standard format, a universal spectrum identifier, the qcML quality control format, and the Protein Expression Interface (PROXI) web services Application Programming Interface). In this work we review the current status of all of these aspects of the PSI, describe synergies with other efforts such as the ProteomeXchange Consortium, the Human Proteome Project, and the metabolomics community, and provide a look at future directions of the PSI.

Multi-Protease Strategy Identifies Three PE2 Missing Proteins in Human Testis Tissue

Although 5 years of the missing proteins (MPs) study have been completed, searching for MPs remains one of the core missions of the Chromosome-Centric Human Proteome Project (C-HPP). Following the next-50-MPs challenge of the C-HPP, we have focused on the testis-enriched MPs by various strategies since 2015. On the basis of the theoretical analysis of MPs (2017-01, neXtProt) using multiprotease digestion, we found that nonconventional proteases (e.g. LysargiNase, GluC) could improve the peptide diversity and sequence coverage compared with Trypsin. Therefore, a multiprotease strategy was used for searching more MPs in the same human testis tissues separated by 10% SDS-PAGE, followed by high resolution LC-MS/MS system (Q Exactive HF). A total of 7838 proteins were identified. Among them, three PE2 MPs in neXtProt 2017-01 have been identified: beta-defensin 123 ( Q8N688 , chr 20q), cancer/testis antigen family 45 member A10 ( P0DMU9 , chr Xq), and Histone H2A-Bbd type 2/3 ( P0C5Z0 , chr Xq). However, because only one unique peptide of ≥9 AA was identified in beta-defensin 123 and Histone H2A-Bbd type 2/3, respectively, further analysis indicates that each falls under the exceptions clause of the HPP Guidelines v2.1. After a spectrum quality check, isobaric PTM and single amino acid variant (SAAV) filtering, and verification with a synthesized peptide, and based on overlapping peptides from different proteases, these three MPs should be considered as exemplary examples of MPs found by exceptional criteria. Other MPs were considered as candidates but need further validation. All MS data sets have been deposited to the ProteomeXchange with identifier PXD006465.

The 24th annual Nucleic Acids Research database issue: a look back and upcoming changes

This year's Database Issue of Nucleic Acids Research contains 152 papers that include descriptions of 54 new databases and update papers on 98 databases, of which 16 have not been previously featured in NAR As always, these databases cover a broad range of molecular biology subjects, including genome structure, gene expression and its regulation, proteins, protein domains, and protein-protein interactions. Following the recent trend, an increasing number of new and established databases deal with the issues of human health, from cancer-causing mutations to drugs and drug targets. In accordance with this trend, three recently compiled databases that have been selected by NAR reviewers and editors as 'breakthrough' contributions, denovo-db, the Monarch Initiative, and Open Targets, cover human de novo gene variants, disease-related phenotypes in model organisms, and a bioinformatics platform for therapeutic target identification and validation, respectively. We expect these databases to attract the attention of numerous researchers working in various areas of genetics and genomics. Looking back at the past 12 years, we present here the 'golden set' of databases that have consistently served as authoritative, comprehensive, and convenient data resources widely used by the entire community and offer some lessons on what makes a successful database. The Database Issue is freely available online at the https://academic.oup.com/nar web site. An updated version of the NAR Molecular Biology Database Collection is available at http://www.oxfordjournals.org/nar/database/a/.

A subcellular map of the human proteome

Resolving the spatial distribution of the human proteome at a subcellular level can greatly increase our understanding of human biology and disease. Here we present a comprehensive image-based map of subcellular protein distribution, the Cell Atlas, built by integrating transcriptomics and antibody-based immunofluorescence microscopy with validation by mass spectrometry. Mapping the in situ localization of 12,003 human proteins at a single-cell level to 30 subcellular structures enabled the definition of the proteomes of 13 major organelles. Exploration of the proteomes revealed single-cell variations in abundance or spatial distribution and localization of about half of the proteins to multiple compartments. This subcellular map can be used to refine existing protein-protein interaction networks and provides an important resource to deconvolute the highly complex architecture of the human cell.

Identification and Validation of Novel Subtype-Specific Protein Biomarkers in Pancreatic Ductal Adenocarcinoma

OBJECTIVES

Pancreatic ductal adenocarcinoma (PDAC) has been subclassified into 3 molecular subtypes: classical, quasi-mesenchymal, and exocrine-like. These subtypes exhibit differences in patient survival and drug resistance to conventional therapies. The aim of the current study is to identify novel subtype-specific protein biomarkers facilitating subtype stratification of patients with PDAC and novel therapy development.

METHODS

A set of 12 human patient-derived primary cell lines was used as a starting material for an advanced label-free proteomics approach leading to the identification of novel cell surface and secreted biomarkers. Cell surface protein identification was achieved by in vitro biotinylation, followed by mass spectrometric analysis of purified biotin-tagged proteins. Proteins secreted into a chemically defined serum-free cell culture medium were analyzed by shotgun proteomics.

RESULTS

Of 3288 identified proteins, 2 pan-PDAC (protocadherin-1 and lipocalin-2) and 2 exocrine-like-specific (cadherin-17 and galectin-4) biomarker candidates have been validated. Proximity ligation assay analysis of the 2 exocrine-like biomarkers revealed their co-localization on the surface of exocrine-like cells.

CONCLUSIONS

The study reports the identification and validation of novel PDAC biomarkers relevant for the development of patient stratification tools. In addition, cadherin-17 and galectin-4 may serve as targets for bispecific antibodies as novel therapeutics in PDAC.

A Golden Age for Working with Public Proteomics Data

Data sharing in mass spectrometry (MS)-based proteomics is becoming a common scientific practice, as is now common in the case of other, more mature ‘omics’ disciplines like genomics and transcriptomics. We want to highlight that this situation, unprecedented in the field, opens a plethora of opportunities for data scientists. First, we explain in some detail some of the work already achieved, such as systematic reanalysis efforts. We also explain existing applications of public proteomics data, such as proteogenomics and the creation of spectral libraries and spectral archives. Finally, we discuss the main existing challenges and mention the first attempts to combine public proteomics data with other types of omics data sets.

The field of proteomics has matured and diversified substantially over the past 10 years.

Proteomics data are increasingly shared through centralized, public repositories.

Standardization efforts have ensured that a large proportion of these public data can be read and processed by any interested researcher.

Because any proteomics data set is only partially understood, there is great opportunity for (orthogonal) reuse of public data.

While public proteomics data has so far remained outside ethics and privacy discussions, recent work indicates that there is an inherent risk.

GenomeCRISPR - a database for high-throughput CRISPR/Cas9 screens

Over the past years, CRISPR/Cas9 mediated genome editing has developed into a powerful tool for modifying genomes in various organisms. In high-throughput screens, CRISPR/Cas9 mediated gene perturbations can be used for the systematic functional analysis of whole genomes. Discoveries from such screens provide a wealth of knowledge about gene to phenotype relationships in various biological model systems. However, a database resource to query results efficiently has been lacking. To this end, we developed GenomeCRISPR (http://genomecrispr.org), a database for genome-scale CRISPR/Cas9 screens. Currently, GenomeCRISPR contains data on more than 550 000 single guide RNAs (sgRNA) derived from 84 different experiments performed in 48 different human cell lines, comprising all screens in human cells using CRISPR/Cas published to date. GenomeCRISPR provides data mining options and tools, such as gene or genomic region search. Phenotypic and genome track views allow users to investigate and compare the results of different screens, or the impact of different sgRNAs on the gene of interest. An Application Programming Interface (API) allows for automated data access and batch download. As more screening data will become available, we also aim at extending the database to include functional genomic data from other organisms and enable cross-species comparisons.

Cytogenetic Resources and Information

The main databases devoted stricto sensu to cancer cytogenetics are the "Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer" ( http://cgap.nci.nih.gov/Chromosomes/Mitelman ), the "Atlas of Genetics and Cytogenetics in Oncology and Haematology" ( http://atlasgeneticsoncology.org ), and COSMIC ( http://cancer.sanger.ac.uk/cosmic ).However, being a complex multistep process, cancer cytogenetics are broadened to "cytogenomics," with complementary resources on: general databases (nucleic acid and protein sequences databases; cartography browsers: GenBank, RefSeq, UCSC, Ensembl, UniProtKB, and Entrez Gene), cancer genomic portals associated with recent international integrated programs, such as TCGA or ICGC, other fusion genes databases, array CGH databases, copy number variation databases, and mutation databases. Other resources such as the International System for Human Cytogenomic Nomenclature (ISCN), the International Classification of Diseases for Oncology (ICD-O), and the Human Gene Nomenclature Database (HGNC) allow a common language.Data within the scientific/medical community should be freely available. However, most of the institutional stakeholders are now gradually disengaging, and well-known databases are forced to beg or to disappear (which may happen!).

The Challenge of Human Spermatozoa Proteome: A Systematic Review

Currently, there are 20,197 human protein-coding genes in the most expertly curated database (UniProtKB/Swiss-Pro). Big efforts have been made by the international consortium, the Chromosome-Centric Human Proteome Project (C-HPP) and independent researchers, to map human proteome. In brief, anno 2017 the human proteome was outlined. The male factor contributes to 50% of infertility in couples. However, there are limited human spermatozoa proteomic studies. Firstly, the development of the mapping of the human spermatozoa was analyzed. The human spermatozoa have been used as a model for missing proteins. It has been shown that human spermatozoa are excellent sources for finding missing proteins. Y chromosome proteome mapping is led by Iran. However, it seems that it is extremely challenging to map the human spermatozoa Y chromosome proteins based on current mass spectrometry-based proteomics technology. Post-translation modifications (PTMs) of human spermatozoa proteome are the most unexplored area and currently the exact role of PTMs in male infertility is unknown. Additionally, the clinical human spermatozoa proteomic analysis, anno 2017 was done in this study.

Databases and Associated Tools for Glycomics and Glycoproteomics

The access to biodatabases for glycomics and glycoproteomics has proven to be essential for current glycobiological research. This chapter presents available databases that are devoted to different aspects of glycobioinformatics. This includes oligosaccharide sequence databases, experimental databases, 3D structure databases (of both glycans and glycorelated proteins) and association of glycans with tissue, disease, and proteins. Specific search protocols are also provided using tools associated with experimental databases for converting primary glycoanalytical data to glycan structural information. In particular, researchers using glycoanalysis methods by U/HPLC (GlycoBase), MS (GlycoWorkbench, UniCarb-DB, GlycoDigest), and NMR (CASPER) will benefit from this chapter. In addition we also include information on how to utilize glycan structural information to query databases that associate glycans with proteins (UniCarbKB) and with interactions with pathogens (SugarBind).

Integrative methods for analyzing big data in precision medicine

We provide an overview of recent developments in big data analyses in the context of precision medicine and health informatics. With the advance in technologies capturing molecular and medical data, we entered the area of "Big Data" in biology and medicine. These data offer many opportunities to advance precision medicine. We outline key challenges in precision medicine and present recent advances in data integration-based methods to uncover personalized information from big data produced by various omics studies. We survey recent integrative methods for disease subtyping, biomarkers discovery, and drug repurposing, and list the tools that are available to domain scientists. Given the ever-growing nature of these big data, we highlight key issues that big data integration methods will face.

The neXtProt knowledgebase on human proteins: 2017 update

The neXtProt human protein knowledgebase (https://www.nextprot.org) continues to add new content and tools, with a focus on proteomics and genetic variation data. neXtProt now has proteomics data for over 85% of the human proteins, as well as new tools tailored to the proteomics community.Moreover, the neXtProt release 2016-08-25 includes over 8000 phenotypic observations for over 4000 variations in a number of genes involved in hereditary cancers and channelopathies. These changes are presented in the current neXtProt update. All of the neXtProt data are available via our user interface and FTP site. We also provide an API access and a SPARQL endpoint for more technical applications.

AAgAtlas 1.0: a human autoantigen database

Autoantibodies refer to antibodies that target self-antigens, which can play pivotal roles in maintaining homeostasis, distinguishing normal from tumor tissue and trigger autoimmune diseases. In the last three decades, tremendous efforts have been devoted to elucidate the generation, evolution and functions of autoantibodies, as well as their target autoantigens. However, reports of these countless previously identified autoantigens are randomly dispersed in the literature. Here, we constructed an AAgAtlas database 1.0 using text-mining and manual curation. We extracted 45 830 autoantigen-related abstracts and 94 313 sentences from PubMed using the keywords of either ‘autoantigen’ or ‘autoantibody’ or their lexical variants, which were further refined to 25 520 abstracts, 43 253 sentences and 3984 candidates by our bio-entity recognizer based on the Protein Ontology. Finally, we identified 1126 genes as human autoantigens and 1071 related human diseases, with which we constructed a human autoantigen database (AAgAtlas database 1.0). The database provides a user-friendly interface to conveniently browse, retrieve and download human autoantigens as well as their associated diseases. The database is freely accessible at http://biokb.ncpsb.org/aagatlas/. We believe this database will be a valuable resource to track and understand human autoantigens as well as to investigate their functions in basic and translational research.

Detection of Missing Proteins Using the PRIDE Database as a Source of Mass Spectrometry Evidence

The current catalogue of the human proteome is not yet complete, as experimental proteomics evidence is still elusive for a group of proteins known as the missing proteins. The Human Proteome Project (HPP) has been successfully using technology and bioinformatic resources to improve the characterization of such challenging proteins. In this manuscript, we propose a pipeline starting with the mining of the PRIDE database to select a group of data sets potentially enriched in missing proteins that are subsequently analyzed for protein identification with a method based on the statistical analysis of proteotypic peptides. Spermatozoa and the HEK293 cell line were found to be a promising source of missing proteins and clearly merit further attention in future studies. After the analysis of the selected samples, we found 342 PSMs, suggesting the presence of 97 missing proteins in human spermatozoa or the HEK293 cell line, while only 36 missing proteins were potentially detected in the retina, frontal cortex, aorta thoracica, or placenta. The functional analysis of the missing proteins detected confirmed their tissue specificity, and the validation of a selected set of peptides using targeted proteomics (SRM/MRM assays) further supports the utility of the proposed pipeline. As illustrative examples, DNAH3 and TEPP in spermatozoa, and UNCX and ATAD3C in HEK293 cells were some of the more robust and remarkable identifications in this study. We provide evidence indicating the relevance to carefully analyze the ever-increasing MS/MS data available from PRIDE and other repositories as sources for missing proteins detection in specific biological matrices as revealed for HEK293 cells.

"Omics"-Informed Drug and Biomarker Discovery: Opportunities, Challenges and Future Perspectives

The pharmaceutical industry faces unsustainable program failure despite significant increases in investment. Dwindling discovery pipelines, rapidly expanding R&D budgets and increasing regulatory control, predict significant gaps in the future drug markets. The cumulative duration of discovery from concept to commercialisation is unacceptably lengthy, and adds to the deepening crisis. Existing animal models predicting clinical translations are simplistic, highly reductionist and, therefore, not fit for purpose. The catastrophic consequences of ever-increasing attrition rates are most likely to be felt in the developing world, where resistance acquisition by killer diseases like malaria, tuberculosis and HIV have paced far ahead of new drug discovery. The coming of age of Omics-based applications makes available a formidable technological resource to further expand our knowledge of the complexities of human disease. The standardisation, analysis and comprehensive collation of the "data-heavy" outputs of these sciences are indeed challenging. A renewed focus on increasing reproducibility by understanding inherent biological, methodological, technical and analytical variables is crucial if reliable and useful inferences with potential for translation are to be achieved. The individual Omics sciences-genomics, transcriptomics, proteomics and metabolomics-have the singular advantage of being complimentary for cross validation, and together could potentially enable a much-needed systems biology perspective of the perturbations underlying disease processes. If current adverse trends are to be reversed, it is imperative that a shift in the R&D focus from speed to quality is achieved. In this review, we discuss the potential implications of recent Omics-based advances for the drug development process.

Tiered Human Integrated Sequence Search Databases for Shotgun Proteomics

The results of analysis of shotgun proteomics mass spectrometry data can be greatly affected by the selection of the reference protein sequence database against which the spectra are matched. For many species there are multiple sources from which somewhat different sequence sets can be obtained. This can lead to confusion about which database is best in which circumstances-a problem especially acute in human sample analysis. All sequence databases are genome-based, with sequences for the predicted gene and their protein translation products compiled. Our goal is to create a set of primary sequence databases that comprise the union of sequences from many of the different available sources and make the result easily available to the community. We have compiled a set of four sequence databases of varying sizes, from a small database consisting of only the ∼20,000 primary isoforms plus contaminants to a very large database that includes almost all nonredundant protein sequences from several sources. This set of tiered, increasingly complete human protein sequence databases suitable for mass spectrometry proteomics sequence database searching is called the Tiered Human Integrated Search Proteome set. In order to evaluate the utility of these databases, we have analyzed two different data sets, one from the HeLa cell line and the other from normal human liver tissue, with each of the four tiers of database complexity. The result is that approximately 0.8%, 1.1%, and 1.5% additional peptides can be identified for Tiers 2, 3, and 4, respectively, as compared with the Tier 1 database, at substantially increasing computational cost. This increase in computational cost may be worth bearing if the identification of sequence variants or the discovery of sequences that are not present in the reviewed knowledge base entries is an important goal of the study. We find that it is useful to search a data set against a simpler database, and then check the uniqueness of the discovered peptides against a more complex database. We have set up an automated system that downloads all the source databases on the first of each month and automatically generates a new set of search databases and makes them available for download at http://www.peptideatlas.org/thisp/ .

State of the Art of Chromosome 18-Centric HPP in 2016: Transcriptome and Proteome Profiling of Liver Tissue and HepG2 Cells

A gene-centric approach was applied for a large-scale study of expression products of a single chromosome. Transcriptome profiling of liver tissue and HepG2 cell line was independently performed using two RNA-Seq platforms (SOLiD and Illumina) and also by Droplet Digital PCR (ddPCR) and quantitative RT-PCR. Proteome profiling was performed using shotgun LC-MS/MS as well as selected reaction monitoring with stable isotope-labeled standards (SRM/SIS) for liver tissue and HepG2 cells. On the basis of SRM/SIS measurements, protein copy numbers were estimated for the Chromosome 18 (Chr 18) encoded proteins in the selected types of biological material. These values were compared with expression levels of corresponding mRNA. As a result, we obtained information about 158 and 142 transcripts for HepG2 cell line and liver tissue, respectively. SRM/SIS measurements and shotgun LC-MS/MS allowed us to detect 91 Chr 18-encoded proteins in total, while an intersection between the HepG2 cell line and liver tissue proteomes was ∼66%. In total, there were 16 proteins specifically observed in HepG2 cell line, while 15 proteins were found solely in the liver tissue. Comparison between proteome and transcriptome revealed a poor correlation (R² ≈ 0.1) between corresponding mRNA and protein expression levels. The SRM and shotgun data sets (obtained during 2015-2016) are available in PASSEL (PASS00697) and ProteomeExchange/PRIDE (PXD004407). All measurements were also uploaded into the in-house Chr 18 Knowledgebase at http://kb18.ru/protein/matrix/416126 .

Human Proteome Project Mass Spectrometry Data Interpretation Guidelines 2.1

Every data-rich community research effort requires a clear plan for ensuring the quality of the data interpretation and comparability of analyses. To address this need within the Human Proteome Project (HPP) of the Human Proteome Organization (HUPO), we have developed through broad consultation a set of mass spectrometry data interpretation guidelines that should be applied to all HPP data contributions. For submission of manuscripts reporting HPP protein identification results, the guidelines are presented as a one-page checklist containing 15 essential points followed by two pages of expanded description of each. Here we present an overview of the guidelines and provide an in-depth description of each of the 15 elements to facilitate understanding of the intentions and rationale behind the guidelines, for both authors and reviewers. Broadly, these guidelines provide specific directions regarding how HPP data are to be submitted to mass spectrometry data repositories, how error analysis should be presented, and how detection of novel proteins should be supported with additional confirmatory evidence. These guidelines, developed by the HPP community, are presented to the broader scientific community for further discussion.

Looking for Missing Proteins in the Proteome of Human Spermatozoa: An Update

The Chromosome-Centric Human Proteome Project (C-HPP) aims to identify "missing" proteins in the neXtProt knowledgebase. We present an in-depth proteomics analysis of the human sperm proteome to identify testis-enriched missing proteins. Using protein extraction procedures and LC-MS/MS analysis, we detected 235 proteins (PE2-PE4) for which no previous evidence of protein expression was annotated. Through LC-MS/MS and LC-PRM analysis, data mining, and immunohistochemistry, we confirmed the expression of 206 missing proteins (PE2-PE4) in line with current HPP guidelines (version 2.0). Parallel reaction monitoring acquisition and sythetic heavy labeled peptides targeted 36 ≪one-hit wonder≫ candidates selected based on prior peptide spectrum match assessment. 24 were validated with additional predicted and specifically targeted peptides. Evidence was found for 16 more missing proteins using immunohistochemistry on human testis sections. The expression pattern for some of these proteins was specific to the testis, and they could possibly be valuable markers with fertility assessment applications. Strong evidence was also found of four "uncertain" proteins (PE5); their status should be re-examined. We show how using a range of sample preparation techniques combined with MS-based analysis, expert knowledge, and complementary antibody-based techniques can produce data of interest to the community. All MS/MS data are available via ProteomeXchange under identifier PXD003947. In addition to contributing to the C-HPP, we hope these data will stimulate continued exploration of the sperm proteome.