Published and perished? The influence of the searched protein database on the long-term storage of proteomics data

Serum Proteomics Identified TAFI as a Potential Molecule Facilitating the Migration of Peripheral Monocytes to Damaged White Matter During Chronic Cerebral Hypoperfusion

Neuroinflammation is assumed as the critical pathophysiologic mechanism of white matter lesions (WMLs), and infiltrated peripheral monocyte-derived macrophages are implicated in the development of neuroinflammation. This study sought to explore the blood molecules that promote the migration of peripheral monocytes to the sites of WMLs. The serum protein expression profiles of patients and Sprague-Dawley rat models with WMLs were detected by data-independent acquisition (DIA) proteomics technique. Compared with corresponding control groups, we acquired 62 and 41 differentially expressed proteins (DEPs) in the serum of patients and model rats with WMLs respectively. Bioinformatics investigations demonstrated that these DEPs were linked to various Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO) terms involved in neuroinflammation. Afterward, we identified thrombin-activatable fibrinolysis inhibitor (TAFI) as a shared and overexpressed protein in clinical and animal serum samples, which was further verified by enzyme-linked immunosorbent assay. Additionally, an upregulation of TAFI was also observed in the white matter of rat models, and the inhibition of TAFI impeded the migration of peripheral monocytes to the area of WMLs. In vitro experiments suggested that TAFI could enhance the migration ability of RAW264.7 cells and increase the expression of Ccr2. Our study demonstrates that neuroinflammatory signals can be detected in the peripheral blood of WMLs patients and model rats. TAFI may serve as a potential protein that promotes the migration of peripheral monocytes to WMLs regions, thereby providing a novel molecular target for further investigation into the interaction between the central and peripheral immune systems.

Beyond blast: enabling microbiologists to better extract literature, taxonomic distributions and gene neighbourhood information for protein families

Capturing the published corpus of information on all members of a given protein family should be an essential step in any study focusing on specific members of that family. Using a previously gathered dataset of more than 280 references mentioning a member of the DUF34 (NIF3/Ngg1-interacting Factor 3) family, we evaluated the efficiency of different databases and search tools, and devised a workflow that experimentalists can use to capture the most information published on members of a protein family in the least amount of time. To complement this workflow, web-based platforms allowing for the exploration of protein family members across sequenced genomes or for the analysis of gene neighbourhood information were reviewed for their versatility and ease of use. Recommendations that can be used for experimentalist users, as well as educators, are provided and integrated within a customized, publicly accessible Wiki.

Beyond Blast: Enabling Microbiologists to Better Extract Literature, Taxonomic Distributions and Gene Neighborhood Information for Protein Families

Capturing the published corpus of information on all members of a given protein family should be an essential step in any study focusing on specific members of that said family. Using a previously gathered dataset of more than 280 references mentioning a member of the DUF34 (NIF3/Ngg1-interacting Factor 3), we evaluated the efficiency of different databases and search tools, and devised a workflow that experimentalists can use to capture the most published information on members of a protein family in the least amount of time. To complement this workflow, web-based platforms allowing for the exploration of protein family members across sequenced genomes or for the analysis of gene neighborhood information were reviewed for their versatility and ease of use. Recommendations that can be used for experimentalist users, as well as educators, are provided and integrated within a customized, publicly accessible Wiki.

An effective approach for annotation of protein families with low sequence similarity and conserved motifs: identifying GDSL hydrolases across the plant kingdom

Background

The massive accumulation of protein sequences arising from the rapid development of high-throughput sequencing, coupled with automatic annotation, results in high levels of incorrect annotations. In this study, we describe an approach to decrease annotation errors of protein families characterized by low overall sequence similarity. The GDSL lipolytic family comprises proteins with multifunctional properties and high potential for pharmaceutical and industrial applications. The number of proteins assigned to this family has increased rapidly over the last few years. In particular, the natural abundance of GDSL enzymes reported recently in plants indicates that they could be a good source of novel GDSL enzymes. We noticed that a significant proportion of annotated sequences lack specific GDSL motif(s) or catalytic residue(s). Here, we applied motif-based sequence analyses to identify enzymes possessing conserved GDSL motifs in selected proteomes across the plant kingdom.

Results

Motif-based HMM scanning (Viterbi decoding-VD and posterior decoding-PD) and the here described PD/VD protocol were successfully applied on 12 selected plant proteomes to identify sequences with GDSL motifs. A significant number of identified GDSL sequences were novel. Moreover, our scanning approach successfully detected protein sequences lacking at least one of the essential motifs (171/820) annotated by Pfam profile search (PfamA) as GDSL. Based on these analyses we provide a curated list of GDSL enzymes from the selected plants. CLANS clustering and phylogenetic analysis helped us to gain a better insight into the evolutionary relationship of all identified GDSL sequences. Three novel GDSL subfamilies as well as unreported variations in GDSL motifs were discovered in this study. In addition, analyses of selected proteomes showed a remarkable expansion of GDSL enzymes in the lycophyte, Selaginella moellendorffii. Finally, we provide a general motif-HMM scanner which is easily accessible through the graphical user interface (http://compbio.math.hr/).

Conclusions

Our results show that scanning with a carefully parameterized motif-HMM is an effective approach for annotation of protein families with low sequence similarity and conserved motifs. The results of this study expand current knowledge and provide new insights into the evolution of the large GDSL-lipase family in land plants.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-0919-7) contains supplementary material, which is available to authorized users.

2016 update of the PRIDE database and its related tools

The PRoteomics IDEntifications (PRIDE) database is one of the world-leading data repositories of mass spectrometry (MS)-based proteomics data. Since the beginning of 2014, PRIDE Archive (http://www.ebi.ac.uk/pride/archive/) is the new PRIDE archival system, replacing the original PRIDE database. Here we summarize the developments in PRIDE resources and related tools since the previous update manuscript in the Database Issue in 2013. PRIDE Archive constitutes a complete redevelopment of the original PRIDE, comprising a new storage backend, data submission system and web interface, among other components. PRIDE Archive supports the most-widely used PSI (Proteomics Standards Initiative) data standard formats (mzML and mzIdentML) and implements the data requirements and guidelines of the ProteomeXchange Consortium. The wide adoption of ProteomeXchange within the community has triggered an unprecedented increase in the number of submitted data sets (around 150 data sets per month). We outline some statistics on the current PRIDE Archive data contents. We also report on the status of the PRIDE related stand-alone tools: PRIDE Inspector, PRIDE Converter 2 and the ProteomeXchange submission tool. Finally, we will give a brief update on the resources under development 'PRIDE Cluster' and 'PRIDE Proteomes', which provide a complementary view and quality-scored information of the peptide and protein identification data available in PRIDE Archive.

Exploring the potential of public proteomics data

In a global effort for scientific transparency, it has become feasible and good practice to share experimental data supporting novel findings. Consequently, the amount of publicly available MS-based proteomics data has grown substantially in recent years. With some notable exceptions, this extensive material has however largely been left untouched. The time has now come for the proteomics community to utilize this potential gold mine for new discoveries, and uncover its untapped potential. In this review, we provide a brief history of the sharing of proteomics data, showing ways in which publicly available proteomics data are already being (re-)used, and outline potential future opportunities based on four different usage types: use, reuse, reprocess, and repurpose. We thus aim to assist the proteomics community in stepping up to the challenge, and to make the most of the rapidly increasing amount of public proteomics data.

Making proteomics data accessible and reusable: current state of proteomics databases and repositories

Compared to other data-intensive disciplines such as genomics, public deposition and storage of MS-based proteomics, data are still less developed due to, among other reasons, the inherent complexity of the data and the variety of data types and experimental workflows. In order to address this need, several public repositories for MS proteomics experiments have been developed, each with different purposes in mind. The most established resources are the Global Proteome Machine Database (GPMDB), PeptideAtlas, and the PRIDE database. Additionally, there are other useful (in many cases recently developed) resources such as ProteomicsDB, Mass Spectrometry Interactive Virtual Environment (MassIVE), Chorus, MaxQB, PeptideAtlas SRM Experiment Library (PASSEL), Model Organism Protein Expression Database (MOPED), and the Human Proteinpedia. In addition, the ProteomeXchange consortium has been recently developed to enable better integration of public repositories and the coordinated sharing of proteomics information, maximizing its benefit to the scientific community. Here, we will review each of the major proteomics resources independently and some tools that enable the integration, mining and reuse of the data. We will also discuss some of the major challenges and current pitfalls in the integration and sharing of the data.

Identifying novel biomarkers through data mining-a realistic scenario?

In this article we discuss the requirements to use data mining of published proteomics datasets to assist proteomics-based biomarker discovery, the use of external data integration to solve the issue of inadequate small sample sizes and finally, we try to estimate the probability that new biomarkers will be identified through data mining alone.

ProteomeScout: a repository and analysis resource for post-translational modifications and proteins

ProteomeScout (https://proteomescout.wustl.edu) is a resource for the study of proteins and their post-translational modifications (PTMs) consisting of a database of PTMs, a repository for experimental data, an analysis suite for PTM experiments, and a tool for visualizing the relationships between complex protein annotations. The PTM database is a compendium of public PTM data, coupled with user-uploaded experimental data. ProteomeScout provides analysis tools for experimental datasets, including summary views and subset selection, which can identify relationships within subsets of data by testing for statistically significant enrichment of protein annotations. Protein annotations are incorporated in the ProteomeScout database from external resources and include terms such as Gene Ontology annotations, domains, secondary structure and non-synonymous polymorphisms. These annotations are available in the database download, in the analysis tools and in the protein viewer. The protein viewer allows for the simultaneous visualization of annotations in an interactive web graphic, which can be exported in Scalable Vector Graphics (SVG) format. Finally, quantitative data measurements associated with public experiments are also easily viewable within protein records, allowing researchers to see how PTMs change across different contexts. ProteomeScout should prove useful for protein researchers and should benefit the proteomics community by providing a stable repository for PTM experiments.

Plant proteomics: from genome sequencing to proteome databases and repositories

Proteomic approaches are useful for the identification of functional proteins. These have been enhanced not only by the development of proteomic techniques but also in concert with genome sequencing. In this chapter, 30 databases and Web sites relating to plant proteomics are reviewed and recent technologies relating to data collection and annotation are surveyed.

Chromosome-centric approach to overcoming bottlenecks in the Human Proteome Project

The international Human Proteome Project (HPP), a logical continuation of the Human Genome Project, was launched on 23 September 2010 in Sydney, Australia. In accordance with the gene-centric approach, the goals of the HPP are to prepare an inventory of all human proteins and decipher the network of cellular protein interactions. The greater complexity of the proteome in comparison to the genome gives rise to three bottlenecks in the implementation of the HPP. The main bottleneck is the insufficient sensitivity of proteomic technologies, hampering the detection of proteins with low- and ultra-low copy numbers. The second bottleneck is related to poor reproducibility of proteomic methods and the lack of a so-called 'gold' standard. The last bottleneck is the dynamic nature of the proteome: its instability over time. The authors here discuss approaches to overcome these bottlenecks in order to improve the success of the HPP.

Crowdsourcing in proteomics: public resources lead to better experiments

With the growing interest in the field of proteomics, the amount of publicly available proteome resources has also increased dramatically. This means that there are many useful resources available for almost all aspects of a proteomics experiment. However, it remains vital to use the right resource, for the right purpose, at the right time. This review is therefore meant to aid the reader in obtaining an overview of the available resources and their application, thus providing the necessary background to choose the appropriate resources for the experiment at hand. Many of the resources are also taking advantage of so-called crowdsourcing to maximize the potential of the resource. What this means and how this can improve future experiments will also be discussed. The text roughly follows the steps involved in a proteomics experiment, starting with the planning of the experiment, via the processing of the data and the analysis of the results, to the community-wide sharing of the produced data.

Multidimensional protein identification technology for direct-tissue proteomics of heart

Multidimensional protein identification technology (MudPIT) is an invaluable approach to identify proteins at large-scale level. Here, we describe a procedure of investigation to functional characterize the proteomic profile of complex samples such as those from cardiac tissues. In particular, we focus on the main steps concerning sample preparation, MudPIT analysis, tandem mass spectra processing, and biomarker discovery using label-free approaches. Finally, we report a data-derived systems biology approach to identify groups of proteins of over-, under-, and normal expression.

The PRoteomics IDEntifications (PRIDE) database and associated tools: status in 2013

The PRoteomics IDEntifications (PRIDE, http://www.ebi.ac.uk/pride) database at the European Bioinformatics Institute is one of the most prominent data repositories of mass spectrometry (MS)-based proteomics data. Here, we summarize recent developments in the PRIDE database and related tools. First, we provide up-to-date statistics in data content, splitting the figures by groups of organisms and species, including peptide and protein identifications, and post-translational modifications. We then describe the tools that are part of the PRIDE submission pipeline, especially the recently developed PRIDE Converter 2 (new submission tool) and PRIDE Inspector (visualization and analysis tool). We also give an update about the integration of PRIDE with other MS proteomics resources in the context of the ProteomeXchange consortium. Finally, we briefly review the quality control efforts that are ongoing at present and outline our future plans.

Improvements in the Protein Identifier Cross-Reference service

The Protein Identifier Cross-Reference (PICR) service is a tool that allows users to map protein identifiers, protein sequences and gene identifiers across over 100 different source databases. PICR takes input through an interactive website as well as Representational State Transfer (REST) and Simple Object Access Protocol (SOAP) services. It returns the results as HTML pages, XLS and CSV files. It has been in production since 2007 and has been recently enhanced to add new functionality and increase the number of databases it covers. Protein subsequences can be Basic Local Alignment Search Tool (BLAST) against the UniProt Knowledgebase (UniProtKB) to provide an entry point to the standard PICR mapping algorithm. In addition, gene identifiers from UniProtKB and Ensembl can now be submitted as input or mapped to as output from PICR. We have also implemented a 'best-guess' mapping algorithm for UniProt. In this article, we describe the usefulness of PICR, how these changes have been implemented, and the corresponding additions to the web services. Finally, we explain that the number of source databases covered by PICR has increased from the initial 73 to the current 102. New resources include several new species-specific Ensembl databases as well as the Ensembl Genome ones. PICR can be accessed at http://www.ebi.ac.uk/Tools/picr/.

Analysis of high accuracy, quantitative proteomics data in the MaxQB database

MS-based proteomics generates rapidly increasing amounts of precise and quantitative information. Analysis of individual proteomic experiments has made great strides, but the crucial ability to compare and store information across different proteome measurements still presents many challenges. For example, it has been difficult to avoid contamination of databases with low quality peptide identifications, to control for the inflation in false positive identifications when combining data sets, and to integrate quantitative data. Although, for example, the contamination with low quality identifications has been addressed by joint analysis of deposited raw data in some public repositories, we reasoned that there should be a role for a database specifically designed for high resolution and quantitative data. Here we describe a novel database termed MaxQB that stores and displays collections of large proteomics projects and allows joint analysis and comparison. We demonstrate the analysis tools of MaxQB using proteome data of 11 different human cell lines and 28 mouse tissues. The database-wide false discovery rate is controlled by adjusting the project specific cutoff scores for the combined data sets. The 11 cell line proteomes together identify proteins expressed from more than half of all human genes. For each protein of interest, expression levels estimated by label-free quantification can be visualized across the cell lines. Similarly, the expression rank order and estimated amount of each protein within each proteome are plotted. We used MaxQB to calculate the signal reproducibility of the detected peptides for the same proteins across different proteomes. Spearman rank correlation between peptide intensity and detection probability of identified proteins was greater than 0.8 for 64% of the proteome, whereas a minority of proteins have negative correlation. This information can be used to pinpoint false protein identifications, independently of peptide database scores. The information contained in MaxQB, including high resolution fragment spectra, is accessible to the community via a user-friendly web interface at http://www.biochem.mpg.de/maxqb.

Consequences of the discontinuation of the International Protein Index (IPI) database and its substitution by the UniProtKB "complete proteome" sets

The International Protein Index (IPI) database has been one of the most widely used protein databases in MS proteomics approaches. Recently, the closure of IPI in September 2011 was announced. Its recommended replacement is the new UniProt Knowledgebase (UniProtKB) "complete proteome" sets, launched in May 2011. Here, we analyze the consequences of IPI's discontinuation for human and mouse data, and the effect of its substitution with UniProtKB on two levels: (i) data already produced and (ii) newly performed experiments. To estimate the effect on existing data, we investigated how well IPI identifiers map to UniProtKB accessions. We found that 21% of human and 10% of mouse identifiers do not map to UniProtKB and would thus be "lost." To investigate the impact on new experiments, we compared the theoretical search space (i.e. the tryptic peptides) of both resources and found that it is decreased by 14.0% for human and 8.9% for mouse data through IPI's closure. An analysis on the experimental evidence for these "lost" peptides showed that the vast majority has not been identified in experiments available in the major proteomics repositories. It thus seems likely that the search space provided by UniProtKB is of higher quality than the one currently provided by IPI.