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Vesiclepedia (http://www.microvesicles.org) is a free web-based compendium of DNA, RNA, proteins, lipids and metabolites that are detected or associated with extracellular vesicles (EVs) and extracellular particles (EPs). EVs are membranous vesicles that are secreted ubiquitously by cells from all domains of life from archaea to eukaryotes. In addition to EVs, it was reported recently that EPs like exomeres and supermeres are secreted by some mammalian cells. Both EVs and EPs contain proteins, nucleic acids, lipids and metabolites and has been proposed to be implicated in several key biological functions. Vesiclepedia catalogues proteins, DNA, RNA, lipids and metabolites from both published and unpublished studies. Currently, Vesiclepedia contains data obtained from 3533 EV studies, 50 550 RNA entries, 566 911 protein entries, 3839 lipid entries, 192 metabolite and 167 DNA entries. Quantitative data for 62 822 entries from 47 EV studies is available in Vesiclepedia. The datasets available in Vesiclepedia can be downloaded as tab-delimited files or accessible through the FunRich-based Vesiclepedia plugin., (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Extracellular vesicles (EVs) are membranous vesicles that are released by both prokaryotic and eukaryotic cells into the extracellular microenvironment. EVs can be categorised as exosomes, ectosomes or shedding microvesicles and apoptotic bodies based on the mode of biogenesis. EVs contain biologically active cargo of nucleic acids, proteins, lipids and metabolites that can be altered based on the precise state of the cell. Vesiclepedia (http://www.microvesicles.org) is a web-based compendium of RNA, proteins, lipids and metabolites that are identified in EVs from both published and unpublished studies. Currently, Vesiclepedia contains data obtained from 1254 EV studies, 38 146 RNA entries, 349 988 protein entries and 639 lipid/metabolite entries. Vesiclepedia is publicly available and allows users to query and download EV cargo based on different search criteria. The mode of EV isolation and characterization, the biophysical and molecular properties and EV-METRIC are listed in the database aiding biomedical scientists in assessing the quality of the EV preparation and the corresponding data obtained. In addition, FunRich-based Vesiclepedia plugin is incorporated aiding users in data analysis.

Motivation: RNA-seq has become the technology of choice for interrogating the transcriptome. However, most methods for RNA-seq differential expression (DE) analysis do not utilize prior knowledge of biological networks to detect DE genes. With the increased availability and quality of biological network databases, methods that can utilize this prior knowledge are needed and will offer biologists with a viable, more powerful alternative when analyzing RNA-seq data., Results: We propose a three-state Markov Random Field (MRF) method that utilizes known biological pathways and interaction to improve sensitivity and specificity and therefore reducing false discovery rates (FDRs) when detecting differentially expressed genes from RNA-seq data. The method requires normalized count data (e.g. in Fragments or Reads Per Kilobase of transcript per Million mapped reads (FPKM/RPKM) format) as its input and it is implemented in an R package pathDESeq available from Github. Simulation studies demonstrate that our method outperforms the two-state MRF model for various sample sizes. Furthermore, for a comparable FDR, it has better sensitivity than DESeq, EBSeq, edgeR and NOISeq. The proposed method also picks more top Gene Ontology terms and KEGG pathways terms when applied to real dataset from colorectal cancer and hepatocellular carcinoma studies, respectively. Overall, these findings clearly highlight the power of our method relative to the existing methods that do not utilize prior knowledge of biological network., Availability and Implementation: As an R package at https://github.com/MalathiSIDona/pathDESeq., To Install the Package Type: install_github("MalathiSIDona/pathDESeq",build_vignettes = TRUE). After installation, type vignette("pathDESeq") to access the vignette., Contact: a.salim@latrobe.edu.au., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com)

In order to advance our understanding of colorectal cancer (CRC) development and progression, biomedical researchers have generated large amounts of OMICS data from CRC patient samples and representative cell lines. However, these data are deposited in various repositories or in supplementary tables. A database which integrates data from heterogeneous resources and enables analysis of the multidimensional data sets, specifically pertaining to CRC is currently lacking. Here, we have developed Colorectal Cancer Atlas (http://www.colonatlas.org), an integrated web-based resource that catalogues the genomic and proteomic annotations identified in CRC tissues and cell lines. The data catalogued to-date include sequence variations as well as quantitative and non-quantitative protein expression data. The database enables the analysis of these data in the context of signaling pathways, protein-protein interactions, Gene Ontology terms, protein domains and post-translational modifications. Currently, Colorectal Cancer Atlas contains data for >13 711 CRC tissues, >165 CRC cell lines, 62 251 protein identifications, >8.3 million MS/MS spectra, >18 410 genes with sequence variations (404 278 entries) and 351 pathways with sequence variants. Overall, Colorectal Cancer Atlas has been designed to serve as a central resource to facilitate research in CRC., (© The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Motivation: Extracellular vesicles (EVs) are spherical bilayered proteolipids, harboring various bioactive molecules. Due to the complexity of the vesicular nomenclatures and components, online searches for EV-related publications and vesicular components are currently challenging., Results: We present an improved version of EVpedia, a public database for EVs research. This community web portal contains a database of publications and vesicular components, identification of orthologous vesicular components, bioinformatic tools and a personalized function. EVpedia includes 6879 publications, 172 080 vesicular components from 263 high-throughput datasets, and has been accessed more than 65 000 times from more than 750 cities. In addition, about 350 members from 73 international research groups have participated in developing EVpedia. This free web-based database might serve as a useful resource to stimulate the emerging field of EV research., Availability and Implementation: The web site was implemented in PHP, Java, MySQL and Apache, and is freely available at http://evpedia.info., (© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Plasma Proteome Database (PPD; http://www.plasmaproteomedatabase.org/) was initially described in the year 2005 as a part of Human Proteome Organization's (HUPO's) pilot initiative on Human Plasma Proteome Project. Since then, improvements in proteomic technologies and increased throughput have led to identification of a large number of novel plasma proteins. To keep up with this increase in data, we have significantly enriched the proteomic information in PPD. This database currently contains information on 10,546 proteins detected in serum/plasma of which 3784 have been reported in two or more studies. The latest version of the database also incorporates mass spectrometry-derived data including experimentally verified proteotypic peptides used for multiple reaction monitoring assays. Other novel features include published plasma/serum concentrations for 1278 proteins along with a separate category of plasma-derived extracellular vesicle proteins. As plasma proteins have become a major thrust in the field of biomarkers, we have enabled a batch-based query designated Plasma Proteome Explorer, which will permit the users in screening a list of proteins or peptides against known plasma proteins to assess novelty of their data set. We believe that PPD will facilitate both clinical and basic research by serving as a comprehensive reference of plasma proteins in humans and accelerate biomarker discovery and translation efforts.

Exosomes are membraneous nanovesicles of endocytic origin released by most cell types from diverse organisms; they play a critical role in cell-cell communication. ExoCarta (http://www.exocarta.org) is a manually curated database of exosomal proteins, RNA and lipids. The database catalogs information from both published and unpublished exosomal studies. The mode of exosomal purification and characterization, the biophysical and molecular properties are listed in the database aiding biomedical scientists in assessing the quality of the exosomal preparation and the corresponding data obtained. Currently, ExoCarta (Version 3.1) contains information on 11,261 protein entries, 2375 mRNA entries and 764 miRNA entries that were obtained from 134 exosomal studies. In addition to the data update, as a new feature, lipids identified in exosomes are added to ExoCarta. We believe that this free web-based community resource will aid researchers in identifying molecular signatures (proteins/RNA/lipids) that are specific to certain tissue/cell type derived exosomes and trigger new exosomal studies.

Sharing proteomic data with the biomedical community through a unified proteomic resource, especially in the context of individual proteins, is a challenging prospect. We have developed a community portal, designated as Human Proteinpedia (http://www.humanproteinpedia.org/), for sharing both unpublished and published human proteomic data through the use of a distributed annotation system designed specifically for this purpose. This system allows laboratories to contribute and maintain protein annotations, which are also mapped to the corresponding proteins through the Human Protein Reference Database (HPRD; http://www.hprd.org/). Thus, it is possible to visualize data pertaining to experimentally validated posttranslational modifications (PTMs), protein isoforms, protein-protein interactions (PPIs), tissue expression, expression in cell lines, subcellular localization and enzyme substrates in the context of individual proteins. With enthusiastic participation of the proteomics community, the past 15 months have witnessed data contributions from more than 75 labs around the world including 2710 distinct experiments, >1.9 million peptides, >4.8 million MS/MS spectra, 150,368 protein expression annotations, 17,410 PTMs, 34,624 PPIs and 2906 subcellular localization annotations. Human Proteinpedia should serve as an integrated platform to store, integrate and disseminate such proteomic data and is inching towards evolving into a unified human proteomics resource.

Human Protein Reference Database (HPRD--http://www.hprd.org/), initially described in 2003, is a database of curated proteomic information pertaining to human proteins. We have recently added a number of new features in HPRD. These include PhosphoMotif Finder, which allows users to find the presence of over 320 experimentally verified phosphorylation motifs in proteins of interest. Another new feature is a protein distributed annotation system--Human Proteinpedia (http://www.humanproteinpedia.org/)--through which laboratories can submit their data, which is mapped onto protein entries in HPRD. Over 75 laboratories involved in proteomics research have already participated in this effort by submitting data for over 15,000 human proteins. The submitted data includes mass spectrometry and protein microarray-derived data, among other data types. Finally, HPRD is also linked to a compendium of human signaling pathways developed by our group, NetPath (http://www.netpath.org/), which currently contains annotations for several cancer and immune signaling pathways. Since the last update, more than 5500 new protein sequences have been added, making HPRD a comprehensive resource for studying the human proteome.