OPD is a public database for storing and disseminating mass spectrometry based proteomics data. It covers Escherichia coli, Homo sapiens, Saccharomyces cerevisiae, Mycobacterium smegmatis, and Mus musculus. The database currently contains roughly 3,000,000 spectra representing experiments from these 5 different organisms. The mirror url is provided below as the OPD website is no longer functional (http://bioinformatics.icmb.utexas.edu/OPD/).

The identification of peptide sequences and their post-translational modifications (PTMs) is a crucial step in the analysis of bottom-up proteomics data. The recent development of open modification search (OMS) engines allows virtually all PTMs to be searched for. This not only increases the number of spectra that can be matched to peptides but also greatly advances the understanding of biological roles of PTMs through the identification, and thereby facilitated quantification, of peptidoforms (peptide sequences and their potential PTMs). While the benefits of combining results from multiple protein database search engines has been established previously, similar approaches for OMS results are missing so far. Here, we compare and combine results from three different OMS engines, demonstrating an increase in peptide spectrum matches of 8-18%. The unification of search results furthermore allows for the combined downstream processing of search results, including the mapping to potential PTMs. Finally, we test for the ability of OMS engines to identify glycosylated peptides. The implementation of these engines in the Python framework Ursgal facilitates the straightforward application of OMS with unified parameters and results files, thereby enabling yet unmatched high-throughput, large-scale data analysis.

This dataset includes all relevant results files, databases, and scripts that correspond to the accompanying journal article. Specifically, the following files are deposited:

• Homo_sapiens_PXD004452_results.zip: result files from OMS and CS for the dataset PXD004452
• Homo_sapiens_PXD013715_results.zip: result files from OMS and CS for the dataset PXD013715
• Haloferax_volcanii_PXD021874_results.zip: result files from OMS and CS for the dataset PXD021874
• Escherichia_coli_PXD000498_results.zip: result files from OMS and CS for the dataset PXD000498
• databases.zip: target-decoy databases for Homo sapiens, Escherichia coli and Haloferax volcanii as well as a glycan database for Homo sapiens
• scripts.zip: example scripts for all relevant steps of the analysis
• mzml_files.zip: mzML files for all included datasets
• ursgal.zip: current version of Ursgal (0.6.7) that has been used to generate the results (for most recent versions see https://github.com/ursgal/ursgal)

To enable the identification of mutated peptide sequences in complex biological samples, in this work, a cancer protein database with mutation information collected from several public resources such as COSMIC, IARC P53, OMIM and UniProtKB, was developed. In-house developed Perl-scripts were used to search and process the data, and to translate each gene-level mutation into a mutated peptide sequence. The cancer mutation database comprises a total of 872,125 peptide entries from 25,642 protein IDs. A description line for each entry provides the parent protein ID and name, the cDNA- and protein-level mutation site and type, the originating database, and the cancer tissue type and corresponding hits. The database is FASTA formatted to enable data retrieval by commonly used tandem MS search engines.

Accompanying MaxQuant, Percolator and Picked Protein Group FDR files to reproduce results in the publication "Re-analysis of ProteomicsDB using an accurate, sensitive and scalable false discovery rate estimation approach for protein groups". The code for reproducing Protein Group FDRs is available on GitHub at https://github.com/kusterlab/picked_group_fdr

Bioinformatics Services Market size was valued at USD 11.1 Billion in 2023 and is projected to reach USD 3.58 Billion by 2031, growing at a CAGR of 15.06% from 2024-2031.

Bioinformatics Services Market: Definition/ Overview

Bioinformatics services cover a wide range of computational tools and methods for managing, analyzing, and interpreting biological data. These services enable the integration of data from domains such as genomics, proteomics, transcriptomics, and metabolomics to provide insights into biological systems. Drug discovery, customized medicine, gene sequencing, and biological data management are some of the most important applications of bioinformatics. Researchers and healthcare professionals use these services to analyze big datasets, detect disease markers, and develop tailored medicines, considerably improving the precision and efficiency of life science research.

Malaria morbidity and mortality caused by both Plasmodium falciparum and Plasmodium vivax extend well beyond the African continent, and, although P. vivax causes 80-300 million severe cases each year, vivax transmission remains poorly understood. Plasmodium parasites are transmitted by Anopheles mosquitoes, and the critical site of interaction between parasite and host is at the mosquitos luminal midgut brush border. While the genome of the "model" African P. falciparum vector, Anopheles gambiae, has been sequenced, evolutionary divergence limits its utility as a reference across anophelines, especially non-sequenced P. vivax vectors such as Anopheles albimanus. Clearly, enabling technologies and platforms that bridge this substantial scientific gap are required in order to provide public health scientists key transcriptomic and proteomic information that could spur the development of novel interventions to combat this disease. To our knowledge, no approaches have been published which address this issue. To bolster our understanding of P. vivax-An. albimanus midgut interactions, we developed an integrated bioinformatic-hybrid RNA-Seq-LC-MS/MS approach involving An. albimanus transcriptome (15,764 contigs) and luminal midgut subproteome (9,445 proteins) assembly, which, when used with our custom Diptera protein database (685,078 sequences), facilitated a comparative proteomic analysis of the midgut brush borders of two important malaria vectors, An. gambiae and An. albimanus. Summary from: http://www.mcponline.org/content/early/2012/10/17/mcp.M112.019596.long The An. albimanus transcriptome dataset is available at http://funcgen.vectorbase.org/RNAseq/Anopheles_albimanus/INSP/v2

The chromosome-centric human proteome project aims to systematically map all human proteins, chromosome by chromosome, in a gene-centric manner through dedicated efforts from national and international teams. This mapping will lead to a knowledge-based resource defining the full set of proteins encoded in each chromosome and laying the foundation for the development of a standardized approach to analyze the massive proteomic data sets currently being generated. The neXtProt database lists 946 proteins as the human proteome of chromosome 7. However, 170 (18%) proteins of human chromosome 7 have no evidence at the proteomic, antibody, or structural levels and are considered “missing” in this study as they lack experimental support. We have developed a protocol for the functional annotation of these “missing” proteins by integrating several bioinformatics analysis and annotation tools, sequential BLAST homology searches, protein domain/motif and gene ontology (GO) mapping, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Using the BLAST search strategy, homologues for reviewed non-human mammalian proteins with protein evidence were identified for 90 “missing” proteins while another 38 had reviewed non-human mammalian homologues. Putative functional annotations were assigned to 27 of the remaining 43 novel proteins. Proteotypic peptides have been computationally generated to facilitate rapid identification of these proteins. Four of the “missing” chromosome 7 proteins have been substantiated by the ENCODE proteogenomic peptide data.

Objective: To screen for novel predictive serum markers of preeclampsia (PE). Method: Blood samples were collected from 7 women with PE and 5 with healthy pregnancies. Serum proteins were identified using ITRAQ technology combined with liquid chromatography mass spectrometry analysis. The differential expressed proteins in the PE samples were identified using the SwissProt database, and functionally annotated by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses Results: We identified 121 differential expressed proteins, of which 76 were up-regulated and 45 were down-regulated, and 14 were differential expressed by more than 2-folds. The top GO terms for Cellular Components (CC) were high-density lipoprotein particles and plasma lipoprotein particles, defense response for Biological Processes (BP), and glycosaminoglycan binding, heparin binding and sulfur compound for Molecular functions (MF). The pathway hsa04979 for Cholesterol metabolism was significantly enriched among the upregulated proteins, while structural domain was enriched in immunoglobulin subtype 2. Conclusion: PE pathogenesis is related to lipid metabolism and inflammation, and proteins related to these pathways are potential early diagnostic markers for PE.

According to Cognitive Market Research, the Global Bioinformatics Services Market Size will be USD XX Billion in 2023 and is set to achieve a market size of USD XX Billion by the end of 2031 growing at a CAGR of XX% from 2024 to 2031.

• The global Bioinformatics services Market will expand significantly by XX% CAGR between 2024 and 2031.

• Based on technology, Because of the growing number of platform applications and the need for improved tools for drug development, the bioinformatics platforms segment dominated the market.

• In terms of service type, The sequencing services segment held the largest share and is anticipated to grow over the coming years

• Based on application, The genomic segment dominated the bioinformatics market

• Based on End-user, academic institutes and research centers segment hold the largest share.

• Based on speciality segment, The medical bioinformatics segment holds the large share and is anticipated to expand at a substantial CAGR during the forecast period.

• The North America region accounted for the highest market share in the Global Bioinformatics Services Market. CURRENT SCENARIO OF THE BIOINFORMATICS SERVICES

Driving Factors of the Bioinformatics Services Market

Expansive uses of bioinformatics across multiple sectors is propelling the market's growth.

Several industries, such as the food, bioremediation, agriculture, forensics, and consumer industries, are also using bioinformatics services to improve the quality of their products and supply chain processes. Companies in a variety of sectors are rapidly utilizing bioinformatics services such as data integration, manipulation, lead generation, data management, in silico analysis, and advanced knowledge discovery.

• Bioinformatics Approaches in Food Sciences

In order to meet the needs of food production, food processing, enhancing the quality and nutritional content of food sources, and many other areas, bioinformatics plays a significant role in forecasting and evaluating the intended and undesired impacts of microorganisms on food, genomes, and proteomics research. Furthermore, bioinformatics techniques can be applied to produce crops with high yields and resistance to disease, among other desirable qualities. Additionally, there are numerous databases with information about food, including its components, nutritional value, chemistry, and biology.

Genome Canada is proud to partner with five Institutes where there are five funding pools within this opportunity and Genome Canada is partnering on the Bioinformatics, Computational Biology and Health Data Sciences pool. (Source:https://genomecanada.ca/genome-canada-partners-with-cihr-to-launch-health-research-training-platform-2024-25/)

• Bioinformatics in agriculture

Bioinformatics is becoming more and more crucial in the gathering, storing, and processing of genomic data in the field of agricultural genomics, or agri-genomics. Generally referred to as agri-informatics, some of the various applications of bioinformatics tools and methods in agriculture focus on improving plant resistance against biotic and abiotic stressors as well as enhancing the nutritional quality in depleted soils. Beyond these uses, computer software-assisted gene discovery has enabled researchers to create focused strategies for seed quality enhancement, incorporate extra micronutrients into plants for improved human health, and create plants with phytoremediation potential.

India/UK-based Agri-Genomics startup, Piatrika Biosystems has raised $1.2 Million in a seed round led by Ankur Capital. The company is bringing sustainable seeds and agri chemicals to market faster and cheaper. The investment will be used to build a strong Product Development team, also for more profound research, and to accelerate the productionising and commercialization of MVP. (Source:https://pressroom.icrisat.org/agri-genomics-startup-piatrika-biosystems-raises-12-million-in-seed-funding-led-by-ankur-capital)

This expansion in the application areas of bioinformatics services is likely to drive the overall market growth. Bioinformatics services such as data integration, manipulation, lead discovery, data management, in silico analysis, and advanced knowledge discovery are increasingly being adopted by companies across various industries.&...

A complete proteomics dataset of Pseudoalteromonas tunicata D2 liquid cultures grown for 8 h (planktonic), 26 h (biofilm), 42 h (biofilm), and 68 h (biofilm). This Scaffold (.sf3) file contains all MS/MS based peptide and protein identifications for each of the four samples. PEAKS Studio v. 8.5 was used as a search engine with the NCBI P. tunicata D2 proteome (4503 entries, NCBI database, May 7, 2018) as a reference database. Additional details can be found within the .sf3 file.

Proteomics market size and share projected to reach USD 380.7 Billion by 2034 from USD 37.0 Billion in 2024 and is expected to grow at CAGR of 29.20% during the forecast period. The proteomics market is segmented based on instrumentation technology, services and software, application, and region.

Supplementary Material 1

Proteomics and bioinformatics are a useful combined technology for the characterization of protein expression level and modulation associated with the response to a drug and with its mechanism of action. The folate pathway represents an important target in the anticancer drugs therapy. In the present study, a discovery proteomics approach was applied to tissue samples collected from ovarian cancer patients who relapsed after the first-line carboplatin-based chemotherapy and were treated with pemetrexed (PMX), a known folate pathway targeting drug. The aim of the work is to identify the proteomic profile that can be associated to the response to the PMX treatment in pre-treatement tissue. Statistical metrics of the experimental Mass Spectrometry (MS) data were combined with a knowledge-based approach that included bioinformatics and a literature review through ProteinQuest™ tool, to design a protein set of reference (PSR). The PSR provides feedback for the consistency of MS proteomic data because it includes known validated proteins. A panel of 24 proteins with levels that were significantly different in pre-treatment samples of patients who responded to the therapy vs. the non-responder ones, was identified. The differences of the identified proteins were explained for the patients with different outcomes and the known PMX targets were further validated. The protein panel herein identified is ready for further validation in retrospective clinical trials using a targeted proteomic approach. This study may have a general relevant impact on biomarker application for cancer patients therapy selection.

The objective of this study was to characterize differentially regulated proteins and biological processes in hydrogen-treated hyperoxic primary type II alveolar epithelial cells (AECIIs) to elucidate the protective mechanism of hydrogen using quantitative proteomics. AECIIs were divided into three groups that were cultured for 24 h in three different conditions: control (21% oxygen), hyperoxia (95% oxygen), and hyperoxia + hydrogen. The TMT labeling quantitative proteome technique was used to detect changes in the protein expression profile, and bioinformatics analysis was performed.

FASTA files for training, validating, and testing a neural network for coevolution-based metal binding site identification.

This dataset contains the result of clustering the Unified Human Gastrointestinal Proteome (UHGP) using the DPCfam algorithm.

More details on the DPCfam clustering algorithm can be found in the original publication:

Russo, Elena Tea, et al. "DPCfam: Unsupervised protein family classification by Density Peak Clustering of large sequence datasets." PLOS Computational Biology 18.10 (2022): e1010610. https://doi.org/10.1371/journal.pcbi.1010610

All of the putative protein families obtained through DPCfam (including previous results) can be browsed online at our dedicated webserver: https://dpcfam.areasciencepark.it/uhgp

The original protein dataset is version 1.0 of the UHGP-50 dataset, available for download from MGnify at https://www.ebi.ac.uk/metagenomics/.

FILES DESCRIPTION:

Only MCs with seeds with 1) more than 50 elements and 2) average length larger than 50 aminoacids are reported.

metaclusters_xml.tar.gz:

• dpcfam_uhgp_metaclusters.xml: Metaclusters' seeds. Metaclusters entries include also some statistical information about each MC (such as size, average length, low complexity fraction, etc.) and Pfam comparison (Dominant Architecture).
• dpcfam_metaclusters.xsd: XML schema file for the data.
• MCxml_to_tables.awk: Awk script to convert from XML to tabular text files. Use through the parse.sh script.
• parse.sh: XML parser.
• README.md

uhgp_xml.tar.gz:

• uhgp_seed_match.xml: XML file containing all of UHGP-50 proteins and its corresponding sequences, annotated with Pfam and DPCfam metacluster data. Annotations comprise the membership of a protein as a seed or matches found though the profile-hmms of the DPCfam-UHGP and the DPCfam-Uniref clusterings.
• uhgp_matches.xsd: XML schema file for the data.
• xml_to_list.awk: Awk script to convert from XML to tabular text files. Use through the parse.sh script.
• xml_to_list_mcfiles.awk: Awk script to convert from XML to tabular text files (including individual files for metaclusters' seeds). Use through the parse.sh script.
• parse.sh: XML parser.
• README.md

Metacluster Files:

• seeds.zip: Metaclusters' seed sequences. A fasta file for each metacluster before filtering.
• filtered_seeds.zip: Metaclusters' seed sequences after clustering at 60 percent identity.
• metaclusters_hmms.tar.gz: Metaclusters' profile-hmms. A ".hmm" file for each metacluser.
• metaclusters_msas.tar.gz: Metaclusters' multiple sequence alignments, in fasta format.

uhgp_protein_mapping.txt:

• Contains a mapping between the identifiers of versions 1.0 and 2.0.2 of UHGP. The first column corresponds to the ID in UHGP-50 1.0 (representatives for the clustering at 50% protein identity), the second column to the ID in version 2.0.2 and the third column to the ID of the representative of the protein for clustering at 100% sequence identity, for which the protein sequence can be found in UHGP-100.

Modern proteomics approaches can explore whole proteomes within a single mass spectrometry (MS) run. However, the enormous amount of MS data generated often remains incompletely analyzed due to a lack of sophisticated bioinformatic tools and expertise needed from a diverse array of fields. In particular, in the field of microbiology, efforts to combine large-scale proteomic datasets have so far largely been missing. Thus, despite their relatively small genomes, the proteomes of most archaea remain incompletely characterized. This in turn undermines our ability to gain a greater understanding of archaeal cell biology.

Therefore, we have initiated the Archaeal Proteome Project (ArcPP), a community effort that works towards a comprehensive analysis of archaeal proteomes. Starting with the model archaeon Haloferax volcanii, using state-of-the-art bioinformatic tools, we have:

• reanalyzed more than 26 Mio. spectra
• optimized the analysis using parameter sweeps, multiple search engines implemented in Ursgal, and the combination of results through the combined PEP approach
• thoroughly controlled false discovery rates for high confidence protein identifications using the picked protein FDR approach and limiting FDR to 0.5%
• identified more than 45k peptides, corresponding to 3069 proteins (>75% of the proteome) with a median sequence coverage of 55%.
• analyzed N-terminal protein processing, including N-terminal acetylation and signal peptide cleavage
• performed a detailed glycoproteomic analysis, identifying >230 glycopeptides corresponding to 45 glycoproteins

Benefiting from the established bioinformatic infrastructure, we will follow up on this analysis focusing on H. volcanii proteogenomics as well as the characterization of additional post-translational modifications. Furthermore, ArcPP will integrate quantitative results obtained from the individual datasets in order to identify common regulatory mechanisms. These studies on the H. volcanii proteome can serve as a blueprint for comprehensive proteomic analyses performed on a diverse range of archaea and bacteria.

For further details, please refer to the following publications. Please also cite this work if you use these results for further analyses:

Schulze, S., Adams, Z., Cerletti, M. et al. The Archaeal Proteome Project advances knowledge about archaeal cell biology through comprehensive proteomics. Nat Commun 11, 3145 (2020). https://doi.org/10.1038/s41467-020-16784-7

Schulze, S.; Pfeiffer, F.; Garcia, B.A.; Pohlschroder, M. (2021). Comprehensive glycoproteomics shines new light on the complexity and extent of glycosylation in archaea. PLOS Biol. https://doi.org/10.1371/journal.pbio.3001277

An interactive website to explore the combined results can be found at https://archaealproteomeproject.org/

Scripts and metadata used for the analysis can be found at https://github.com/arcpp/ArcPP

Updates version 1.3.0:

- includes dataset PXD021827

Updates version 1.2.0:

- Includes dataset PXD021874
- Includes results from a comprehensive glycoproteomic analysis of ArcPP datasets

Updates version 1.1.0:
- Natrialba magadii results are included in PXD009116.zip

Bioinformatics Software Market Outlook

The global bioinformatics software market size was valued at approximately USD 10 billion in 2023, and it is projected to reach around USD 25 billion by 2032, growing at a robust CAGR of 11% during the forecast period. This remarkable growth is fueled by the increased application of bioinformatics in drug discovery and development, the rising demand for personalized medicine, and the ongoing advancements in sequencing technologies. The convergence of biology and information technology has led to the optimization of biological data management, propelling the market's expansion as it transforms the landscape of biotechnology and pharmaceutical research. The rapid integration of artificial intelligence and machine learning techniques to process complex biological data further accentuates the growth trajectory of this market.

An essential growth factor for the bioinformatics software market is the burgeoning demand for sequencing technologies. The decreasing cost of sequencing has led to a massive increase in the volume of genomic data generated, necessitating advanced software solutions to manage and interpret this data efficiently. This demand is particularly evident in genomics and proteomics, where bioinformatics software plays a critical role in analyzing and visualizing large datasets. Additionally, the adoption of cloud computing in bioinformatics offers scalable resources and cost-effective solutions for data storage and processing, further fueling market growth. The increasing collaboration between research institutions and software companies to develop innovative bioinformatics tools is also contributing positively to market expansion.

Another significant driver is the growth of personalized medicine, which relies heavily on bioinformatics for the analysis of individual genetic information to tailor therapeutic strategies. As healthcare systems worldwide move towards precision medicine, the demand for bioinformatics software that can integrate genetic, phenotypic, and environmental data becomes more pronounced. This trend is not only transforming patient care but also significantly impacting drug development processes, as pharmaceutical companies aim to create more effective and targeted therapies. The strategic partnerships and collaborations between biotech firms and bioinformatics software providers are critical in advancing personalized medicine and enhancing patient outcomes.

The increasing prevalence of complex diseases such as cancer and neurological disorders necessitates comprehensive research efforts, driving the need for robust bioinformatics software. These diseases require multi-omics approaches for better understanding, diagnosis, and treatment, where bioinformatics tools are indispensable. The ongoing research and development activities in this area, supported by government funding and private investments, are fostering innovation in bioinformatics solutions. Furthermore, the development of user-friendly and intuitive software interfaces is expanding the market beyond specialized research labs to include clinical settings and hospitals, broadening the potential user base and enhancing market penetration.

From a regional perspective, North America currently leads the bioinformatics software market, thanks to its advanced technological infrastructure, significant investment in healthcare R&D, and the presence of numerous key market players. The region accounted for the largest market share in 2023 and is expected to maintain its dominance throughout the forecast period. Meanwhile, the Asia Pacific region is anticipated to exhibit the highest CAGR, driven by increasing investments in biotechnology and pharmaceutical research, expanding healthcare infrastructure, and the rising adoption of bioinformatics in emerging economies like China and India. Europe's market growth is also significant, supported by substantial funding for genomic research and a strong focus on precision medicine initiatives.

Lifesciences Data Mining and Visualization are becoming increasingly vital in the bioinformatics software market. As the volume of biological data continues to grow exponentially, the need for sophisticated tools to mine and visualize this data is paramount. These tools enable researchers to uncover hidden patterns and insights from complex datasets, facilitating breakthroughs in genomics, proteomics, and other life sciences fields. The integration of advanced data mining techniques with visualization capabilities allows for a more intuitive

This file contains: - Overlap between all the sets (transcriptomic set, UniProtKB, Text-mining and proteomics set) - Overlap between the transcriptomic and the proteomic set - The list of gene–tissue associations unique to each set

The Bioinformatics Services Market Report is Segmented by Service Type (Data Analysis, Database Management, Sequencing, and Others), by Application (Drug Design, Genomics & Proteomics, Metabolomics, Transcriptomics, and Others), by End-User (Pharmaceutical & Biotechnology Companies, Contract Research Organization, Academic Institutes & Research Centers, and Others), and Geography (North America, Europe, Asia-Pacific, Middle East and Africa, and South America). The Report Offers the Value (in USD) for the Above Segments.