The size of the Bioinformatics Platforms Market market was valued at USD 16.36 Million in 2023 and is projected to reach USD 27.93 Million by 2032, with an expected CAGR of 7.94% during the forecast period. The Bioinformatics Platforms Market includes the software and tools required to understand biological data that contain genomic, proteomic, or metabolic data. These platforms include support for various applications like drug discovery, individualized medicine, and clinically related diagnostics through helps of data integration, statistical analysis and visualization. Some of the emerging trends that are driving the bioinformatics market are cloud-based bioinformatics solutions to support scalability and collaboration, advanced machine learning and artificial intelligence (AI) technologies to accurately analyze raised significance of multi-omics data integration for profound tumor bioinformatics analysis. Such factors pulling the market ahead include increasing volume of biological data in facets like research and clinical trials, evolving sequencing technologies, along with the increasing requirement for enhanced data management and analysis in genomics and proteomics. Further, the rising usage of bioinformatics for customized treatment and the growing number of research studies in genomics complement the market’s growth. Recent developments include: In June 2022, California's biotechnology research startup LatchBio launched an end-to-end bioinformatics platform for handling big biotech data to accelerate scientific discovery., In March 2022, ARUP launched Rio, a bioinformatics pipeline and analytics platform for better, faster next-generation sequencing test results.. Key drivers for this market are: Increasing Demand for Nucleic Acid and Protein Sequencing, Increasing Initiatives from Governments and Private Organizations; Accelerating Growth of Proteomics and Genomics; Increasing Research on Molecular Biology and Drug Discovery. Potential restraints include: Lack of Well-defined Standards and Common Data Formats for Integration of Data, Data Complexity Concerns and Lack of User-friendly Tools. Notable trends are: Sequence Analysis Platform Segment is Expected Hold a Significant Share Over the Forecast Period.

Supplementary Table 2 showing the success and willingness in reproducing published experiments stratified by the frequency of using bioinformatics tools. Chi square statistic: 0.53333, df = 3, p-value = 0.9115Conclusion: There was no evidence for a difference in the ability and willingness to reproduce published results between the respondents who use bioinformatics tools often and those who use them rarely or never.

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.&...

Access the summary of the Bioinformatics market report, featuring key insights, executive summary, market size, CAGR, growth rate, and future outlook.

Cumulative number of data packages in the Knowledge Network for Biocomplexity until 2007-06-21This data set records the cumulative number of data packages in the Knowledge Network for Biocomplexity (KNB) data repository through 2007-06-21. A data package represents a set of data files and metadata files that together make a coherent, citable unit for some particular scientific activity. Each data package in the KNB is described by a scientific metadata document and can be composed of one or more data files that contain various segments of the data in question.cumdatasets-20070622.csv

Studies describing the expression patterns and biomarkers for the tumoral process increase in number every year. The availability of new datasets, although essential, also creates a confusing landscape where common or critical mechanisms are obscured amidst the divergent and heterogeneous nature of such results. In this work, we manually curated the Gene Expression Omnibus using rigorous filtering criteria to select the most homogeneous and highest quality microarray and RNA-seq datasets from multiple types of cancer. By applying systems biology approaches, combined with machine learning analysis, we investigated possible frequently deregulated molecular mechanisms underlying the tumoral process. Our multi-approach analysis of 99 curated datasets, composed of 5,406 samples, revealed 47 differentially expressed genes in all analyzed cancer types, which were all in agreement with the validation using TCGA data. Results suggest that the tumoral process is more related to the overexpression of core deregulated machinery than the underexpression of a given gene set. Additionally, we identified gene expression similarities between different cancer types not described before and performed an overall survival analysis using 20 cancer types. Finally, we were able to suggest a core regulatory mechanism that could be frequently deregulated.

Data file 2. Statistic of ONT-sequencing in this study

The Bioinformatics Services market has emerged as a critical component of modern biological research, applying computational tools and statistical methods to analyze complex biological data. This rapidly evolving field is instrumental in various sectors, including pharmaceuticals, biotechnology, agriculture, and env

The Bioinformatics Service market is a rapidly expanding sector that merges biology, computer science, and information technology, providing essential tools and services for the analysis and interpretation of complex biological data. This field has gained tremendous significance due to the exponential growth of biol

The excel spreadsheet includes sample IDs and labeling information for DNA sequencing raw data. In addition, DNA concentrations for all the biofilm samples analyzed are presented. This dataset is associated with the following publication: Jeon, Y., l. li, J. Calvillo, H. Ryu, J. Santo Domingo, O. Choi, J. Brown, and Y. Seo. Impact of algal organic matter on the performance, cyanotoxin removal, and biofilms of biologically-active filtration systems. WATER RESEARCH. Elsevier Science Ltd, New York, NY, USA, 184: 116120, (2020).

The Bioinformatics Cloud Platform market is experiencing robust growth, driven by the increasing volume of biological data generated from genomics research, personalized medicine initiatives, and drug discovery programs. The need for scalable, cost-effective, and secure data storage and analysis solutions is fueling the adoption of cloud-based platforms. This market is segmented by service type (SaaS, PaaS, IaaS) and application (academic & research, pharmaceutical, others). While precise market size figures are not provided, based on industry reports and observed growth in related sectors like cloud computing and genomics, we can estimate the 2025 market size to be approximately $5 billion, with a Compound Annual Growth Rate (CAGR) of 20% projected from 2025 to 2033. This strong CAGR reflects the continuous advancements in sequencing technologies, the expansion of big data analytics in life sciences, and the growing adoption of cloud computing across various organizations. The pharmaceutical sector is a major contributor to this growth, driven by the need for faster and more efficient drug development pipelines that leverage powerful computational capabilities. Academic and research institutions also play a crucial role in market expansion through their active engagement in genomic research and data sharing initiatives. The market's growth is further propelled by several key trends, including the increasing accessibility of cloud-based bioinformatics tools, the development of advanced analytics techniques like AI and machine learning for data interpretation, and the rising emphasis on data security and compliance within the life sciences industry. However, challenges such as data privacy concerns, the complexity of integrating diverse data sources, and the need for specialized expertise to effectively utilize these platforms represent potential restraints. Nevertheless, the long-term outlook for the Bioinformatics Cloud Platform market remains exceptionally positive, driven by the continuous rise in genomic data and the increasing reliance on cloud-based solutions for efficient data management and analysis within the life sciences domain. Major players like Amazon Web Services, Google Cloud, Microsoft Azure, and specialized bioinformatics companies are actively competing and innovating within this rapidly expanding space.

Species trees provide insight into basic biology, including the mechanisms of evolution and how it modifies biomolecular function and structure, biodiversity and co-evolution between genes and species. Yet, gene trees often differ from species trees, creating challenges to species tree estimation. One of the most frequent causes for conflicting topologies between gene trees and species trees is incomplete lineage sorting (ILS), which is modelled by the multi-species coalescent. While many methods have been developed to estimate species trees from multiple genes, some which have statistical guarantees under the multi-species coalescent model, existing methods are too computationally intensive for use with genome-scale analyses or have been shown to have poor accuracy under some realistic conditions. Results: We present ASTRAL, a fast method for estimating species trees from multiple genes. ASTRAL is statistically consistent, can run on datasets with thousands of genes and has outstanding accuracy—improving on MP-EST and the population tree from BUCKy, two statistically consistent leading coalescent-based methods. ASTRAL is often more accurate than concatenation using maximum likelihood, except when ILS levels are low or there are too few gene trees. Methods Availability and implementation: ASTRAL is available in open source form at https://github.com/smirarab/ASTRAL/. Datasets studied in this article are available at http://www.cs.utexas.edu/users/phylo/datasets/astral. Contact: warnow@illinois.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Statistics of the cp genome assemblies of the 18 sweetpotato cultivars and the I. triloba line NCNSP-0323

In this paper, we aim at using genetic algorithms for gene selection and propose silhouette statistics as a discriminant function to classify breast cancers on microarray data for pattern discovery. In order to see the causality among these genes, we use the Bayesian method to construct a probability network for the pattern discovered. Consequently, we found a set of genes that is effective to discriminate breast cancer subtypes and present their probability dependencies to construct a diagnostic system. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1

Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.

Database of curated links to molecular resources, tools and databases selected on the basis of recommendations from bioinformatics experts in the field. This resource relies on input from its community of bioinformatics users for suggestions. Starting in 2003, it has also started listing all links contained in the NAR Webserver issue. The different types of information available in this portal: * Computer Related: This category contains links to resources relating to programming languages often used in bioinformatics. Other tools of the trade, such as web development and database resources, are also included here. * Sequence Comparison: Tools and resources for the comparison of sequences including sequence similarity searching, alignment tools, and general comparative genomics resources. * DNA: This category contains links to useful resources for DNA sequence analyses such as tools for comparative sequence analysis and sequence assembly. Links to programs for sequence manipulation, primer design, and sequence retrieval and submission are also listed here. * Education: Links to information about the techniques, materials, people, places, and events of the greater bioinformatics community. Included are current news headlines, literature sources, educational material and links to bioinformatics courses and workshops. * Expression: Links to tools for predicting the expression, alternative splicing, and regulation of a gene sequence are found here. This section also contains links to databases, methods, and analysis tools for protein expression, SAGE, EST, and microarray data. * Human Genome: This section contains links to draft annotations of the human genome in addition to resources for sequence polymorphisms and genomics. Also included are links related to ethical discussions surrounding the study of the human genome. * Literature: Links to resources related to published literature, including tools to search for articles and through literature abstracts. Additional text mining resources, open access resources, and literature goldmines are also listed. * Model Organisms: Included in this category are links to resources for various model organisms ranging from mammals to microbes. These include databases and tools for genome scale analyses. * Other Molecules: Bioinformatics tools related to molecules other than DNA, RNA, and protein. This category will include resources for the bioinformatics of small molecules as well as for other biopolymers including carbohydrates and metabolites. * Protein: This category contains links to useful resources for protein sequence and structure analyses. Resources for phylogenetic analyses, prediction of protein features, and analyses of interactions are also found here. * RNA: Resources include links to sequence retrieval programs, structure prediction and visualization tools, motif search programs, and information on various functional RNAs.

These datasets contains shotgun metagenomic sequencing taxonomic results, an amplicon sequence variant ID (ASV) table from 16S rRNA gene amplicon sequencing, and metagenomic sequencing nitrogen cycling genes from samples collected in Kongsfjorden and Rijpfjorden, in 2016, during the MOSJ and ICE cruises. Accompanying physical and biogeochemical data may be found in another dataset (https://doi.org/10.21334/npolar.2024.4d4de169). The sampling and analysis of this genetic data was carrried out by colleagues from CIIMAR – Interdisciplinary Centre of Marine and Environmental Research (Portugal) in collaboration with the NPI.

Title: Shotgun metagenomic sequencing taxonomic results relative to Kongsfjorden and Rijpfjorden 2016

File: “metagenomes_taxonomy.csv”

This dataset contains the results of raw read processing of shotgun metagenomics, relative to taxonomy.

Variables: - Level: taxonomic level; - Name: name of taxon; - Taxon_id: unique id for each taxonomic name; - Reads: number of reads attributed to Taxon_id in specific sample; - Percentage: percentage of reads in sample.

Bioinformatic processing of shotgun metagenomics sequencing results

Full details are available in Costa et al., 2024 (in review): «The raw shotgun metagenomic reads were trimmed with Trimmomatic v0.36, to remove adapter sequences, short reads (<36 bp), and reads with an average quality score <15 within 4-base windows (Bolger et al., 2014). Taxonomic annotation of the paired reads was carried out using Kaiju v1.9.0, with default parameters (Menzel et al., 2016). De novo assembly of the reads was performed using metaSPAdes - v3.15.3 (Nurk et al., 2017; Prjibelski et al., 2020), with a minimum contig length of 2000 bp.»

Keywords: metagenomics; microbial composition

# References Bolger, A. M., Lohse, M., & Usadel, B. (2014). Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics, 30(15), 2114–2120. https://doi.org/10.1093/bioinformatics/btu170 Menzel, P., Ng, K. L., & Krogh, A. (2016). Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nature Communications, 7, 1–9. https://doi.org/10.1038/ncomms11257 Nurk, S., Meleshko, D., Korobeynikov, A., & Pevzner, P. A. (2017). MetaSPAdes: A new versatile metagenomic assembler. Genome Research, 27(5), 824–834. https://doi.org/10.1101/gr.213959.116 Prjibelski, A., Antipov, D., Meleshko, D., Lapidus, A., & Korobeynikov, A. (2020). Using SPAdes De Novo Assembler. Current Protocols in Bioinformatics, 70(1). https://doi.org/10.1002/cpbi.102

Title: ASV table from 16S rRNA gene amplicon sequencing

File: ASV_table_16S.csv

This dataset contains the abundance table of ASVs obtained from 16S rRNA gene amplicon sequencing.

Variables: - ASV - amplicon sequence variant ID; - Abundance - abundance score of each ASV; - Kingdom - equivalent to domain level taxonomy of the ASV; - Phylum - phylum level taxonomy of ASV; - Class - class level taxonomy of ASV; - Order - order level taxonomy of ASV; - Family - family level taxonomy of ASV; - Genus - genus level taxonomy of ASV; - Species - species level taxonomy of ASV.

Bioinformatic processing of V4-V5 16S rRNA gene amplicon sequencing results

From Costa et al., 2024 (in review): «Bioinformatic analysis was conducted as described in detail in Semedo et al. (2021). Primers from the raw FastQ files obtained from Illumina MiSeq sequencing were removed using “cutadapt v.1.16”. Files were imported into R (v 4.1.1) and analyzed following the DADA2 R package (v 1.20.0) (Callahan et al., 2016). Sample filtering, trimming (Forward = 240 nt, Reverse = 160 nt), error rates learning, dereplication and Amplicon Sequence Variant (ASV) inference were performed with default settings. Chimeras were removed using the function removeBimeraDenovo with the “consensus” method. Taxonomy was assigned using the DADA2 native implementation of the naive Bayesian classifier (Wang et al., 2007) with the GTDB v202 reference database (Cole et al., 2014; Parks et al., 2018). Taxonomy was filtered by removing the undesirable lineages “Eukaryota”, “Mitochondria”, “Chloroplast” and “unknown” from the dataset.»

Keywords: 16S rRNA gene amplicon sequencing; prokaryotic taxonomy; microbial composition

References Semedo, M., Lopes, E., Baptista, M. S., Oller-Ruiz, A., Gilabert, J., Tomasino, M. P., & Magalhães, C. (2021). Depth Profile of Nitrifying Archaeal and Bacterial Communities in the Remote Oligotrophic Waters of the North Pacific. Frontiers in Microbiology, 12(3), 1–18. https://doi.org/10.3389/fmicb.2021.624071 Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A., & Holmes, S. P. (2016). DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods, 13(7), 581–583. https://doi.org/10.1038/nmeth.3869 Wang, Q., Garrity, G. M., Tiedje, J. M., & Cole, J. R. (2007). Naive Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy. Applied and Environmental Microbiology, 73(16), 5261–5267. https://doi.org/10.1128/AEM.00062-07 Cole, J. R., Wang, Q., Fish, J. A., Chai, B., McGarrell, D. M., Sun, Y., Brown, C. T., Porras-Alfaro, A., Kuske, C. R., & Tiedje, J. M. (2014). Ribosomal Database Project: Data and tools for high throughput rRNA analysis. Nucleic Acids Research, 42(D1), 633–642. https://doi.org/10.1093/nar/gkt1244 Parks, D. H., Chuvochina, M., Waite, D. W., Rinke, C., Skarshewski, A., Chaumeil, P.-A., & Hugenholtz, P. (2018). A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nature Biotechnology, 36(10), 996–1004. https://doi.org/10.1038/nbt.4229

Title: Shotgun metagenomic sequencing nitrogen cycling genes results relative to Kongsfjorden and Rijpfjorden 2016

File: “genes_meta_complete.csv”

This dataset contains the results of raw read processing of shotgun metagenomics, relative to genes associated with the nitrogen cycle.

Variables: - originalFile: internal file used in construction of the dataset; - geneCallersID: unique ID relative to a gene accession; - source: software source for the gene accession; - accession: gene obtained from the source; - gene: common name for gene accession; - geneFunction: function of the gene; - contig: name of the contig used to identify gene; - start: start position of contig; - stop: stop position of contig; - contigLength: length of contig; - mappedReads: number of reads from the contig; - geneCoverage: coverage of the gene; - refGeneCoverage: coverage of reference gene; - normalizedCoverage: normalized gene coverage.

Bioinformatic processing of shotgun metagenomics sequencing results

Full details are available in Costa et al., 2024 (in review): «The raw shotgun metagenomic reads were trimmed with Trimmomatic v0.36, to remove adapter sequences, short reads (<36 bp), and reads with an average quality score <15 within 4-base windows (Bolger et al., 2014). De novo assembly of the reads was performed using metaSPAdes - v3.15.3 (Nurk et al., 2017; Prjibelski et al., 2020), with a minimum contig length of 2000 bp. Functional annotation of genes of the assembled contigs was done using PROKKA v1.14.5 (Seemann, 2014), and gene abundance was estimated by mapping the trimmed paired reads back into the contigs using bowtie2 (Langmead & Salzberg, 2012), with local alignment mode and allowing 1 bp mismatch. The number of reads mapped to the target genes of this study (genes implicated in the nitrogen cycle, see Table S4) was counted, in each of the metagenomes, using SAMtools (Danecek et al., 2021). To quantify the coverage of each of the nitrogen cycle target genes found in the metagenomes, the number of reads mapping to the contig was divided by the length (in bp) of the gene. To take in account the differences of sequencing depth between samples, the gene coverage was then normalized against the mean coverage of three reference single-copy genes: RecA protein (recA), DNA gyrase subunit B (gyrB), and DNA-directed RNA polymerase subunit beta (rpoB), and expressed as “Average Genomic Copy Number”, according to what has been described in detail by Semedo and Song (2023).»

Keywords: metagenomics; nitrogen cycle

# References Bolger, A. M., Lohse, M., & Usadel, B. (2014). Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics, 30(15), 2114–2120. https://doi.org/10.1093/bioinformatics/btu170 Menzel, P., Ng, K. L., & Krogh, A. (2016). Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nature Communications, 7, 1–9. https://doi.org/10.1038/ncomms11257 Nurk, S., Meleshko, D., Korobeynikov, A., & Pevzner, P. A. (2017). MetaSPAdes: A new versatile metagenomic assembler. Genome Research, 27(5), 824–834. https://doi.org/10.1101/gr.213959.116 Prjibelski, A., Antipov, D., Meleshko, D., Lapidus, A., & Korobeynikov, A. (2020). Using SPAdes De Novo Assembler. Current Protocols in Bioinformatics, 70(1). https://doi.org/10.1002/cpbi.102 Seemann, T. (2014). Prokka: Rapid prokaryotic genome annotation. Bioinformatics, 30(14), 2068–2069. https://doi.org/10.1093/bioinformatics/btu153 Semedo, M., & Song, B. (2023). Sediment metagenomics reveals the impacts of poultry industry wastewater on antibiotic resistance and nitrogen cycling genes in tidal creek ecosystems. Science of the Total Environment, 857(July 2022), 159496. https://doi.org/10.1016/j.scitotenv.2022.159496

The Single Cell Bioinformatics Software and Service market is rapidly evolving as a crucial segment within the field of genomics and bioinformatics, focusing on the analysis of individual cells to uncover insights that traditional bulk methods often miss. This market, which has gained significant traction over the p

The global bioinformatics market size reached USD 13.9 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 39.4 Billion by 2033, exhibiting a growth rate (CAGR) of 11.69% during 2025-2033. Rapid technological advancements, increasing genomic sequencing, surging demand for personalized medicine, data analytics growth, investment in research and development (R&D), expanding biological databases, and the rising focus on preventive care are some of the factors fostering the market growth.

IMARC Group provides an analysis of the key trends in each segment of the market, along with forecasts at the global, regional, and country levels for 2025-2033. Our report has categorized the market based on the product and service, application, and end-use sector.

The organizations that contribute to the longevity of 67 long-lived molecular biology databases published in Nucleic Acids Research (NAR) between 1991-2016 were identified to address two research questions 1) which organizations fund these databases? and 2) which organizations maintain these databases? Funders were determined by examining funding acknowledgements in each database's most recent NAR Database Issue update article published (prior to 2017) and organizations operating the databases were determine through review of database websites.

Accompanying dataset to the protocol published in Methods in Molecular Biology. Click on the PURL link below in the "External Files" section to download the dataset.