Abstract Public databases are essential to the development of multi-omics resources. The amount of data created by biological technologies needs a systematic and organized form of storage, that can quickly be accessed, and managed. This is the objective of a biological database. Here, we present an overview of human databases with web applications. The databases and tools allow the search of biological sequences, genes and genomes, gene expression patterns, epigenetic variation, protein-protein interactions, variant frequency, regulatory elements, and comparative analysis between human and model organisms. Our goal is to provide an opportunity for exploring large datasets and analyzing the data for users with little or no programming skills. Public user-friendly web-based databases facilitate data mining and the search for information applicable to healthcare professionals. Besides, biological databases are essential to improve biomedical search sensitivity and efficiency and merge multiple datasets needed to share data and build global initiatives for the diagnosis, prognosis, and discovery of new treatments for genetic diseases. To show the databases at work, we present a a case study using ACE2 as example of a gene to be investigated. The analysis and the complete list of databases is available in the following website .

THIS RESOURCE IS NO LONGER IN SERVICE, documented on 8/12/13. An expanded version of the Alternative Splicing Annotation Project (ASAP) database with a new interface and integration of comparative features using UCSC BLASTZ multiple alignments. It supports 9 vertebrate species, 4 insects, and nematodes, and provides with extensive alternative splicing analysis and their splicing variants. As for human alternative splicing data, newly added EST libraries were classified and included into previous tissue and cancer classification, and lists of tissue and cancer (normal) specific alternatively spliced genes are re-calculated and updated. They have created a novel orthologous exon and intron databases and their splice variants based on multiple alignment among several species. These orthologous exon and intron database can give more comprehensive homologous gene information than protein similarity based method. Furthermore, splice junction and exon identity among species can be valuable resources to elucidate species-specific genes. ASAP II database can be easily integrated with pygr (unpublished, the Python Graph Database Framework for Bioinformatics) and its powerful features such as graph query, multi-genome alignment query and etc. ASAP II can be searched by several different criteria such as gene symbol, gene name and ID (UniGene, GenBank etc.). The web interface provides 7 different kinds of views: (I) user query, UniGene annotation, orthologous genes and genome browsers; (II) genome alignment; (III) exons and orthologous exons; (IV) introns and orthologous introns; (V) alternative splicing; (IV) isoform and protein sequences; (VII) tissue and cancer vs. normal specificity. ASAP II shows genome alignments of isoforms, exons, and introns in UCSC-like genome browser. All alternative splicing relationships with supporting evidence information, types of alternative splicing patterns, and inclusion rate for skipped exons are listed in separate tables. Users can also search human data for tissue- and cancer-specific splice forms at the bottom of the gene summary page. The p-values for tissue-specificity as log-odds (LOD) scores, and highlight the results for LOD >= 3 and at least 3 EST sequences are all also reported.

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.

Harvester is a Web-based tool that bulk-collects bioinformatic data on human proteins from various databases and prediction servers. It is a meta search engine for gene and protein information. It searches 16 major databases and prediction servers and combines the results on pregenerated HTML pages. In this way Harvester can provide comprehensive gene-protein information from different servers in a convenient and fast manner. As full text meta search engine, similar to Google trade mark, Harvester allows screening of the whole genome proteome for current protein functions and predictions in a few seconds. With Harvester it is now possible to compare and check the quality of different database entries and prediction algorithms on a single page. Sponsors: This work has been supported by the BMBF with grants 01GR0101 and 01KW0013.

To extend proteome coverage obtained from bottom-up mass spectrometry approaches, three complementary ion activation methods, higher energy collision dissociation (HCD), ultraviolet photodissociation (UVPD), and negative mode UVPD (NUVPD), are used to interrogate the tryptic peptides in a human hepatocyte lysate using a high performance Orbitrap mass spectrometer. The utility of combining results from multiple activation techniques (HCD+UVPD+NUVPD) is analyzed for total depth and breadth of proteome coverage. This study also benchmarks a new version of the Byonic algorithm, which has been customized for database searches of UVPD and NUVPD data. Searches utilizing the customized algorithm resulted in over 50% more peptide identifications for UVPD and NUVPD tryptic peptide data sets compared to other search algorithms. Inclusion of UVPD and NUVPD spectra resulted in over 600 additional protein identifications relative to HCD alone.

Explore the booming Molecular Biology Software market, projected to reach $3.7 billion by 2033. Discover key drivers, trends in bioinformatics, DNA analysis, and drug discovery.

PROSITE consists of documentation entries describing protein domains, families and functional sites as well as associated patterns and profiles to identify them [More... / References / Commercial users ]. PROSITE is complemented by ProRule , a collection of rules based on profiles and patterns, which increases the discriminatory power of profiles and patterns by providing additional information about functionally and/or structurally critical amino acids [More...].

Data to be used with SingleM in "pipe" mode. https://github.com/wwood/singlem

This is the HQSNP DB (high-quality SNP database) developed by CHG bioinformatics group. The high-quality SNP is defined as a SNP having allele frequency or genotyping data. The majority of the HQSNPs come from HapMap, others come from JSNP (Japanese SNP database), TSC (The SNP Consortium), Affymetrix 120K SNP, and Perlegen SNP. There are four kinds of SNP search you can do: * Get SNPs by dbSNP rs#: Choose this search if you have already selected a list of SNPs and you just want to get the SNP information. The program will generate a Excel file containing the SNP flanking sequence, variation, quality, function, etc. In the Excel file, there are 10 highlighted fields. You can send only those highlighted information to Illumina to get SNP pre-score. (The same fields are presented in other types of searches as well.) * Get gene SNPs by gene names: Choose this search if you have a list of gene names and you want to get the SNP information in these genes. The gene name can be official gene symbol, Ensembl gene ID, RefSeq accession ID, LocusLink number, etc. * Get gene SNPs by genome regions: Choose this search if you have a list of genome regions and you want to get all gene SNP information in these regions. The software will find all the Ensembl genes in the regions and find SNPs associated to each Ensembl gene. * Get genome scan SNPs by genome regions: Choose this search if you have a list of genome regions and you want to get evenly spaced SNPs in these regions. A SNP selection tool (SNPselector) was built upon HQSNP. It took snp ID list, gene name list, or genome region list as input and searched SNPs for genome scan or gene assoctiation study. It could take an optional ABI SNP file (exported from ABI SNP search web page) as input for checking whether the candidate SNP is available from ABI. It could also take an optional Illumina SNP pre-score file as input to select SNP for Illumina SNP assay. It generated results sorted by tag SNP in LD block, SNP quality, SNP function, SNP regulatory potential, and SNP mutation risk. SNPselector is now retired from public use (as of September 30, 2010).

Bio Resource for array genes is a free online resource for easy access to collective and integrated information from various public biological resources for human, mouse, rat, fly and c. elegans genes. The resource includes information about the genes that are represented in Unigene clusters. This resource provides interactive tools to selectively view, analyze and interpret gene expression patterns against the background of gene and protein functional information. Different query options are provided to mine the biological relationships represented in the underlying database. Search button will take you to the list of query tools available. This Bio resource is a platform designed as an online resource to assist researchers in analyzing results of microarray experiments and developing a biological interpretation of the results. This site is mainly to interpret the unique gene expression patterns found as biological changes that can lead to new diagnostic procedures and drug targets. This interactive site allows users to selectively view a variety of information about gene functions that is stored in an underlying database. Although there are other online resources that provide a comprehensive annotation and summary of genes, this resource differs from these by further enabling researchers to mine biological relationships amongst the genes captured in the database using new query tools. Thus providing a unique way of interpreting the microarray data results based on the knowledge provided for the cellular roles of genes and proteins. A total of six different query tools are provided and each offer different search features, analysis options and different forms of display and visualization of data. The data is collected in relational database from public resources: Unigene, Locus link, OMIM, NCBI dbEST, protein domains from NCBI CDD, Gene Ontology, Pathways (Kegg, Genmapp and Biocarta) and BIND (Protein interactions). Data is dynamically collected and compiled twice a week from public databases. Search options offer capability to organize and cluster genes based on their Interactions in biological pathways, their association with Gene Ontology terms, Tissue/organ specific expression or any other user-chosen functional grouping of genes. A color coding scheme is used to highlight differential gene expression patterns against a background of gene functional information. Concept hierarchies (Anatomy and Diseases) of MESH (Medical Subject Heading) terms are used to organize and display the data related to Tissue specific expression and Diseases. Sponsors: BioRag database is maintained by the Bioinformatics group at Arizona Cancer Center. The material presented here is compiled from different public databases. BioRag is hosted by the Biotechnology Computing Facility of the University of Arizona. 2002,2003 University of Arizona.

SUPFAM is a database that consists of clusters of potentially related homologous protein domain families, with and without three-dimensional structural information, forming superfamilies. The present release (Release 3.0) of SUPFAM uses homologous families in Pfam (Version 23.0) and SCOP (Release 1.69) which are examples of sequence -alignment and structure classification databases respectively. The two steps involved in setting up of SUPFAM database are * Relating Pfam and SCOP families using a new profile-profile alignment algorithm AlignHUSH. This results in identifying many Pfam families which could be related to a family or superfamily of known structural information. * An all-against-all match among Pfam families with yet unknown structure resulting in identification of related Pfam families forming new potential superfamilies. The SUPFAM database can be used in either the Browse mode or Search mode. In Browse mode you can browse through the Superfamilies, Pfam families or SCOP families. In each of these modes you will be presented with a full list which can be easily browsed. In Search mode, you can search for Pfam families, SCOP families or Superfamilies based on keywords or SCOP/Pfam identifiers of families and superfamilies., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

The European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) is international, innovative and interdisciplinary, and a champion of open data in the life sciences. The EMBL-EBI captures and presents globally comprehensive sequence data as part of the International Nucleotide Sequence Database Collaboration. Data provided to GBIF include geotagged environmental sequences with user-provided taxonomic identifications. This dataset contains INSDC sequences associated with environmental sample identifiers. The dataset is prepared periodically using the public ENA API (https://www.ebi.ac.uk/ena/portal/api/) by querying data with the search parameters: environmental_sample=True & host="" EMBL-EBI also publishes other records in separate datasets (https://www.gbif.org/publisher/ada9d123-ddb4-467d-8891-806ea8d94230). The data was then processed as follows: 1. Human sequences were excluded. 2. For non-CONTIG records, the sample accession number (when available) along with the scientific name were used to identify sequence records corresponding to the same individuals (or group of organism of the same species in the same sample). Only one record was kept for each scientific name/sample accession number. 3. Contigs and whole genome shotgun (WGS) records were added individually. 4. The records that were missing some information were excluded. Only records associated with a specimen voucher or records containing both a location AND a date were kept. 5. The records associated with the same vouchers are aggregated together. 6. A lot of records left corresponded to individual sequences or reads corresponding to the same organisms. In practise, these were "duplicate" occurrence records that weren't filtered out in STEP 2 because the sample accession sample was missing. To identify those potential duplicates, we grouped all the remaining records by scientific_name, collection_date, location, country, identified_by, collected_by and sample_accession (when available). Then we excluded the groups that contained more than 50 records. The rationale behind the choice of threshold is explained here: Deduplication v2 gbif/embl-adapter#10 (comment) 7. To improve the matching of the EBI scientific name to the GBIF backbone taxonomy, we incorporated the ENA taxonomic information. The kingdom, Phylum, Class, Order, Family, and genus were obtained from the ENA taxonomy checklist available here: http://ftp.ebi.ac.uk/pub/databases/ena/taxonomy/sdwca.zip More information available here: https://github.com/gbif/embl-adapter#readme You can find the mapping used to format the EMBL data to Darwin Core Archive here: https://github.com/gbif/embl-adapter/blob/master/DATAMAPPING.md

Background: Viruses that infect prokaryotes (phages) constitute the most abundant group of biological agents, playing pivotal roles in microbial systems. They are known to impact microbial community dynamics, microbial ecology, and evolution. Efforts to document the diversity, host range, infection dynamics, and effects of bacteriophage infection on host cell metabolism are still at the surface level. Among phages, some adopt the lysogenic mode of infection, where the genome integrates into the host cell genome, forming a prophage. Prophages enable viral genome replication without host cell lysis and often contribute novel and beneficial traits to the host genome. Despite their importance, research on prophages is limited. Current phage research predominantly focuses on lytic phages, leaving a significant gap in knowledge regarding prophages, including their biology, diversity, and ecological roles. Results: To bridge this gap, the creation of Prophage-DB, a prophage database, aims to a..., , , # Prophage-DB: A comprehensive database to explore diversity, distribution, and ecology of prophages

https://doi.org/10.5061/dryad.3n5tb2rs5

This dataset contains prophage sequences (available as .fna files) identified from prokaryotic genomes from three public databases (Genome Taxonomy Database (GTDB) (release 207), National Center for Biotechnology Information (NCBI) Reference Sequence (RefSeq) database (accessed March 2023), and Searchable Planetary-scale mIcrobiome REsource (SPIRE). The downloaded prokaryotic genomes from these databases contained both archaeal and bacterial representative genomes (SPIRE also included data from unknown hosts).Â

Methods

Prophage identification from downloaded representative genomes was carried out using VIBRANT (v1.2.1). We used the default arguments when using VIBRANT (minimum scaffold length requirement = 1000 base pairs, minimum number of open readings frames (ORFs, or proteins) per scaffold requi...

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 29, 2016. The EBI SRS server is a primary gateway to major databases in the field of molecular biology produced and supported at EBI as well as European public access point to the MEDLINE database provided by US National Library of Medicine (NLM). It is a reference server for latest developments in data and application integration. Features include: concept of virtual databases, integration of XML databases like the Integrated Resource of Protein Domains and Functional Sites (InterPro), Gene Ontology (GO), MEDLINE, Metabolic pathways, etc., user friendly data representation in ''Nice views'', SRSQuickSearch bookmarklets. Quick Searches allow users to make a number of searches without needing to learn how to use SRS in depth. The searches query some of the common databanks without having to go and select them explicitly and without the need to understand the SRS Query Forms. Quick Searches can be performed from either the Start page (when you first open SRS) or the SRS Quick Search page (when you are already in a project). SRS also has the ability to search for links between your current results and related information in other databanks. Additionally, it is able to analyze the results of your search using many bioinformatics analysis tools or applications. This enables you to seek out further information that may be relevant to your initial search.

One sub-project within the global Chromosome 19 Consortium, part of the Chromosome-Centric Human Proteome Project, is to define chromosome 19 gene and protein expression in glioma-derived cancer stem cells (GSCs). Chromosome 19 is notoriously linked to glioma by 1p/19q co-deletions and clinical tests are established to detect that specific aberration. GSCs are tumor-initiating cells and are hypothesized to provide a repository of cells in tumors that can self-replicate and be refractory to radiation and chemotherapeutic agents developed for the treatment of tumors. In this pilot study, we performed RNA-Seq, label-free quantitative protein measurements in six GSC lines, and targeted transcriptomic analysis using a chromosome 19 specific microarray in an additional 6 GSC lines. Here, we present insights into differences in GSC gene and protein expression, including the identification of proteins listed as having no or low evidence at the protein level (such as small nuclear ribonucleoprotein G-like protein, RUXGL_HUMAN), as correlated to chromosome 19 and GSC subtype. Furthermore, the upregulation of proteins downstream of adenovirus-associated viral integration site 1 (AAVS1) in GSC11 in response to oncolytic adenovirus treatment was demonstrated. Taken together, our results may indicate new roles for chromosome 19, beyond the 1p/19q co-deletion, in the future of personalized medicine for glioma patients. Data analysis: MS files (.raw) were imported into Progenesis LC-MS (version 18.214.1528, Nonlinear Dynamics) for m/z and retention time alignment. The top 5 spectra for each feature were exported (charge deconvolution, top 1000 peaks) as a combined .mgf file for database searching in PEAKS (version 6, Bioinformatics Solutions Inc., Waterloo, ON) against the UniprotKB/Swissprot-Human database (July 2013 version, 20,264 proteins), appended with the cRAP contaminant database. PEAKS DB and Mascot (version 2.3.02, Matrix Science) searches were performed with a parent ion tolerance of 10 ppm, fragment ion tolerance of 0.025 Da, fixed carbamidomethyl cysteine, and variable modifications of oxidation (M), phosphorylation (STY), and deamidation (NQ). Trypsin was specified as the enzyme, allowing for 2 missed cleavages and a maximum of 3 PTMs per peptide. An additional search for unexpected modifications was performed with the entire Unimod database. Finally, homology searching was performed using the SPIDER algorithm to identify peptides resulting from nonspecific cleavages or amino acid substitutions. Mascot and PEAKS SPIDER searches were combined (inChorus), using a 1% false discovery rate cutoff for both search engines. The resulting peptide-spectrum matches (95% peptide probability) were imported into Progenesis LC-MS. Conflict resolution was performed manually to ensure that a single peptide sequence was assigned to each feature by removing lower scoring peptides. The resulting normalized peptide intensity data were exported, and the peptide list was filtered to remove non-unique peptides, methionine-containing peptides, and all modified peptides except cysteine carbamidomethylation. For quantification, the filtered list of peptide intensities was imported into DanteR (version 0.1.1), and intensities for peptides of the same sequence were combined to form a single entry. The resulting peptide intensities were log2 transformed and combined to protein abundances (RRollup) using the default settings, excluding one-hit wonders (50% minimum presence of at least one peptide, minimum dataset presence 3, p-value cutoff of 0.05 for Grubbs’ test, minimum of 5 peptides for Grubbs’ test). The resulting proteins were quantified by 1-way ANOVA relative to M37; p-value adjustment for multiple testing was performed according to Benjamini and Hochberg.

The global molecular biology software market is projected to reach USD 4.2 billion by 2033, exhibiting a CAGR of 10.5% during the forecast period (2023-2033). The market growth is primarily driven by the increasing demand for efficient and accurate tools for molecular biology research. The advancements in genomics and personalized medicine, coupled with the growing need for bioinformatics analysis, are further fueling the market expansion. The market is highly competitive, with established players such as QIAGEN, DNASTAR, Inc., SCIEX, SoftGenetics, LLC., and Thermo Fisher Scientific holding significant market shares. These companies offer a wide range of software solutions for plasmid mapping, DNA/protein database search and analysis, primer design, and other bioinformatics applications. The market also includes emerging players such as Benchling, CapitalBio Technology, and Biomatters, who are gaining traction with innovative software solutions and collaborations with research institutions. The market is segmented based on type, application, and region, with the research segment accounting for the largest market share due to the extensive use of molecular biology software in academic and medical research.

DATA for "A learned score function improves the power of mass spectrometry database search"

These data files are associated with the following publication:

Varun Ananth, Justin Sanders, Melih Yilmaz, Sewoong Oh and William Stafford Noble. "A learned score function improves the power of mass spectrometry database search". Bioinformatics (Proceedings of the ISMB). 2024.

For the benchmarking data, we used a dataset that is publicly available on ProteomeXchange (PXD028735). The paper that introduced this dataset is:

Van Puyvelde, B., Daled, S., Willems, S., Gabriels, R., Gonzalez de Peredo, A., Chaoui, K., Mouton-Barbosa, E., Bouyssié, D., Boonen, K., Hughes, C. J., Gethings, L. A., Perez-Riverol, Y., Bloomfield, N., Tate, S., Schiltz, O., Martens, L., Deforce, D., & Dhaenens, M. (2022). A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics. In Scientific Data (Vol. 9, Issue 1). Springer Science and Business Media LLC. https://doi.org/10.1038/s41597-022-01216-6

More specifically, the following .raw files were downloaded:

LFQ_Orbitrap_DDA_Ecoli_01.raw

LFQ_Orbitrap_DDA_Human_01.raw

LFQ_Orbitrap_DDA_Yeast_01.raw

Those files can be accessed via FTP here.

We upload here the annotated .mgf files created from these .raw files, as described in our paper.

The human, yeast, and E. coli .fasta files used in all database searches were downloaded from UniProt on 11/6/23, 4:30 PM.

Bateman, A., Martin, M.-J., Orchard, S., Magrane, M., Ahmad, S., Alpi, E., Bowler-Barnett, E. H., Britto, R., Bye-A-Jee, H., Cukura, A., Denny, P., Dogan, T., Ebenezer, T., Fan, J., Garmiri, P., da Costa Gonzales, L. J., Hatton-Ellis, E., Hussein, A., … Zhang, J. (2022). UniProt: the Universal Protein Knowledgebase in 2023. In Nucleic Acids Research (Vol. 51, Issue D1, pp. D523–D531). Oxford University Press (OUP). https://doi.org/10.1093/nar/gkac1052

We include these files here, with only minor modifications to replace U amino acids with X so that all amino acids fall into Casanovo-DB's vocabulary.

Rhodococcus jostii RHA1 is a catabolically versatile soil actinomycete that can utilize a wide range of organic compounds as growth substrates including steroids. To globally assess the adaptation of the protein composition in the membrane fraction to steroids, the membrane proteomes of RHA1 grown on each of cholesterol and cholate were compared to pyruvate-grown cells using gel-free SIMPLE-MudPIT technology. Label-free quantification by spectral counting revealed 59 significantly regulated proteins, many of them present only during growth on steroids. Cholesterol and cholate induced distinct sets of steroid-degrading enzymes encoded by paralogous gene clusters, consistent with transcriptomic studies. CamM and CamABCD, two systems that take up cholate metabolites, were found exclusively in cholate-grown cells. Similarly, 9 of the 10 Mce4 proteins of the cholesterol uptake system were found uniquely in cholesterol-grown cells. Bioinformatic tools were used to construct a model of Mce4 transporter within the RHA1 cell envelope. Finally, comparison of the membrane and cytoplasm proteomes indicated that several steroid-degrading enzymes are membrane-associated. The implications for the degradation of steroids by actinomycetes, including cholesterol by the pathogen Mycobacterium tuberculosis , are discussed. Bioinformatics and data processing: All database searches were performed using SEQUEST algorithm, embedded in BioworksTM (Rev. 3.3.1, Thermo Fisher Scientific Inc., Waltham, MA), with a RHA1 database containing 9145 sequences. Only tryptic peptides with up to two missed cleavages were accepted. No fixed modifications were considered. Oxidation of methionine was permitted as variable modification. The mass tolerance for precursor ions was set to 10 ppm; the mass tolerance for fragment ions was set to 1 amu. For protein identification a threshold for deltaCn (0.08) and for XCorr values was defined, depending on the peptide charge (>2.5 (+2); > 3.5 (+3)). A protein was considered identified if at least two different peptides met these criteria. To assess the false discovery rate (FDR) of protein identification, a database with reversed protein sequences was searched retaining the search parameters and filter criteria. The FDR was calculated by dividing the absolute number of hits from the reversed database through the sum of hits from both database searches (reversed database and original database). Using the stringent criteria described above, no reversed database hit was found; therefore, the FDR was 0% in all measurements. This low error rate can be attributed to the additional demand for two different peptide matches per protein, which eliminated all otherwise observed protein hits against the decoy database.

We sought to systematically remove assembled non-elaterid contaminant sequence from Ilumi1.0. Using the blobtools toolset (v.1.0.1), we taxonomically annotated our scaffolds by performing a blastn (v2.6.0+) nucleotide sequence similarity search against the NCBI nt database, and a diamond (v.0.9.10.111) translated nucleotide sequence similarity search against the of Uniprot reference proteomes (July 2017). Using this similarity information, we taxonomically annotated the scaffolds with blobtools using parameters “-x bestsumorder --rank phylum”.

A summary of the number of GO terms that were significantly enriched (having a p-value of