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104 results for “functional annotation”
DATASET: De novo assembly and functional annotation of the heart + hemolymph transcriptome in the Caribbean spiny lobster Panulirus argus
<p>The spiny lobster <em>Panulirus argus</em> is an ecologically relevant species in shallow water coral reefs and target of the most lucrative fishery in the greater Caribbean region. This study reports, for the first time, the heart + hemolymph transcriptome of the Caribbean spiny lobster<em> Panulirus argus</em> assembled from short Illumina 150 bp PE raw reads. A total 80,152,094 raw reads were assembled using the Oyster River Protocol pipeline that aspires to become the standard protocol for <em>de novo</em> transcriptome assembly. The assembly resulted in a total of 254,773 transcripts. Functional gene annotation was conducted using the software package 'dammit' that also aspires to become the standard protocol for <em>de novo</em> transcriptome annotation. Lastly, gene enrichment analyses were conducted using the Gene Ontology (GO), KEGG pathway analyses (Kaas), and KOG (WebMGA) databases. This resource will be of utmost importance in future research aiming at exploring the effect of local and regional anthropogenic disturbances as well as global climate change on the molecular physiology of this overexploited species.</p>
Genome, repeat, and functional annotation associated with the naked mole-rat genome assembly, mHetGlaV3 (GCA_964261345.1)
<p>The naked mole-rat (NMR; Heterocephalus glaber) is a eusocial subterranean rodent with a highly unusual set of physiological traits, such as extreme longevity, that has attracted great interest amongst the scientific community. However, the genetic basis of most of these traits has not been elucidated. To facilitate our understanding of the molecular mechanisms underlying NMR physiology and behaviour, we generated a long-read chromosomal-level genome assembly of the NMR. This genome, mHetGlaV2, was subsequently annotated and incorporated into a “91 eutherian mammals” multiple whole genome alignment in Ensembl. </p> <p>We identified intra-chromosomal misassemblies within mHetGlaV2. We fixed these misassemblies by comparing syntenic blocks between this assembly and the Canadian Porcupine (EreDor) genome assembly (https://www.ncbi.nlm.nih.gov/datasets/genome/GCA_028451465.1/) and a FISH-Karyotype of the naked mole-rat completed by Romanenko et al., 2023 (PMID: 380307020) to address any misassemblies and place centromeres. Chromosome numbering was identified from a composite karyogram of karyotypes from over 350 cells. This scaffold-corrected assembly is labelled mHetGlaV3 (https://www.ebi.ac.uk/ena/browser/view/GCA_964261345.1).</p> <p>This repository stores the repeat, genome, and epigenome annotations for HetGlaV3.</p> <p>mHetGlaV3.primary.gtf.gz. Gene structures and gene symbols are transferred from ENSEMBL annotations of mHetGlaV2 using liftOff with default parameters. Additional gene symbols were identified using TOGA and manual curation.</p> <p>mHetGlaV3.primary.gtf.gz. Simple repetitive regions and transposable elements were annotated using EarlGrey (https://github.com/TobyBaril/EarlGrey) using "Rodentia" annotations for RepeatMasker.</p> <p>mHetGlaV3.primary.genesymbol_table.txt.txt.gz. A tab-delimited file where rows are gene IDs and columns are gene symbols generated with each method. "Consensus" shows the best matching gene symbol for each gene ID.</p> <p>mHetGlaV3.primary_annotated_blacklist.bed.gz. Provides an assembly "blacklist" for mHetGlaV3. This blacklist is a bed file annotating assembly breakpoints between HetGlaV2 and HetGlaV3. This blacklist contains additional columns (e.g., closest gene, overlapping TE etc.) and should therefore be filtered to the first column before being incorporated into traditional genomic pipelines.</p> <p>mHetGlaV3.primary_hypothalamus_ABC_enhancer.bedpe.gz. Activity-By-Contact enhancers (https://github.com/broadinstitute/ABC-Enhancer-Gene-Prediction) generated in the female subordinate naked mole-rat hypothalamus using Hi-C-seq, ChIP-seq of H3K27Ac data, ATAC-seq, and RNA-seq information.</p> <p>mHetGlaV3.primary_hypothalamus_chromHMM.bed.gz. Chromatin states (using Chromhmm) annotating the female subordinate naked mole-rat hypothalamus using H3K4me3 (promoter), H4K4me2 (promoter-enhancer), H3K27Ac (active enhancer), H3K36me3 (elongated), H3K27me3 (polycomb repressed), H3K9me3 (heterochromatin), and CTCF (whole brain) ChIP-seq data, as well as ATAC-seq and RNA-seq data.</p> <p>mHetGlaV3.primary.fa.gz. Genome assembly fasta file for the naked mole-rat (V3, primary assembly). This assembly matches the primary assembly stored on ENA, however the chromosome names match these files, rather than have chromosome names processed by ENA (e.g. chr 1 instead of "OZ179169.1 Heterocephalus glaber genome assembly, chromosome: 1").</p> <p> </p> <p>UPDATES:</p> <p>* The 1.2 update fixed unscaffolded contig names from those used in-lab to those compatible with ENA.</p> <p>* The 1.3 update added small (50~100kbp) contigs onto mHetGlaV3.primary.fa.gz that were filtered before the ENA submission.</p> <p>* The 1.4 update fixed a small chromosome naming inconsistency spotted in the 1.3 update.</p>
Annotated Benchmark of Real-World Data for Approximate Functional Dependency Discovery
<p><strong>Annotated Benchmark of Real-World Data for Approximate Functional Dependency Discovery</strong></p> <p>This collection consists of ten open access relations commonly used by the data management community. In addition to the relations themselves (please take note of the references to the original sources below), we added three lists in this collection that describe approximate functional dependencies found in the relations. These lists are the result of a manual annotation process performed by two independent individuals by consulting the respective schemas of the relations and identifying column combinations where one column implies another based on its semantics. As an example, in the <em>claims.csv</em> file, the <em>AirportCode</em> implies <em>AirportName</em>, as each code should be unique for a given airport.</p> <p>The file <em>ground_truth.csv</em> is a comma separated file containing approximate functional dependencies. <em>table</em> describes the relation we refer to, <em>lhs</em> and <em>rhs</em> reference two columns of those relations where semantically we found that <em>lhs</em> implies <em>rhs</em>.</p> <p>The file <em>excluded_candidates.csv</em> and <em>included_candidates.csv</em> list all column combinations that were excluded or included in the manual annotation, respectively. We excluded a candidate if there was no tuple where both attributes had a value or if the <em>g3_prime</em> value was too small.</p> <p><strong>Dataset References</strong></p> <ul> <li><em>adult.csv</em>: Dua, D. and Graff, C. (2019). <a href="http://archive.ics.uci.edu/ml">UCI Machine Learning Repository</a>. Irvine, CA: University of California, School of Information and Computer Science.</li> <li><em>claims.csv</em>: TSA Claims Data 2002 to 2006, <a href="https://www.dhs.gov/tsa-claims-data">published by the U.S. Department of Homeland Security</a>.</li> <li><em>dblp10k.csv</em>: Frequency-aware Similarity Measures. Lange, Dustin; Naumann, Felix (2011). 243–248. <a href="https://hpi.de/naumann/projects/repeatability/datasets/dblp-dataset.html">Made available as DBLP Dataset 2</a>.</li> <li><em>hospital.csv</em>: Hospital dataset used in Johann Birnick, Thomas Bläsius, Tobias Friedrich, Felix Naumann, Thorsten Papenbrock, and Martin Schirneck. 2020. Hitting set enumeration with partial information for unique column combination discovery. Proc. VLDB Endow. 13, 12 (August 2020), 2270–2283. https://doi.org/10.14778/3407790.3407824. <a href="https://owncloud.hpi.de/s/j6Z0yvXC0qhtGCk/download">Made available as part the dataset collection to that paper.</a></li> <li><em>t_biocase_...</em> files: t_bioc_... files used in Johann Birnick, Thomas Bläsius, Tobias Friedrich, Felix Naumann, Thorsten Papenbrock, and Martin Schirneck. 2020. Hitting set enumeration with partial information for unique column combination discovery. Proc. VLDB Endow. 13, 12 (August 2020), 2270–2283. https://doi.org/10.14778/3407790.3407824. <a href="https://owncloud.hpi.de/s/j6Z0yvXC0qhtGCk/download">Made available as part the dataset collection to that paper.</a></li> <li><em>tax.csv</em>: Tax dataset used in Johann Birnick, Thomas Bläsius, Tobias Friedrich, Felix Naumann, Thorsten Papenbrock, and Martin Schirneck. 2020. Hitting set enumeration with partial information for unique column combination discovery. Proc. VLDB Endow. 13, 12 (August 2020), 2270–2283. https://doi.org/10.14778/3407790.3407824. <a href="https://owncloud.hpi.de/s/j6Z0yvXC0qhtGCk/download">Made available as part the dataset collection to that paper.</a></li> </ul>
Metagenomes: gene function and family annotations
<p>Functional annotations of genes for all contigs in 1,782 metagenomes.</p> <p>Genes were annotated to three sources: (1) COGs, (2) Pfams, and (3) <em>de novo</em> families from reference sequences. These gene annotations are used to train and run PlasX.</p>
Planet Microbe Functional and Taxonomic annotation of Illumina WGS Prokaryotic Fraction for Semantic Web Analysis
<p>Functional and Taxonomic annotations computed from a subset of Illumina Whole-Genome Sequencing samples from the prokaryotic fraction of the <a href="https://www.planetmicrobe.org/">Planet Microbe</a> database. Data was computed using the pipeline available from https://github.com/hurwitzlab/planet-microbe-functional-annotation/, and post processing scripts from https://github.com/hurwitzlab/planet-microbe-semantic-web-analysis. Files contain total annotation counts of Interpro, GO and NCBITaxon annotations, as well as additional sample metadata. See readme.txt file for more information.</p>
PR2_V9, a SSU V9 rDNA reference database with functional annotations.
<p>The present data set provides a tab separated text file compressed in a gzip archive. The file includes 63,401 18S V9 rDNA reference sequences for 44,084 unique eukaryotic taxa and 9,759 16S V9 rDNA references sequences for 9,661 unique prokaryotic taxa. It includes the following fields : sequence = nucleic acid sequence of reference; lineage = taxonomic path of the reference sequence; refs = original accession numbers corresponding to the reference sequence; name = reference sequence identifier; taxogroup = high-taxonomic level assignation of the reference sequence. The file also includes six categories of functional annotations: (1) chloroplast: yes, presence of permanent chloroplast; no, absence of permanent chloroplast ; NA, undetermined. (2) symbiont (small partner): parasite, the species is a parasite; commensal, the species is a commensal; mutualist, the species is a mutualist symbiont, most often a microalgal taxon involved in photosymbiosis; no the species is not involved in a symbiosis as small partner; NA, undetermined. (3) symbiont (host): photo, the host species relies on a mutualistic microalgal photosymbiont to survive (obligatory photosymbiosis); photo_falc, same as photo, but facultative relationship; photo_klep, the host species maintains chloroplasts from microalgal prey(s) to survive; photo_klep_falc, same as photo_klep, but facultative; Nfix, the host species must interact with a mutualistic symbiont providing N2 fixation to survive; Nfix_falc, same as Nfix, but facultative; no, the species is not involved in any mutualistic symbioses; NA, undetermined. (4) silicification; yes, the species has a silicified skeleton; no, it does not; NA, undetermined. (5) calcification; yes, the species has a calcified skeleton; no, it does not; NA, undetermined. (6) strontification; yes, the species has a skeleton made of strontium; no, it does not; NA, undetermined.</p>
Training data for 'Functional annotation of protein sequences' tutorial (Galaxy Training Material)
<p>The data provided here are part of a Galaxy Training Network tutorial for functional annotation of protein sequences.</p>
FAPM: Functional annotation of proteins using multi-modal models beyond structural modeling
<p>Assigning accurate property labels to proteins, like functional terms and catalytic activity, is challenging, especially for proteins without homologs and "tail labels" with few known examples. Unlike previous methods that mainly focused on protein sequence features, we use a pretrained large natural language model to understand the semantic meaning of protein labels. Specifically, we introduce FAPM, a contrastive multi-modal model that links natural language with protein sequence language. This model combines a pretrained protein sequence model with a pretrained large language model to generate labels, such as Gene Ontology (GO) functional terms and catalytic activity predictions, in natural language. Our results show that FAPM excels in understanding protein properties, outperforming models based solely on protein sequences or structures. It achieves state-of-the-art performance on public benchmarks and in-house experimentally annotated phage proteins, which often have few known homologs. Additionally, FAPM's flexibility allows it to incorporate extra text prompts, like taxonomy information, enhancing both its predictive performance and explainability. This novel approach offers a promising alternative to current methods that rely on multiple sequence alignment for protein annotation.</p>
The North Pacific Eukaryotic Gene Catalog: metatranscriptome assemblies with taxonomy, function and abundance annotations
<p>This data continues with the development of the unprocessed NPEGC Trinity <em>de novo</em> metatranscriptome assemblies, uploaded to this Zenodo repository for raw assemblies: <a href="../records/7332796">The North Pacific Eukaryotic Gene Catalog: Raw assemblies from Gradients 1, 2 and 3</a><br><br>A full description of this data is published in Scientific Data, available here: <a href="https://www.nature.com/articles/s41597-024-04005-5" target="_blank" rel="noopener">The North Pacific Eukaryotic Gene Catalog of metatranscriptome assemblies and annotations</a>. Please cite this publication if your research uses this data:<br><br>Groussman, R. D., Coesel, S. N., Durham, B. P., Schatz, M. J., & Armbrust, E. V. (2024). The North Pacific Eukaryotic Gene Catalog of metatranscriptome assemblies and annotations. <em>Scientific Data</em>, <em>11</em>(1), 1161.</p> <p><br>Excerpts of key processing steps are sampled below with links to the detailed code on the main github code repository: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog">https://github.com/armbrustlab/NPac_euk_gene_catalog</a></p> <p><br>Processing and annotation of protein-level NPEGC metatranscripts is done in 6 primary steps:<br>1. Six-frame translation into protein sequences<br>2. Frame-selection of protein-coding translation frames<br>3. Clustering of protein sequences at 99% sequence identity<br>4. Taxonomic annotation against MarFERReT v1.1 + MARMICRODB v1.0 multi-kingdom marine reference protein sequence library with DIAMOND<br>5. Functional annotation against Pfam 35.0 protein family HMM profiles using HMMER3<br>6. Functional annotation against KOfam HMM profiles (KEGG release 104.0) using KofamScan v1.3.0<br><br><code># Define local NPEGC base directory here:</code><br><code>NPEGC_DIR="/mnt/nfs/projects/armbrust-metat"</code></p> <p><code># Raw assemblies are located in the /assemblies/raw/ directory</code><br><code># for each of the metatranscriptome projects</code><br><code>PROJECT_LIST="D1PA G1PA G2PA G3PA G3PA_diel"</code></p> <p><code># raw Trinity assemblies:</code><br><code>RAW_ASSEMBLY_DIR="${NPEGC_DIR}/${PROJECT}/assemblies/raw"</code><br><br><strong>Translation</strong><br>We began processing the raw metatranscriptome assemblies by six-frame translation from nucleotide transcripts into three forward and three reverse reading frame translations, using the transeq function in the EMBOSS package. We add a cruise and sample prefix to the sequence IDs to ensure unique identification downstream (ex, `>TRINITY_DN2064353_c0_g1_i1_1` to `>G1PA_S09C1_3um_TRINITY_DN2064353_c0_g1_i1_1` for the S09C1_3um sample in the G1PA assemblies). See <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/aa_data/NPEGC.6tr_frame_selection_clustering.sh">NPEGC.6tr_frame_selection_clustering.sh</a> for full code description.<br><br>Example of six-frame translation using transeq<br><code>transeq -auto -sformat pearson -frame 6 -sequence 6tr/${PREFIX}.Trinity.fasta -outseq 6tr/${PREFIX}.Trinity.6tr.fasta</code><br><br><strong>Frame selection</strong><br>We use a custom frame-selection python script <a href="https://github.com/armbrustlab/marferret/blob/main/scripts/python/keep_longest_frame.py">keep_longest_frame.py</a> to determine the longest coding length in each open reading frame and retain this sequence (or multiple sequences if there is a tie) for downstream analyses. See <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/aa_data/NPEGC.6tr_frame_selection_clustering.sh">NPEGC.6tr_frame_selection_clustering.sh</a> for full code description.<br><br><strong>Clustering by sequence identity</strong><br>To reduce sequence redundancy and near-identical sequences, we cluster protein sequences at the 99% sequence identity level and retain the sequence cluster representative in a reduced-size FASTA output file. See <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/aa_data/NPEGC.6tr_frame_selection_clustering.sh">NPEGC.6tr_frame_selection_clustering.sh</a> for full code description of linclust/mmseqs clustering.<br><br>Sample of linclust clustering script: core mmseqs function<br><code>function NPEGC_linclust {</code><br><code># make an index of the fasta file:</code><br><code>$MMSEQS_DIR/mmseqs createdb $FASTA_PATH/$FASTA_FILE NPac.$STUDY.bf100.db</code><br><code># cluster sequences at $MIN_SEQ_ID</code><br><code>$MMSEQS_DIR/mmseqs linclust NPac.${STUDY}.bf100.db NPac.${STUDY}.clusters.db NPac_tmp --min-seq-id ${MIN_SEQ_ID}</code><br><code># retieve cluster representatives:</code><br><code>$MMSEQS_DIR/mmseqs result2repseq NPac.${STUDY}.bf100.db NPac.${STUDY}.clusters.db NPac.${STUDY}.clusters.rep</code><br><code># generate flat FASTA output with cluster reps</code><br><code>$MMSEQS_DIR/mmseqs result2flat NPac.${STUDY}.bf100.db NPac.${STUDY}.bf100.db NPac.${STUDY}.clusters.rep NPac.${STUDY}.bf100.id99.fasta --use-fasta-header</code><br><code>}</code><br><br>Corresponding files uploaded to this repository: Gzip-compressed FASTA files after translation, frame-selection, and clustering at 99% sequence identity (.bf100.id99.aa.fasta.gz)<br><strong> </strong><em> NPac.G1PA.bf100.id99.aa.fasta.gz</em><br><em> NPac.G2PA.bf100.id99.aa.fasta.gz</em><br><em> NPac.G3PA.bf100.id99.aa.fasta.gz</em><br><em> NPac.G3PA_diel.bf100.id99.aa.fasta.gz</em><br><em> NPac.D1PA.bf100.id99.aa.fasta.gz</em><br><br><strong>MarFERReT + MARMICRODB taxonomic annotation with DIAMOND</strong></p> <p>Taxonomy was inferred for the NPEGC metatranscripts with the DIAMOND fast read alignment software against the <a href="../records/10586950">MarFERReT v1.1 + MARMICRODB v1.0 multi-kingdom marine reference protein sequence library (v1.1)</a>, a combined database of the <a href="https://doi.org/10.1038/s41597-023-02842-4">MarFERReT v1.1 marine microbial eukaryote sequence library</a> and <a href="https://doi.org/10.5281/zenodo.3520509">MARMICRODB v1.0 </a>prokaryote-focused marine genome database. See <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/aa_data/NPEGC.diamond_taxonomy.log.sh">NPEGC.diamond_taxonomy.log.sh</a> for full description of DIAMOND annotation.</p> <p>Excerpt of core DIAMOND function:<br><code>function NPEGC_diamond {</code><br><code># FASTA filename for $STUDY</code><br><code>FASTER_FASTA="NPac.${STUDY}.bf100.id99.aa.fasta"</code><br><code># Output filename for LCA results in lca.tab file:</code><br><code>LCA_TAB="NPac.${STUDY}.MarFERReT_v1.1_MMDB.lca.tab"</code><br><code>echo "Beginning ${STUDY}"</code><br><code>singularity exec --no-home --bind ${DATA_DIR} \</code><br><code> "${CONTAINER_DIR}/diamond.sif" diamond blastp \</code><br><code> -c 4 --threads $N_THREADS \</code><br><code> --db $MFT_MMDB_DMND_DB -e $EVALUE --top 10 -f 102 \</code><br><code> --memory-limit 110 \</code><br><code> --query ${FASTER_FASTA} -o ${LCA_TAB} >> "${STUDY}.MarFERReT_v1.1_MMDB.log" 2>&1</code><br><code>}</code><br><br>Corresponding files uploaded to this repository: Gzip-compressed diamond lowest common ancestor predictions with NCBI Taxonomy against a combined MarFERReT + MARMICRODB taxonomic library (*.Pfam35.domtblout.tab.gz)<br><em> NPac.G1PA.MarFERReT_v1.1_MMDB.lca.tab.gz</em><br><em> NPac.G2PA.MarFERReT_v1.1_MMDB.lca.tab.gz</em><br><em> NPac.G3PA.MarFERReT_v1.1_MMDB.lca.tab.gz</em><br><em> NPac.G3PA_diel.MarFERReT_v1.1_MMDB.lca.tab.gz</em><br><em> NPac.D1PA.MarFERReT_v1.1_MMDB.lca.tab.gz</em><br><br><strong>Pfam 35.0 functional annotation using HMMER3</strong><br>Clustered protein sequences were annotated against the Pfam 35.0 collection of 19,179 protein family Hidden Markov Models (HMMs) using <a href="http://hmmer.org/">HMMER 3.3 </a> with the <a href="https://academic.oup.com/nar/article/49/D1/D412/5943818">Pfam 35.0 protein family database</a>. Pfam annotation code is documented here: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/aa_data/NPEGC.hmmer_function.sh">NPEGC.hmmer_function.sh</a><br><br>Excerpt of core hmmsearch function:<br><br><code>function NPEGC_hmmer {</code><br><code># Define input FASTA</code><br><code>INPUT_FASTA="NPac.${STUDY}.bf100.id99.aa.fasta"</code><br><code># hmmsearch call:</code><br><code>hmmsearch --cut_tc --cpu $NCORES --domtblout $ANNOTATION_DIR/${STUDY}.Pfam35.domtblout.tab $HMM_PROFILE ${INPUT_FASTA}</code><br><code># compress output file:</code><br><code>gzip $ANNOTATION_DIR/${STUDY}.Pfam35.domtblout.tab</code><br><code>}</code><br><br>Corresponding files uploaded to this repository: Gzip-compressed hmmsearch domain table files for Pfam35 queries (*.Pfam35.domtblout.tab.gz)<br><em> G1PA.Pfam35.domtblout.tab.gz</em><br><em> G2PA.Pfam35.domtblout.tab.gz</em><br><em> G3PA.Pfam35.domtblout.tab.gz</em><br><em> G3PA_diel.Pfam35.domtblout.tab.gz</em><br><em> D1PA.Pfam35.domtblout.tab.gz</em><br><br></p> <p><strong>KEGG functional annotation using KofamScan v1.3.0</strong></p> <p>Clustered protein sequences were annotated against the KEGG collection (release 104.0) of 20,819 protein family Hidden Markov Models (HMMs) using <a href="https://github.com/takaram/kofam_scan" target="_blank" rel="noopener">KofamScan </a>and KofamKOALA. Kofam annotation code is documented here: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/aa_data/NPEGC.kofamscan_function.sh">NPEGC.kofamscan_function.sh</a></p> <p>Excerpt of core NPEGC_kofam function:</p> <p><code># Core function to perform KofamScan annotation</code><br><code>function NPEGC_kofam {</code><br><code> # Define input FASTA</code><br><code> local INPUT_FASTA="NPac.${STUDY}.bf100.id99.aa.fasta"</code></p> <p><code> # KofamScan call</code><br><code> ${KOFAM_DIR}/kofam_scan-1.3.0/exec_annotation -f detail-tsv -E ${EVALUE} -o ${ANNOTATION_DIR}/NPac.${STUDY}.bf100.id99.aa.tsv ${FASTA_DIR}/${INPUT_FASTA}</code></p> <p><code> # Keep best hit (data is already sorted by KofamScan)</code><br><code> sort -uk1,1 ${ANNOTATION_DIR}/NPac.${STUDY}.bf100.id99.aa.tsv > ${ANNOTATION_DIR}/NPac.${STUDY}.bf100.id99.aa.best.kofam.tsv</code></p> <p><code> # Compress output file</code><br><code> gzip ${ANNOTATION_DIR}/NPac.${STUDY}.bf100.id99.aa.tsv</code></p> <p><code> # Compress best.kofam output file</code><br><code> gzip ${ANNOTATION_DIR}/NPac.${STUDY}.bf100.id99.aa.best.kofam.tsv</code><br><code>}</code></p> <p><br><code># filter hits with a score > 30 in R</code></p> <p>Corresponding files uploaded to this repository: Gzip-compressed KofamScan domain table files for Kofam queries (*.best.Kofam.incT30.csv.gz):<em><br> NPac.G1PA.bf100.id99.aa.best.Kofam.incT30.csv.gz</em><em><br> NPac.G2PA.bf100.id99.aa.best.Kofam.incT30.csv.gz</em><br><em> NPac.G3PA.UW.bf100.id99.aa.best.Kofam.incT30.csv.gz</em><br><em> NPac.G3PA.diel.bf100.id99.aa.best.kofam.incT30.csv.gz</em><br><em> NPac.D1PA.diel.bf100.id99.aa.best.kofam.incT30.csv.gz<br><br></em>The full kofamscan tables with score >30 are deposited here: <a title="The North Pacific Eukaryotic Gene Catalog: KOfam protein function annotations" href="../records/13743267" target="_blank" rel="noopener">https://zenodo.org/records/13743267</a></p>
The North Pacific Eukaryotic Gene Catalog: KOfam protein function annotations
<p><strong>KEGG functional annotation using KofamScan v1.3.0</strong></p> <p>These tables are larger alternative versions to the KOfam tables included in the North Pacific Eukaryotic Gene Catalog protein data repository here: <a href="../records/12630398">https://zenodo.org/records/12630398</a><br><br>A full description of this data is published in Scientific Data, available here: <a href="https://www.nature.com/articles/s41597-024-04005-5" target="_blank" rel="noopener">The North Pacific Eukaryotic Gene Catalog of metatranscriptome assemblies and annotations</a>. Please cite this publication if your research uses this data:<br><br>Groussman, R. D., Coesel, S. N., Durham, B. P., Schatz, M. J., & Armbrust, E. V. (2024). The North Pacific Eukaryotic Gene Catalog of metatranscriptome assemblies and annotations. <em>Scientific Data</em>, <em>11</em>(1), 1161.<br><br>Clustered protein sequences were annotated against the KEGG collection (release 104.0) of 20,819 protein family Hidden Markov Models (HMMs) using <a href="https://github.com/takaram/kofam_scan" target="_blank" rel="noopener">KofamScan </a>and KofamKOALA. Kofam annotation code is documented in the project github repository here: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/aa_data/NPEGC.kofamscan_function.sh">NPEGC.kofamscan_function.sh</a></p> <p>Excerpt of core NPEGC_kofam function:</p> <p><code># Define input FASTA</code><br><code>local INPUT_FASTA="NPac.${STUDY}.bf100.id99.aa.fasta"</code></p> <p><code># KofamScan call</code><br><code>${KOFAM_DIR}/kofam_scan-1.3.0/exec_annotation -f detail-tsv -E ${EVALUE} -o ${ANNOTATION_DIR}/NPac.${STUDY}.bf100.id99.aa.tsv ${FASTA_DIR}/${INPUT_FASTA}</code></p> <p>Unprocessed annotation results were filtered with a minimum score of 30 to remove low-scoring matches:<br><br><code>zcat NPac.<em><u>NPacID</u></em>.kofam.tsv.gz | awk -F'\t' '{ gsub(/"/, "", $5); $5 = $5 + 0; if ($5 >= 30) print }' | gzip > NPac.<em><u>NPacID</u></em>.UW.bf100.id99.aa.incT30.tsv.gz</code></p>
ProtNote: a multimodal method for protein-function annotation
<div> <div>Understanding protein sequence-function relationships is essential for advancing protein biology and engineering. However, fewer than 1% of known protein sequences have human-verified functions, and scientists continually update the set of possible functions. While deep learning methods have demonstrated promise for protein function prediction, current models are limited to predicting only those functions on which they were trained. Here, we introduce ProtNote, a multimodal deep learning model that leverages free-form text to enable both supervised and zero-shot protein function prediction. ProtNote not only maintains near state-of-the-art performance for annotations in its train set, but also generalizes to unseen and novel functions in zero-shot test settings. We envision that ProtNote will enhance protein function discovery by enabling scientists to use free text inputs, without restriction to predefined labels – a necessary capability for navigating the dynamic landscape of protein biology.</div> </div>
Annotation-based Modeling of Non-functional Requirements and Analysis Results in Domain-driven Design
<p>This repo contains all supplementary data sets that we have created and used throughout this thesis. In particular, it contains<br> - expert interview material (elicitation): consent form and question catalogue for requirements elicitation<br> - expert interview material (evaluation): consent form and task description for expert evaluation<br> - Diagrams related to our modeling concept and Dqualizer<br> - Screenshots of the Domain Story Modeler with our Modeling Concept</p>
Functional Annotations of Bacillus stercoris BHUJPV-SS7
<p>This dataset provides comprehensive genome annotations of Bacillus stercoris BHUJPV-SS7, a cellulose-degrading bacterium with potential applications in biomass bioconversion and biotechnology. The strain was isolated from the rhizosphere soil of a mango tree at the experimental field of Banaras Hindu University, India.</p> <p>The genome annotations included in this dataset were generated using three different tools: Prokka (Prokaryotic Genome Annotation), RAST (Rapid Annotations using Subsystems Technology), and CAZy (Carbohydrate-Active Enzymes database). These annotations provide insights into the functional potential of Bacillus stercoris BHUJPV-SS7, including the presence of genes involved in cellulose degradation, lignin degradation, and other carbon degradation processes.</p> <p>This dataset aims to facilitate further research on the biotechnological potential of Bacillus stercoris BHUJPV-SS7 in areas such as biofuel production, waste management, and sustainable development.</p>
FAPM: Functional annotation of proteins using multi-modal models beyond structural modeling
Open the record for dataset details and reuse information.
Updated functional annotation of the Mycobacterium bovis AF2122/97 reference genome - datasets
<p>This is an archive of the repository held under https://github.com/dmnfarrell/gordon-group/tree/master/mbovis_annotation</p> <p>It contains a notebook and required input files for updating the MTB/Mbovis AF2122/97 genomes with new protein product annotations from literature.</p> <p>D Farrell UCD February 2020</p> <p>Updates to M.bovis genome (Aug 2019):<br> added 611 product annotations<br> added 689 gene names<br> added 5 manually edited entries from pdb hits<br> removed locus_tags for repeat_region features<br> added prefix to tRNA and mobile_element features for consistency</p> <p><br> References<br> Updated functional annotation of the Mycobacterium bovis AF2122/97 reference genome (https://www.biorxiv.org/content/10.1101/757823v1)</p>
DeepAnnotation: A novel interpretable deep learning-based genomic selection model that integrates comprehensive functional annotations
<p>1. Update package, example dataset, and demo code of DeepAnnotation</p> <p>2. Update the transformed genotype data, the phenotype data, the comprehensive functional annotation data for Duroc prepared by RNAfold, DeepSEA, easyMF models, and the four types of input data for training DeepAnnotation model</p> <p>3. Add the conserved functional annotation</p> <p> </p>
Supplementary datasets for eRNA community and Functional annotations
<p>Changes in land use and agricultural intensification threaten biodiversity and ecosystem functioning of small water bodies. We studied 67 kettle holes (KH) in an agricultural landscape in northeastern Germany using landscape-scale metatranscriptomics, to understand the responses of active bacterial, archaeal, and eukaryotic communities, to land-use type. These KH are proxies of the millions of small standing water bodies of glacial origin spread across the northern hemisphere. Like other landscapes in Europe, the study area has been used for intensive agriculture since the 1950s. In contrast to a parallel eDNA study which revealed the homogenization of biodiversity across KH conceivably resulting from long-lasting intensive agriculture, land-use type affected the structure of the active KH communities during spring crop fertilization, but not a month later. This effect was more pronounced in eukaryotes than in bacteria. In contrast, gene expression patterns did not differ between months or across land-use type, suggesting a high degree of functional redundancy across the KH communities. Variability in gene expression was best explained by active bacterial and eukaryotic community structures, suggesting that these changes in functioning are primarily driven by interactions between organisms. Our results show that influences of the surrounding landscape result in temporary changes in the activity of different community members. Thus, even in KH where biodiversity has been homogenized, communities continue to respond to land management. This needs to be considered when developing sustainable management options for restoration purposes and for successful mitigation of further biodiversity loss in agricultural landscapes.</p>
Data accompanying MetaChrom and "Annotating functional effects of non-coding variants in neuropsychiatric cell types by Deep Transfer Learning"
<p>This is the data accompanying the paper " Annotating functional effects of non-coding variants in neuropsychiatric cell types by Deep Transfer Learning" and the GitHub repository https://github.com/bl-2633/MetaChrom. </p> <p><strong>/data/bed_files/ </strong>contains the unprocessed bed file used in analysis</p> <p><strong>/data/seq_data/</strong> contains processed data from the bed files with corresponding partition and labels for each sequence segment.</p> <p><strong>/trained_model/MetaChrom_model/</strong> contains the pre-trained MetaChrom model on neural developmental context</p> <p><strong>/trained_models/MetaFeat_model/ </strong>contains the MetaFeat model used in training</p> <p><strong>/tool/</strong> contains files and software necessary for the processing pipeline.</p>
Haematococcus lacustris genome assembly and functional annotation
<p>Nuclear, chloroplast and mitochondrial genome assembly and functional annotation of Haematococcus lacustris (formely Haematococcus pluvialis) K-0084</p>
FURNA: a database of functional annotations of RNA structures (part 2)
<p>A copy of the FURNA database curated on June 9, 2024 (part 2). Download both part 1 (10.5281/zenodo.11664059) and part 2 (10.5281/zenodo.11672037) to combine them by:</p> <p>$ cat xaa xab > furna.tar.bz2</p> <p>$ rm xaa xab</p> <p>$ tar -xvf furna.tar.bz2</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.