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220 results for “functional sequencing”
Whole genome sequencing of Turkish genomes reveals functional private alleles and impact of genetic interactions with Europe, Asia and Africa.
<p>BACKGROUND:</p> <p>Turkey is a crossroads of major population movements throughout history and has been a hotspot of cultural interactions. Several studies have investigated the complex population history of Turkey through a limited set of genetic markers. However, to date, there have been no studies to assess the genetic variation at the whole genome level using whole genome sequencing. Here, we present whole genome sequences of 16 Turkish individuals resequenced at high coverage (32×-48×).</p> <p>RESULTS:</p> <p>We show that the genetic variation of the contemporary Turkish population clusters with South European populations, as expected, but also shows signatures of relatively recent contribution from ancestral East Asian populations. In addition, we document a significant enrichment of non-synonymous private alleles, consistent with recent observations in European populations. A number of variants associated with skin color and total cholesterol levels show frequency differentiation between the Turkish populations and European populations. Furthermore, we have analyzed the 17q21.31 inversion polymorphism region (MAPT locus) and found increased allele frequency of 31.25% for H1/H2 inversion polymorphism when compared to European populations that show about 25% of allele frequency.</p> <p>CONCLUSION:</p> <p>This study provides the first map of common genetic variation from 16 western Asian individuals and thus helps fill an important geographical gap in analyzing natural human variation and human migration. Our data will help develop population-specific experimental designs for studies investigating disease associations and demographic history in Turkey.</p>
IBP-database and environmental IBP sequences for functional analysis of microalgae
<p>Database for analysis of ice binding protein (IBP) sequences (Uhlig et al. (2015)):</p> <p>(1) DUF3494_seqs_Uniprot.fasta: full length sequences with DUF3494 domain used for the calculation of the backbone tree in the phylogenetic placement</p> <p>(2) env_IBPs.fasta: potential IBP sequences from one Arctic and five Antarctic sea ice metatranscriptomes (Sanger or 454)</p> <p>(3) DUF3494_substree_fig2a_UniprotIDs.txt: UniProtIDs for subtree in Fig 2a</p> <p>(4) DUF3494_confirmed_IBPactivity_UniprotIDs.txt: UniProtIDs for sequences with confirmed IBP function of the protein</p> <p>If using this dataset please cite the following publication: Uhlig, C., Kilpert, F., Frickenhaus, S., Kegel, J.U., Krell, A., Mock, T., Valentin, K., Beszteri, B., (2015) The significance of antifreeze proteins for eukaryotic microbial communities of Arctic and Antarctic sea ice, The ISME Journal, 9, 2537–2540, doi:10.1038/ismej.2015.43</p>
Supplementary Data Files for the paper "Intrinsically disordered compositional bias in proteins: Sequence traits, region clustering, and generation of hypothetical functional associations"
<div> <div> <div> <div> <p><strong>Supplementary data files relating to <a href="https://doi.org/10.1177/11779322241287485">https://doi.org/10.1177/11779322241287485. </a></strong></p> <p><strong><span>Suppl. File 1: Protein Family Clusters.</span></strong></p> <p><strong><span>Suppl. File 2: Cluster GO enrichments/depletions. </span></strong></p> <p><strong><span>Suppl. File 3: The raw ID-CBR data with annotations. </span></strong></p> <p><strong><span>Suppl. File 4: ­ID-CBR Cluster membership.</span></strong></p> <p><strong><span>Each file has an explanatory header. </span></strong></p> <p> </p> </div> </div> </div> </div>
Supporting data for Novel functional sequences uncovered through a bovine multi-assembly graph
<p><strong>Description of the datasets</strong></p> <p>Data are organized as a folder and compressed with tar.gz.</p> <p>You need to unzip the folder using the command <em>tar -xz</em><em>v</em><em>f</em> data.tar.gz. Unzipping will output a folder named <em>data_tidy</em>, which is organized as follow:</p> <ul> <li>graph.gfa : Graph in GFA format constructed from 6 cattle assemblies</li> <li>nonref.fa : Non-reference sequences extracted from the graph</li> <li>nonref.fa.masked: Hard masked repetitive regions version of nonref.fa</li> <li>nonref_woflanking.fa: Nonref.fa without flanking sequences</li> <li>nonref_woflanking.fa.masked: Masked version of nonref_woflanking.fa</li> <li>augustus_predict.gtf: Annotated gene models of Augustus from non-ref sequences</li> <li>augustus_prot.fa: Protein fasta of the predicted gene models from Augustus</li> <li>breeds_assembled.gtf: Annotation of the StringTie assembled across-breed transcriptome</li> <li>breeds_expressed.tsv: Expression data of breeds_assembled.gtf</li> <li>de_assembled.gtf: Annotation of the StringTie assembled differentially-expressed transcriptome on non-ref sequences</li> <li>de_expression.tsv: Differential expression results from de_assembled.gtf</li> <li>variant_nonref.tsv: Variants called from non-ref sequences (-1, 0, 1, 2 indicates no call, hom ref, het, and hom alt respectively)</li> </ul>
common function paralog pairs and sequence similarity features
<p>This repository contains datasets of selected paralog pairs (Ensembl 111), labeled with various "common function" annotations, including PPI, SL, and GO datasets for both human (<em>Homo sapiens</em>) and budding yeast (<em>Saccharomyces cerevisiae</em>). These paralog pairs are characterized using different sequence similarity features, such as AlphaFold-predicted structures, Protein Language Model embeddings, and similarity searches from various databases.</p> <p> </p> <p>These datasets are used in the following manuscript: <a href="https://www.biorxiv.org/content/10.1101/2024.10.11.617835v1">Evaluating Sequence and Structural Similarity Metrics for Predicting Shared Paralog Functions</a></p> <p> </p> <p>For the corresponding analysis notebooks, see: <a href="https://github.com/cancergenetics/paralog_seq_similarity/tree/main">github.com/cancergenetics/paralog_seq_similarity/</a></p>
Sequence-structure-function relationships in the microbial protein universe
<p>The Microbiome Immunity Project (MIP) dataset contains models predicted with both Rosetta and DMPFold (folder `dataset/`). It also contains DeepFRI function predictions for all models. </p> <p>The `metadata` folder contains additional data which may be useful for searching the MIP database (FASTA files, BLAST databases and useful scripts for structure/function search) as well as retrieving the sequence/structural annotations.</p> <p>The `intermediate_data` folder contains preprocessed output for reproducing many of the figures in our manuscript in conjunction with scripts and Juypter notebooks found in our git repository: https://github.com/microbiome-immunity-project/protein_universe .</p> <p>More information about the dataset and associated metadata is provided in the `README.md` file).</p> <p>We are also providing workflows to search the MIP database against a protein sequence or structure or function of interest (see `SEARCHING.md` for more details).</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>
Data from: Latent generative landscapes as maps of functional diversity in protein sequence space
<p>Variational autoencoders are unsupervised learning models with generative capabilities, when applied to protein data, they classify sequences by phylogeny and generate de novo sequences which preserve statistical properties of protein composition. While previous studies focus on clustering and generative features, here, we evaluate the underlying latent manifold in which sequence information is embedded. To investigate properties of the latent manifold, we utilize direct coupling analysis and a Potts Hamiltonian model to construct a latent generative landscape. We showcase how this landscape captures phylogenetic groupings, functional and fitness properties of several systems including Globins, β-lactamases, ion channels, and transcription factors. We provide support on how the landscape helps us understand the effects of sequence variability observed in experimental data and provides insights on directed and natural protein evolution. We propose that combining generative properties and functional predictive power of variational autoencoders and coevolutionary analysis could be beneficial in applications for protein engineering and design.</p>
Genome sequencing of 2,000 canids advances the understanding of demography, genome function and architecture
<p><strong>Background: </strong>The international Dog10K project aims to sequence and analyze several thousand canine genomes. Incorporating 20x data from 1,987 individuals, including 1,611 dogs (321 breeds), 309 village dogs, 63 wolves and four coyotes, we identify genomic variation across the canid family, setting the stage for detailed studies of domestication, behavior, morphology, disease susceptibility and genome architecture and function.</p> <p><strong>Results: </strong>We report the analysis of >48M single nucleotide, indel, and structural variants spanning the autosomes, X chromosome and mitochondria. We discover more than 75% of variation for 239 sampled breeds. Allele sharing analysis indicates that 94.9% of breeds form monophyletic clusters and 25 major clades. German Shepherd Dogs and related breeds show the highest allele sharing with independent breeds from multiple clades. On average, each breed dog differs from the UU_Cfam_GSD_1.0 reference at 26,960 deletions and 14,034 insertions greater than 50bp, with wolves having 14% more variants. Discovered variants include retrogene insertions from 926 parent genes. To aid functional prioritization, single nucleotide variants were annotated with SnpEff and Zoonomia phyloP constraint scores. Constrained positions were negatively correlated with allele frequency. Finally, the utility of the Dog10K data as an imputation reference panel is assessed, generating high confidence calls across varied genotyping platform densities including for breeds not included in the Dog10K collection.</p> <p><strong>Conclusions:</strong> We have developed a dense dataset of 1,987 sequenced canids that reveals patterns of allele sharing, identifies likely functional variants, informs breed structure, and enables accurate imputation. Dog10K data are publicly available</p>
FIG. 2 in Structural and functional genes, and highly repetitive sequences commonly used in the phylogeny and species concept of the phylum Cyanobacteria
FIG. 2. — Phylogeny of common or less studied genetic markers. According to the literature review,less common studied genetic marker has been highlighted.
FIG. 1. — A in Structural and functional genes, and highly repetitive sequences commonly used in the phylogeny and species concept of the phylum Cyanobacteria
FIG. 1. — A summary of structural and functional genes, and highly repetitive sequences commonly used in the phylogeny of cyanobacteria.
Data from: Latent generative landscapes as maps of functional diversity in protein sequence space
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Sequence-structure-function relationships in class I MHC: a local frustration perspective
<p>Class I Major Histocompatibility Complex (MHC) binds short antigenic peptides with the help of Peptide Loading Complex (PLC), and presents them to T-cell Receptors (TCRs) of cytotoxic T-cells and Killer-cell Immunglobulin-like Receptors (KIRs) of Natural Killer (NK) cells. With more than 10000 alleles, the Human Leukocyte Antigen (HLA) chain of MHC is the most polymorphic protein in humans. This allelic diversity provides a wide coverage of peptide sequence space, yet does not affect the three-dimensional structure of the complex. Moreover, TCRs mostly interact with pMHC in a common diagonal binding mode, and KIR-pMHC interaction is allele-dependent. With the aim of establishing a framework for understanding the relationships between polymorphism (sequence), structure (conserved fold) and function (protein interactions) of the MHC, we performed here a local frustration analysis on pMHC homology models covering 1436 HLA I alleles. An analysis of local frustration profiles indicated that (1) variations in MHC fold are unlikely due to minimally-frustrated and relatively conserved residues within the HLA peptide-binding groove, (2) high frustration patches on HLA helices are either involved in or near interaction sites of MHC with the TCR, KIR, or Tapasin of the PLC, and (3) peptide ligands mainly stabilize the F-pocket of HLA binding groove.</p>
MUFFIN : A suite of tools for the analysis of functional sequencing data - Example input data
<p>This repository contains the data required to run the example notebooks and to reproduce the figures from the paper : </p> <div> <div><strong>MUFFIN : A suite of tools for the analysis of functional sequencing data</strong></div> </div> <div><em>Pierre de Langen, Benoit Ballester</em></div> <div>bioRxiv 2023.12.11.570597; doi: <a href="https://doi.org/10.1101/2023.12.11.570597" target="_blank" rel="noopener">https://doi.org/10.1101/2023.12.11.570597</a></div> <div> </div> <div>Source code is located here :</div> <div><a href="https://github.com/pdelangen/Muffin" target="_blank" rel="noopener">https://github.com/pdelangen/Muffin</a></div> <div> </div> <ul> <li><strong>10k_pbmc_gene/ </strong>contains the data for 10k pbmc dataset in standard 10x sparse count table format.</li> <li><strong>genome_annot/</strong> contains gencode v38 and chromosomes for the human (used for gene set enrichment analyses)</li> <li><strong>GO_files/</strong> contains gene set information retrieved from the g:ProfileR website.</li> <li><strong>immune_chip/ </strong>contains the data required to re-run the ChIP-seq analyses, it will require to also launch the dl_data.smk to retrieve the data from ENCODE.</li> <li><strong>tcga_atac/</strong> contains the sample-genomic region ATAC tag count table, as well as the sample metadata and a gene set file of cancer hallmark genes.</li> <li><strong>scATAC/</strong> contains the cell barcode-genomic region ATAC tag count table, as well as the barcode metadata and the 10k pbmc dataset pre-analyzed in h5 AnnData format.</li> </ul> <div> </div>
Shallow shotgun sequencing of the microbiome recapitulates 16S amplicon results and provides functional insights
<p>Prevailing 16S rRNA gene-amplicon methods for characterizing the bacterial microbiome of wildlife are economical, but result in coarse taxonomic classifications, are subject to primer and 16S copy number biases, and do not allow for direct estimation of microbiome functional potential. While deep shotgun metagenomic sequencing can overcome many of these limitations, it is prohibitively expensive for large sample sets. We evaluated the ability of shallow shotgun metagenomic sequencing to characterize taxonomic and functional patterns in the fecal microbiome of a model population of feral horses (Sable Island, Canada). Since 2007, this unmanaged population has been the subject of an individual-based, long-term ecological study. Using deep shotgun metagenomic sequencing, we determined the sequencing depth required to accurately characterize the horse microbiome. In comparing conventional versus high-throughput shotgun metagenomic library preparation techniques, we validate the use of more cost-effective lab methods. Finally, we characterize similarities between 16S amplicon and shallow shotgun characterization of the microbiome and demonstrate that the latter recapitulates biological patterns first described in a published amplicon dataset. Unlike amplicon data, we further demonstrate how shallow shotgun metagenomic data provide useful insights about microbiome functional potential which support previously hypothesized diet effects in this study system.</p>
Figure 3. Describing the sequence of input sensations sent by the system.-Designing a Growing Functional Modules "Artificial Brain"
<p>To design the controller, first of all, the user should click the button “new” on the toolbar to<br> create an empty canvas and to assign it a controller's name, presently “pathfinder”. Then, the first<br> convenient step is to describe the feedback given by the system as a sequence of sensations (see<br> figure 3). The first sensation named “free”, indicates if the vehicle has been able or not to cross the<br> range of obstacles. Each of the next sixteen values is associated to its corresponding proximity<br> sensors that points out the presence of an obstacle. They are given a name beginning with “s”<br> followed by a number. The red color of the last sensation's field indicates that the given name is<br> invalid; presently, because it contains a blank character.</p>
The stability of massive main-sequence stars as a function of metallicity
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2012MNRAS.423.3397S">The stability of massive main-sequence stars as a function of metallicity</a></p>
Input Data for "Protein Function Prediction for newly sequenced organisms"
<p>The input sequence files in FASTA format and the detailed list of all organisms excluded when testing each specific bacterium.</p>
Dataset - Functional annotation for protein sequences
<p>These are the input and output files for the functional annotation of protein sequences workflow tests.</p>
Fromsight to sequence: Underwater visual census vs eDNA metabarcoding for the monitoring of taxonomic and functional fish diversity
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.