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832 results for “genomics and transcriptomics”
The evolution of genomic, transcriptomic, and single-cell protein markers of metastatic upper tract urothelial carcinoma
<p>The molecular characteristics of metastatic upper tract urothelial carcinoma (UTUC) are unknown. The genomic and transcriptomic differences between primary and metastatic UTUC is not well described either. We combined whole-exome sequencing, RNA-sequencing, and Imaging Mass Cytometry<sup>TM</sup> (IMC<sup>TM</sup>) of 44 tumor samples from 28 patients with high-grade primary and metastatic UTUC. IMC enables spatially resolved single-cell analyses to examine the evolution of cancer cell, immune cell, and stromal cell markers using mass cytometry with lanthanide metal-conjugated antibodies. We discovered that actionable genomic alterations are frequently discordant between primary and metastatic UTUC tumors in the same patient. In contrast, molecular subtype membership and immune depletion signature were stable across primary and matched metastatic UTUC. Molecular and immune subtypes were consistent between bulk RNA-sequencing and mass cytometry of protein markers from 340,798 single-cells. Molecular subtyping at the single cell level was highly conserved between primary and metastatic UTUC tumors within the same patient.</p>
Genome- and transcriptome-wide association summary statistics for outcome from traumatic brain injury
<p>The dataset contains summary statistics for the genome- and transcriptome-wide association studies (GWAS, TWAS) of genetic effects on outcome in traumatic brain injury (TBI). The study participants attended hospital within 24 hours of TBI, and underwent head computed tomography imaging.</p> <p><strong>Study participants</strong></p> <p>European ancestry data set contains 4710 individuals; multi-ethnic cohort 5268 individuals, including Europeans (n = 4710), Africans (n = 245) and Admixed Americans (n = 313).</p> <p>The largest European population contribution was from CENTER-TBI (Collaborative European NeuroTrauma Effectiveness Research, https://www.center-tbi.eu), where each participating center (60 centers from 20 countries in Europe) recruited patients between December 2013 and December 2017. The patients recruited in CENTER-TBI were supplemented by subjects from cohorts recruited at two European centres (Cambridge, UK, and Turku, Finland).</p> <p>The majority of patients in the US cohort were recruited between 2014 and 2018 to TRACK-TBI (Transforming Research and Clinical Knowledge in TBI, https://tracktbi.ucsf.edu) by the 18 US participant sites. The subjects recruited to the US cohort from TRACK-TBI were supplemented by patients recruited to an institutional research initiative at Mass General Brigham (MGB).</p> <p><strong>Outcome definition</strong></p> <p>Outcomes were measured using the extended Glasgow Outcome Scale (GOSE), ranging from 1 (dead) to 8 (upper good recovery), measured 6 months post-TBI. TBI severity was specified using the Glasgow Coma Score (GCS), with TBI classified as mild (GCS 13-15), moderate (GCS 9-12), or severe (GCS 3-8).</p> <p>To account for the effect of injury severity on outcome, sliding dichotomization was used to categorize outcome as favourable or unfavourable. A GOSE ≤ 4 was used to define an unfavourable outcome for patients with either moderate (GCS 9-12) or severe (GCS 3-8) TBI, while the unfavourable group was extended to patients with GOSE ≤ 7 if they had mild (GCS 13-15) TBI.</p> <p><strong>Genotype data and imputation</strong></p> <p>Genotyping was completed at FIMM Technology Center for CENTER-TBI, Cambridge, Turku patients and the Broad Institute for TRACK-TBI, using the Illumina Global Screening Array (GSA-24v2-0 + Multi-Disease). The MGB cohort were genotyped using Illumina’s Multi-Ethnic Global array (MEGA) and the pre-releases forms, including MEGA and MEGA-Ex arrays at Illumina at the MGB Translational Genomics Core.</p> <p>A unified quality control procedure was applied for each study cohort and the array-based genotypes were imputed using the Haplotype Reference Consortium panel. Autosomal chromosomes were considered, post-imputation data was filtered by imputation quality (INFO > 0.4 for CENTER-TBI, Cambridge and Turku; R2 > 0.4 for TRACK-TBI and MGB) and MAF > 1%.</p> <p><strong>Genome-wide association analysis and meta-analysis</strong></p> <p>Genome-wide single-marker scans were performed using a penalized likelihood-based Firth logistic regression, and implemented in PLINK v2.0. Using favourable outcome as reference, models were fitted on the basis of imputed allelic dosages. Age, sex, major extracranial injury, pupillary reactivity, and the first 10 principal components were included as covariates. Study cohort (CENTER-TBI, Cambridge, Turku) was an additional covariate in the CENTER-TBI GWAS.</p> <p>Fixed-effects meta-analysis of the three European ancestry GWAS was performed using METAL. For trans-ethnic meta-analysis, summary statistics of five GWASs in patients of European, African and Admixed Americans were aggregated via MR-MEGA.</p> <p><strong>Transcriptome-wide association study</strong></p> <p>Genetically regulated gene expression (GREx) was imputed using a regression model fitted on a separate gene expression database. Elastic net models provided by PrediXcan for all available GTEx brain tissues and whole blood were used. For TWAS, the same sliding dichotomy model for outcome with the same set of covariates as in the GWAS, but PCA components were replaced with the top five principal components of the respective gene expression data. </p> <p><strong>Column headers - GWAS</strong></p> <p>rsID: variant rsID<br> Chrom: chromosome<br> Pos: position (build GRCh38)<br> A1: effect allele<br> A2: reference allele<br> EAF: allele frequency of effect allele<br> Effect: effect size of effect allele<br> StdErr: standard error of effect size<br> P: p value of association (with genomic correction)<br> N: sample size</p> <p>Note. 'Effect' and 'StdErr' are only available for the European ancestry meta-analysis.</p> <p><br> <strong>Column headers - TWAS</strong></p> <p>tissue: GTEx tissue type<br> id: ensembl gene id<br> coef: model coefficient<br> se: model standard error for coefficient<br> p: model-based p value<br> symbol: gene symbol<br> name: gene name written out<br> chr: chromosome<br> start: gene start position (build GRCh38)</p>
Applications and raw data for SciPipe genomics and transcriptomics case studies
<p>Accompanying applications and raw data for the genomics and transcriptomics (RNA-Seq) case studies for SciPipe [1] available at https://github.com/pharmbio/scipipe-demo </p> <p>[1] http://scipipe.org</p>
Genome alignments for the project "Whole transcriptome analysis of thousands of FACS-sorted single cells with the single cell nanoCAGE protocol" - Protocol optimization
<p>Genome alignments for data generated in the project "<em>Whole transcriptome analysis of thousands of FACS-sorted single cells with the single cell nanoCAGE protocol – Optimization of the protocol.</em>" Files names indicate unique identifiers of MOIRAI workflow runs, with the following structure: library name, dot, workflow ID (OP-WORKFLOW-CAGEscan-short-reads-v2.0.), dot, timestamp. The raw (FASTQ) data of each library is also deposited in Zenodo (<a href="https://doi.org/10.5281/zenodo.250156">10.5281/zenodo.250156</a>). Library names correspond to the following runs:</p> <ul> <li> NC33: 151007_M00528_0161_000000000-AEBDC</li> <li> NC37: 151204_M00528_0173_000000000-AEBEF</li> <li> NC38: 151211_M00528_0175_000000000-AE9PJ</li> <li> NC39: 160122_M00528_0185_000000000-AEB18</li> <li> NC42: 160302_M00528_0192_000000000-AELYK</li> </ul> <p>This data can be analysed using the "CAGEr" software package available from Bioconductor. The "multiplex_files.zip" file contains tables indicating which samples are biological replicates of each other or negative controls.</p>
GWAS to single cell: Intersecting single-cell transcriptomics and genome wide association studies identifies crucial cell-populations and candidate genes for atherosclerosis.
<p><strong>Background</strong></p> <p>Genome-wide association studies (GWAS) have discovered hundreds of common genetic variants for atherosclerotic disease and cardiovascular risk factors. The translation of susceptibility loci into biological mechanisms and targets for drug discovery remains challenging. Intersecting genetic and gene expression data has led to identification of candidate genes. However, the assayed tissues are often non-diseased and heterogeneous in cell composition confounding the candidate prioritization. We collected single-cell transcriptomics (scRNA-seq) from atherosclerotic plaques and aimed to identify cell-type-specific expression of disease-associated genes. </p> <p> </p> <p><strong>Methods and Results</strong></p> <p>To identify disease-associated candidate genes, we applied gene-based analyses using GWAS summary statistics from 46 atherosclerotic, cardiometabolic, and other traits. Next we intersected these candidates with single-cell transcriptomics (scRNA-seq) to identify those genes that are specifically expressed in individual cell (sub)populations of atherosclerotic plaques. We derive an enrichment score and show that loci that associated with coronary artery disease demonstrated a prominent substrate in plaque smooth muscle cells (<em>SKI</em>, <em>KANK2</em>, <em>SORT1</em>), endothelial cells (<em>SLC44A1</em>, <em>ATP2B1</em>), and macrophages (<em>APOE</em>, <em>HNRNPUL1</em>). Further sub clustering of SMC-subtypes revealed genes in risk loci for coronary calcification specifically enriched in a synthetic cluster of SMCs. To verify the robustness of our approach, we used liver-derived scRNAseq-data and showed enrichment of circulating lipids-associated loci in hepatocytes.</p> <p><br> <strong>Conclusion</strong></p> <p>We confirm known gene-cell pairs relevant for atherosclerotic disease, and discovered novel pairs pointing to new biological mechanisms amenable for therapy. We present an intuitive single-cell transcriptomics driven workflow rooted in human large-scale genetic studies to identify putative candidate genes and affected cells associated with cardiovascular traits.</p> <p> </p>
Genomic and transcriptomic data for the frog Platyplectrum ornatum
<p>The diversity of genome sizes across the tree of life is of key interest in evolutionary biology. Various correlates of variation in genome size, such as accumulation of transposable elements or rate of DNA gain and loss, are well known, but the underlying molecular mechanisms that drive or constrain genome size are poorly understood. Here we study one of the smallest genomes among frogs characterized thus far, that of the ornate burrowing frog<b> (</b><i>Platyplectrum ornatum</i>) from Australia, and compare it to other published frog and vertebrate genomes to examine the forces driving reduction in genome size. At ~1.06 Gb, the <i>P. ornatum </i>genome is like that of birds, revealing four major mechanisms underlying TE dynamics: reduced abundance of all major classes of transposable elements (TEs); increased net deletion bias in TEs; drastic reduction in the lengths of introns; and expansion via gene duplication of the repertoire of TE-suppressing Piwi genes, accompanied by increased expression of piRNA-based TE-silencing pathway genes in germline cells. Transcriptome data from multiple tissues in both sexes corroborate these results and provide insight into sex-differentiation pathways in <i>Platyplectrum</i>. Genome skimming of two closely related frog species (<i>Lechriodus fletcheri </i>and <i>Limnodynastes fletcheri</i>) confirms a reduction in TEs as a major driver of genome reduction in <i>Platyplectrum</i> and supports a macroevolutionary scenario of small genome size in frogs driven by convergence in life history, especially rapid tadpole development and tadpole diet. The <i>P. ornatum</i> genome offers a model for future comparative studies on mechanisms of genome size reduction in amphibians and in vertebrates generally.</p>
Raw data used for COI delineation of the Eupolybothrus species: Authors: Stoev et al. 2013 Data type: genomic The archive contains the following data: 1) fasta-Alignment as the basis for all analyses (.FASTA), 2) mega-file for the calculation of the genetic distances and the NJ tree (.MDSX), 3) NJ-tree in Newick format (.NWK), 4) graph of the TCS Software for the Statistical Parsimony method (.GRAPH) File: E_cavernicolus.rar from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013
<p>Authors: Stoev et al. 2013 Data type: genomic The archive contains the following data: 1) fasta-Alignment as the basis for all analyses (.FASTA), 2) mega-file for the calculation of the genetic distances and the NJ tree (.MDSX), 3) NJ-tree in Newick format (.NWK), 4) graph of the TCS Software for the Statistical Parsimony method (.GRAPH) File: E_cavernicolus.rar</p>
Kellet's whelk genome and transcriptome assembly
<p>Understanding genomic characteristics of non-model organisms can help bridge gaps in ecology and evolutionary sciences, but lack of a reference genome and transcriptome for these species challenges their study. We advance this goal by conducting the first full genome and transcriptome sequence assembly and analysis of the non-model organism Kellet's whelk, <em>Kelletia kelletii</em>, a marine gastropod and fisheries species exhibiting a northern range expansion along the US west coast that is potentially driven by climate change. We used a combination of Oxford Nanopore Technologies, PacBio, and Illumina platforms for sequencing, and integrated a set of bioinformatic pipelines to create a comprehensive and contiguous de novo genome assembly. Our results represent the most complete and continuous documented genome among the <em>Buccinoidea</em> superfamily to date. Genome validation revealed its relatively high completeness with low missing metazoan BUSCOs, and an average coverage of ~70x for all contigs, indicating a robust assembly. Characteristics of the <em>K. kelletii</em> genome showed that short-read data contributed significantly to genome coverage and accuracy; however, long-read data was imperative to the completeness and continuity of the genome assembly. Genome annotation identified a large number of protein-coding genes compared to other closely related species, suggesting the presence of a complex genome structure. We conducted the transcriptome assembly and analysis of individuals during their period of peak embryonic development, and revealed highly expressed genes associated with specific GO terms and metabolic pathways, most notably lipid, carbohydrate, glycan, and phospholipid metabolism. We also identified numerous heat shock proteins (HSPs) in the transcriptome and genome with a potential association between the transcriptional expansion of HSP families and the marine environment experienced by the sessile life history stage of the developing embryo. This study offers a valuable reference genome and transcriptome for conducting comprehensive bioinformatic analyses of the non-model organism <em>K. kelletii</em>. Such resources will enhance our understanding of its ecology and evolution, as well as that of other coastal marine species facing environmental changes.</p>
Corallorhiza maculata genomic and transcriptomic analysis
<p>Novoplasty assemblies of plastid genomes from two different Corallorhiza maculata plants (Circularized_assembly_1_CM_1A.fasta and Circularized_assembly_1_CM_2A.fasta).</p> <p>Spades assembly of total cellular genomic DNA from one Corallorhiza maculata plant (scaffolds.fasta.gz). This assembly includes scaffolds of mitochondrial origin (as well as plastid and nuclear).</p> <p>Trinity assembly of rRNA-depleted RNA-seq reads from one Corallorhiza maculata plant (trinity_out_dir.Trinity.fasta.gz).</p>
Figure 3. Designing a in Omics in Weed Science: A Perspective from Genomics, Transcriptomics, and Metabolomics Approaches
Figure 3. Designing a metabolomics study. (A) The various approaches for performing a metabolomics experimental study. GC-MS, gas chromatography–mass spectrometry; HILIC-LC-MS/MS, hydrophilic interaction chromatography for liquid chromatography–tandem mass spectrometry; LC-MS/MS, liquid chromatography–tandem mass spectrometry. (B) The general metabolomics workflow. It involves formulating a biological question, setting up an experimental design to test the hypothesis, sample treatment and harvest, metabolite extraction, clean-up, chromatographic separation, identification, statistical validation, and functional interpretation.
Figure 1 in Omics in Weed Science: A Perspective from Genomics, Transcriptomics, and Metabolomics Approaches
Figure 1. Classical systems biology concept and omics organization. The central dogma of molecular biology covers the progressive functionalization of the genotype to the phenotype. The omics techniques track and capture various molecular entities across the biological system.
Supplemental Results for Assembly, Annotation, and Analysis from HiFi reads of Gulf Toadfish Genome and Transcriptome fOpsBet2.1
<p>This repository contains gzipped tarballs of the results of the various assembly, annotation, and analysis steps performed during the assembly of the fOpsBet2.1 genome assembly for Opsanus beta at the University of Miami Rosenstiel School of Marine, Atmospheric, and Earth Science for the McDonald Toadfish Lab. These results are too numerous to include as supplemental data for a journal publication and so are available here for review. In this repository you will find results for:</p><p>Scripts:</p><p>-all bash and LSF scheduler job scripts used as part of the analysis, both exploratory and final. </p><p>QC:</p><p>-GenomeScope2 estimation of genome metrics from HiFi Reads</p><p>-QUAST genome statistics for each assembly step</p><p>-BUSCO completeness assessments for each assembly step </p><p>-inspector logs for polishing of initial assembly</p><p>-logs from Kraken2 contaminant screen</p><p>Assembly and Scaffolding:</p><p>-ntLINKS logs and intermediates for initial scaffolding</p><p>-ragtag logs and metrics for super-scaffolding to the ThaAma1.1 T. amazonica reference assembly</p><p>-mitoHIFI results for mitogenome assembly from HiFi reads, primary assembly, and purged alternate assembly</p><p>Annotation:</p><p>-PASA directory with full input and output for SQLite PASA assembly of transcriptome for gene predictors</p><p>-Results folder for Funannotate::annotate for gene models, annotations, and CDS/mRNA/protein fastas</p><p>-InterProSCan5 results for protein annotation used as input into Funannotate</p><p>-ghostKOALA KEGG assignment results for predicted proteins from funannotate results</p><p>Repetitive Elements:</p><p>-tidk telomere repeat analysis results</p><p>-TRAH satellite DNA analysis with subsequent analysis with HiCAT and StainedGlass</p><p>-RepeatModeler results for de novo TE prediction</p><p>-repclassifier results for TE curation</p><p>Comparative Analysis:</p><p>-OrthoFinder ortholog search for O. beta to several other vertebrates</p><p>-CAFE5 gene family expansion and contraction of Orthogroups from OrthoFinder results</p><p> </p>
Shared and distinct genetic risk factors for childhood-onset and adult-onset asthma: genome-wide and transcriptome-wide studies
<p>GWAS summary results from the paper</p> <p>The Lancet Respiratory Medicine: http://dx.doi.org/10.1016/S2213-2600(19)30055-4</p> <p>Preprint: https://doi.org/10.1101/427427</p>
Genome and transcriptome analysis of the beet armyworm Spodoptera exigua reveals targets for pest control
<p>The genus <i>Spodoptera</i> (Lepidoptera: Noctuidae) includes some of the most infamous insect pests of cultivated plants including <i>Spodoptera frugiperda</i>, <i>Spodoptera litura</i> and <i>Spodoptera exigua</i>. To effectively develop targeted pest control strategies for diverse <i>Spodoptera</i> species, genomic resources are highly desired. To this aim, we provide the genome assembly and developmental transcriptome comprising all major life stages of <i>S. exigua</i>, the beet armyworm. <i>Spodoptera exigua</i> is a polyphagous herbivore that can feed from > 130 host plants including several economically important crops.</p> <p>The 419 Mb beet armyworm genome was sequenced from a female <i>S. exigua</i> pupa. Using a hybrid genome sequencing approach (Nanopore long read data and Illumina short read), a high-quality genome assembly was achieved (N50=1.1 Mb). An official gene set (OGS, 18,477 transcripts) was generated by automatic annotation and by using transcriptomic RNA-seq data sets of 18 <i>S. exigua</i> samples as supporting evidence. In-depth analyses of developmental stage-specific expression in combination with gene tree analyses of identified homologous genes across Lepidoptera genomes revealed four potential genes of interest (three of them <i>Spodoptera</i>-specific) upregulated during 1<sup>st</sup> and 3<sup>rd</sup> instar larval stages for targeted pest-outbreak management.</p> <p>The beet armyworm genome sequence and developmental transcriptome covering all major developmental stages provides critical insights into the biology of this devastating polyphagous insect pest species with a worldwide distribution. In addition, comparative genomic analyses across Lepidoptera significantly advance our knowledge to further control other invasive <i>Spodoptera</i> species and reveals potential lineage-specific target genes for pest control strategies.</p>
Supplementary Data for AGouTI - flexible Annotation of Genomic and Transcriptomic Intervals
<p>The data allows to replicate the use-case scenario described in the manuscript "AGouTI - flexible Annotation of Genomic and Transcriptomic Intervals" by Jan G. Kosiński and Marek Żywicki.</p>
Reproducible data, example subsets, and analysis pipeline for the extended TAaCGH study of breast cancer genomic and transcriptomic profiles
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Genome and transcriptome analysis of the beet armyworm Spodoptera exigua reveals targets for pest control
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Data from: Using transcriptome sequencing and pooled exome capture to study local adaptation in the giga-genome of Pinus cembra
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Genomic and transcriptomic data for the frog Platyplectrum ornatum
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Kellet's whelk genome and transcriptome assembly
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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)
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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.