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175 results for “Long read sequencing”
Long-read sequencing and structural variant characterization in 1,019 samples from the 1000 Genomes Project
SV analysis of the long-read sequencing data of 1,019 samples from the 1000 Genomes Project. The data is hosted at the International Genome Sample Resource (IGSR) in the <a href="https://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1KG_ONT_VIENNA/">1KG_ONT_VIENNA</a> directory. Please see the <a href="https://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1KG_ONT_VIENNA/README_1KG_ONT_VIENNA.md">README</a> and <a href="https://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1KG_ONT_VIENNA/README_1KG_ONT_VIENNA_datareuse_statement.md">data reuse statement</a> for further information about this dataset.
The SV callsets of the HG002 human sample produced by cuteSV with multi long-read sequencing platforms.
<p>The SV callsets of the HG002 human sample produced by cuteSV with multi long-read sequencing platforms.</p>
Long-read sequencing reveals extensive gut phageome structural variations driven by genetic exchange with bacterial hosts
<p><span>Genetic variations are instrumental for unraveling phage evolution and deciphering their functional implications. Here we explore the underlying fine-scale genetic variations in the gut phageome, especially structural variations (SVs). By employing virome-enriched long-read metagenomics sequencing across 91 individuals, we identified a total of 14,438 non-redundant phage SVs, and revealed their prevalence within the human gut phageome. These SVs are mainly enriched in genes involved in recombination, DNA methylation, and antibiotic resistance. Strikingly, a substantial fraction of phage SV sequences share close homology with bacterial fragments, with most SVs enriched for horizontal gene transfer (HGT) mechanism. Further investigations showed that these SV sequences were genetic exchanged between specific phage-bacteria pairs, particularly between phages and their respective bacterial hosts. Temperate phages exhibits a higher frequency of genetic exchange with bacterial chromosomes then virulent phages. Collectively, our findings provide novel insights into the genetic landscape of the human gut phageome.</span></p>
Parent-of-origin detection and chromosome-scale haplotyping using long-read DNA methylation sequencing and Strand-seq
<p>Hundreds of loci in human genomes have alleles that are methylated differentially according to their parent of origin. These imprinted loci generally show little variation across tissues, individuals, and populations. We show that such loci can be used to distinguish the maternal and paternal homologs for all autosomes, without the need for the parental DNA. We integrate methylation-detecting nanopore sequencing with the long-range phase information in Strand-seq data to determine the parent of origin of chromosome-length haplotypes for both DNA sequence and DNA methylation in five trios with diverse genetic backgrounds.</p>
Genome annotations of Drosophila melanogaster and Drosophila simulans wild-type strains from long read sequencing assemblies
<p>Genome assemblies were performed for eight wild-type strains of Drosophila melanogaster and Drosophila simulans from Oxford Nanopore long read sequencing (please refer to Mohamed et al. Cells 2020 (doi:10.3390/cells9081776)). Assemblies were deposited in the European Nucleotide Archive (ENA) at EMBL-EBI under accession number PRJEB50024 (<a href="https://www.ebi.ac.uk/ena/browser/view/PRJEBxxxx">https://www.ebi.ac.uk/ena/browser/view/</a>PRJEB50024).</p> <p>Transposable Element annotations: we used RepeatMasker 4.1.0 (<a href="http://repeatmasker.org/">http://repeatmasker.org/</a>) -species Drosophila, followed by OneCodeToFindThemAll (Bailly-Bechet et al. 2014) with default parameters.</p> <p>Gene annotations: We retrieved gtf files from FlyBase : <a>ftp.flybase.net/genomes/D</a><a>rosophila_melanogaster/dmel_r6,46_FB2022_03/gft/dmel-all-r6.46.gtf.gz</a> and <a>ftp.flybase.net/genomes/Drosophila_simulans/dsim_r2,02_FB2017_04/gtf/dsim-all-</a><a>r2,02.gtf.gz</a>. The corresponding fasta files were also downloaded from FlyBase: <a>ftp.flybase.net/genomes/Drosophila_melanogaster/dmel_r6,46_FB2022_03/</a><a>fasta</a><a>/dmel-all-</a><a>chromosome-</a><a>r6.46.</a><a>fasta</a><a>.gz</a> and <a>ftp.flybase.net/genomes/Drosophila_simulans/dsim_r2,02_FB2017_04/</a><a>fasta</a><a>/dsim-all-</a><a>chromosome-</a><a>r2,02.</a><a>fasta</a><a>.gz</a>. We used Liftoff (Shumate and Salzberg, 2020) to lift over gene annotations from the references to our genome assemblies. We used -flank 0.2 and only kept the “gene” and “exon” terms.</p>
Data from: Improved genome assembly of the whiteleg shrimp Penaeus (Litopenaeus) vannamei using long- and short-read sequences from public databases
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Reconstructing NOD-like receptor alleles with high internal conservation in Podospora anserina using long-read sequencing
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A novel method to assess the integrity of frozen archival DNA samples: Alpha-diversity ratios of short and long-read 16S rRNA gene sequences
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Short and Long Read Sequences from VIM-1 producing (Klebsiella pneumoniae test-dataset)
<p>Creation of a downsized version of the long and short reads data of VIM-1 producing <i>Klebsiella pneumoniae</i>. Samples were obtained from the Hospital Infanta Cristina (Badajoz, Spain) on October 11th, 2019. </p><p>- Long read data: Oxford Nanopore Technology (A1403KPN.fastq.gz).</p><p>- Short read data : Illumina (A1403KPN_R2_filtered.fastq.gz && A1403KPN_R2_filtered.fastq.gz)</p>
TAGET: A toolkit for analyzing full-length transcripts from long-read sequencing
<p>Polished transcripts of COLO829 from the PacBio platform. The original web link: https://downloads-ap.pacbcloud.com/public/dataset/Melanoma2019_IsoSeq/PolishedMappedTranscripts/before-SQANTI2filter/.</p>
A multiomic characterization of the leukemia cell line REH using short- and long-read sequencing
<p>This is a public repository containing secondary datasets described in the publication <a href="https://doi.org/10.26508/lsa.202302481">"A multiomic characterization of the leukemia cell line REH using short- and long-read sequencing"</a>. Primary data for this project are available at NCBI/SRA under the BioProject accession numbers PRJNA600820 and PRJNA834955, and include the following sequencing datasets:</p> <p>REH cell line:</p> <ul> <li>PacBio WGS</li> <li>ONT Ultralong WGS</li> <li>Illumina short-read PCR-free WGS</li> <li>IsoSeq RNA-seq</li> <li>Illumina short-read RNA-seq</li> </ul> <p>GM12878 cell line:</p> <ul> <li>Illumina short-read RNA-seq</li> </ul> <p>This dataset includes the following files:</p> <p><strong>Depth of Coverage analysis</strong></p> <ul> <li>Output from `samtools coverage`: <em>samtools.coverage.illumina.txt, samtools.coverage.ont.txt, samtools.coverage.pb.txt</em></li> <li>Output from `copycat` (binned coverage): <em>copycat.ont.coverage.10kb.csv, copycat.pb.coverage.10kb.csv, copycat.pcrfree.coverage.10kb.csv</em></li> </ul> <p><strong>Structural Variant (SV) callsets</strong></p> <ul> <li><strong>Raw:</strong> <em>illumina.tiddit.vcf, ont.sniffles.vcf, pb.sniffles.vcf</em></li> <li><strong>Filtered: </strong><em>REH.svs.filtered.csv</em></li> </ul> <p><strong>SNV callsets</strong></p> <ul> <li><strong>Filtered and annotated:</strong> <em>REH.mutect.filtered.ann.vcf.gz</em></li> </ul> <p><strong>Fusion gene callsets</strong></p> <ul> <li><strong>Short-read: </strong><em>GM12878.fusionreport.txt, illumina.all.txt, illumina.filtered.csv, REH.arriba.fusions.tsv, REH.fusioncatcher.fusion-genes.txt, REH.pizzly.txt, REH.squid.fusions.annotated.txt, REH.starfusion.abridged.tsv, REH.pdf</em></li> <li><strong>Long-read: </strong><em>cupcake.long.csv, cupcake.std.csv, jaffa_results.csv</em></li> <li><strong>Filtered: </strong><em>REH.fusions.filtered.csv</em><br> </li> </ul> <p> </p>
Resolution, validation and divergence of heterozygous haplotypes from pooled long read sequencing of the diamondback moth (Lepidoptera: Plutellidae)
<p>This data sets includes the full set of intermediate genome assemblies produced during our analyses. We make these available for researchers who may be interested in the variation between assembly results and variation within the study organism (<em>Plutella xylostella</em>) prior to removal during subsequent genome processing.</p>
Data from: Benchmarking ultra-high molecular weight DNA preservation methods for long-read and long-range sequencing
<p>Studies in vertebrate genomics require sampling from a broad range of tissue types, taxa, and localities. Recent advancements in long-read and long-range genome sequencing have made it possible to produce high-quality chromosome-level genome assemblies for almost any organism. However, adequate tissue preservation for the requisite ultra-high molecular weight DNA (uHMW DNA) remains a major challenge. Here we present a comparative study of preservation methods for field and laboratory tissue sampling, across vertebrate classes and different tissue types. We find that no single method is best for all cases. Instead, the optimal storage and extraction methods vary by taxa, by tissue, and by down-stream application. Therefore, we provide sample preservation guidelines that ensure sufficient DNA integrity and amount required for use with long-read and long-range sequencing technologies across vertebrates. Our best practices generate the uHMW DNA needed for the high-quality reference genomes for Phase 1 of the Vertebrate Genomes Project (VGP), whose ultimate mission is to generate chromosome-level reference genome assemblies of all ~70,000 extant vertebrate species.</p>
Long-read-based draft genome sequence of Indian black gram IPU-94-1 'Uttara': Insights into disease resistance and seed storage protein genes
<p>Black gram [Vigna mungo (L.) Hepper var. <em>mung<a>o</a></em>] [LAV1] is a warm-season legume highly prized for its protein content along with significant folate and iron proportions. To expedite the genetic enhancement of black gram, a high-quality draft genome from the center of origin of the crop is indispensable. Here, we established a draft genome sequence of an Indian black gram cultivar, 'Uttara' (IPU 94-1), known for its high resistance to mungbean yellow mosaic virus. Pacific Biosciences of California, Inc. (PacBio) single-molecule real-time (SMRT) and Illumina sequencing assembled a draft reference-guided assembly with a cumulative size of ~454.4 Mb, of which, 444.4 Mb was anchored on 11 pseudomolecules corresponding to 11 chromosomes. Uttara assembly denotes features of a high-quality draft genome illustrated through high N50 value (42.88 Mb), gene completeness (benchmarking universal single-copy ortholog [BUSCO] score 94.17%), and low levels of ambiguous nucleotides (N) percent (0.0005%). Gene discovery using transcript evidence predicted 28,881 protein-coding genes, from which, ~95% were functionally annotated. A global survey of genes associated with disease resistance revealed 119 nucleotide binding site–leucine rich repeat (NBS-LRR) proteins, while 23 genes encoding seed storage proteins (SSPs) were discovered in black gram. A large set of microsatellite loci were discovered for marker development in the crop. Our draft genome of an Indian black gram provides the foundational genomic resources for the improvement of important agronomic traits and ultimately will help in accelerating black gram breeding programs.</p>
Genome-resolved metagenomics using short-, long-read and metaHiC sequencing
<p>Reference-quality metagenome-assembled genomes (MAGs) are the key to exploring microbial compositions and microbe-phenotype associations. They can be recovered by different sequencing technologies and computational tools, which need an unbiased and comprehensive assessment to identify best practices. This work systematically evaluates 40 distinct strategies to recover high-quality MAGs generated by eight assemblers, eight metagenomics binners, and four sequencing technologies, including short-, long-read and metaHiC sequencing. We notice that the hybrid assemblies of short- and long-reads outperform either short- or long-read assemblies and generate more contigs with high contiguity. When the hybrid assemblies are combined with metaHiC-based binning (Hybrid-HiC), more high-quality MAGs with higher taxonomic diversity are recovered, and more tRNA and rRNA genes, phages, plasmids and antibiotic resistance genes are identified in the mock, simulated and real datasets.</p>
Simulated data from: Reference-free assembly of long-read transcriptome sequencing data with RNA-Bloom2
<p><span>Long-read sequencing technologies have improved significantly since their emergence. Their read lengths, potentially spanning entire transcripts, is advantageous for reconstructing transcriptomes. Existing long-read transcriptome assembly methods are primarily reference-based and to date, there is little focus on reference-free transcriptome assembly. We introduce RNA-Bloom2, a reference-free assembly method for long-read transcriptome sequencing data. </span>RNA-Bloom2 is available on GitHub at: <a href="https://github.com/bcgsc/RNA-Bloom">https://github.com/bcgsc/RNA-Bloom</a>.</p> <p><span>We benchmarked the assembly quality and the computational performance of RNA-Bloom2 on simulated data. We prepared two mouse simulated datasets with Trans-NanoSim</span><span> for the cDNA and dRNA sequencing protocols model on experimental ONT data</span><span>. The datasets were simulated </span><span>based on the mouse ENSEMBL annotation for GRCm39.</span><span> To investigate the effect of sequencing depth, we subsampled each dataset to 2, 10, and 18 million reads, resulting in a total of six sets of reads for our benchmarking experiments. Using the simulated data, w</span><span>e showed that the transcriptome assembly quality of RNA-Bloom2 is competitive to those of reference-based methods.</span></p>
Strainy: phasing and assembly of strain haplotypes from long-read metagenome sequencing - Real and mock datasets
<p>This repository contains the reads, assemblies, and references required to replicate the <strong>real and mock</strong> results presented in the paper: https://doi.org/10.1101/2023.01.31.526521</p>
Strainy: phasing and assembly of strain haplotypes from long-read metagenome sequencing - Simulated datasets
<p>This repository contains the reads, assemblies, and references required to replicate the <strong>simulated</strong> results presented in the paper: https://doi.org/10.1101/2023.01.31.526521</p>
Comprehensive identification of pathogenic variants in retinoblastoma by long- and short-read sequencing
<p>Retinoblastoma (RB) is the most common intraocular malignancy in childhood. The causal variants in RB are mostly characterized by previously used short-read sequencing (SRS) analysis, which has technical limitations in identifying structural variants (SVs) and phasing information. Long-read sequencing (LRS) technology has significant advantages over SRS in detecting SVs, phased genetic variants, and methylation. In this study, we comprehensively characterized the genetic landscape of RB using combinatorial LRS and SRS of 16 RB tumors and 16 matched blood samples. We detected a total of 232 somatic SVs, with an average of 14.5 SVs per sample across the cohort. We identified 20 distinct pathogenic variants, including <span>three</span> novel small variants and <span>five</span> <span>somatic</span> SVs. Furthermore, our analysis shows that the vast majority (93.8%) of <em>RB1</em>-disrupted patients fit the two-hit hypothesis through diverse types, including the biallelic hypermethylated promoter as well as small and large compound heterozygous mutations which were missing in SRS analysis. By constructing the evolutionary history of all genetic variants, we reveal the evolution trends that <em>RB1</em> disruption early and followed by copy number changes, including amplifications of Chr2p, and deletions of Chr16q, during RB tumorigenesis. Altogether, we characterize the comprehensive genetic landscape of RB, providing novel insights into the genetic alterations and mechanisms contributing to RB initiation and development. Our work also establishes a framework to analyze genomic landscape of cancers based on LRS data. <br><br>We took IGV screenshots of SV and SNV/indels. The IGV naming conventions for SV is: sample name, chromosome 1, start, chromosome 2, end, SV type, SV length, SV breakpoint. The IGV naming convention for SNV/indels is: sample name, chromosome, position, reference allele, alternate allele.</p>
Long reads and Hi-C sequencing illuminate the two-compartment genome of the model arbuscular mycorrhizal symbiont Rhizophagus irregularis
<p>This repository contains annotations for the strains of <em>R. irregularis</em> chromosome assemblies.</p>
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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)
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.