COMPASS-AdvancedPancreaticCancer-WGS-mapped-reads - samples
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ABSTRACT:
PROVIDER: EGAD00001003585 | EGA |
REPOSITORIES: EGA
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Project description:Genomics-Driven Precision Medicine for Advanced Pancreatic Cancer - Early Results from the COMPASS Trial - RNA-Seq mapped reads
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Project description:UnlabelledWe describe a tool for quantifying the uniformity of mapped reads in high-throughput sequencing experiments. Our statistic directly measures the uniformity of both read position and fragment length, and we explain how to compute a P-value that can be used to quantify biases arising from experimental protocols and mapping procedures. Our method is useful for comparing different protocols in experiments such as RNA-Seq.Availability and implementationWe provide a freely available and open source python script that can be used to analyze raw read data or reads mapped to transcripts in BAM format at http://www.math.miami.edu/~vhower/ReadSpy.html.
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Project description:Whole Genomes Define Concordance in Matched Primary, Xenograft, and Organoid Models of Pancreas Cancer - WGS mapped reads
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Project description:The advent of high-throughput sequencing has enabled sequencing based measurements of cellular function, with an individual measurement potentially consisting of more than 108 reads. While tools are available for aligning sets of reads to genomes and interpreting the results, fewer tools have been developed to address the storage and retrieval requirements of large collections of aligned datasets. We present ReadDB, a network accessible column store database system for aligned high-throughput read datasets.ReadDB stores collections of aligned read positions and provides a client interface to support visualization and analysis. ReadDB is implemented as a network server that responds to queries on genomic intervals in an experiment with either the set of contained reads or a histogram based interval summary. Tests on datasets ranging from 105 to 108 reads demonstrate that ReadDB performance is generally within a factor of two of local-storage based methods and often three to five times better than other network-based methods.ReadDB is a high-performance foundation for ChIP-Seq and RNA-Seq analysis. The client-server model provides convenient access to compute cluster nodes or desktop visualization software without requiring a shared network filesystem or large amounts of local storage. The client code provides a simple interface for fast data access to visualization or analysis. ReadDB provides a new way to store genome-aligned reads for use in applications where read sequence and alignment mismatches are not needed.
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Project description:MotivationStructural variants are defined as genomic variants larger than 50 bp. They have been shown to affect more bases in any given genome than single-nucleotide polymorphisms or small insertions and deletions. Additionally, they have great impact on human phenotype and diversity and have been linked to numerous diseases. Due to their size and association with repeats, they are difficult to detect by shotgun sequencing, especially when based on short reads. Long read, single-molecule sequencing technologies like those offered by Pacific Biosciences or Oxford Nanopore Technologies produce reads with a length of several thousand base pairs. Despite the higher error rate and sequencing cost, long-read sequencing offers many advantages for the detection of structural variants. Yet, available software tools still do not fully exploit the possibilities.ResultsWe present SVIM, a tool for the sensitive detection and precise characterization of structural variants from long-read data. SVIM consists of three components for the collection, clustering and combination of structural variant signatures from read alignments. It discriminates five different variant classes including similar types, such as tandem and interspersed duplications and novel element insertions. SVIM is unique in its capability of extracting both the genomic origin and destination of duplications. It compares favorably with existing tools in evaluations on simulated data and real datasets from Pacific Biosciences and Nanopore sequencing machines.Availability and implementationThe source code and executables of SVIM are available on Github: github.com/eldariont/svim. SVIM has been implemented in Python 3 and published on bioconda and the Python Package Index.Supplementary informationSupplementary data are available at Bioinformatics online.
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