Raw read archive of EPIGEM
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ABSTRACT:
PROVIDER: PRJEB11695 | ENA |
REPOSITORIES: ENA
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Project description:The sequence read archive (SRA) contains over 52 terabases or 482 billion reads from Drosophila melanogaster (as of June 2018). These data are massively underused by the community and include 14,423 RNA-Seq samples, that is roughly 7 times the size of modENCODE. Currently the major challenge is finding high quality datasets that are suitable for inclusion in new studies. To help the community overcome this hurdle, we re-processed all D. melanogaster RNA-Seq SRA experiments (SRXs) using an identical workflow. This workflow uses a data driven approach to identify technical metadata (i.e., strandedness and layout) for each sample in order to optimize mapping parameters. The workflow generates QC metrics, coverage tracks based on the dm6 assembly, and calculates gene level, junction level, and intergenic counts against FlyBase r6.11. This resource will allow any researcher to visualize browser tracks for any publicly available dataset, quickly identify high quality data sets for use in their own research, and download identically processed counts tables. There is a treasure trove of underused data sitting in the SRA and this work addresses the first challenge to make data integration a common laboratory practice.
2018-07-18 | GSE117217 | GEO
Project description:Remapping the SRA: Drosophila melanogaster RNA-Seq data from the Sequence Read Archive
| PRJNA481740 | ENA
Project description:The Sequence Read Archive (SRA) contains over one million publicly available sequencing runs from various studies using a variety of sequencing library strategies. These data inherently contain information about underlying genomic sequence variants which we exploit to extract allelic read counts on an unprecedented scale. We reprocessed over 250,000 human sequencing runs (>1000 TB data worth of raw sequence data) into a single unified dataset of allelic read counts for nearly 300,000 variants of biomedical relevance curated by NCBI dbSNP, where germline variants were detected in a median of 912 sequencing runs, and somatic variants were detected in a median of 4,876 sequencing runs, suggesting that this dataset facilitates identification of sequencing runs that harbor variants of interest. Allelic read counts obtained using a targeted alignment were very similar to read counts obtained from whole-genome alignment. Analyzing allelic read count data for matched DNA and RNA samples from tumors, we find that RNA-seq can also recover variants identified by Whole Exome Sequencing (WXS), suggesting that reprocessed allelic read counts can support variant detection across different library strategies in SRA. This study provides a rich database of known human variants across SRA samples that can support future meta-analyses of human sequence variation.
| S-EPMC6415672 | biostudies-literature
Project description:BACKGROUND:Vast amounts of next generation sequencing RNA data has been deposited in archives, accompanying very diverse original studies. The data is readily available also for other purposes such as genome annotation or transcriptome assembly. However, selecting a subset of available experiments, sequencing runs and reads for this purpose is a nontrivial task and complicated by the inhomogeneity of the data. RESULTS:This article presents the software VARUS that selects, downloads and aligns reads from NCBI's Sequence Read Archive, given only the species' binomial name and genome. VARUS automatically chooses runs from among all archived runs to randomly select subsets of reads. The objective of its online algorithm is to cover a large number of transcripts adequately when network bandwidth and computing resources are limited. For most tested species VARUS achieved both a higher sensitivity and specificity with a lower number of downloaded reads than when runs were manually selected. At the example of twelve eukaryotic genomes, we show that RNA-Seq that was sampled with VARUS is well-suited for fully-automatic genome annotation with BRAKER. CONCLUSIONS:With VARUS, genome annotation can be automatized to the extent that not even the selection and quality control of RNA-Seq has to be done manually. This introduces the possibility to have fully automatized genome annotation loops over potentially many species without incurring a loss of accuracy over a manually supervised annotation process.
| S-EPMC6842140 | biostudies-literature
Project description:The goal of this study, started as a part of the modENCODE project, is to detect and characterize previously unannotated transcripts of the C. elegans genome. This dataset has been imported from the Sequence Read Archive and curated by the WormBase and ArrayExpress teams. It is a subset of study E-MTAB-2683 (SRP00401) and only contains assays on worms not infected by pathogens.
2010-02-26 | E-MTAB-2812 | biostudies-arrayexpress
Project description:MotivationThe NCBI's Sequence Read Archive (SRA) promises great biological insight if one could analyze the data in the aggregate; however, the data remain largely underutilized, in part, due to the poor structure of the metadata associated with each sample. The rules governing submissions to the SRA do not dictate a standardized set of terms that should be used to describe the biological samples from which the sequencing data are derived. As a result, the metadata include many synonyms, spelling variants and references to outside sources of information. Furthermore, manual annotation of the data remains intractable due to the large number of samples in the archive. For these reasons, it has been difficult to perform large-scale analyses that study the relationships between biomolecular processes and phenotype across diverse diseases, tissues and cell types present in the SRA.ResultsWe present MetaSRA, a database of normalized SRA human sample-specific metadata following a schema inspired by the metadata organization of the ENCODE project. This schema involves mapping samples to terms in biomedical ontologies, labeling each sample with a sample-type category, and extracting real-valued properties. We automated these tasks via a novel computational pipeline.Availability and implementationThe MetaSRA is available at metasra.biostat.wisc.edu via both a searchable web interface and bulk downloads. Software implementing our computational pipeline is available at http://github.com/deweylab/metasra-pipeline.Contactcdewey@biostat.wisc.edu.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC5870770 | biostudies-literature