Remapping the SRA: Drosophila melanogaster RNA-Seq data from the Sequence Read Archive
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PROVIDER: PRJNA481740 | 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:BackgroundThe mitochondrial genomes (mitogenomes) of metazoans generally include the same set of protein-coding genes, which ensures the homology of mitochondrial genes between species. The mitochondrial genes are often used as reference data for species identification based on genetic data (DNA barcoding). The need for such reference data has been increasing due to the application of environmental DNA (eDNA) analysis for environmental assessments. Recently, the number of publicly available sequence reads obtained with next-generation sequencing (NGS) has been increasing in the public database (the NCBI Sequence Read Archive, SRA). Such freely available NGS reads would be promising sources for assembling mitochondrial protein-coding genes (mPCGs) of organisms whose mitochondrial genes are not available in GenBank. The present study aimed to assemble annelid mPCGs from raw data deposited in the SRA.MethodsThe recent progress in the classification of Annelida was briefly introduced. In the present study, the mPCGs of 32 annelid species of 19 families in clitellates and allies in Sedentaria (echiurans and polychaetes) were newly assembled from the reads deposited in the SRA. Assembly was performed with a recently published pipeline mitoRNA, which includes cycles of Bowtie2 mapping and Trinity assembly. Assembled mPCGs were deposited in GenBank as Third Party Data (TPA) data. A phylogenetic tree was reconstructed with maximum likelihood (ML) analysis, together with other mPCGs deposited in GenBank.Results and discussionmPCG assembly was largely successful except for Travisia forbesii; only four genes were detected from the assembled contigs of the species probably due to the reads targeting its parasite. Most genes were largely successfully obtained, whereas atp8, nad2, and nad4l were only successful in 22-24 species. The high nucleotide substitution rates of these genes might be relevant to the failure in the assembly although nad6, which showed a similarly high substitution rate, was successfully assembled. Although the phylogenetic positions of several lineages were not resolved in the present study, the phylogenetic relationships of some polychaetes and leeches that were not inferred by transcriptomes were well resolved probably due to a more dense taxon sampling than previous phylogenetic analyses based on transcriptomes. Although NGS data are generally better sources for resolving phylogenetic relationships of both higher and lower classifications, there are ensuring needs for specific loci of the mitochondrial genes for analyses that do not require high resolutions, such as DNA barcoding, eDNA, and phylogenetic analysis among lower taxa. Assembly from publicly available NGS reads would help design specific primers for the mitochondrial gene sequences of species, whose mitochondrial genes are hard to amplify by Sanger sequencing using universal primers.
| S-EPMC10693233 | biostudies-literature
Project description:We reanalyzed published RNA-seq data to study 1) the genomic landscapes near surrounding regions of transcriptional start sites with regard to the gene expression activities and 2) the gene expression change upon transcription factor (MYBL1, ATF4) depletion. Raw data were downloaded from Sequence Read Archive (SRA) in National Center for Biotechnology Information (NCBI) database. FASTQ files were extracted with the SRA Toolkit version 2.5.5 and aligned using STAR 2.4.2 onto the mouse and human genome (mm9 and hg19, respectively). Gene expression was calculated as RPKM values using rpkmforgenes.py (Ramsköld et al., 2009).
2018-02-15 | GSE80793 | GEO
Project description:This archive contains raw reads produced by the Epidemiology and Genomics unit at the Laboratoire National de Santé, mainly on isolates of foodborne pathogens
| PRJEB11695 | 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:In order to elucidate the general rules for gene localization and regulation mediated by CpG islands, we reanalyzed published ChIP-seq data of CXXC domain, H3K9me3, KDM2A, SUV39H1, ATF4, MYBL1, MYOD1, SPI1, and CTCF. Raw data were downloaded from Sequence Read Archive (SRA) in National Center for Biotechnology Information (NCBI) database. FASTQ files were extracted with the SRA Toolkit version 2.5.5 and aligned using Bowtie 2.2.5 onto the mouse and human genome (mm9 and hg19, respectively). For the identification of factor binding sites, model-based analysis for ChIP-seq peak caller (MACS 1.4.2) was used with a p-value cutoff of 1e-5.
2018-02-15 | GSE80791 | GEO
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:Drosophila melanogaster RNA sequencing with Illumina Genome Analyzer. High-throughput sequencing of Drosophila melanogaster RNAs. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf
2008-11-04 | GSE13441 | GEO
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