The sequence read archive: explosive growth of sequencing data

By Yuichi Kodama, Martin Shumway, Rasko Leinonen. 2011

901 citations recorded by the source corpus.

New generation sequencing platforms are producing data with significantly higher throughput and lower cost. A portion of this capacity is devoted to individual and community scientific projects. As these projects reach publication, raw sequencing datasets are submitted into the primary next-generation sequence data archive, the Sequence Read Archive (SRA). Archiving experimental data is the key to the progress of reproducible science. The SRA was established as a public repository for next-generation sequence data as a part of the International Nucleotide Sequence Database Collaboration (INSDC). INSDC is composed of the National Center for Biotechnology Information (NCBI), the European Bioinformatics Institute (EBI) and the DNA Data Bank of Japan (DDBJ). The SRA is accessible at www.ncbi.nlm.nih.gov/sra from NCBI, at www.ebi.ac.uk/ena from EBI and at trace.ddbj.nig.ac.jp from DDBJ. In this article, we present the content and structure of the SRA and report on updated metadata structures, submission file formats and supported sequencing platforms. We also briefly outline our various responses to the challenge of explosive data growth.

Subfields: Ecology, Molecular Biology

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Citations in the corpus

Clustering huge protein sequence sets in linear time (2017, methodology): “59 billion protein sequence fragments predicted by Prodigal [19] in 2200 metagenomic and metatranscriptomic datasets [3, 20, 21] downloaded mainly 200”
Expression Atlas: gene and protein expression across multiple studies and organisms (2017, methodology): “All RNA-Seq data sets from ENA, including the data from the Sequence Read Archive, SRA (14), corresponding to species covered by Expression Atlas are processed automatically using the open source iRAP pipeline and made available via the RNAseq-er Application Programming Interface (API) (15).”
PlantPAN3.0: a new and updated resource for reconstructing transcriptional regulatory networks from ChIP-seq experiments in plants (2018, methodology): “The plant ChIP-seq data were collected systematically from Gene Expression Omnibus (GEO) and Sequence Read Archive (SRA) (19,20).”
DIANA-TarBase v8: a decade-long collection of experimentally supported miRNA–gene interactions (2017, methodology): “Interactions supported from high-throughput experiments, have been extracted either from relevant publications or from the analysis of raw libraries retrieved from Gene Expression Omnibus (GEO) (17) and DNA Data Bank of Japan (DDBJ) (18) repositories.”
Transcriptomics technologies (2017, methodology): “SRA.”
The SIB Swiss Institute of Bioinformatics’ resources: focus on curated databases (2015, background): “Typical examples of primary databases are the European Nucleotide Archive (ENA) (9) and the Sequence Read Archive (SRA) (10) which store raw data from sequencing technologies, or the Gene Expression Omnibus database (GEO) (11), a data repository for high-throughput gene expression data.”
The International Nucleotide Sequence Database Collaboration (2011, background): “Details have been published in this issue (1).”
Salmon: fast and bias-aware quantification of transcript expression using dual-phase inference (2017, background): “Traditional quantification algorithms, that use alignments of the sequencing reads to the genome or transcriptome, require significant computational resources [7] and do not scale well with the rate at which data is produced [8].”
Improvements to PATRIC, the all-bacterial Bioinformatics Database and Analysis Resource Center (2016, background): “Since the submission of metadata with a genome is almost entirely voluntary, and many researches have been electing to submit their genomes to SRA (5) or the European Nucleotide Archive (ENA) (42) instead of assembling and annotating them, we have been searching the literature for publications with…”
miRBase: annotating high confidence microRNAs using deep sequencing data (2013, background): “Large numbers of deep sequencing data sets are publicly available in databases such as the Gene Expression Omnibus (15) and the Short Read Archive (16), and these data sets allow us to assess post hoc the confidence in any given microRNA annotation.”
GenBank (2012, background): “Although neither the Trace Archive nor SRA is a part of GenBank, they are part of the INSDC and provide access to the data underlying these assemblies (4,6).”
Database resources of the National Center for Biotechnology Information (2014, background): “Data are deposited into SRA as supporting evidence for a wide range of study types, including de novo genome assemblies, genome-wide association studies (GWAS), single nucleotide polymorphism and structural variation analysis, pathogen identification, transcript assembly, metagenomic community profi”