Abstract
High-throughput sequencing technology, also called next-generation sequencing (NGS), has the potential to revolutionize the whole process of genome sequencing, transcriptomics, and epigenetics. Sequencing data is captured in a public primary data archive, the Sequence Read Archive (SRA). As of January 2013, data from more than 14,000 projects have been submitted to SRA, which is double that of the previous year. Researchers can download raw sequence data from SRA website to perform further analyses and to compare with their own data. However, it is extremely difficult to search entries and download raw sequences of interests with SRA because the data structure is complicated, and experimental conditions along with raw sequences are partly described in natural language. Additionally, some sequences are of inconsistent quality because anyone can submit sequencing data to SRA with no quality check. Therefore, as a criterion of data quality, we focused on SRA entries that were cited in journal articles. We extracted SRA IDs and PubMed IDs (PMIDs) from SRA and full-text versions of journal articles and retrieved 2748 SRA ID-PMID pairs. We constructed a publication list referring to SRA entries. Since, one of the main themes of -omics analyses is clarification of disease mechanisms, we also characterized SRA entries by disease keywords, according to the Medical Subject Headings (MeSH) extracted from articles assigned to each SRA entry. We obtained 989 SRA ID-MeSH disease term pairs, and constructed a disease list referring to SRA data. We previously developed feature profiles of diseases in a system called “Gendoo”. We generated hyperlinks between diseases extracted from SRA and the feature profiles of it. The developed project, publication and disease lists resulting from this study are available at our web service, called “DBCLS SRA” (http://sra.dbcls.jp/). This service will improve accessibility to high-quality data from SRA.
Highlights
High-throughput sequencing technology is a powerful technique for determination of an entire genome sequence and quantification of the transcriptome at base-pair resolution with a large dynamic range
The Sequence Read Archive (SRA) database is a primary archive of public highthroughput sequencing data, and provides experimental designs such as project titles and sequencers along with raw sequences as six objects of metadata XML files
In SRA, additional experiments are often assigned to a previous project and deposited as a new submission, the number of submission exceeds that of projects, and many submissions contain only a partial set of metadata files, even excluded ‘‘analysis’’ files from consideration, which are optional for submission (Figure 1)
Summary
High-throughput sequencing technology is a powerful technique for determination of an entire genome sequence and quantification of the transcriptome at base-pair resolution with a large dynamic range. The sequencers using massively parallel sequencing technology, called next-generation sequencer (NGS), drastically reduce the cost and time of sequencing, compared with previous methods, and is rapidly becoming the technology of choice for such purposes [1] This type sequencers yields a vast quantity of captured images, in-process files, and numerous sequence reads, requiring an extensive amount of disk space [2]. Such data are important for researchers and should be shared, as are the nucleotide sequences in GenBank and microarray data in the Gene Expression Omnibus (GEO).
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