Dataset Information

Sequence database versioning for command line and Galaxy bioinformatics servers.

ABSTRACT: There are various reasons for rerunning bioinformatics tools and pipelines on sequencing data, including reproducing a past result, validation of a new tool or workflow using a known dataset, or tracking the impact of database changes. For identical results to be achieved, regularly updated reference sequence databases must be versioned and archived. Database administrators have tried to fill the requirements by supplying users with one-off versions of databases, but these are time consuming to set up and are inconsistent across resources. Disk storage and data backup performance has also discouraged maintaining multiple versions of databases since databases such as NCBI nr can consume 50?Gb or more disk space per version, with growth rates that parallel Moore's law.Our end-to-end solution combines our own Kipper software package-a simple key-value large file versioning system-with BioMAJ (software for downloading sequence databases), and Galaxy (a web-based bioinformatics data processing platform). Available versions of databases can be recalled and used by command-line and Galaxy users. The Kipper data store format makes publishing curated FASTA databases convenient since in most cases it can store a range of versions into a file marginally larger than the size of the latest version.Kipper v1.0.0 and the Galaxy Versioned Data tool are written in Python and released as free and open source software available at https://github.com/Public-Health-Bioinformatics/kipper and https://github.com/Public-Health-Bioinformatics/versioned_data, respectively; detailed setup instructions can be found at https://github.com/Public-Health-Bioinformatics/versioned_data/blob/master/doc/setup.md: Damion.Dooley@Bccdc.Ca or William.Hsiao@Bccdc.CaSupplementary information: Supplementary data are available at Bioinformatics online.

SUBMITTER: Dooley DM

PROVIDER: S-EPMC4824126 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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Sequence database versioning for command line and Galaxy bioinformatics servers.

Dooley Damion M DM Petkau Aaron J AJ Van Domselaar Gary G Hsiao William W L WW

Bioinformatics (Oxford, England) 20151212 8

<h4>Motivation</h4>There are various reasons for rerunning bioinformatics tools and pipelines on sequencing data, including reproducing a past result, validation of a new tool or workflow using a known dataset, or tracking the impact of database changes. For identical results to be achieved, regularly updated reference sequence databases must be versioned and archived. Database administrators have tried to fill the requirements by supplying users with one-off versions of databases, but these are ...[more]

PMID: 26656932

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Project description:Chinese hamster ovary (CHO) cell lines represent the most commonly used mammalian expression system for the production of therapeutic proteins. In this context, detailed knowledge of the CHO cell transcriptome might help to improve biotechnological processes conducted by specific cell lines. Nevertheless, very few assembled cDNA sequences of CHO cells were publicly released until recently, which puts a severe limitation on biotechnological research. Two extended annotation systems and web-based tools, one for browsing eukaryotic genomes (GenDBE) and one for viewing eukaryotic transcriptomes (SAMS), were established as the first step towards a publicly usable CHO cell genome/transcriptome analysis platform. This is complemented by the development of a new strategy to assemble the ca. 100 million reads, sequenced from a broad range of diverse transcripts, to a high quality CHO cell transcript set. The cDNA libraries were constructed from different CHO cell lines grown under various culture conditions and sequenced using Roche/454 and Illumina sequencing technologies in addition to sequencing reads from a previous study. Two pipelines to extend and improve the CHO cell line transcripts were established. First, de novo assemblies were carried out with the Trinity and Oases assemblers, using varying k-mer sizes. The resulting contigs were screened for potential CDS using ESTScan. Redundant contigs were filtered out using cd-hit-est. The remaining CDS contigs were re-assembled with CAP3. Second, a reference-based assembly with the TopHat/Cufflinks pipeline was performed, using the recently published draft genome sequence of CHO-K1 as reference. Additionally, the de novo contigs were mapped to the reference genome using GMAP and merged with the Cufflinks assembly using the cuffmerge software. With this approach 28,874 transcripts located on 16,492 gene loci could be assembled. Combining the results of both approaches, 65,561 transcripts were identified for CHO cell lines, which could be clustered by sequence identity into 17,598 gene clusters.

Dataset Information

Sequence database versioning for command line and Galaxy bioinformatics servers.

Publications

Sequence database versioning for command line and Galaxy bioinformatics servers.

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