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BMC Genomics

, 12:507

Transcriptomic methods


BackgroundThe growth of high-throughput technologies such as microarrays and next generation sequencing has been accompanied by active research in data analysis methodology, producing new analysis methods at a rapid pace. While most of the newly developed methods are freely available, their use requires substantial computational skills. In order to enable non-programming biologists to benefit from the method development in a timely manner, we have created the Chipster software.

ResultsChipster brings a powerful collection of data analysis methods within the reach of bioscientists via its intuitive graphical user interface. Users can analyze and integrate different data types such as gene expression, miRNA and aCGH. The analysis functionality is complemented with rich interactive visualizations, allowing users to select datapoints and create new gene lists based on these selections. Importantly, users can save the performed analysis steps as reusable, automatic workflows, which can also be shared with other users. Being a versatile and easily extendable platform, Chipster can be used for microarray, proteomics and sequencing data. In this article we describe its comprehensive collection of analysis and visualization tools for microarray data using three case studies.

ConclusionsChipster is a user-friendly analysis software for high-throughput data. Its intuitive graphical user interface enables biologists to access a powerful collection of data analysis and integration tools, and to visualize data interactively. Users can collaborate by sharing analysis sessions and workflows. Chipster is open source, and the server installation package is freely available.

AbbreviationsaCGHarray comparative genomic hybridization

altCDFalternative Affymetrix library file

CCACanonical Correspondence Analysis

ChIP-seqchromatin immunoprecipitation sequencing

CNVcopy number variation

FDRfalse discovery rate

GEOGene Expression Omnibus

GNUGeneral Public License

GOGene Ontology

KEGGKyoto Encyclopedia of Genes and Genomes

KNNk-nearest neighbor

LDALinear Discriminant Analysis

NGSnext generation sequencing

NMDSNon-metric Multi-Dimensional Scaling

NUSENormalized Unscaled Standard Error

PCAPrincipal Component Analysis

RLERelative Log Expression

RMARobust Multi-Array Average

ROTSReproducibility-Optimized Test Statistic

SAMSignificance Analysis of Microarrays

SOAPSimple Object Access Protocol: SNP: single nucleotide polymorphism

SOMself-organizing map

SVMsupport vector machine.

Electronic supplementary materialThe online version of this article doi:10.1186-1471-2164-12-507 contains supplementary material, which is available to authorized users.

M Aleksi Kallio, Jarno T Tuimala contributed equally to this work.

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Autor: M Aleksi Kallio - Jarno T Tuimala - Taavi Hupponen - Petri Klemelä - Massimiliano Gentile - Ilari Scheinin - Mikko Koski


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