Genomics | Bioinformatics
iDiscover – an intelligent assistant for integrative analysis of transcriptome data
Tomaž Curk*, Črtomir Gorup, Gregor Rot, Jernej Ule, Blaž Zupan
*Corresponding author: Tomaž Curk
Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
F1000Posters 2011, 2: 1500 (poster) [ENGLISH]
Poster [2.22 MB]
ISMB/ECCB 2011 , 20 - 22 Jul 2011, X68
International Society for Computational Biology
Next-generation high-throughput RNA-Seq and ChIP-Seq technologies produce enormous quantities of data. Even after filtering, reduction and summarization the user is confronted with an overwhelmingly large quantity of results that require tedious manual sifting before identification of potentially novel regulatory patterns. iDiscover is a knowledge-based, interactive web application that enables an efficient and effective scientific discovery through workflows that guide the researcher to the most interesting and unexpected patterns in transcriptome data.
As an input it accepts data and results from RNA-Seq and ChIP-Seq analyses performed by iCount – a computational pipeline that we have implemented for the analysis of iCLIP, ncRNA-Seq, mRNA-Seq, CLIP, CLAP, HITS-CLIP, and similar data. The main output of iCount is a nucleotide resolution and precisely quantified genome map of RNA-protein binding sites and mRNA expression. iCount also reports on quality of reads and mapping, summarizes statistics of expressed and bound regions, and provides detailed genome annotation of binding sites, peak analysis, binding sequence motifs, differential binding, RNA maps, gene expression, and differential expression.
While important on their own, all these results require the user to consider many different combinations and take an overwhelming large number of steps to identify interesting patterns in the data. Instead, iDiscover infers rules that relate the experimentally identified transcriptome elements in an experiment and combines the results and patterns from all experiments. Candidate patterns identified in this way are then ranked, allowing the user to focus on best candidates for novel hypotheses on regulatory rules.
No relevant conflicts of interest declared.
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