Optimizing bioinformatics workflows for data analysis using cloud management techniques
Proceedings of the 6th workshop on Workflows in support of large-scale science
Adaptive resource configuration for Cloud infrastructure management
Future Generation Computer Systems
Managing and Optimizing Bioinformatics Workflows for Data Analysis in Clouds
Journal of Grid Computing
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Motivation: Measurement precision determines the power of any analysis to reliably identify significant signals, such as in screens for differential expression, independent of whether the experimental design incorporates replicates or not. With the compilation of large-scale RNA-Seq datasets with technical replicate samples, however, we can now, for the first time, perform a systematic analysis of the precision of expression level estimates from massively parallel sequencing technology. This then allows considerations for its improvement by computational or experimental means. Results: We report on a comprehensive study of target identification and measurement precision, including their dependence on transcript expression levels, read depth and other parameters. In particular, an impressive recall of 84% of the estimated true transcript population could be achieved with 331 million 50 bp reads, with diminishing returns from longer read lengths and even less gains from increased sequencing depths. Most of the measurement power (75%) is spent on only 7% of the known transcriptome, however, making less strongly expressed transcripts harder to measure. Consequently, Contact: rnaseq10@boku.ac.at Supplementary information: Supplementary data are available at Bioinformatics online.