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Old 07-02-2014, 03:45 AM   #1
plabaj
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Location: Vienna

Join Date: Oct 2010
Posts: 85
Exclamation Power and Limitations of RNA-Seq: findings from the SEQC (MAQC-III) consortium

Dear All,

It is a pleasure to announce that during the Highlight Track of the ISMB 2014 conference we will give a talk where we will present the key findings of the SEQC/MAQC-III Consortium.

The main manuscript of the SEQC Consortium:
A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequence Quality Control consortium
is already at the copy-editing stage in the Nature Biotechnology and should be available shortly.

We therefore invite you to take part in the talk (HT-PP27 more details below) and following discussion as well as visit our posters adversing selected key results of the study: F45, F46, F47, F48, and N56

PP27 (HT)
Power and Limitations of RNA-Seq: findings from the SEQC (MAQC-III) consortium
Date: Monday, July 14, 11:00 am - 11:25 am
Room: 304
https://www.iscb.org/cms_addon/confe...sentations.php

Abstract:
We present an extensive multi-centre multi-platform study of the US-FDA MAQC/SEQC-consortium, introducing a landmark RNA-Seq reference dataset comprising 30 billion reads. Several next-generation-sequencing, microarray, and qPCR platforms were examined. The study design features known mixtures, wide-dynamic range ERCC spikes, and a nested replication structure -- together allowing a large variety of complementary benchmarks and metrics. We find that none of the examined technologies can provide a ‘gold standard,’ making the built-in truths of this reference set a critical device for the development and validation of novel or improved algorithms and data processing pipelines. In contrast to absolute expression-levels, for relative expression measures, good inter-site reproducibility and agreement of across platforms could be achieved with additional filtering steps. Comparisons with microarrays identified complementary strengths, with RNA-Seq at sufficient read-depth detecting differential expression more sensitively, and microarrays achieving higher rank-reproducibility. At the gene level, comparable performance was reached at widely varying read-depths, depending on the application scenario. On the other hand, RNA-Seq has heralded a gold-rush for the study of alternative gene-transcripts. Even at read-depths beyond 100 million, we find thousands of novel junctions, with good agreement between platforms. Remarkably, junctions supported by only ~10 reads achieved qPCR validation-rates >80-100%, illustrating the unique discovery power of RNA-Seq. Finally, the modelling approaches for inferring alternative transcripts expression-levels from read counts along a gene can similarly be applied to probes along a gene in high-density next-generation microarrays. We show that this has advantages in quantitative transcript-resolved expression profiling. There is still much to do!
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