Hi Marco,

A nice overview how all these tools for gene/transcript expression ptofilling work can be found here:

http://arxiv.org/PS_cache/arxiv/pdf/1104/1104.3889v2.pdf

Althought it is already a few years old it can give you the theoretical overview.

I would just maybe add to the list mentioned by Lior one more position:

http://bioinformatics.oxfordjournals.org/content/28/13/1721.short

Within a few weeks there should apear online a Naure Biotech paper from a SEQC consortium (I am one of the leading authors; manuscript has just been accepted) about cross-site and cross-platform RNA-seq study, which also compares different RNA-seq piplelines.

Once we will get pre-prints can send a copy to you.

Best wishes,

Paweł

Hi Marco,

A nice overview how all these tools for gene/transcript expression ptofilling work can be found here:

http://arxiv.org/PS_cache/arxiv/pdf/1104/1104.3889v2.pdf

Althought it is already a few years old it can give you the theoretical overview.

I would just maybe add to the list mentioned by Lior one more position:

http://bioinformatics.oxfordjournals.org/content/28/13/1721.short

Within a few weeks there should apear online a Naure Biotech paper from a SEQC consortium (I am one of the leading authors; manuscript has just been accepted) about cross-site and cross-platform RNA-seq study, which also compares different RNA-seq piplelines.

Once we will get pre-prints can send a copy to you.

Best wishes,

Paweł

Thank you very much for you answer,

I had a look at many reviews in the last week and things are becoming more clear to me.

A copy of your paper would be really highly appreciated!

Thanks,

Marco

Since I received private messages asking further information on RNASeqV2, this is TCGA pipeline for generating Level3 RnaSeqV2 data (from cghub)

https://www.cghub.ucsc.edu/docs/tcga/UNC_mRNAseq_summary.pdf

Steps: (Mapslice+RSEM)

1. Format fastq1+fastq2 (paired ends) for Mapsplice

2. Mapsplice

3. Add read groups

4. Convert back to phred33

5. Sort by coordinate (samtools)

6. Flagstat (samtools)

7. Indexing (samtools)

8. Sort by chromosome, then read id

9. Translate to transcriptome coords

10. Filter indels, large inserts, zero mapping quality from transcriptome bam

11. RSEM

12. Strip trailing tabs from rsem.isoforms.results

13. Prune isoforms from gene quant file

14. Normalize gene quant

15. Normalize isoform quant

16. Junction counts

17. Exon counts

18. Cleanup large intermediate output

Dear Marco,

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 (http://www.fda.gov/ScienceResearch/BioinformaticsTools/MicroarrayQualityControlProject/#MAQC-IIIalsoknownasSEQC).

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/conferences/ismb2014/paperpresentations.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!