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There were lots of interesting developments this month that didn’t work their way into a full blog post. Here is an incomplete list of what
I’ve been tweeting about over the last few weeks. But first I want to draw your attention to the latest manuscript for a new bioconductor package for doing RNA-seq in R.
DEXSeq vs Cuffdiff. See the pre-publication manuscript from Simon Anders, Alejandro Reyes, and Wolfgang Huber: “
Detecting differential usage of exons from RNA-Seq data.”
DEXSeq is an R package by the same guys who developed the
DESeq R package and the
HTSeq python scripts. (Incidentally, both DESeq and DEXSeq are rare examples of bioconductor vignettes which are well developed and are a pleasure to read). I often use cufflinks/cuffdiff in the bioinformatics core was because many other tools and methods only allow you to interrogate differential expression at the gene level. Using cufflinks for transcriptome assembly enables you to interrogate transcript/isoform expression, differential splicing, differential coding output, differential promoter usage, etc. DEXSeq uses similar methodology as DESeq, but can give you exon-level differential expression, without going through all the assembly business that cufflinks does. In one of the supplementary tables in their pre-pub manuscript, they compare several versions of cuffdiff to DEXSeq on two datasets. Both of these datasets had biological replicates for treatment and control conditions. They compared treatment to controls, and found DEXseq gave you more significant hits than cuffdiff. Then they compared controls to other controls (ideally should have zero hits) and found cufflinks had way more hits. See p13, p23, tables S1 and S2.
Proper comparison treatment vs control, # significant hits:
DEXSeq: 159
Cuffdiff 1.1: 145
Cuffdiff 1.2: 69
Cuffdiff 1.3: 50
Mock comparison controls vs controls, # significant hits:
DEXSeq: 8
Cuffdiff 1.1: 314
Cuffdiff 1.2: 650
Cuffdiff 1.3: 639
In the UVA Bioinformatics core we strive for reproducibility, scalability, and transparency using the most robust tools and methodology available. It gives me pause to see such alarmingly different results with each new version and each
new protocol of a particular tool. What are your thoughts and experiences with using Cufflinks/Cuffdiff, DESeq/DEXSeq, or the many, many other tools for RNA-Seq (
MISO,
ExpressionPlot,
EdgeR,
RSEM,
easyRNASeq, etc.)? Please share in the comments.
Everything else:
| Webinar from @goldenhelixinc: Learning From Our GWAS Mistakes: From experimental design to scientific method https://t.co/KkxAn18p |
| [bump] Questions on cutoff setting of FPKM value & know genes filtering in Cuffmerge result http://t.co/iKMZ7Dsd #bioinformatics |
| Very cool: DNAse-Seq+RNA-seq used to show DNaseI sensitivity eQTLs are a major determinant of gene expression variation http://t.co/nPo3xHVa |
| Beware using UCSC GTFs in HTSeq/CovergeBed for counting RNA-seq reads. “transcript_id” is repeated as “gene_id”! https://t.co/ADg1Pi6U |
| Identification of allele-specific alternative mRNA processing via RNA-seq http://t.co/fig9cLlH #bioinformatics @myen |
| prepub on arXiv + analysis tutorial/walkthrough + AWS EC2 AMI + git repo + ipython notebook = reproducible research done right http://t.co/GPNmpdJD |
| NSF-NIH Interagency Initiative: Core Techniques and Technologies for Advancing Big Data Science and Engineering http://t.co/W3LUdCsG |
| New approach from @MarylynRitchie lab to collapsing/combining: using biological pathways rather than positional info http://t.co/ywWj0MNn |
| Cloud BioLinux: pre-configured and on-demand #bioinformatics computing for the genomics community. @myen http://t.co/3kCE0ktH |
| SCOTUS remands AMP v Myriad (BRCA) patent case to CAFC to consider in light of prometheus decision http://t.co/7CkTa4l0 |
| 57 year experiment, Drosophila kept in dark for 1400 generations, many evolutionary changes (record longest postdoc!) http://t.co/wukq8fAf |
| IsoLasso: a LASSO regression approach to RNA-Seq based transcriptome assembly http://t.co/FN1sYM8f #bioinformatics |
| Complex disease genetics is complex. Imagine that. Hirschhorn, Visscher, & the usual consortium suspects: http://t.co/Bwopxlx6 |
| MAnorm: a robust model for quantitative comparison of ChIP-Seq data sets http://t.co/pffuHIlO #bioinformatics |
| Nat Protocols: Differential gene & transcript expression analysis of RNA-seq w/ TopHat & Cufflinks http://t.co/U1ZpSE7V #bioinformatics |
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