At last week's 2013 useR! conference in Albacete, Spain, Martin Morgan and Marc Carlson led a course on using R/Bioconductor for analyzing next-gen sequencing data, covering alignment, RNA-seq, ChIP-seq, and sequence annotation using R. The course materials are online here, including R code for running the examples, the PDF vignette tutorial, and the course material itself as a package. Course Materials from useR!

Two of the most common questions at the beginning of an RNA-seq experiments are "how many reads do I need?" and "how many replicates do I need?". This paper describes a web application for designing RNA-seq applications that calculates an appropriate sample size and read depth to satisfy user-defined criteria such as cost, maximum number of reads or replicates attainable, etc.

I've said it before: Twitter makes me a lazy blogger. Lots of stuff came across my radar this week that didn't make it into a full blog post.

In case you missed it, a new paper was published in Nature Biotechnology on a method for detecting isoform-level differential expression with RNA-seq Data: Trapnell, Cole, et al. "Differential analysis of gene regulation at transcript resolution with RNA-seq." Nature Biotechnology (2012). THE PROBLEM RNA-seq enables transcript-level resolution of gene expression, but there is no proven methodology for simultaneously accounting for

There's a new kid on the block for RNA-seq alignment. Dobin, Alexander, et al. "STAR: ultrafast universal RNA-seq aligner." Bioinformatics (2012). Aligning RNA-seq data is challenging because reads can overlap splice junctions. Many other RNA-seq alignment algorithms (e.g. Tophat) are built on top of DNA sequence aligners.

Update (Dec 18, 2012): Please see this related post I wrote about differential isoform expression analysis with Cuffdiff 2. DESeq and edgeR are two methods and R packages for analyzing quantitative readouts (in the form of counts) from high-throughput experiments such as RNA-seq or ChIP-seq.

Recently published in Nucleic Acids Research: F. Zambelli, G. M. Prazzoli, G. Pesole, G. Pavesi, Cscan: finding common regulators of a set of genes by using a collection of genome-wide ChIP-seq datasets., Nucleic acids research 40 , W510–5 (2012). Cscan web interface screenshot This paper presents a methodology and software implementation that allows users to discover a set of transcription factors or epigenetic modifications that

I just got this announcement from EMBL-EBI about an RNA-seq/ChIP-seq analysis hands-on course.

I found the slides below on the education page from Bioinformatics & Research Computing at the Whitehead Institute. The first set (PDF) gives an overview of the methods and software available for quality assessment of microarray and RNA-seq experiments using the FastX toolkit and FastQC. The second set (PDF)  gives an example RNA-seq workflow using TopHat, SAMtools, Python/HTseq, and R/DEseq.

James Taylor came to UVA last week and gave an excellent talk on how Galaxy enables transparent and reproducible research in genomics. I'm gearing up to take on several projects that involve next-generation sequencing, and I'm considering installing my own Galaxy framework on a local cluster or on the cloud. If you've used Galaxy in the past you're probably aware that it allows you to share data, workflows, and histories with other users.