This week we published a new, high-quality genome sequence of one of Australia’s most iconic animals, the short beaked echidna. The almost gapless genome sequence of this egg-laying mammal helps researchers to track genomic reshuffling events that gave rise to a perplexing  sex determination system. At first glance they may be mistaken for a weird-looking hedgehog.

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.