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.
I just got an email from Illumina about a webinar that looks interesting this Wednesday at 9am PST (noon EST) on clinical applications of next-gen sequencing. Date: Wednesday, December 7, 2011Time: 9:00 AM (PST)Speaker: Rick Dewey, MD, Stanford Center for Inherited Cardiovascular Disease Next-generation sequencing (NGS) presents both challenges and opportunities for clinical care.
BioMart recently got a facelift. I'm not sure if this was always available in the old BioMart, but there's now a link to a gene ID converter that worked pretty well for me for converting S. cerevisiae gene IDs to standard gene names. It looks like the tool will convert nearly any ID you could imagine. Looks like it will also map Affy probe IDs to gene, transcript, or protein IDs and names.
Gene Expression Omnibus is NCBI's repository for publicly available gene expression data with thousands of datasets having over 600,000 samples with array or sequencing data. You can download data from GEO using FTP, or download and load the data directly into R using the GEOquery bioconductor package written (and well documented) by Sean Davis, and analyze the data using the limma package.
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.
In general, the standard practice for correcting for population stratification in genetic studies is to use principal components analysis (PCA) to categorize samples along different ethnic axes . Price et al. published on this in 2006, and since then PCA plots are a common component of many published GWAS studies.
I’m a bit exhausted from a week of excellent science at ICHG. First, let me say that Montreal is a truly remarkable city with fantastic food and a fascinating blend of architectural styles, all making the meeting a fun place to be…. Now on to the genomics – I’ll recap a few of the most exciting sessions I attended. You can find a live-stream of tweets from the meeting by searching the #ICHG2011 and #ICHG hashtags.
This week, I'm off to Montreal for the International Congress on Human Genetics and I hope to see you there! If you are attending and are already a part of the Twitterverse, bring a tablet or phone and tweet away about the meeting using the official hashtag, #ICHG2011.
Like most bioinformatics nerds (or anyone with a facebook account), I’m fascinated by networks. Most people immediately think of protein-protein interaction networks, or biological pathways when thinking about networks, but sometimes representing a problem as a network makes solving problems easier. Recently, some collaborators from the PAGE study had a list of a few hundred SNPs gathered from multiple loci across the genome.
I just accepted an offer for a faculty position at the University of Virginia in the Center for Public Health Genomics / Department of Public Health Sciences. Starting in October I will be developing and directing a new centralized bioinformatics core in the UVA School of Medicine. Over the next few weeks I'm taking a much-needed vacation next door in Kauai and then packing up for the move to Charlottesville.