"NGS without bioinformatics expertise"

This showed up in my inbox today:

Integromics launches SeqSolve, a Next Generation Sequencing functional ...
PharmaLive.com (press release)
SeqSolve is the first NGS analysis software on the market specifically developed for data interpretation without requiring bioinformatics expertise. ...

Might be a bit presumptious, but I would guess that a lot of scientists would like to have someone with "bioinformatics expertise" do data analysis for their Next-Gen Sequencing projects. But hey, if someone wants to spend $10-$50K on sequencing, but doesn't want an expert to look at the data, good luck with that. Our "Sequencing Informatics Group" at NYU is now taking outside consulting work for all types of NGS bioinformatics projects. It is sort of like fixing your Porche - you can go to the Foreign Car specialist mechanic, or you can go to PepBoys and buy some spark plugs and a wrench kit.

We (Ross Smith) have been developing our own visualization toolkit for NGS. Latest version allows us to integrate RNAseq and ChIPseq with RefSeq or other annotation. Net result is much more accurate assignment of TF or histone modification sites to genes, and the ability to clearly see which of multiple TSS are actually being used in a particular sample/cell type. It is very beautiful.

ChIPseq QC

I am speaking at the CHI Next-Gen Sequencing conf in Providence 9.26.2010 (Sunday short course). My topic is going to be about the role of bioinformatics in QC for NG sequencing, with examnples from ChIPseq, where I have the most experience.

My main point is that the informatics team works hardest on experiments that produce poor data - or where the data contradict the investigator's expectations. When the experiment is beautiful, then you can use your automated (or semi-automated) pipeline, and hand over the analyzed data with a standard report. For a transcription factor type ChIPseq, the standard result is a set of peaks with p-value and fold change vs. an input DNA, annotated by distance to the nearest gene's Transcription Start Site. If pressed, we can deliver this about 2 days after the sequencing run is complete. For an epigenomics type ChIPseq (histone methylation, acetylation, etc) we deliver both peaks vs input DNA and some type of fold-change for each peak comparing one biological condition vs. another.

However, we spend a lot more time squabbling about runs with high PCR duplication, weird artifacts, low yield, peaks in the input DNA lane, etc. To deal with this, we have been developing a variety of tools to quantify overall data quality in a ChIPseq run. We are looking as the overall clustering of mapped reads on the Reference genome (average spacing of adjacent/overlapping reads), as well as coverage at various depths. Some of these metrics make intersting graphs, but we have not completely pinned down their predictive power for understanding the data.

We have recently been playing with selecting sets of genes based on external data such as gene exprssion values from microarray or RNAseq experiments, and looking at the aggregate profile of reads mapped near the TSS of groups of genes that are upregulated, downregulated, unchanged, etc. By combining reads for a bunch of genes, we get smooter curves and you can actually say fairly clearly that upregualted genes have (or do NOT have) a change in histone methylation near the TSS as compared with downreg or unchanged genes.

Correlation does not equal causation

I am not a biostatistican, but I can play on on Blogger. Attention science news writers: "Correlation does not equal causation." Can we institute a simple electroshock penalty for those who cite such fraudulent statistics?

Check this out:

"new study finds that older women who use multivitamins may be more likely than non-users to develop breast cancer"

http://www.reuters.com/article/idUSTRE62S4F520100329

Is there any chance of a sampling bias in this study? Any chance at all that the population of elderly women who take multivitamins may be different in any health parameters from the population that finds no need for such supplements? Furthermore, is there any chance that the study subjects taking the vitamins are more likely to be examined more frequently and therefore more likely to have cancer detected?

This reminds me of the brilliant study (cited from the grocery store tabloids by my sister-in-law) that diet soda makes you fat, because a study found that people who drink diet soda were more likely to be overweight than those who do not drink it.

Esther Dyson disrespects geneticists

I was reading the NY Times on Sunday (3/21/2010). I do that. In the Business section there was an article about the relatively poor sales of the direct-to-consumer genetic tests offered by the startup company 23andMe. Other competitors in this market have done even worse. Several scientists interviewed in the article said basically that there is very little predictive medical value to these SNP profile tests. Then they interviewed Esther Dyson, who is apparently on the board of directors of 23andMe. She provided this clunker of a quote:

Ms. Dyson called it “appallingly paternalistic,” to think consumers could not interpret genetic information without help of a doctor. “People can understand statistics about baseball,” she said, “and I think they ought to understand statistics about genetics.”

Does this not trivialize all of the work of medical geneticists, biostatisticians, and bioinformaticians. Is our work really no more challenging than interpreting baseball statistics? Gee thanks Ms. Dyson.