[lre1234]

As some one who did a solely molecular biology based PhD and has since transferred into the computational world, I think that this site is somewhat biased towards the bioinformatics people. (this is not necessarily a bad thing, and it has been exceedingly helpful to me over the past few years.) But, one thing I have noticed is that many bioinformaticians do not always understand the finer points of a lab experiment. Not in terms of the concept of using biological or technical replicates, but the physical act of doing cell culture, western blots, etc. I have met some people, who would look at a paper in Cell for example, and see 7 figures of western blots, and just think that al you have to do is buy an antibody and it's done, when in reality there is lots of optimization and other work behind all of that. The same goes for the lab people who think that analyzing a number of sequence datasets is just a click away.

So, no 1 person can do it all, and good communication between all parties is need.

As a side and maybe unrelated note, I think that one problem is that in many if not the majority of papers that come out, the data analysis sections are rather poorly described, and just give a reference to an older paper, especially when something custom or unique was done. Maybe there should be better standards on how to present the methods on how data was analyzed, which may give a better explanation to the lab people. The same goes the other way, better description of methods for the lab people. Who knows, this may fix the lack of reproducibility in many papers!

[Jessica_L]

...I think that this site is somewhat biased towards the bioinformatics people. (this is not necessarily a bad thing, and it has been exceedingly helpful to me over the past few years.) But, one thing I have noticed is that many bioinformaticians do not always understand the finer points of a lab experiment.

I was just having a variation on this conversation the other day. I said then, as I'll say now, that I think it's been a natural progression over time. Way back when, none of the protocols were nailed down and Illumina was constantly changing reagents and preps to address performance issues. There was a lot of discussion here about best practices or does-your-data-look-better-with-the-new-chemistry, etc. A lot of those forums settled down as performance issues were worked out.

While a lot of the wet lab work was eventually simplified into kits, the way data gets processed hasn't been that straightforward or static-- you might adjust the way data is trimmed based on your run QC metrics, or you might want to adjust a filter in a variant caller if you're interested in novel variant detection for a particular sample. Questions come up around how to do that, or the best practices for doing so. Then there are people like me that have been doing the bench work for years and there's a lot to learn on the analysis end, so we ask questions. Also, Illumina used to have next to zero technical support for bioinformatics/pipeline problems, so we came here for answers; now most analysis has shifted to open tools like tophat/bowtie/cufflinks or BBTools or SAMTools and if we have issues, we still come here for answers.