Sep 282016

When I was a graduate student at Stanford, fellow graduate students hang out together all the time, going to social activities, consulting on class or research projects, or gossiping about different advisors or laboratories. One topic we discuss often is how long it takes for our respective advisors to revise our manuscripts. A common complaint from students is, “I have sent this manuscript to my advisor for two weeks, and still haven’t heard a word from my advisor about it. What is s/he doing??”. Having been a faculty for 14 years, I finally understand the answer to this question.

First of all, I appreciated how busy a faculty’s schedule is, and it gets busier as the faculty becomes more senior. Since I wrote the “Fit Tasks to Schedules” blog, I feel much less guilty saying No to people who requests me to review papers / grants or write letters of references / evaluations on a short notice. Also, I now categorize manuscripts from my trainees into 4 categories based on how important and well written the ms is.

A. For ms that is very important (clever method, significant finding, potential for high profile publication) and well written, I am extremely motivated to revise it as soon as I receive it. I am very happy to use my skills and knowledge to improve the writing of the ms, and check it off my to-do list.

B. For a ms that is well written but not very important, or not so central to the research program in my group, I usually take a quick look and give some brief feedback quickly, and mostly rely on the first and senior authors to revise the ms in detail.

C. For a ms that is very important and yet not so well written, I feel strongly that the ms has potential but also the pain associated with the revision / rewriting. It would take the trainees persistence to find time and work with me on the revision, and my perseverance to revise the ms to a good shape in a couple weeks or months.

D. For a ms that is not so important and not so well written, it usually languish on my computer for months. The ms is often not in shape for submission but it will take too much time and efforts to revise it.

So when trainees wonder why their advisors still haven’t revised their ms, it is often because their ms is in category C or D. When this happens, the trainees often feel that the ball is in the court of the advisors (I already give the ms to her, and it is her responsibility to revise it), but in fact it is still in the court of the trainees (I better revise this ms myself more so it is in better shape for my advisor to revise). So trainee should take the initiatives and schedule a time (e.g. 30-60 min) to talk to the advisors about the ms to get some quick comments, or to sit and work on the revision together with the advisor. After all, if a ms drags on too long or never gets published, it probably hurts the trainees more than the advisors.

Sep 232016

Recently I went on a seminar trip, and happened to discuss DNase-seq data analysis with colleagues. I realized that many people didn’t know about our paper on the potential issues with DNase-seq footprint analysis.

After our original paper, the Stam lab submitted a correspondence to Nature Method challenging our study, and we were asked to submit a response. Both were submitted to reviewers, who turned out to be overwhelmingly supportive of our study. Unfortunately based on these reviews, the editor decided not to publish the correspondence and our response, which could have been informative to the research community. I still see people toil with DNase-seq footprint analysis now, only to reach similar conclusions as we did in 2014. So, instead of a lengthy blog about our original paper, I would like to include this response we wrote, which clearly summarized the technical issues with DNase-seq footprint analysis. I apologize for not being able to include the original correspondence from the Stam lab, because I don’t have their permission to post here, but I hope the readers can guess.

Overall the DNase-seq data from the Stam lab has been very high quality and extremely valuable to the community, and is one of the crowning successes of the ENCODE project. However, we cautioned the liberal calls of DNase-seq footprints, due to DNase I cutting bias and over dispersion of sequencing noise. Similar caution should also be given in ATAC-seq footprint analysis, in fact we see even stronger cutting bias in ATAC-seq. Instead of footprint analysis, we believe DNase/ATAC-seq peak heights (or read count in the peaks) with motif hits could better predict TF binding.