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. 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.

Youthful Explorations

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Jul 152016

I recently finished reading Robb Lowe’s autobiography Stories I Only Tell My Friends. It is a pretty fun read, and as he talked about the movies he made, I started checking out many movies in the 80s which lead to the Brat Pack. The brat pack kids were crazy in the 80s. You kinda wonder what they did everyday except drinking, smoking, drugs and sex. But many of them, after having “been there, done that”, like Robb Lowe and Andrew McCarthy (although many never came back), really got themselves back together and are leading very purposeful lives.

After reading the book, I started discussing with my husband and other friends about raising kids. This generation of kids, especially those grown up in China, are so busy with their daily school and extra curricular activities, that they have very little time to free play and explore the world at their pace. Needless to say how much homework Chinese elementary, middle and high school students have every day, so they really don’t experience the world. College life could be interesting, but many are starting to think about going abroad or making money. Graduate student life could be monotonous (unless they find excitement in research discoveries), and students seem to have limited interactions with people outside their academic environment. After graduation, many will be busy with work to buy a house and raise a family. It is around the time when they are in their 40s when many people had a stable job, house, and family, when suddenly mid-life crisis hits. They look back and regret not having a wild youth and have worked too hard that their youth is now behind them.

I want to urge young students and postdocs, especially those in China, to treasure your valuable youthful years and fully explore the world. In additional to your study and research life, try something different and have fun, just so that you don’t have to regret not doing it later. Young people should try wild and healthy experiences like traveling, sports and dancing, building a robot or a car, picking up a new hobby, making a movie / book, learning to sell or negotiate, reading many different types of books, starting a new social group, news paper/blog, or a business startup. I learned a lot from Kaifu Li and Tony Hsieh‘s books, which are good examples. Also try out the 101 things to do at MIT. Many of these are wild, crazy, and fun :). These not only make life a lot more interesting, but also give us valuable experiences that could benefit us in later life. In summary, experience the youthful life to the fullest.

I know some young people also try things that might be considered bad. I am not advocating decadent living, instead just want to say, I am not going to make a big fuss about it if my kids or students occasionally did something wild and crazy. E.g. drinking (my family is quite against the US 21 drinking age, and we tell our kids they can drink a bit at home if they feel like it), smoking (trying a bit is not going to kill you, I heard every BBQ we eat is like smoking 12 cigarettes), drugs (every US president in the last 30 years probably tried weed), sex (be respectful and don’t hurt people, live-in as a tryout for marriage is not a bad idea), video games (I have heard advice on giving our kids enough video games to play now, so they can get over it in high school when they are busier at school), gambling (we turn green if we loose a few hundred dollars during school years; people in their 40s could gamble away millions). Just two things to be careful: try in moderation and don’t get addicted (esp drugs); and don’t leave long term damages (e.g. drug overdose, car accident from DUI, pregnancy or AIDS from unprotected sex, etc).

People in mid-life crisis also try these crazy “youthful explorations”, unfortunately the stakes are higher at this age. Recently I have seen / heard some sad stories of my very respected colleagues whose careers suffer significant setbacks due to midlife crisis. It is sad because I know some of these colleagues to be really brilliant and hard working scientists. Society is a lot more tolerant when young people at their teens or twenties try these crazy things or make mistakes, but society expects people in their 40s not be confused about anything (the Chinese saying 四十不惑), so expects the “youthful exploration” days to be over whether or not they have been there or done that before.

For me, there seem to be 2 things I could do, now that I have passed the youthful exploration age. The first is that, looking at the 80 movie stars turning 50, I found that those staying thin at least maintained some youthful charm, while those growing fat totally lost it. So we need to maintain our weight. The second is that kids are our saviors. As my kids grow older, I have to work hard to keep up with them (in sports, travel, music, etc; my older son has soundly beaten me in 100 meter IM after the last season), and I hope to enjoy some (if not all) fun explorations with them later. Hope my sons won’t mind having a crazy old mom sticking around sometimes :).

Jul 012016

When I was in my thirties, exercise was nice but not as necessary. With work and kids, exercise simply took lower priorities. After turning 40, my hair started to turn gray, I really felt my age but dying my hair is still a quick fix ;). Recently, when I wake up with a stiff back, notice body hair turning gray (how do you dye that?), have chest pain after a long and stressful day, exercise suddenly becomes the necessity! I now know why some of my colleagues in the forties and fifties exercise so much more regularly, because this enables them to work better on a daily basis.

I previously mentioned the very intensive and painful iPhone exercise program 7-min-work-out and how Yoga Studio was better. Recently this program has an upgrade called Sworkit, and I am switching from Yoga back to Sworkit. Instead of just having 14 30-sec exercises, the new program allows users to select many different exercise, each lasting only 30 second. I made a custom collection of ~180 different exercise, some building strength, some aiming at flexibility or relaxation, some on neck, arm, leg, back, etc. Then every day, I just specify the length of my work out (e.g. 20-30 min), the program will randomly sample exercises from the collection, so every day I get a different yet comprehensive workout. In the beginning, my body sores after an exercise, but now every morning after the workout I feel so GREAT! This exercise is extremely efficient and very easy to do during travel (some exercises on the floor and some on the bed, so no need to bring yoga mat).

I wish I had taken better care of my health when I was younger, but it is not too late to start now.

Jun 302016

Recently I visited Tsinghua University in China for a seminar. It is quite amazing how fast Tsinghua has grown in their biomedical research, the buildings, the faculty, the students, and the research outcome! Meeting with faculty I also saw many faculty collaborating with each other, which is a very healthy trend. This really underlines the importance of having a critical mass. Many young scientists are now unwilling to start their faculty career in the best universities in China, because these universities don’t necessarily offer as attractive startup package, often have worse life quality (air, housing, traffic), but have very high pressure for publication and funding. While these are good considerations, there are also some advantages to being in the best universities. I have worked on transcription factor binding motif before, and know how small differences in every nucleotide could influence the overall transcription factor binding. For example, at a good institution with a critical mass, the faculty might get students that are 10% better, receive 10% more research funding, hear 10% better seminars, have 10% better equipment platforms, find a collaborator 10% more capable, get their samples sequenced 10% faster. Even though each advantage might not look big, the PI only needs to work 10% harder himself, to receive almost twice (1.1^7) the research outcome! I guess that’s why Harvard is so exciting too!

Jan 092016

I sometimes see Chinese students / postdoc use the word “majorly” when they want to use the meaning of “major” / “majority” as an adverb. Instead of meaning “major” or “mostly”, it actually means “very” or “extremely”. So next time you want to say “majorly”, ask yourself “do I really mean extremely?”, “perhaps largely is more appropriate?” In fact, “majorly” is not a formal / academic word, so I would just suggest cutting this completely out of your vocabulary.

Nov 152015

Recently I read a book called SCRUM, which is a strategy used in rugby games. Originally designed for software development, SCRUM can also be used for learning, managing other projects, including research projects. The basic idea for SCRUM is that when you have a team working towards a common goal, they keep good communication and reprioritize tasks as needed. The team first figure out the tasks involved and prioritize the importance and the time needed for each task. Then the team picks the most important few tasks and allocate resources and people to finish them first. During the project, there is usually a project backlog board, an ongoing task board, as well as a finished task board. Sometimes a bigger task is divided into smaller chunks and a person or a team will work to finish individual chunks. Very often a small miscommunication, waiting for other people, as well as getting stuck on a small task waste a lot of time. Therefore, team members spend 10 to 15 minutes going over the progress of tasks daily, moved the finished tasks over to the finished board, then pick the most important task from the backlog board to the ongoing board to be worked on next. Everyone knows what everyone else in the team is working on and they know the overall objectives of the whole team. In a particular team member gets stuck on one task, other members in the team try to help in order to reach the objective for the whole team. The backlog board is actively updated to reflect the priorities of the team. As the project progresses, sometime an initial task is no longer needed or important, so can be moved off from the backlog board.

Our research team tried to use the SCRUM strategy to manage our research projects over the last six months, and were surprised by its effectiveness. There is a free website called Trello which enables teams to use the SCRUM strategy to manage projects. We manage each project with one separate board, and within each board there are often multiple lists: a “Backlog” list, a “Doing” list, and one “Done” list for each week before. Within each list, each “doing” task is assigned to a team member, with deadlines and checklists. Say a project team has a postdoc, a graduate student, a programmer and me (in fact even two-member teams work too). The project owner decides how to prioritize tasks and makes the ultimate call on the direction and finished milestone (e.g. paper), which is me or a senior and independent postdoc. The scrum master is often the postdoc (or potential first author of the paper) who coordinates the daily meetings and updates, maintains the Trello board, helps removing troubles or roadblocks. During daily updates, each team member goes over his card, reports progress or challenges, moves the done card to the Done list, and gets assigned new tasks from the Backlog. The progress could be written as comments in the card, uploaded attachment files, or links to files in directories where the team shares. We found that 15-minute updates everyday works much better than two-hour meetings once a week or two weeks, and the team can move forward very quickly. If someone gets stuck on a challenge which takes longer to trouble shoot, the team members might just use the daily update time to schedule a separate longer meeting to discuss afterwards. Periodically the whole team meets to visit all the milestones, reprioritize the tasks, and discuss ideas on how to make the process more efficient.

Ever since we started using the SCRUM methodology, the productivity of our team has drastically increased and we were able to finish four papers lately. I have also introduced SCRUM to other colleagues and at least some of them told me it is working very well for them too. So I would highly recommend the SCRUM techniques to my other colleagues. What we are using is probably SCRUM in its simplest form, and every time I read the book and wiki page again, I pick up more ideas which SCRUM recommends implementing.

Nov 062015

Recently a colleague suggested an interesting approach to calculate the impact of a specific paper by dividing the citation of the paper by the impact factor of the journal the paper is published. E.g. if paper A is published in Nature (IF ~36) and received 108 citations, while paper B is published in NAR (IF ~9) and received 54 citations, potentially paper B actually has better impact (54/9 > 108/36), because paper A has an unfair advantage of being read more by being published in Nature. Maybe the citation or the IF need to be further scaled (e.g. taking log or square root, or adding pseudo counts to journals with extremely low IF, etc), but this is certainly an interesting thought!

Oct 292015

Recently I finished another round of GCAT review and thought of lessons learned during the meeting. Some of it is similar to another blog I wrote going to GCAT for the first time, but good to refresh those ideas…

    1. Significance plays an important role. Many methods and techniques can be conceptually novel or even fancy, but if the reviewers don’t believe this will work, or answer important biomedical questions, or be used by many people in the community, they will not be the excited supporters of the proposal.

    2. Track record of the team is very important. If the PI is at the forefront of the technology, publishes good work in reputable journal, or has previous developed methods that are widely used, has earlier access to unique and large datasets, it gives reviewer confidence that the proposed work will likely have an impact.

    3. In the biosketch “contribution to science” section, when discussing each contribution, highlight the main idea in a phrase first before describing it in a more detailed paragraph. In fact, anywhere in the main proposal using bold, italic, color, or underline to help highlight the important points will help reviewers read and get the ideas.

    4. In the specific aims page, use the first two paragraphs to establish the motivation and significance, then in each aim try to explain in as much detail as possible the research strategy, what experiments, the idea behind the algorithm, the unique clever ideas, etc. Most reviewers only have this page to refer to during the review, so make the aims description meaty for reviewers to have concrete ideas what you are going to do.

    5. In the Innovation section of the proposal, educate the reviewers on the specific technological or methodological innovation, unique system or clever idea in the proposal.

    6. To develop a computational proposal, the PI should know the state-of-the-art methods, demonstrate clearly the gap in existing methods, the motivation of the proposed method. Sometimes a computer- or statistics-oriented proposal could completely loose the biology reviewers. Explain the intuition or the unique cleverness behind the method, the input / output of the method, so at least reviewers roughly understand what the method is trying to do. Finally, discuss the exact deliverables and metrics to measuring success (e.g. how to evaluate whether a new parameter or new model will make the results better, systematic experimental validation, etc).

    7. Some computational grants include an experimental collaborator to do validation, but sometimes the validation is just some experimental descriptions to generate more data without a clear idea how they validate or help refine the computational method. Some experimental grants will generate a lot of data, and data analysis is only added as an afterthought. Reviewers appreciate solid and coherent combination of the computational and experimental plans.

    8. Large scale ($) comprehensive data generation projects without clear motivation, good biological problem, and deeper mechanistic and functional follow up plans don’t usual fair very well. Also, it is probably wise for new investigators to stick to modular budget.

    9. Positively and fully address the previous reviewers’ comments in a revision. Reviewers give credit to good efforts in addressing previous reviews, especially more preliminary results and strengthened aims, which demonstrate the group is devoted to the project instead of “we will work on this crazy idea if we get $”.

    10. Pay attention to grantsmanship: have logical and coherent aims, discuss work around for potential pitfalls, include support letters to demonstrate significance or expertise or cover weakness, use clear and friendly ext format, check figure and equation visibility.