Archive for category science

On writing my first letter of recommendation

I was asked to write a letter of recommendation for someone. This is the first one I’ve ever written. Well, technically, I’ve written letters of recommendation before…but for myself; some professors I’ve asked letters from, have asked me to provide a draft they could then edit. I know this is usually frown upon by some people, but apparently this is more common that I imagined.

"Are all these letters of recommendation from your mother?"

“Are all these letters of recommendation from your mother?” (Image credit)

Anyway, let’s rephrase my statement from before: this is the first letter I’ve written for someone else. A former undergraduate student in our lab asked me to write a letter for her to apply to grad school in the US. It was great to be asked to do this, but agreeing to write one is a big responsibility. The idea is to describe the applicant’s strength and weaknesses, with regards to the program they are applying to; to describe whether they are a good fit for the program and mention why. Agreeing to write one, in my opinion, should be done with the idea that the writer is actually supportive of the applicant’s plan: you want the person to be able to be accepted into the program and I guess that if you can’t really recommend the person in an honest way, maybe you should simply decline to write it or mention your reservations to the student and let he/she decide whether he/she still would want you to write it.

I’m positive I’ll be writing more letters in the future, so I took this opportunity to study a little more about writing effective letters. I read a bunch of sample letters online and also read an addendum to the HHMI book “Making the Right Moves: A Practical Guide to Scientific Management for Postdocs and New Faculty”, entitled “Writing a Letter of Recommendation”. It was very useful and I recommend it. It includes a bunch of tips which were very helpful for knowing what to mention and what not to mention in the letter.

Before writing

Basically I started by asking the student about her plans and why she wanted to join the program. In the end, I simply asked for her intention letter which included all this information. Knowing her goals and motivation for the specific program was very useful for drafting the letter. Additionally, I asked her to send me her CV and asked if there was any particular aspect she wanted me to specifically discuss in the letter. In her case, she wanted me to center my discussion around her research experience. Note that I did not ask her to tell me which aspect I should compliment; simply which one she wanted me to focus on. I asked this because people usually get letters from different people highlighting different aspects of their CV/training.

What I wrote

In the letter, I basically introduced myself and described my relationship to the student. In my case, we shared a lab bench for over a year and I taught a few classes in which she was a student. I then mentioned how I ranked her among all undergrads I’ve met in a similar setting (i.e. in the lab). People usually do this by saying something like “In my opinion, candidate x is among the top 5 percent of the students I have known”. I then went on to describe the project she worked on while in the lab and her findings, highlighting not only the technical side of the project (her knowledge of lab techniques, the ones she had to implement and troubleshoot, etc.), but also aspects of her personality (personal attributes) that I considered were relevant for its development (i.e. can work independently, has a critical mind, is determined, etc). The idea is to be specific, to denote that you truly know the candidate. I then discussed writing and oral communication skills, as they relate to how she communicated her scientific findings.

I thought it would also be relevant to mention some shortcomings she had when she joined that lab that have now been improved, with specific examples as to how this has changed. As stated in the HHMI document I mentioned above, “You don’t just have to describe the candidate as he or she is right now—you can discuss the development the person has undergone”.

Then I discussed how good a fit her skills are to the specific program she applied to and gave my impression about her likelihood to be a successful student in that program.

I finished the letter summarizing my enthusiasm for the candidate and highlighting the skills I think can make her a good asset to the program. The last line was just my offer to help if further information about the candidate was required. In all, the letter was 2 pages long.

I think I did an acceptable job. When the time comes that I start reading letters from others, I’ll probably learn more tips on writing letters, and I’ll try to make them more effective and help students as much as I can.

I hope she gets in!

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Abstract word cloud from glam journals – 2013 edition.

Quick and simple post, considering it is Jan 1st and I’m still tired from last night, and the fact that I just came back from, you guessed it, the lab.

Anyway, I wanted to know what the most recurring topics were on the top two glam journals during 2013, so I obtained the 2013 PubMed-indexed abstracts from Nature and Science using EBOT and then used Wordle to generate a word cloud.

Here are the results:

1) Nature

Image2) Science

ImageCool, ah? You’ll notice that many words are shared.

It’s pretty easy to do it for any other journal or for any other query in PubMed using Ebot. If you want to do something similar for say, your country or institution and you need help, let me know.

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When I’ve planned and set up an experiment for weeks and fails on the last day because the light went out

falling

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When a competitor asks me what genes I’m studying next

tumblr_inline_mil42jEdrN1qz4rgp

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When someone uses Comic Sans in a presentation

wtf f0jL6

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Publishing like a Boss

Well, that’s a very productive May 2nd, 1997.

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Well played, Jay, well played.

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Reflections of a “Small Journal” Editor

Yet, without a thick skin, the patience of Job, and an enduring sense of humor, the hard-hit small editor would probably not last through a single cycle of his journal, laboring as he must under the curses of author uncomprehension, author uncooperativeness, author irresponsibility,and sometimes even downright stupidity

John O. Corliss

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Watson at the Eagle pub, 60 years later.

The Eagle is a pub at Cambridge, originally opened in 1667, which Jim Watson and the great Francis Crick, among others, used to visit during their time at the Cavendish Laboratory.

If you’ve read the Double Helix, you’ll be very familiar with it. If you haven’t, stop reading this post and go get a copy at Amazon. You won’t regret it.

Anyway, a great pic was posted in Twitter a few days ago, and I wanted to share it with you.

It was on February 28th, 1953, that Watson and Crick claimed they had cracked a big problem they have been working on; in fact Crick is said to have stormed into the Eagle proclaiming that they had ” found the secret of life”: they finally had a model for the structure of DNA.

Later that year, in April, the idea was formalized in the classic Nature paper.

2013 then, marks the 60th anniversary of this event, which opened the path for the explosion of molecular biology as a field.

Watson, now 84, appears in this great pic, taken to commemorate such an important event in the history of biological research. He is, of course, having a beer, like great scientists do.

Although this is not the original Eagle pub in Cambridge, but the one at CSHL, it is still a nice photograph.

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(Picture credit: CSHLnews)

Just for comparison, this is a picture taken of both of them in 1959.

james-watson-and-francis-crick-in-1959

(Image credit)

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When I read an article and notice I’ve been scooped….

crying 14loqba

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The ENCODE “takedown” paper: some quotes

Soon after the recent set of ENCODE papers came out, several scientists raised concerns regarding the estimates about the encodefraction of the genome that appears to be functional, that the authors put forward: according to them, ~80% of the human genome is functional.
This, of course, greatly differs to what most of us think, considering, among other things, that the fraction of the genome that is evolutionarily conserved through purifying selection appears to be under 10% (what about the rest? We think it divides between junk DNA and some “unknowns”).

The problem mainly arose from the definition of “functional” that ENCODE used, one that is so loose, that may not be useful at all.

In fact, “according to ENCODE, for a DNA segment to be ascribed functionality it needs to (1) be transcribed or (2) associated with a modified histone or (3) located in an open-chromatin area or (4) to bind a transcription factors or (5) to contain a methylated CpG dinucleotide” (Graur et al., 2013). You would agree that this criteria is very lenient, hence, the 80% estimate.

A recent paper, ruthlessly discusses the ENCODE paper and takes great issue with the “80%” estimate. The authors “detail the many logical and methodological transgressions involved in assigning functionality to almost every nucleotide in the human genome“. The manuscript reviewers could have suggested the authors to tone it down a little, but from what I found out in the web, evolutionary biologists tend to be very strong about their opinions on paper, when discussing the work of others they disagree with.

I encourage you to read the article, which is freely available. In the meantime, here are a few quotes:

The ENCODE results were predicted by one of its authors to necessitate the rewriting of textbooks. We agree, many textbooks dealing with marketing, mass-media hype, and public relations may well have to be
rewritten.

ENCODE adopted a strong version of the causal role definition of function, according to which a functional element is a discrete genome segment that produces a protein or an RNA or displays a reproducible biochemical signature (for example, protein binding). Oddly, ENCODE not only uses the wrong concept of functionality, it uses it wrongly and inconsistently

We identified three main statistical infractions. ENCODE used methodologies encouraging biased errors in favor of inflating estimates of functionality, it consistently and excessively favored sensitivity over specificity, and it paid unwarranted attention to statistical significance, rather than to the magnitude of the effect.

At this point, we must ask ourselves, what is the aim of ENCODE: Is it to identify every possible functional element at the expense of increasing the number of elements that are falsely identified as functional? Or is it to create a list of functional elements that is as free of false positives as possible

Comparative studies have repeatedly shown that pseudogenes, which have been so defined because they lack coding potential due to the presence of disruptive mutations, evolve very rapidly and are mostly subject to no functional constraint (Pei et al. 2012). Hence, regardless of their transcriptional or translational status, pseudogenes are nonfunctional!

For example, according to ENCODE, the putative function of the H4K20me1 modification is “preference for 5’ end of genes.” This is akin to asserting that the function of the White House is to occupy the lot of land at the 1600 block of Pennsylvania Avenue in Washington, D.C.

So, what have we learned from the efforts of 442 researchers consuming 288 million dollars? According to Eric Lander, a Human Genome Project luminary, ENCODE is the “Google Maps of the human genome” (Durbin et al. 2010). We beg to differ, ENCODE is considerably worse than even Apple Maps.

Evolutionary conservation may be frustratingly silent on the nature of the functions it highlights, but progress in understanding the functional significance of DNA sequences can only be achieved by not
ignoring evolutionary principles

High-throughput genomics and the centralization of science funding have enabled Big Science to generate “high-impact false positives” by the truckload (The PLoS Medicine Editors 2005; Platt et al. 2010; Anonymous 2012; MacArthur 2012; Moyer 2012). Those involved in Big Science will do well to remember the depressingly true popular maxim: “If it is too good to be true, it is too good to be true.”

We conclude that the ENCODE Consortium has, so far, failed to provide a compelling reason to abandon the prevailing understanding among evolutionary biologists according to which most of the human genome is devoid of function

(…) according to the ENCODE Consortium, a biological function can be maintained indefinitely without selection, which implies that at least 80 – 10 = 70% of the genome is perfectly invulnerable to
deleterious mutations, either because no mutation can ever occur in these “functional” regions, or because no mutation in these regions can ever be deleterious. This absurd conclusion was reached through various means (…)

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