Monday, April 14, 2014

56 Different Points on the Gender Spectrum

I was on several panels at the Conference on World Affairs (#CWA2014). One of them (which I was both most excited for, and most nervous for) was also video-recorded:
Panel 3712
56 Different Points on the Gender Spectrum
3:00-4:20 on Wednesday April 9, 2014
UMC Center Ballroom 
Margot Adler
Joel Gallant
Judithe Registre
Melissa Wilson Sayres
Moderator: Mindy Pantiel

I'm not sure how long this will be online, but I have ordered this video-recording, as well as the audio recording from the other panels I was on, and am inquiring about sharing them.

I forgot the awesomeness of the way this panel ended. The 56 refers to the different gender identities allowed in facebook at the time. I have transcribed it here for you.
Mindy: We have time for one more very brief question and this person has been very patient. 
Audience member: The question will be, just how many stories can people be expected to learn? The context is, I don't believe you can accept somebody unless you understand them, and I don't think you understand them until you know their story. 56 genders, 56 stories. It impresses me as a huge burden to learn 56 stories. Just how big a burden can people be expected to take on? How many stories can people be expected to learn? 
Joel Gallant: Well let's not take the 56 too literally. This is a facebook invention. I mean, it's not an invention, these come from somewhere, but the 56 comes from facebook as far as I know. And remember, that if you look at the categories in the facebook list, which I did last night, y'know, there's lots of overlap, these are not biological categories. The stories... there's many more than 56 stories. There's a story for every person, and what you want to know is not their facebook classification, but what's their personal story? And when you find that out, it won't really matter what they call themselves, 'cause you'll know that person as a human being, and that's the story you want to learn. 
Melissa Wilson Sayres: I'll say. According to the world population counter there are currently: seven billion, two hundred twenty-five million, three hundred eighty two thousand, eight hundred and fourty... nine, stories. 
Judithe Registre: I'm just going to add though there's one story. The story is that you feel pain, I feel pain, we hunger, there is one human story. Seven million people and one thing I've been fascinated by, whether I move across different countries around the world, that people have the same desire: to be respected, to live with human dignity. That is one story. And if we can remember that one story, the 56 stories become meaningful. If we can't remember that one story, the 56 stories is meaningless.

Monday, March 31, 2014

Academic startup: What is negotiable?

Okay, so you've been on your job interview (tips here for preparing for that), and now you have an offer. Yay! It's time to negotiate. Oh... 

If you're like me, you've been told over and over that we should negotiate, but not how to negotiate, or what to ask for

So, I asked twitter for advice on what to request in negotiations and startup packages. The #startupwishlist tweets were Storified here, and summarized below. In addition, the book, At the Helm: Leading Your Laboratory was recommended for new PIs, which also has a chapter on job applications.

NOTE: Keep in mind that many of these suggestions come from people at research-heavy institutions. Resources will differ from institution to institution, and especially between institutions with different missions. You will need to prioritize what you need to be successful, and balance that with the resources/facilities of the institution. That said, at least this list can give you some ideas of things that are negotiable.

Be informed when negotiating salary.
"Day in the Life: Lunch Money" by marya via Wikimedia Commons
  • Confirm whether 9-month or 12-month appointment.
  • Ask for summer salary for 2-3 years.
  • X years of guaranteed salary, and any salary that is covered by outside grants to convert to unrestricted funds.
  • When is the start date? Can you start early, or push it back?
  • 10% increase in salary, unless you have better information from inside the department (Can check the web for salaries for public institutions. University library reference might also have this info).

Make sure you have the equipment you need to succeed.
  • Confirm availability of any major equipment and space modifications you need.
  • Ask whether space renovations are included in or separate from startup budget.
  • Negotiate access to equipment that is shared, or in another person's labspace.
  • Negotiate ongoing service contracts for equipment, and non-expiration of these accounts.
  • Computational resources - desktops/laptops, node buy-in, annual HPC fees
  • Software
  • Sofa, table, chairs, coffee maker, fridge

Experiments are run by people.
  • Ask about University rates for overhead for students/postdocs/techs/staff.
  • $$ for 2 graduate students 
  • Access to administrative staff for grants
  • Does the department have a regular source of TAships for students?
  • $$ for postdocs
  • $$ for tech/lab manager

Time limits on spending startup?
  • Know if there are time limits for spending the money (also if $$ able to roll-over).
  • Request some startup to go to an unrestricted account (versus only to personnel or equipment).
  • Is the startup a lump sum or a set amount each year? 
  • What restrictions are there on Startup spending, and are there reporting requirements?
  • Flexibility for how to spend, versus what was requested.
  • Ask for 2-5 years to spend start-up

You need to travel to share your results and network.
Photo by Douglas Paul Perkins, via Wikimedia Commons
  • $$ for travel for you and lab members for first 2-3 years.
  • An annual professional allowance each year (for conference travel, journals, professional membership).

How many new courses will you need to develop in the first 5 years?


  • Protected time (preferably >1year), including teaching reduction and protection from service - get it in writing.
  • Ask for written out %FTE expected of teaching vs research.
  • Ask what %FTE is covered by department versus needing to get grants to fund yourself (mostly for medical schools).
  • Can time off teaching be held and used after the first year?
  • What courses you will teach over the first 4-5 years.

"Almost done" by Lisa Risager via Wikimedia Commons
  • Extension the deadline for you to make your decision.
  • A parking lot near your building
  • Housing/relocation allowance (sometimes you can request a month's extra salary if moving expenses aren't explicitly covered).
  • Slot in the University affiliated daycare/preschool
  • A semester of teaching/service relief for parental leave/dependent care

In summary:

What is negotiable? Everything.

April: I'll be around

It turns out that April is going to be a big travel month for me. If you'll be at any of these events, I'd be very happy to meet up!


Wednesday April 2, 2014 - CEHG Seminar at Stanford

I will be traveling to the South Bay, and will be available to meet with people 10am-3pm. See the announcement and abstract here.

Seminar on Computational, Evolutionary and Human Genetics
Stanford University
1:00pm Lunch; 1:15 Seminar
Clark Center S360

April 6-12, 2014 - tweeting for @realscientists

I will be tweeting for @realscientists!! I'm preparing a series of topics to cover, focusing on sex chromosome and sex-biased evolution.

If you have specific questions about my research, sex chromosomes, sex-determination, or the related, leave them below, and I'll address them during this week.


April 7-11, 2014 - Conference on World Affairs panelist

I am thrilled to have been invited to participate in the 66th Annual Conference on World Affairs at the University of Colorado Boulder. Here's my profile for the conference

I'll be serving as a panelist on seven different panels
1716 I F-ing Love Theoretical Research!
    3:00-4:20 on Monday April 7, 2014
    Visual Arts Complex 1B20

1864 Gentrification, Homelessness and the American City
    4:30-5:50 on Monday April 7, 2014
    UMC East Ballroom

2464 Young Scientists Making Their Mark
    12:30-1:50 on Tuesday April 8, 2014
    UMC West Ballroom

3316 Science Ain’t What It Used to Be
    11:00-12:20 on Wednesday April 9, 2014
    UMC West Ballroom

3712 56 Different Points on the Gender Spectrum
    3:00-4:20 on Wednesday April 9, 2014
    UMC Center Ballroom

4611 Controversies Inside Science
    2:00-3:20 on Thursday April 10, 2014
    UMC 235

5262 Into the Future of Science and Technology
    10:30-11:50 on Friday April 11, 2014
    UMC 235

April 17-18, 2014 - RCN Network for Integrating Bioinformatics into Life Sciences Education

As a computational biologist I am a huge advocate for increasing students' understanding of mathematics and computational approaches. So, I jumped at the opportunity to participate in the Research Coordination Network (RCN), Network for Integrating Bioinformatics into Life Sciences Education (NIBLSE). As it happens, the RCN NIBLSE is organizing a conference in Omaha, Nebraska this April, so I'll be making a whirlwind trip there this month as well. I'll be sure to share what I learn here. 

Other than this, I'll be in Berkeley, getting work done. 

Wednesday, March 19, 2014

Template rejection letters are better than no letter at all.

It is the academic job season, and as offers are made, we receive rejection letters, sometimes. But more often than not, applicants don't hear anything. For someone whose life is built on data and observations I absolutely prefer to receive a rejection letter. At least then I know where I stand. But, today I laughed out loud a the rejection letter I received. I've changed the names of the people and University to italics, but nothing else, including the salutation: 
Dear Dr. $[LastName], 
Thank you for your application for the faculty position in the UniversityDepartment of Biology. 
While we were impressed with your background and research goals, uponcareful review of your application materials we have identified othercandidates who are a better match for our faculty development plans inthe department. This is the result of our need to complement existingfaculty research programs. 
Thank you again for your interest in the University Department of Biology. Wewish you the best of luck in your career endeavors. 
Dr. Forgot to Check the Template and the entire search committee
I know that these are form letters, and I am definitely guilty of making these kinds of silly spelling/grammar/typing errors, but sometimes it is nice to see that I'm not the only one who could use an extra cup of coffee some mornings. :)

And, at least they sent a letter. Knowledge is power, even if it is disappointing knowledge. 


Update: An apology was emailed out this afternoon. 

Monday, March 17, 2014

Tips for job interviews

I have completed my academic job interviews for this season. I am cautiously optimistic. In the meantime, while it is fresh in my mind, I'd like to write out some of my suggestions. For a more light-hearted and graphical overview of the interview process, check out Hope Jahren's, "How to get a faculty job" comic **(see my addendum below). Also, see this list from Matt Might that focuses on the whole academic application process.

Before the interview
  • Prepare presentations. You will need to prepare at least one presentation that features your past and current research. You may also need to prepare a chalk talk, or a teaching talk. 
  • Practice. Practice your research presentation, and your chalk talk and/or teaching talk, to several different audiences if you can. Practicing saying things out loud is useful, even if it is just to an empty room. 
  • Prepare questions. The campus interview is a two-way street, for the department to get to know you, and for you to get to know the department. Think about what is important for you to know before going somewhere (e.g., research facilities, teaching responsibilities, support for students, tenure expectations, etc. For more ideas see this list from Dartmouth.). 
  • Look up scientific backgrounds of each of the people you will be meeting with. Save either as an electronic document or print out for reading on the plane. I've heard some people suggest reading a paper from each person you are going to meet with, but I think this is not reasonable. Some departments are vast, and you will not have the scientific expertise to internalize 16 new disciplines. That said, you should have a general sense of what each person does, how you might potentially collaborate, and prepare one or two notes or questions about their research, to facilitate conversation. 
  • Consider questions they might ask you, especially the ones they aren't supposed to ask, but might anyway: see here for questions you should never be asked on a job interview.
Traveling - Before
  • Pack light, so you don't need to check luggage, and risk having your bag not arrive when you do. Make sure you still have professional clothes and layers. Layers are important.
  • Keep all important items in your carryon (especially **laptop, backup presentation, adaptors, and power chargers**).
  • Pack two snacks. Likely your flights will be delayed or the layover too short, and it will be nice to feel like you don't really need to pay $7 for that bag of almond M&Ms.
  • Bring headphones and non-electronic reading material. The flights can get long.
  • Keep dental floss in your bag, and make a bathroom check after lunch, for your peace of mind.
  • Bring a water bottle or a reusable coffee mug (works for hot or cold) to stay hydrated.
  • Choose mints over gum. Less distracting. Cough drops are also nice to have, just in case.
  • Bring pens and paper so you can take any important notes for following up.
  • Don't over-caffinate. Be careful not to drink too much coffee/tea, it seems to flow non-stop.
  • Decompress for 30 minutes (use the gym, take a bath, listen to music, watch youTube).
  • Don't obsess. If you have a second talk (chalk talk or teaching presentation), go over it one more time, update with potential collaborations after what you've learned on your first day, but then let it go.
  • Get some rest. The first day is so exciting with all the new people and places. You need to be just as enthusiastic the second day.
Traveling - After
  • Thank you notes can be started on the plane ride home, when the visit is fresh in your mind. Just save in a text file and then you can proof and send them out when you return.
  • Stretch. The combo of nerves during the interview and the not-so-comfy airline seats can hurt your neck/back, so take care of yourself.
After the Interview
  • Enjoy your time at home!
  • Catch up on emails, lab work, writing, family and friends.
  • Do not obsess. You've done all you can do. Now, patience, and distraction, are your friends.
  • Good luck!

_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
**My addendum to Hope's comic:

#5: Just ask for a bathroom break when you need it.
#15: I found that ordering an alcoholic drink was an easy way to avoid the "might she be pregnant?" unasked, but burning, question.  
#18: Even if you feel sure that you're a great fit, you still might not get the job. And that really stinks.

Monday, March 10, 2014

March 31st Deadline: Summer Institute for Native Americans in Genomics Workshop


The Institute for Genomic Biology (IGB) at the University of Illinois at Urbana-Champaign has partnered with the Department of Anthropology and the Native American and Indigenous Studies Program at the University of Texas at Austin to host the Summer Internship for Native Americans in Genomics (SING) Workshop for 2014. The workshop will take place from June 1-7, 2014 on the University of Texas campus in Austin, the week immediately after the Native American Indigenous Studies Association (NAISA) meetings take place, also in Austin, Texas.
The goals of the workshop are to facilitate discussions of how genomics research is conducted and to create a support network for Native American students in the sciences. Additional instruction in fundamental concepts and methods in genomics and bioinformatics, including both theoretical aspects and practical laboratory- and computer-based training, will take place.
By combining ethical, legal, and social discussions surrounding historical Native American encounters with science and hands-on training in the latest genomics techniques and analytical programs, the workshop will help prepare participants for future leadership positions in science research and careers.
“The SING workshop fosters a new generation of intellectual leaders with the tools to address the expanding frontiers of genomic science and interactions with society,” says Ripan Malhi, Director of the SING program.
The SING workshop was first held at the IGB in 2011, with 12 attendees and several faculty advisors participating from universities across North America. The upcoming workshop will be the third workshop and will include leading Native American scholars who will be attending the NAISA conference in Austin the week before.
“SING appeals to Native American students and community members who often become interested in genomic research because they see that it presents both risks and opportunities for their tribal communities. SING offers a multi-disciplinary curriculum taught by diverse faculty who recognize that ‘science’ and ‘society’ are not separate, but they continuously shape one another,” says Kim TallBear, SING faculty and associate professor at the University of Texas at Austin.
The Summer Internship for Native Americans in Genomics (SING) Workshop will take place from June 1-7, 2014, at the University of Texas at Austin. The workshop is open to tribal college students, community college students, university undergraduate students and graduate students, and individuals from Native American communities who would like to continue their education in the sciences. Full details and the online application can be found at
About the Native American and Indigenous Studies (NAIS) program at the University of Texas:
The Native American and Indigenous Studies (NAIS) program at the University of Texas at Austin has a global, comparative focus with a particular strength in the Americas.  NAIS fosters and supports teaching and intellectual engagements around the languages, cultures, knowledges, histories, and current political struggles of indigenous peoples. The program is particularly concerned with scholarship and intellectual exchange that contributes to the economic, social, and political advancement of indigenous peoples.  NAIS also contributes to efforts to build a diverse campus by actively working on recruitment of indigenous students and faculty.
For more information, contact the following co-organizers of SING:
Dr. Ripan Malhi (University of Illinois at Urbana-Champaign)
Dr. Deborah Bolnick (University of Texas at Austin)
Dr. Kim TallBear (University of Texas at Austin)

Sunday, February 16, 2014

How is sex determined?

A fun video introducing the variety of sex determination systems:

I like this as an introduction, which sets some good rules of thumb, but there are some excellent exceptions to these rules that we can get into. For example, in mammals (even humans), the part of the Y chromosome that is most responsible for turning on the male-determining pathway doesn't always function as it should, resulting in individuals with an X and a Y chromosome who have female physical characteristics (for more see Ed Yong's article about Chen et al.'s 2013 paper).

Tuesday, February 4, 2014

Convergent evolution: tenrecs and hedgehogs

The hedgehog and tenrec diverged from one another over 100 million years ago. That is a lot of time. To put that in perspective the lineages leading to human and mouse also diverged roughly 100 million years ago (maybe closer to 90ish). And yet, the tenrec and hedgehog have independently evolved very similar features, likely because of similar environmental pressures. This independent evolution of features is called convergent evolution, and it is just fantastic to observe.

Tenrecs are found in Madagascar and Africa:
Tenrecs at Berkeley's Museum of Vertebrate Zoology, Own work
Hedgehogs are found in Europe, Asia, and Africa.
Hedgehog, by Nino Barbieri, via Wikimedia Commons
Based on their physical features, hedgehogs and tenrecs were once thought to be closely related species. But, Murphy et al. (2007) showed that their genomes are very different from each other, suggesting the two species have been separated for more than 100 million years.

Extra tidbit: 
Recent efforts have begun to domesticate hedgehogs, and the result of some of those efforts is the long-eared hedgehog:

It looks like a bat-eared hedgehog to me. 
Have a great day.

Thursday, January 30, 2014

Microseconds matter.

Grace Hopper answers, "Why does it take so damn long to send a message by satellite?" Specifically she does a tremendous job of visualizing the difference between a nanosecond and a microsecond, and what it means to throw away microseconds of computing time.

I am a computational biologist. Sometimes I use computers to compare differences and similarities between the nucleotides (A's, T's, G's and C's) of mammalian genomes.  Sometimes I look at evolution of the human sex chromosomes. I always write computer programs to analyze these datasets.

New technology means that the size of the datasets that we can produce is increasing, and will continue to increase during my lifetime. Computational infrastructure has also been increasing (see a history of computer storage). But, computational power and storage is not going to scale fast enough to keep pace with the size and complexity of the datasets we will need to analyze. That's why Hopper's video above is so relevant.

I've noticed a mentality in biology circles that all we need to do to be able to analyze larger and larger datasets is to increase computing power. But, there are some big problems with this:

1. Computers are not self-sustaining.
Computers need to be maintained, cooled, managed, and cared for. Computing resources need a space where they will be kept cooled. Computers cannot upload or update their own software. Computers cannot turn themselves back on after a power outage, back themselves up (without instructions to do so), or upgrade their own hard drives.

We need good system administrators, and computational lab managers, to maintain computing resources, and these people need to be paid a competitive salary. High-performace computers is not a once-and-done expenditure; it is an investment.

2. Bigger is not enough.
Yes, large datasets do require lots of storage space, and analysis will increase with faster processors, but that isn't enough. Let's think about it this way:

Imagine I have a dataset that takes one week of computing time to analyze using my fast processors, all of the storage I need, and my current code. If my dataset grows to be a hundred times larger, my dataset will now take 100 weeks of computing time to analyze. If I take no time to optimize my code, or parallelize the jobs, or figure out a new, faster, method, I will be waiting two years (assuming no hiccups) just to see what the new results are.

That is unacceptable. We need to code smarter. Similarly, we need to utilize efficient storage formats. There is progress in this direction, but it needs to be a constant focus.

3. Open science.
Despite the wonders of the internet, I would argue that most of us do not take the time to carefully edit and annotate our code, and make it publicly available to others. This is especially true for all of the "in-house" scripts used for data processing. These are small scripts that aren't stand-alone programs for some new type of analysis, just day-to-day analyses or parsers. But without these intermediate scripts an outside person cannot replicate our analysis exactly. I'm guilty of this myself. I do try to comment my codes heavily, and I locally archive all the codes for each project, but I don't always go the extra step of archiving them somewhere *public*. Going forward I am going to change this.

One option is to create a pipeline of all scripts with a clear README file, and deposit into public repositories like GitHub. Another is to incorporate tools into a web-based platform that allows workflows, like Galaxy. A third option is to maintain the code on a local website, but this seems more like a back-up to me.

Grace Hopper chastised programmers for not appreciating the value of a microsecond. Her admonition rings true as much today as it did over 20 years ago.

Sunday, January 26, 2014

Notes on: Single-cell RNA-Seq reveals dynamic, random monoallelic gene expression in mammalian cells

Brief background:

We have two copies of each non-sex gene. Each version of the gene is called an allele: one inherited from your genetic mother, one from your genetic father. It is generally thought that each allele is expressed (turned out) at the same intensity. But, there are some examples where this isn't true. The most notable occurs on the X chromosome. Females with two X chromosomes inherited one X chromosome from each parent, but one of these X chromosomes is almost completely inactivated. That means that instead of having biallelic expression (expression from both the maternal and paternal allele), most genes on the X chromosome exhibit monoallelic expression.

In recent work, Deng et al (2014) isolated single cells from two different stains of mice, where they could detect maternal-alleles and paternal-alleles for over 82% of assayed genes (in the other genes, there were not unique variants that allowed deciphering between the two alleles). For each gene, the authors characterized whether they could detect expression from both the maternal and paternal alleles, or from only the maternal or paternal allele. Although the title says, "mammalian," all of the experiments and analysis were conducted in mouse cells and tissues, so far as I can tell.

My notes and thoughts on the paper:
  • The authors state, "…different SNPs within the same gene gave coherent allelic calls (fig. S2)." I am very interested to see what they did in cases where different SNPs did not give the same estimates of allele-specific expression.
  • Mouse paternal X chromosome inactivation is complicated. In single cells, the paternal X chromosome is inactive initially, reactivated starting at the late 2-cell stage, active at the 4-cell stage, then inactivated starting at the 16-cell stage, and completely inactivated again by the early blastocyst stage. Xist appears to be off during early embryogensis, and is only expressed starting at the 16-cell stage - correlating with the re-silencing of the paternal X chromosome in the mouse.
  • X-inactivation near and far from mouse XIC. The spread of X-inactivation is not directly correlated to the distance from the X-inactivation center (XIC).
  • Technology biases estimates of allele-specific expression. Initial observations of allele-specific expression on the autosomes suggested over half of all genes exhibit mono-allelic expression, but as much as 66% of these are false positives due to the loss of RNA molecules with the available technology. After inferring the proportion of losses RNA molecules, the authors propose that 12-24% of genes exhibit monoallelic expression in single cells.
  • Monoallelic expression evens out in tissues. The authors state, "Pooling cells by embryo removed essentially all monoallelic expression, demonstrating a high degree of cell-specific randomness in monoallelic expression." To me this suggests that studies of single cell gene expression may not give the most accurate picture of gene expression within a tissue. 
Additional thoughts:
  • I would very much like to know how estimates of allele-specific expression on the X chromosome varied between the single cell and multicelluar analyses. 
  • The authors claim the patterns of monoallelic expression on the autosomes is likely due to independent allelic expression, but I would like to understand the mechanism more. Is this simply variance in polymerase activity? 
  • If 12-24% of genes are expressed from only one allele, what can we learn from it? Is dosage of these genes less important? Is selection weaker on genes that are more likely to be mono-allelicly expressed? 

 2014 Jan 10;343(6167):193-6. doi: 10.1126/science.1245316.

Single-cell RNA-seq reveals dynamicrandom monoallelic gene expression in mammalian cells.


Here is the Storify of the discussion on twitter about this result, and how it is transcriptional bursting. I wasn't aware of the term, so here's the wikipedia entry for transcriptional bursting. Given this background, it is not so surprising that there is so much variation in expression across the autosomes, but my questions about what kind of classes exhibit measurable levels of this phenomenon in single cells still stands.

Also, I did focus on the X chromosome results because to me they were the most interesting. In marsupials it is always the paternal X that is inactivated - there is no random X-inactivation. My understanding is that in eutherian mammals the paternal X is inherited as inactivated, it is reactivated, then either the maternal or paternal allele is randomly inactivated. That said, there is some evidence of preferential paternal X-inactivation in mice - see Paternally biased X inactivation in mouse neonatal brain.