Can Twitter help us find out the gender ratio of bioinformaticians?

I'm still collecting survey results to try to understand the extent of gender bias in bioinformatics. I plan to publish an analysis of these results next week and I'll also share all of the the raw survey results via Figshare (in case anyone else wants to dive deeper).

One thing that is hard to accurately know is just what the gender ratio is across everyone who identifies themselves as a bioinformatician. A survey that is trying to ask something about gender bias no doubt introduces its own bias in the types of people who would be interested in completing such a survey.

But maybe Twitter can be of use in trying to determine a 'background' gender ratio among bioinformaticians. The evidence is hardly conclusive, but there are some data that suggests that more women use twitter than men. There's also data that there are comparable numbers of male/female users. In any case, numbers of users doesn't tell the whole story. Other research shows that, on average, men have  15% more followers than women, and a tool called Twee-Q that tries to identify the likely gender of twitter users, finds that men tend to be retweeted almost twice as often as women.

Despite gender biases in how people use twitter, it might still be useful to see what the gender ratio is of people who follow bioinformatics-type accounts. This is something that twitter can show you at analytics.twitter.com. However, this only seems to be enabled on accounts that have a certain number of followers. Here is what the results looks like for the @assemblathon twitter account (click to enlarge):

So twitter identifies — presumably using some sort of gender-guessing-algorithm — that 82% of  the followers are male. I'd love to see what other results look like for other bioinformatics twitter accounts. However, I think it is a better test if the accounts in question are themselves gender-neutral. I.e. affiliated to a resource or institution. If you run a bioinformatics-related twitter account that is gender-neutral, and if you can access analytics.twitter.com, I'd love it you could share your results with me (via comments below or on twitter @kbradnam).

101 questions with a bioinformatician #6: Mario Caccamo

This post is part of a series that interviews some notable bioinformaticians to get their views on various aspects of bioinformatics research. Hopefully these answers will prove useful to others in the field, especially to those who are just starting their bioinformatics careers.

Mario Caccamo is the director of The Genome Analysis Centre, a BBSRC-sponsored research institute focused on genomics and computational biology. You may know of this institute by its shortened name 'TGAC' (of course, this is not the only place where you will see this initialism). As Director, Mario's role is to "ensure TGAC is equipped with the resources and people to deliver good science".

Mario's skills as a bioinformatician are matched only by his prowess on the volleyball court. When he used to play for the Informatics volleyball team at the Wellcome Trust Sanger Institute, he deservedly earned the nickname Super Mario. You can find out more about Mario by following him on twitter (@mcaccamo).

 

001. What's something that you enjoy about current bioinformatics research?

I see bioinformatics as a branch of molecular biology. I love the elegance of molecular biology. Research in bioinformatics is about capturing the beauty of biology in abstractions that can help us to discover new knowledge. Beauty here means complexity, optimisation, economy (in terms of information content) and functionality (among other things). One of the most exciting things about molecular biology is how young it is as a science. Our colleagues working in bioinformatics are the Newtons and the Keplers of molecular biology — so much to be done and discovered. 

 

010. What's something that you *don't* enjoy about current  bioinformatics research?

The flip side is that we still don’t understand enough about the basic building blocks in molecular biology. The language we use to describe biological systems and processes is incomplete. We struggle with issues that sometimes look simple. What did we know about epigenetic modifications 10 years ago for instance? Little compared to what we know today. Bioinformaticians struggle with the incompleteness of the underlying basic knowledge and keep re-inventing the wheel leading to frustration. Perhaps these are growing pains — but pains nevertheless.

 

011. If you could go back in time and visit yourself as an 18 year old, what single piece of advice would you give yourself to help your future bioinformatics career?

My advice would be: “This is good enough. Let it go.” Recognising when you are in the land of diminishing returns is a skill that should be taught at school. This is particularly relevant for bioinformatics. You can always close more gaps, find the missing gene or remove another false positive — you can do this for the next 10 years. My recommendation is...don't. Another perhaps more mundane recommendation is to learn either gawk, Perl one-liners, or some of the basic Unix command line tools to manipulate strings and text; they will give you the data you need for your best presentation the night before your talk. 

 

100. What's your all-time favorite piece of bioinformatics software, and why?

It has to be HMMER — a beautiful super efficient piece of software. I know that we shouldn’t use a hammer for all kind of different nails but somehow HMMER manages to prove that advice wrong. You can HMMER so many nails with this hammer.

 

101. IUPAC describes a set of 18 single-character nucleotide codes that can represent a DNA base: which one best reflects your personality?

I think I would take W. I like W as a strange letter (no explanation for that) — but it is A or T, alpha or omega in the nucleotide alphabet.

A new JABBA award for a particularly bogus bioinformatics acronym

JABBA is an acronym for 'Just Another Bogus Bioinformatics Acronym'. JABBA awards are given when people publish bioinformatics tools that have names which are tenuously derived acronyms (or initialisms). There have been many JABBA award winners, but this one might take some beating. This software tool was published earlier this year in the journal 'Microarrays'. Here is the title of the journal article: 

Pigeons: A Novel GUI Software for Analysing and Parsing High Density Heterologous Oligonucleotide Microarray Probe Level Data

So now the million dollar question is…what is 'Pigeons' an acronym of? Please sit down as  you might feel faint when you read this:

Photographically InteGrated En-suite for the OligoNucleotide Screening

Wow. This rates an 11 on the Bogus Scale (a scale that only goes up to 10).

What is the extent of gender bias in bioinformatics? Please help me find out.

I've been drawing up a short-list of people to interview for my 101 questions with a bioinformatician series, and I've realized that this list is skewed towards males (maybe 2:1). This partly reflects my own biases in choosing people that I know through work and from people that I follow on twitter. 

However, it probably also reflect underlying biases in the bioinformatics field as a whole. The existence of gender biases is STEM subjects is hardly a new concept (see here or here for some recent studies into this area) and anyone who follows Jonathan Eisen's blog will know that there is an all-too-common bias towards male speakers at scientific meetings. In a great blog post from last year (The Magnifying Glass Ceiling: The Plight of Women in Science), Jane Hu discusses the topic of gender bias in science. I encourage everyone to read this post, but I'll highlight one sentence here (emphasis mine):

It is true that women are underrepresented…but not because women aren’t interested in it or can’t handle the work.

Although projects like Girls Who Code and App Camp for Girls are doing a great job at increasing female participation in some STEM subjects, these projects will not help remove the discrimination against women that occurs later in their careers. Fortunately, other fantastic projects like Tools for Change: Boosting the retention of women in the STEM pipeline are helping raise awareness about these problems, and are offering solutions (e.g. encouraging more family friendly policies).

So I'm curious as to the extent of gender bias in bioinformatics. Please help me find out more by completing the really short form (below) and feel free to share this form with others (the Google form can be accessed separately via this link). I will report on the results in a future blog post. Also, I will make more effort to address any gender biases in my 101 questions with a bioinformatician series.