101 questions with a bioinformatician #7: Holly Bik

May 14, 2014 by Keith Bradnam

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.

Holly Bik is currently a Postdoctoral Researcher in Jonathan Eisen’s lab at the UC Davis Genome Center…but not for much longer! Sadly (for us), Holly will soon be leaving Davis to take up a Faculty position in the School of Biosciences at the University of Birmingham, UK.

As a Birmingham Fellow in Bioinformatics, she will no longer be saying things such as "Dude, it's like totally, hella hot" (this is how we all talk in California), and will instead be referring to the weather in the correct British vernacular "I say, one finds this rain jolly bracing". As Curry Capital of Britain she will also be required (under Birmingham law) to dramatically increase her intake of onion bhajjis, aloo gobi, and peshawari naans (something that sadly seem to have been outlawed in Northern California).

During her time at UC Davis, Holly has been working on PhyloSift, a software pipeline for the phylogenetic analysis of genomes and metagenomes. She has also been working on many other things. You can find out more about Holly by following her on twitter (@hollybik) or by visiting www.hollybik.com. And now, on to the 101 questions...

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

Given my biology background, my favorite aspect of any bioinformatics project is interacting with people from different disciplines (project personnel, and/or talking to people at meetings and on Twitter). I learn something new about computing, software, and/or hardware pretty much during every project.

I’m always astounded by how technology and computers have progressed since I got my first personal computer (way back in 1996), and how we’re now leveraging this computing power in conjunction with deep DNA sequencing technologies to address fundamental scientific questions. The power of bioinformatics is really incredible when you stop and think about it!

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

The lack of documentation for a lot of software packages, and to a lesser extent, encountering uninformative error messages when trying to run command line software that’s supposedly designed for researchers. Both can be a prohibitive barrier to testing out different tools that may actually be extremely useful and informative for your own research. I think there’s a reason that QIIME has become such a powerhouse package for microbiome research — biologists have access to a suite of tutorials, test datasets, and they can boot up the software easily as a Virtual Machine or Amazon Cloud instance.

However, the easiest tools to install and use are not necessarily the best to use for your particular research questions. I read so many software papers describing exciting new software (where the authors usually all come from computer science departments), but when I visit the website I find no useable instructions or run into insurmountable errors when trying to install or execute the code. As a biologist, no one ever sat down and taught me the nitty gritty about makefiles or compiling source code; people that publish software shouldn’t assume their users have a computer science degree. Most computational biologists will make a valiant effort to overcome such problems, but at some point you have to do a cost/benefit analysis of whether persevering is worth your time. I’m only going to spend two days trying to install your software if I think its really really worth it, but in most cases I’ll probably decide that it isn't (and so no citation for you).

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?

FILE MANAGEMENT. Read up on the best practices for data management, and start forcing yourself to develop good habits NOW. I guarantee that in 5 years' time you will not remember what data you saved in 'analyses.txt'.

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

I’m going to be shamelessly biased: my favorite software ever is Phinch, a data visualization framework that I’ve been developing in collaboration with Pitch Interactive — a data visualization studio in Berkeley, CA. We’re using solid software engineering and design principles to build exploratory, interactive visualization tools for scientists. And because the visualizations are built in 3D, the user interface is absolutely gorgeous! Who says you can’t create art when doing bioinformatics research?

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

I’m a gap (. or -), because I’m mysterious and don’t like to be classified!

Survey results: The extent of gender bias in bioinformatics

May 13, 2014 by Keith Bradnam

I have completed an analysis of my survey that attempted to see whether there is notable gender bias among bioinformaticians. Thank you to the 370 people that completed the survey! A few things to note:

All survey responses are available on Figshare (in tab-separated value format). Anyone else can come along and play with this data, and maybe ask more intelligent questions about it than I did.
My detailed analysis of these responses is also on Figshare as a separate document.
The original Google survey form remains available (also see my blog post about it). If people continue to complete the survey, I will update the main data file on Figshare.

I encourage people to read the full document on Figshare. Because of the high response to this survey, I had enough data to compare gender bias at different career stages, and also between different countries (for a small number of countries).

I'll leave you with just one result from my analysis. I had asked people to identify their current career position, and I offered 10 possible career stages as answers:

Currently pursuing undergraduate degree (with focus on bioinformatics/genomics
Undergraduate level position in academia or industry (e.g. Research officer / Junior specialist)
Currently pursuing postgraduate qualification (with focus on bioinformatics/genomics)
Postgraduate level position (e.g. Research assistant). MSc or PhD required for role.
Postdoctoral scholar / Fellow / Research Associate
Lecturer / Instructor/ Senior Fellow / Project Scientist (3+ years post-PhD research experience)
Assistant Professor / Reader / Senior Lecturer (5+ years post-PhD research experience)
Associate or Full Professor / Team Leader (7+ years post-PhD research experience)
Senior Professorial role (e.g. head of a department, 10+ years post-PhD research experience)
Super Senior role (e.g. Dean of a school or CEO, 15+ years post-PhD research experience)

Because these categories are a little bit subjective, and because some of the categories (levels 1, 9, and 10) had the least number of responses, I decided to smooth the data by combining adjacent categories. I.e. 1&2, 2&3, etc.

So this is what the percentage of male and female bioinformaticians looks like with respect to progress through their scientific career:

Things start off looking quite equitable but proceed to diverge around the time that people are becoming Associate Professors. However, the situation is more complex than this (see Figure 3 in my full analysis).

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

May 09, 2014 by Keith Bradnam

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

May 07, 2014 by Keith Bradnam

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

May 06, 2014 by Keith Bradnam

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.

May 05, 2014 by Keith Bradnam

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.

Ewan Birney's EBI press conference on being elected to the Royal Society

May 02, 2014 by Keith Bradnam

Speaker: And that concludes this EBI press conference to congratulate Ewan Birney on being elected to the Royal Society. We just have time for one or two questions. Ah okay...the first question goes to…Ewan Birney.

Ewan: Hi Ewan. Just wanted to say that this is all great and I've found your work to be really interesting. Can I just ask whether you've looked at the opportunity of widening this effort by joining other Royal Societies as well? This would allow for a much better comparative analysis of the scope and impact of Royal Society members? The Royal Statistical Society may be a good choice to begin with, or maybe the Royal Society of Marine Artists.

Ewan: Thanks Ewan, that's a really good question. It is something that I'm considering and I think there is a lot to gain from such a comparative approach. But to do this properly I think it needs to be part of a much larger effort. So I'm hopeful of trying to join every Royal Society and then see what can be learned from a cross-societal analysis of such memberships. Furthermore I'm hopeful that Her Majesty could be persuaded to start a new Royal Society for the Promotion of Questions by People Named Ewan at Academic Conferences…something that is very near and dear to my heart.

Speaker: Okay, I think we have time for just one more question. Oh, Ewan…again.

Ewan: Just to follow up Ewan, given the advanced age of many Royal Society members, have you thought about trying to assess what fraction of the Royal Society is functional?

Ewan: That's a fantastic question Ewan, very perceptive of you. This is something else that I have a strong interest in. I am currently involved in some preliminary discussions with various people to form a new pan-European working group that will investigate how much of the Royal Society is functional. This effort will hopefully be called ENCODEMBLIXIR…or something snappy like that.

Jesting aside, congratulations Ewan this is great news!

101 questions with a bioinformatician #5: Laura Clarke

April 30, 2014 by Keith Bradnam

Laura Clarke is the Project Coordinator for Resequencing Informatics, part of the Vertebrate Genomics team led by Paul Flicek at EMBL-EBI. Before joining the EBI, she was applying her considerable bioinformatics skills at the Wellcome Trust Sanger Institute (a move ranked #1 on the annual list of Easiest-employers-to-transition-between).

Her role sees her help with the analysis and coordination of high throughput genomics efforts such as the 1000 Genomes project, BLUEPRINT (deciphering the epigenome of blood cells), and HipSci (the Human Induced Pluripotent Stem Cells Initiative). If you're wondering what this actually entails, I'll hand you over to Laura:

"This work boils down to making sure that data gets into and out of the sequence archives; running primary analysis and QC; and then making sure the resulting analysis makes it out to the community".

You can find out more about Laura by following her on twitter (@laurastephen), and of course you can also follow @blueprint_eu and @hipsci. And now, on to the 101 questions...

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

The possibility. With modern sequencing technologies, computation techniques have the ability to draw together these new data types and massive volumes of data, allowing us to get much closer to a proper understanding of cellular biology, which of course brings us closer to understanding organismal biology.

Add to that the diverse range of species being sequenced and what that can teach us about evolution and the forces which drive evolution.

That is of course before you consider how it might impact medicine or food security or any real world applications.

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

Extracting data from people. My life would be easier if people weren't so begrudging about sharing data and describing the data they do share well. I work with many people who do share data freely and easily but there are still too many people who are too reticent or reluctant to make data publicly available from within a consortium.

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?

For data coordination purposes we produce a lot of tab-delimited text files, cut is a wonderful Unix command for making those easier to work with and manipulate, learning about cut sooner would have at least made mucking about with various types of GFF files easier I suspect.

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

I have to say I did enjoy pairedends.com, very funny

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

R: this is because Adenine and Guanine are the same molecule type (purines) as both Theobromine and Caffeine, both of which are quite important to me and at least influence my personality.

An 18 Kbp read from a MinION sequencer!

April 28, 2014 by Keith Bradnam

The UC Davis Genome Center was fortunate to receive a few MinIONs from Oxford Nanopore the other week:

One of the things that we have been trying to do with these wondrous machines is to study variation in a mixed pan-European population. For this study, we simply combined saliva samples from individuals that represent 32 distinct European ethnicities (but no Belgiums, obviously), and the combined sample was applied directly to the MinION using the WF10 setting (WF = warp factor).

The preliminary results look very promising with an N50 read length of 12.2 Kbp (and this was before applying N50 Booster!!!). Here is the very first read from the device...18,731 bp of pan-European goodness (though note that there was a problem with base quality at the end of the read...contamination with Belgium DNA maybe?).

>PanEuroMix_read00001

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ACGT...TGCA — has every possible DNA-based initialism been used by the bioinformatics/genomics community?

April 25, 2014 by Keith Bradnam

Short answer

Yes.

Long answer…

You might work in a field that's related to biology, genetics, genomics, or bioinformatics. You might be working on a new piece of software, or a research proposal, or you need to form a committee. Maybe you have even been given the power to name a new research facility.

Suddenly you have an inspiration...why don't we name our new software, proposal, committee, or facility after a DNA-based initialism! That would be clever and make us stand out from the crowd, right? Maybe...maybe not.

What follows is a fairly exhaustive list of — presumably intentional — DNA-based initialisms that are in use (or have been used). As of 2020-07-20 the current list contains 67 names in total with all 24 possible combinations of [ACGT] being used. The additions since I first created this page are included at the end.

See also this related blog post by David Lawrence from 2014, which I only discovered in mid-2020. His post — which beat me to the punch by just a couple of weeks! — has provided me with a few additional examples which I hadn’t heard about and which have now been included here.

Please let me know of any errors or omissions, though note that potential names have to be initialisms and has to be somewhat related to to the fields of genetics, genomics, or bioinformatics.

AGTC

Applied Genomics Technology Center — Core Facility? — 1998
Advanced Genome Technologies Core — Core Facility — ???
University of Kentucky Advanced Genetic Technologies Center — Core Facility (now defunct?) — ???

ATCG

Applied Technology in Conservation Genetics — Research Lab — ???

ATGC

Arabidopsis Thaliana Genome Center — Core Facility? — 2000?
Another Tool for Genome Comparison — Software — 2001
Advanced Thermal Gradient dna Chip — Patent — 2002
Another Tool for Genomic Comprehension — Database & web tool — 2012
Alignable Tight Genomic Clusters - Database - 2009

CAGT

Center for Advanced Genomic Technology — Research Facility — 2000?
Center for Applied Genetics and Technology — Research Facility — 2004
Center for Applied Genetic Technologies) — Research Facility — ???
Clustering AGgregation Tool — Software — 2012?

CATG

Cross-legume Advances Through Genomics — Conference — 2004?
Center for Advanced Technologies in Genomics — Research Facility — 2008

CGAT

Comparative Genome Analysis Tool — Software — 2006
Computational Genomics Analysis and Training — Training program — 2010
Computational Genomics Analysis Toolkit — Software — 2013
Centre for Gene Analysis and Technology — Research Facility — ???
Canadian Genome Analysis and Technology program — Research program (now defunct) — 1992

CGTA

CNS Gene therapy Translation Acceleration - Research Group - ???

CTAG

Corn Transcriptome Analysis Group — Working Group — 2014
Canadian Triticum Advancement Through Genomics - Research project - 2011

CTGA

the Catalogue for Transmission Genetics in Arabs — Database — 2006

GACT

The Center for Genetic Architecture of Complex Traits - Research Center - 2013

GATC

Genetic Analysis Technology Consortium — Biotech Consortium (now defunct?) — circa 1997?

GCAT

Genome Comparison & Analytic Testing — Software? — ???
Genome Consortium for Active Teaching — Teaching Consortium — 2007?
Gene-set Cohesion Analysis Tool — Software — 2011 (or 2007) 4.Genotype-Conditional Association Test — Statistical method — 2015
Genomics, Computational biology And Technology - study section - ???

GCTA

Genome-wide Complex Trait Analysis — Software — 2011

GTAC

Gene Technology Access Center — Teaching Facility — 2000
Genomics Technology Access Center — Core Facility — 2009?
Genome Technology Access Center — Core Facility — 2010
Genomics/Transcriptomics Analysis Core — Core Facility — ???
Genomes and Transcriptomes of Arctic Chromists — Research Program — 2012
Gene Technology Advisory Committee — Government Committee — ???

GTCA

Genomic Tetranucleotide Composition Analysis — Database — 2006
Genome Transcriptome Correlation Analysis — Software — 2007

TACG

Talking About Computing and Genomics — Workshop — 2013

TAGC

The Applied Genomics Core — Core Facility — 1998
The Ashkenazi Genome Consortium — Consortium — 2012
Technological Advances for Genomics and Clinics — Research Lab/Program? — ???
The Arts & Genomics Centre — An Arts/Science Center — ???
The Allied Genetics Conference — Conference — 2016?
Taxon-Annotated GC plots — software visualisation method/tool — 2013

TCAG

The Centre for Applied Genomics — Research Facility — 2007?
The Center for the Advancement of Genomics — Research Facility (superseded by this) — ???

TCGA

The Centre for Genetic Anthropology — Research Facility — 1996
The Tayside Centre for Genomic Analysis — Core facility — 2001 (?)
The Center for Genomic Application — Core Facility — 2004
The Cancer Genome Atlas — Research Program — 2006

TGAC

The Genome Access Course — Training Course — 2002
The Genome Analysis Center — Research Facility — 2009

TGCA

The Genome Counselling App — iOS Application — 2014

Updates:

2020-08-20 Added 5th example of ATGC, 3rd example of AGTC, 2nd example of CTAG, and 4th example of GCAT (all courtesy of David Lawrence)
2020-07-18 Added 10th example of ACGT
2019-07-23 Added 9th example of ACGT (thanks to Sam Lent @samanthalent)
2016-09-03 Added 4th example of TCGA (thanks to @malcolmacaulay)
2016-02-16 Added 6th example of TAGC
2015-09-11 - Added 5th example of TAGC
2015-07-06 - Added 8th example of ACGT
2015-04-06 - Added 4th example of GCTA (thanks to John Didion)
2014-12-12 - Added first usage of TACG (thanks to @NazeefaFatima)
2014-04-25 - Added Jeff Ross-Ibarra's planned use of CTAG
2014-04-25 - Included a second instance of AGTC
2014-05-18 - Included a fourth example of TAGC
2014-09-08 - Included first usage of CGTA, GACT, and TGCA

Short answer

Long answer…

ACGT

ACTG

AGCT

AGTC

ATCG

ATGC

CAGT

CATG

CGAT

CGTA

CTAG

CTGA

GACT

GATC

GCAT

GCTA

GTAC

GTCA

TACG

TAGC

TCAG

TCGA

TGAC

TGCA