Nice story from Dan Potter on KQED about Women Science PhDs

Nice story on KQED from Dan Potter: Women Getting Science Ph.D.s Still Face Gender Barriers    

Time to boycott Oxford Global meetings due to blatant sexism

I don't even know what to say or do about this it is so stunningly pathetic.  I saw this Tweet earlier in the day:

Oxford Global’s Pharmaceutical IT Congress: All 38 speakers male! (it seems)
http://t.co/ctu18mKLAw
@phylogenomics @GenderAvenger #YAMMM

— Elisabeth Bik (@MicrobiomDigest) May 13, 2015

I figured even in an era of blatant sexism in science, this must be a mistake right?  How could there be a conference with 38 male speakers and 0 female speakers.  So I went to the site: Who is Speaking – Oxford Global's 13th Pharmaceutical IT Congress, September 2015.  And, well, as far as I can tell Elisabeth Bik has the numbers right.  (See a list at the end of this post).  They even have a running slideshow of the speakers faces.

This is even worse than the 25:1 ratio of the qBio meeting I lost it over a few years ago.  I have never seen anything like this. I note - a 38:0 ratio is nearly impossible by chance in any field and I think pretty clearly an indication of massive bias of some kind.

I note - this is not the first case of a mostly male meeting from Oxford Global.  See for example:
Oxford Global Sequencing Meetings: Where MEN Tell You About Sequencing #YAMMM

I think it is time to just boycott meetings meetings from Oxford Global.  The only way they will change is if people stop speaking at or going to their meetings.  So please - stop going to their meetings.  Stop speaking at their meetings.

Speakers 2015:

• Sebastien Lefebvre 
  Director Data Engineering and Technology – Global Data Office, Biogen Idec
• Uwe Barlage
  EDC Project Leader, Bayer Healthcare
• Marc Berger
  Vice President, Real World Data and Analytics, Pfizer
• Michael Braxenthaler
  Pharma Research and Early Development Informatics, Global Head Strategic Alliances, Roche, & President, Pistoia Alliance
• Arnaub Chatterjee
  Associate Director - Data Science, Insights and Partnerships, Merck
• James Connelly
  Global Head, Research Data Management, Sanofi
• Jos Echelpoels
  Director IT, Regional Initiatives, Janssen
• Brian Ellerman
  ‎Head of Technology Scouting and Information Science Innovation, Sanofi
• Peter Elsig Raun
  Director & Head Business Analysis, Lundbeck
• Dimitrios Georgiopoulos
  Chief Scientific Officer UK, Novartis
• Charles Gerrits
  Vice President, Innovative Patient-Centric Endpoints and Solutions, Sanofi
• Yike Guo
  Professor of Computing Science, Imperial College London and Chief Technology Officer, tranSMART Foundation
• Sergio H. Rotstein
  Director, Research Business Technology, Pfizer
• Juergen Hammer
  Global Head Data Science, Center Head Pharma Research and Early Development Informatics, Roche
• Jan Hauss
  Head Central Analytics Informatics, Merck
• Athula Herath
  Statistical Director, Translational Sciences, MedImmune
• Nigel Hughes
  Director Integrative Healthcare Informatics, Janssen Research and Development
• Michael Hvalsøe Brinkløv
  BI Architect, IT Platforms & Infrastructure, Lundbeck
• Robert J. Boland
  Senior Manager, Translational Informatics & External Innovation R&D IT, Janssen
• Adrian Jones
  Associate Director, Business Intelligence Systems, Astellas
• Srivatsan Krishnan
  Director and Head of R&D Operations and IT, Bristol-Myers Squibb
• Philippe Marc
  Global Head of Preclinical Informatics, Novartis Institutes for Biomedical Research
• Dermot McCaul
  Director, Preclinical Development and Biologics IT, Merck
• Pantaleo Nacci
  Head Statistical Safety & Epidemiology/PV, Novartis Vaccine and Diagnostics Srl (a GSK company)
• Gerhard Noelken
  Global Business IT Lead for Pharmaceutical Science, Pfizer WRD
• Emmanuel Pham
  VP Biométrie, Ipsen 
• Andrew Porter
  Director, Enterprise Architecture, Merck
• Gabriele Ricci
  Vice President of TechOpps IT, Shire
• Anthony Rowe
  Director, Translational Informatics and External Innovation, Johnson & Johnson
• Martin Ryzl
  Director, GIC Analytics Platform Engineering, Merck
• Wolfgang Seemann
  Senior Project Manager, Bayer Business Services
• Aziz Sheikh
  Professor of Primary Care Research & Development and Co-Director Center for Population Health Sciences, The University of Edinburgh
• Yan Song
  Associate Director, Bioanalysis Operations, AbbVie
• Devry Spreitzer
  Director, Global Electronic Systems Quality Assurance, Astellas
• Jason Swift
  Head R&D Information UK, AstraZeneca
• Kevin Teburi
  Director – iMed Team Leader, R&D Information, AstraZeneca
• Simon Thornber
  Director, Data Analytics, Informatics and Innovation, GlaxoSmithKline
• Tjeerd Van Staa
  Professor of Health eResearch, University of Manchester

Some past meetings from Oxford Global to consider

http://www.bmsystems.net/download/BioMarkers-BMsystems-conferenceprogramme.pdf

https://web.archive.org/web/20120514151415/http://www.ngsasia-congress.com/

Koalas, Chlamydia, Antibiotics and Microbiomes - what else do you need?

Katie Dahlhausen, a PhD student in my lab, has become really really interested (perhaps a bit obsessed) with a really interesting case study regarding koalas, Chlamydia, antibiotics, and microbiomes.  Since we do not have funds to work on this in the lab, she has started an Indiegogo campaign to raise funds to work on this.  For more information on this project and how Koalas, Chlamydia, antibiotics and microbiomes are connected see "The Koala Project" page.

9 things PBS Newshour famously gets horrible wrong in story on fast food and microbiomes

Well, this is one of the worst microbiome news stories in a long time: Fast food kills gut bacteria that can keep you slim, book claims.  So many things wrong with it I don't even know where to go.  Here are nine:

1. The original headline: "Fast food kills gut bacteria that can keep you slim, study finds"

Here is the Tweet

Fast food kills gut bacteria that can keep you slim, study finds http://t.co/3fPIhJTuAB | @newshour

— PBS (@PBS) May 11, 2015

2. The correction:

is just completely lame and they should, as the New York Times does when it makes a correction, say what it used to say before they changed it

3. The sentence with the reference to Rob Knight is just bad reporting #1

Here is the quote:

Previous studies made similar findings: Professor Rob Knight of the University of Colorado Boulder, who collaborates with Spector, famously showed that transferring gut bacteria from obese humans to mice could make the rodents gain weight.

First of all - the paper they link to does include Rob Knight as a co-author, but the corresponding and senior author is Jeffrey Gordon and Rob is fourth to last (mind you I love Rob and his work, but in this case, saying this is something Rob showed without mentioning Gordon is just not right).

4 . The sentence with the reference to Rob Knight is just bad reporting #2

What the *$*$# does "famously showed" mean? Really.  What does it mean?

5 . The sentence with the reference to Rob Knight is just bad reporting #3

The statement "Previous studies made similar findings" is just so incredibly misleading.   It seems to be referring back to the previous sentences:

“What is emerging is that changes in our gut microbe community , or microbiome, are likely to be responsible for much of our obesity epidemic, and consequences like diabetes, cancer and heart disease,” he said. “It is clear that the more diverse your diet, the more diverse your microbes and the better your health at any age.”

This is just completely overblown.  The more diverse your diet the better your health at any age?  Oh #FFS that is just not based on any science.  And the "likely responsible for" is silly too.

6 . The sentence with the reference to Rob Knight is just bad reporting #4

Why exactly tell us he is collaborating with Rob Knight?  So some of Rob's good work rubs off?  I mean, Spector may do some fine work (and he has done some really good stuff).  But casually mentioning he collaborates with Knight who famously showed something (when actually it was more Jeff Gordon's work) which did not actually show what the article implies it showed.  Aaaaaaaaaaarg.

7. Good news.

Spector’s book claims that the diversity of microbes in the human body has decreased almost a third over the last century. But there’s also good news: Foods like dark chocolate, garlic, coffee and Belgian beer may help increase gut microbes.

Really?  Thinking about microbes MAY also increase gut microbes.  And so might listening to NPR.  Not something worth reporting here.

8. This sentence

This discovery suggested to his father that many cases of obesity may not simply be due to overeating.

That is right.  Looking at his son's poop and the microbes in it is the key to knowing that obesity might actually be fuc$*@#@ complex and not only caused by overeating.  Oh, that and 100 years of epidemiology and research.

9. This sentence

“Once on the diet I rapidly lost 1,300 species of bacteria and my gut was dominated by a different group called bacteroidetes. The implication is that the McDonald’s diet killed 1,300 of my gut species,” he said.

Sorry but that is NOT the implication.


UPDATE 1: May 11, 2015. 8:00 PM

Thanks to a Tweet from Jennifer Gunter I changed the title of my post from " 9 things horribly wrong with Newshour story on fast food and microbiomes" to "9 things PBS Newshour famously gets horrible wrong in story on fast food and microbiomes"

@PBS famously showed bad science reporting https://t.co/8VuGq5njWU

— Jennifer Gunter (@DrJenGunter) May 12, 2015

Cell Symposia have a problem with gender balance of speakers

With apologies I don't have time right now to tease apart all the details on these meetings. But, yuck. Cell Symposia have a big and persistent problem with gender balance of speakers. See the Storify below:

Coming today to a cat near you - microbes

Only three years after first imagined ...

Guest post from Rachid Ounit on CLARK: Fast and Accurate Classification of Metagenomic and Genomic Sequences

Recently I received and email from Rachid Ounit pointing me to a new open access paper he had on a metagenomics analysis tool called CLARK.  I asked him if he would be willing to write a guest post about it and, well, he did.  Here is it:

CLARK: Accurate metagenomic analysis of a million reads in 20 seconds or less…

At the University of California, Riverside, we have developed a new lightweight algorithm to classify accurately metagenomic samples while minimizing computational resources better than any other classifiers (e.g., Kraken).  While CLARK and Kraken have comparable accuracy, CLARK is significantly faster (cf. Fig. a) and uses less RAM and disk space (cf. Fig. b-c). In default mode and single-threaded, CLARK’s classification speed is higher than 3 million short reads per minute (cf. Fig. a), and it also scales better in multithreading (cf. Fig. d). Like Kraken, CLARK uses k-mers (short DNA words of length k) to solve the classification problem. However, while Kraken and other k-mers based classifiers consider the whole taxonomy tree and must resolve k-mers that match genomes from different taxa (by using the concept of “lowest common ancestor” from MEGAN), CLARK rather considers taxa defined for a unique taxonomy rank (e.g. species/genus), and, during the preprocessing, discards any k-mers that can be found in any pair of taxon. In other words, CLARK exploits specificities of each taxon (against all others) to populate its light and efficient data structure. It uses a customized dictionary of k-mers, in which each k-mer is associated to at most one taxon and results in fast k-mer queries. Then, the read is assigned to the taxon that has the highest amount of k-mers matches with it. Since these matches are discriminative, CLARK assignments are highly accurate. We also show that the choice of the value of k is critical for the optimal performance, and long k-mers (e.g., 31-mers) are not necessarily the best choice to perform accurate identification.  For example, high confidence assignments using 20-mers from real metagenomes show strong consistency with several published and independent results. 

Finally, CLARK can be used for detecting contamination in draft reference genome or, in genomics, chimera in sequenced BACs. We are currently investigating new techniques for improving the sensitivity and the speed of the tool, and we plan to release a new version later this year. We are also extending the tool for comparative genomics/metagenomics purposes. A “RAM-light” version of CLARK for your 4 GB RAM laptop is also available. CLARK is user-friendly (i.e., easy to use, it does not require strong background in programming/bioinformatics) and self-contained (i.e., does not need depend on any external software tool). The latest version of CLARK (v1.1.2) contains several features to analyze your results and is freely available under the GNU GPL license (for more details, please visit CLARK’s webpage). Experimental results and algorithm details can be found in the BMC genomics manuscript.

Performance of Kraken (v0.10.4-beta) and CLARK (v1.0) for the classification of a metagenome sample of 10,000 reads (average reads length 92bp).  a) The classification speed (in 103 reads per minute) in default mode. b) RAM usage (in GB) for the classification. c) Disk space (in GB) required for the database (bacterial genomes from NCBI/RefSeq). d) Classification speed (in 10^3 reads per minute) using 1, 2, 4 and 8 threads.