Correction to eLife paper published

24 Jan

We just published a correction to our 2016 eLife paper.

While we were preparing the code to share it on Github, we discovered an error in calling drug resistance mutations (DRMs) in the protease gene in 140 sequences in our dataset (out of 6,717 sequences). The mistake was related to how R reads in data using read.table(). Even though the error only affected 2% of the sequences, it affected all downstream analysis and multiple figures had to be updated to reflect this correction. The resulting changes are minor and do not substantially change the conclusions and in some cases make them stronger.

When the analysis is updated with new numbers for these 140 sequences, all of our conclusions hold qualitatively, although the points in some figures shift slightly quantitatively. In fact, updating to the correct DRM calling for these sequences results in estimates for two treatments that are more in line with the expectations laid out in our paper.

As an illustration of this effect, we show here a version of Figure 4, which appeared in the paper (although has been slightly altered here for readability), and the shift of the model coefficients after correcting the 140 sequences of DRM calling. As you can see, the shifts are minor in most cases, and only serve to strengthen our conclusions for two of the PI treatments (points in the middle of the figure in light blue).

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Figure 4, from the paper (slightly altered here for readability) shows the shift of the model coefficients after correcting the 140 sequences of DRM calling. The shifts are minor in most cases, and only serve to strengthen our conclusions for two of the PI treatments (points in the middle of the figure in light blue).

After finding the original mistake, we spent a lot of time going through all of the code for the paper and found a few other small mistakes in the description of the analysis. In all cases, the mistakes were minor.

The eLife editors and staff were very helpful in the entire process.

Reference

Feder, Alison F., et al. “More effective drugs lead to harder selective sweeps in the evolution of drug resistance in HIV-1.” Elife 5 (2016): e10670. Link.

 

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Happy holidays from the CoDE lab!

20 Dec

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PINC students teach coding to middle school girls

28 Nov

SFSU PINC Program Blog

Three PINC students (Kimmie Richardson-Kubitsky-Tsui, Darleen Franklin and Olivia Pham) and Kadie Williams from the CoDE Lab taught a coding workshop for middle school girls as part of the  Expanding Your Horizons day at SFSU. 

The girls learned to make an app for an Android phone.

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Lab member Dwayne Evans wins ARCS scholarship

18 Nov

Dwayne joined the lab in 2015 and works on drug resistance and Prep (a drug that prevents HIV infection). He is now an ARCS scholar! Congrats, Dwayne!

The ARCS website writes about Dwayne: An MBRS-RISE (Research Initiative for Scientific Enhancement) and Genentech Dissertation Scholar, Dwayne’s research interests include the evolution of drug resistance in the Human Immunodeficiency Virus type-1 when exposed to Pre Exposure Prophylaxis (PrEP). At SFSU, his research is focused on determining whether PrEP increases the number of patients with drug resistance. Dwayne plans on pursuing a Ph.D. in bioinformatics and continuing to study the relationship between HIV drug resistance and patient infection rates. Aside from research, Dwayne enjoys mentoring undergraduates, learning R programming language and teaching locking choreography in dance classes.

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Dwayne Evans, Code Lab member since 2015, at the ARCS scholarship dinner. 

 

Code Lab walks to Fort Funston

23 Oct

Our campus is close to the ocean, but we are usually too busy to take advantage of that.

Last week we took the afternoon off to walk to Fort Funston.

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Members of the Code Lab at Fort Funston. 

How you can help the Clinton campaign from California and why you should

15 Oct

Being A Better Scientist

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I would like to convince you to join me in helping the Clinton campaign.

Why help the campaign?

First of all: does the campaign still need help? It seems like a sure win at this point!

1. Things can change quickly. Chances of Trump winning are small, but if it does happen it would be a major disaster for the country and the world, so I want to do my part to prevent it.

2. The senate is not a sure win, and right next door, in Nevada, is one of the tightest races for a senate seat. A democratic majority in the senate is within reach and would make a huge difference.

What to do?

For the longest time, I didn’t realize how I could help the Clinton campaign. Now I do, and I thought I share it with you!

1. Send money.

2. Volunteer for a…

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CoDE Lab visits Seven Bridges Genomics

26 Sep

After the Summer CoDE Program ended, we had the opportunity to visit Seven Bridges Genomics (https://www.sbgenomics.com/) here in San Francisco for an academic tour. It was a great experience seeing how bioinformatics is used in the industry!

Seven Bridges is a cloud-based company that offers different apps they have created to store, analyze, and interpret bioinformatic data. Some examples of the apps they offer are: RNA-Seq alignments (TopHat), whole genome analysis, ChimeraScan and more.

At Seven Bridges, we met a few of their bioinformaticians and they showed us what they have been working on and how we as students can use their apps and tools in our projects. Marion, Kadie, and Olivia each gave a brief presentation on their research and the summer students Kayla, Gabriella, and Abdul presented their posters on questions they were interested in using R to analyze data.

A special thanks to :

Zeynep Onder
Rohit Reja
Jing Zhao
Luke Chan
Anurag Sethi
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Kayla presenting her work on Facebook Birthday data

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Abdul presenting his work on Facebook Friend Count based on gender

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Gabriella presenting her work on sugar consumption around the world

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The Code Lab and Bioinformaticians at Seven Bridges

 

 

 

BAPGXIV meeting at SF State

18 Sep

Yesterday we hosted the 14th Bay Area Population Genomics meeting. It was the first time this meeting was held at SF State. A big thanks to all the speakers, poster presenters, lunch-career session volunteers and our sponsors (Ancestry.com, the College of Science and Engineering and the Biology Department). Plus, a really big thanks to all the student volunteers who did an amazing job to make this meeting run smoothly! Many of them are on the picture below.

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Bay Area Population Genomics at SF State

9 Sep

We are proud to host the Bay Area Population Genomics meeting at SF State!

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Fitness cost paper on bioRxiv

27 Jun

A little while ago we published a new manuscript on fitness costs on the bioRxiv. I’m very excited about this paper, because it is on a new topic for me (fitness costs) and we found some exciting results (for example, I never expected to find that CpG sites were so costly for HIV).

I am also excited about the paper because it is the first paper from my lab at SFSU and it is the first paper that resulted from our collaboration with Adi Stern in Tel Aviv.

The work was done by Marion Hartl (SFSU), Kristof Theys (University of Leuven and SFSU), Alison Feder (Stanford), Maoz Gelbart (University of Tel Aviv), Adi Stern (University of Tel Aviv) and myself.

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Fig 2 from the manuscript. Selection coefficients for transitions at every nucleotide site in the pol sequence show that CpG-forming mutations are more costly than non-CpG-forming mutations and that mutations that involve a drastic amino acid change are more costly than mutations that do not.
Selection coefficients were estimated using a generalized linear model and sequence data from 160 HIV-infected patients. Shown are predicted selection coefficients for synonymous (left) and non-synonymous (right) mutations that do not involve a drastic amino acid change and either create CpG sites (green) or do not (orange). For non-synonymous mutations, predictions are also shown for mutations that do involve drastic amino acid changes and either create CpG sites (pink) or do not (blue).

 

 

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