Getting Things Done in Genetics & Bioinformatics Research

Stephen Turner

A few folks have asked me how I get my news and stay on top of what's going on in my field, so I thought I'd share my strategy.

How to Stay Current in Bioinformatics/Genomics

Note 2010-11-17: there's more than one way to do this.

Embed R Code with Syntax Highlighting on your Blog

I first mentioned Coursera about a year ago, when I hired a new analyst in my core. This new hire came in as a very competent Python programmer with a molecular biology and microbial ecology background, but with very little experience in statistics.

Coursera Specializations: Data Science, Systems Biology, Python Programming

Nice review on the genetics of quantitative traits with good figures and references published this morning in Nature Reviews Genetics. Abstract:The genetics of quantitative traits: challenges and prospects (NRG AOP)Getting Genetics Done by Stephen Turner is licensed under a Creative Commons Attribution (CC BY) License.

The genetics of quantitative traits: challenges and prospects

Reposted from

 https://blog.stephenturner.us/p/tech-im-thankful-for-2024

Data science and bioinformatics tech I'm thankful for in 2024: tidyverse, RStudio, Positron, Bluesky, blogs, Quarto, bioRxiv, LLMs for code, Ollama, Seqera Containers, StackOverflow, ...It’s a short week here in the US. As I reflect on the tools that shape modern bioinformatics and data science it’s striking to see how far we’ve come in the 20 years I’ve

Tech I'm thankful for (repost)

This is reposted from the original at https://blog.stephenturner.us/p/python-for-r-users.

---A Google search for “R vs Python” returns thousands of hits across sites like Reddit, IBM, Datacamp, Coursera, Kaggle, and many others.

Python for R users (repost)

Reposted from https://blog.stephenturner.us/p/duckdb-vs-dplyr-vs-base-r.


 
 TL;DR
 
 : For a very simple analysis (means by group on 100M rows), duckdb was 125x faster than base R, and 28x faster than readr+dplyr, without having to read data from disk into memory. The duckplyr package wraps DuckDB's analytical query processing techniques in a dplyr-compatible API.

DuckDB vs dplyr vs base R

This blog has moved. This is reposted from Paired Ends:

 https://blog.stephenturner.us/p/planes-plausibility-analysis-of-epidemiological-signals-rplanes-r-package

PLANES provides a set of methods for evaluating the plausibility of epidemiological signals and forecasts.

PLANES: Plausibility Analysis of Epidemiological Signals

Here are the papers we talked about in yesterday's Journal Club: PLoS Biol. 2010 Jan 26;8(1):e1000294. Rare variants create synthetic genome-wide associations. See my previous coverage of this paper and the comments. Dickson SP, Wang K, Krantz I, Hakonarson H, Goldstein DB. PLoS Genet. 2010 Jan;6(1):e1000798.

Papers from Feb 8 2010 Journal Club

This is reposted from the original article:

Use R to prompt a local LLM with ollamar

Three years ago I wrote a blog post on how to create manhattan plots in R. After hundreds of comments pointing out bugs and other issues, I've finally cleaned up this code and turned it into an R package.

qqman: an R package for creating Q-Q and manhattan plots from GWAS results

One of the most powerful tools you can learn to use in genomics research is a relational database system, such as MySQL.  These systems are fairly easy to setup and use, and provide users the ability to organize and manipulate data and statistical results with simple commands.  As a graduate student (during the height of GWAS), this single skill quickly turned me into an “expert”.

Using Database Joins to Compare Results Sets

Our Program  in  Computation Genomics Journal Club is starting again, now the 3rd   Friday of each month. The next meeting is this Friday, April 16, at 3pm  in the CHGR conference room. As usual, please bring in any articles  you've  found recently and give a brief overview of why you thought it  was  interesting.

Journal Club 4/16/2010

A while back I wrote this post about how I stay current in bioinformatics &amp; genomics. That was

 nearly five years ago

. A lot has changed since then. A few links are dead.

Staying Current in Bioinformatics &amp; Genomics: 2017 Edition

A couple of months ago I posted about how to visualize exome coverage with bedtools and R. But if you're looking to get a basic handle on genome arithmetic, take a look at Aaron Quinlan's bedtools tutorials from the 2013 CSHL course.

Bedtools tutorial from 2013 CSHL course

Reposted from the original at https://blog.stephenturner.us/p/github-repo-to-text-for-llm-input.

---  If you use ChatGPT, Claude, or even some local model through Ollama or HuggingFace Assistants, you’ll know that the chat interface makes it challenging to feed in an entire repo like a Python or R package, because functions, tests, etc. can be scattered across many files throughout a repo.

Turn a GitHub repo into a single text file for LLM-friendly input (repost)

Reposted from the original at https://blog.stephenturner.us/p/bluesky-analysis-claude-llama-tidyverse.

Repost: Bluesky conversation analysis with local and frontier LLMs with R/Tidyverse

Reposted from

 Paired Ends

at https://blog.stephenturner.us/p/llm-translate-documentation. ---The lang package overrides the ? and help() functions in your R session. The translated help page will appear in the help pane in RStudio or Positron. It can also translate your Roxygen documentation.

Use an LLM to translate help documentation on-the-fly

This is reposted from the original at https://blog.stephenturner.us/p/expand-your-bluesky-network-with-r.

---I’m encouraging everyone I know online to join the scientific community on Bluesky.Bluesky for Science Stephen Turner·Nov 16Read full storyIn that post I link to several starter packs — lists of accounts posting about a topic that you can follow individually or all at once to start filling out your network.I started

Expand your Bluesky network with R (repost)

Reposted from the original at https://blog.stephenturner.us/p/python-cli-click-cookiecutter.


 ---

In the spirit of Learning in Public, I wanted an excuse to explore (1)

 click

for creating command line interfaces, (2)

 Cookiecutter

project templates, and (3) modern tools in the Python packaging ecosystem.

Build a Python CLI with Click+Cookiecutter (repost)

This is reposted from the original at https://blog.stephenturner.us/p/use-nanoparquet-instead-of-readr-csv.Parquet is interoperable between Python and R, fast to read+write, works well with databases, and stores complex data types (e.g., tibble listcols). Use it instead of CSV. Many pros, few (no?) cons. Yesterday I wrote about base R vs. dplyr vs. duckdb for a simple summary analysis.

Use nanoparquet instead of readr/CSV

This blog has moved. This is reposted from Paired Ends:


 https://blog.stephenturner.us/p/create-a-free-llama-405b-llm-chatbot-github-repo-huggingface

Llama 3.1 405B is the first open-source LLM on par with frontier models GPT-4o and Claude 3.5 Sonnet.

Create a free Llama 3.1 405B-powered chatbot on any GitHub repo in 1 minute (cross-posted from Paired Ends)

This is re-posted from my newsletter, where I'll be posting from now on: https://blog.stephenturner.us/p/biorecap-r-package-for-summarizing-biorxiv-preprints-local-llm    ---TL;DR I wrote an R package that summarizes recent bioRxiv preprints using a locally running LLM via Ollama+ollamar, and produces a summary HTML report from a parameterized RMarkdown template.

biorecap: an R package for summarizing bioRxiv preprints with a local LLM

How many reads do I need? What's my sequencing depth?

These are common questions I get all the time. Calculating how much sequence data you need to hit a target depth of coverage, or the inverse, what's the coverage depth given a set amount of sequencing, are both easy to answer with some basic algebra. Given one or the other, plus the genome size and read length/configuration, you can calculate either.

Covcalc: Shiny App for Calculating Coverage Depth or Read Counts for Sequencing Experiments

A while back I showed you how to make volcano plots in base R for visualizing gene expression results. This is just one of many genome-scale plots where you might want to show all individual results but highlight or call out important results by labeling them, for example, with a gene name. But if you want to annotate lots of points, the annotations usually get so crowded that they overlap one another and become illegible.

Repel overlapping text labels in ggplot2

I work with

 gene lists

on a nearly daily basis. Lists of genes near ChIP-seq peaks, lists of genes closest to a GWAS hit, lists of differentially expressed genes or transcripts from an RNA-seq experiment, lists of genes involved in certain pathways, etc. And lots of times I’ll need to convert these gene IDs from one identifier to another. There’s no shortage of tools to do this. I use Ensembl Biomart.

Annotables: R data package for annotating/converting Gene IDs

Joanna Zhao’s and Jenny Bryan’s R graph catalog is meant to be a complement to the physical book, Creating More Effective Graphs, but it’s a really nice gallery in its own right. The catalog shows a series of different data visualizations, all made with R and ggplot2. Click on any of the plots and you get the R code necessary to generate the data and produce the plot.

R + ggplot2 Graph Catalog

Anscombe’s quartet comprises four datasets that have nearly identical simple statistical properties, yet appear very different when graphed. Each dataset consists of eleven (

 x

,

 y

) points. They were constructed in 1973 by the statistician Francis Anscombe to demonstrate both the importance of graphing data before analyzing it and the effect of outliers on statistical properties. Let’s load and view the data.

Using and Abusing Data Visualization: Anscombe's Quartet and Cheating Bonferroni

I have a noteworthy blogs tag on this blog that I sort of forgot about, and haven't used in years. But I started reading one recently that's definitely qualified for the distinction. The Microbiome Digest is written by Elisabeth Bik, a scientist studying the microbiome at Stanford.

Microbiome Digest Blog

Last week I taught a three-hour introduction to R workshop for life scientists at UVA's Health Sciences Library. I broke the workshop into three sections: In the first half hour or so I presented slides giving an overview of R and why R is so awesome. During this session I emphasized reproducible research and gave a demonstration of using knitr + rmarkdown in RStudio to produce a PDF that can easily be recompiled when data updates.

Introduction to R for Life Scientists: Course Materials

Data “janitor-work”  The New York Times recently ran a piece on wrangling and cleaning data: “For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights” Whether you call it “janitor-work,” wrangling/munging,  cleaning/cleansing/scrubbing, tidying, or something else, the article above is worth a read (even though it implicitly denigrates the important work that your housekeeping staff does). It’s one of the few “Big Data” pieces that

Do your "data janitor work" like a boss with dplyr

Software Carpentry is an international collaboration backed by Mozilla and the Sloan Foundation comprising a team of volunteers that teach computational competence and basic programming skills to scientists.

Software Carpentry at UVA, Redux

I've been asked a few times how to make a so-called volcano plot from gene expression results. A volcano plot typically plots some measure of effect on the x-axis (typically the fold change) and the statistical significance on the y-axis (typically the -log10 of the p-value). Genes that are highly dysregulated are farther to the left and right sides, while highly significant changes appear higher on the plot.

Using Volcano Plots in R to Visualize Microarray and RNA-seq Results

I recently found this little gem of a web app that analyzes the clarity of your writing. Hemingway highlights long, complex, and hard to read sentences. It also highlights complex words where a simple one would do, and highlights adverbs, suggesting you use a stronger verb instead. It highlights passive voice (bad!), and tells you the minimum reading grade level necessary to understand your writing.

Unsuck your writing

I've seen this question asked and partially answered all around the web. As with anything related to Perl, I'm sure there is more than one way to do it. Here's how I do it with Perl 5.10.1 on CentOS 6.4. First, install local::lib with bootstrapping method as described here.

How To Install BioPerl Without Root Privileges

Boston-based startup Curoverse has announced $1.5 million in funding to develop and support the open-source Arvados platform for cloud-based bioinformatics &amp; genomics data analysis. The Arvados platform was developed in George Church's lab by scientists and engineers led by Alexander Wait Zaranek, now scientific director at Curoverse.

Curoverse raises $1.5M to develop &amp; support an open-source bioinformatics data analysis platform

Manhattan plots have become the standard way to visualize results for genetic association studies, allowing the viewer to instantly see significant results in the rough context of their genomic position.  Manhattan plots are typically shown on a linear X-axis (although the circos package can be used for radial plots), and this is consistent with the linear representation of the genome in online genome browsers.

A Mitochondrial Manhattan Plot

I archived and anlayzed all Tweets with the hashtag #ASHG2013 using my previously mentioned code. Number of Tweets by date shows Wednesday was the most Tweeted day: The top used hashtags other than #ASHG2013: The most prolific users: And what Twitter analysis would be complete without the widely loved, and more widely hated word cloud:

 
 Edit 8:24am
 

: I have gotten notes that some Tweets were not captured in this archive.

Analysis of #ASHG2013 Tweets

Several post-publication peer review forums already exist, such as Faculty of 1000 or PubPeer, that facilitate discussion of papers after they have already been published. F1000 only allows a small number of "faculty" to comment on articles, and access to read commentary requires a paid subscription. PubPeer and similar startup services lack a critical mass of participants to make such a community truly useful.

PubMed Commons: One post-publication peer review forum to rule them all?

At last week's 2013 useR! conference in Albacete, Spain, Martin Morgan and Marc Carlson led a course on using R/Bioconductor for analyzing next-gen sequencing data, covering alignment, RNA-seq, ChIP-seq, and sequence annotation using R. The course materials are online here, including R code for running the examples, the PDF vignette tutorial, and the course material itself as a package. Course Materials from useR!

Course Materials from useR! 2013 R/Bioconductor for Analyzing High-Throughput Genomic Data

Since the near beginning of genome-wide association studies, the PLINK software package (developed by Shaun Purcell’s group at the Broad Institute and MGH) has been the standard for manipulating the large-scale data produced by these studies.

PLATO, an Alternative to PLINK

Metagenomics

is the study of DNA collected from environmental samples (e.g., seawater, soil, acid mine drainage, the human gut, sputum, pus, etc.). While traditional microbial genomics typically means sequencing a pure cultured isolate, metagenomics involves taking a culture-free environmental sample and sequencing a single gene (e.g. the 16S rRNA gene), multiple marker genes, or shotgun sequencing everything in the sample in order to

Comparing Sequence Classification Algorithms for Metagenomics

Two of the most common questions at the beginning of an RNA-seq experiments are "how many reads do I need?" and "how many replicates do I need?". This paper describes a web application for designing RNA-seq applications that calculates an appropriate sample size and read depth to satisfy user-defined criteria such as cost, maximum number of reads or replicates attainable, etc.

Scotty, We Need More Power! Power, Sample Size, and Coverage Estimation for RNA-Seq

For 18 years now, computational biologists have convened on the beautiful islands of Hawaii to present and discuss research emerging from new areas of biomedicine. PSB Conference Chairs Teri Klein (@teriklein), Keith Dunker, Russ Altman (@Rbaltman) and Larry Hunter (@ProfLHunter) organize innovative sessions and tutorials that are always interactive and thought-provoking.

The Pacific Symposium on Biocomputing 2013

It's happened to all of us. You read about a new tool, database, webservice, software, or some interesting and useful data, but when you browse to http://instititution.edu/~home/professorX/lab/data, there's no trace of what you were looking for.

 THE PROBLEM

This isn't an uncommon problem. See the following two articles: The first gives us some alarming statistics.

Stop Hosting Data and Code on your Lab Website

I've said it before: Twitter makes me a lazy blogger. Lots of stuff came across my radar this week that didn't make it into a full blog post.

Twitter Roundup, January 4 2013

This is an issue that has bugged me for years, and I've finally found a good solution on osxdaily and Stack Overflow.

Copy Text to the Local Clipboard from a Remote SSH Session

Many papers have noted the challenges associated with assigning function to non-coding genetic variation, and since the majority of GWAS hits for common traits are non-coding, resources for providing some mechanism for these associations are desperately needed.

RegulomeDB: Identify DNA Features and Regulatory Elements in Non-Coding Regions

The 20th annual ISMB meeting was held over the last week in Long Beach, CA. It was an incredible meeting with lots of interesting and relevant talks, and lots of folks were tweeting the conference, usually with at least a few people in each concurrent session. I wrote the code below that uses the twitteR package to pull all the tweets about the meeting under the #ISMB hashtag. You can download that raw data here.

Plotting the Frequency of Twitter Hashtag Usage Over Time with R and ggplot2

I saw this plot in the supplement of a recent paper comparing microarray results to RNA-seq results. Nothing earth-shattering in the paper - you've probably seen a similar comparison many times before - but I liked how they solved the overplotting problem using heat-colored contour lines to indicate density. I asked how to reproduce this figure using R on Stack Exchange, and my question was quickly answered by Christophe Lalanne.

Fix Overplotting with Colored Contour Lines

Thanks to the excellent work of Lucia Hindorff and colleagues at NHGRI, the GWAS catalog provides a great reference for the cumulative results of GWAS for various phenotypes.  Anyone familiar with GWAS also likely knows about dbGaP – the NCBI repository for genotype-phenotype relationships – and the wealth of data it contains.

Browsing dbGAP Results

I just read an interesting paper on pathogen discovery using next-generation sequencing data, recommended to me by Nick Loman. A previously described algorithm (PathSeq, Kostic et al) for discovering microbes by deep-sequencing human tissue uses computational subtraction, whereby the initial collection of reads is depleted of human DNA by consecutive alignment to the human reference using MAQ and BLAST.

Identifying Pathogens in Sequencing Data

There were lots of interesting developments this month that didn't work their way into a full blog post. Here is an incomplete list of what I've been tweeting about over the last few weeks.

RNA-Seq Methods &amp; March Twitter Roundup

I'm a huge supporter of the Free and Open Source Software movement. I've written more about R than anything else on this blog, all the code I post here is free and open-source, and a while back I invited you to steal this blog under a cc-by-sa license. Every now and then, however, something comes along that just might be worth paying for.

Stepping Outside My Open-Source Comfort Zone: A First Look at Golden Helix SVS

GGD has a new look. I was inspired by Gina Trapani (Smarterware, Lifehacker) to remove any extra lines, links, and other "ink" that doesn't serve any purpose, and I hope the site appears cleaner and easier to read. I also wanted the extra horizontal space for larger images and avoid the dreaded side-scrolling in posts with lots of code like this one.

Redesign by Subtraction

I'm updating my CV and biosketch for a few grant applications, and for some time now, NIH has required you to include the PubMed Central ID for each article you publish that arose from NIH support. I only have a dozen or so papers indexed in PubMed, but I still wanted a way to do this automatically. If you have scores of publications, looking up all the PMCIDs could easily become a hassle. First, create an account at My NCBI.

Your Publications (with PMCID) as a PubMed Query

Kevin White from the University of Chicago will be giving a special guest lecture at NCI next week on systems biology approaches to mine genomics data for biomarkers and therapeutic targets. The lecture will be available online as a videocast.

Webinar: Genomic Networks - Resolving Biomarkers from a Cloud of Data

Title: A Backstage Tour of ggplot2 with Hadley Wickham


 Date: Wednesday, February 8, 2012


 Time: 11:00AM - 12:00PM Pacific


 Presenter: Hadley Wickham, Professor of Statistics, Rice University

Register here.

Hadley Wickham: ggplot2 Webinar (Today!)

I just got an email from Illumina about a webinar that looks interesting this Wednesday at 9am PST (noon EST) on clinical applications of next-gen sequencing. Date: Wednesday, December 7, 2011Time: 9:00 AM (PST)Speaker: Rick Dewey, MD, Stanford Center for Inherited Cardiovascular Disease Next-generation sequencing (NGS) presents both challenges and opportunities for clinical care.

Webinar: Applications of Next-Generation Sequencing in Clinical Care

Lately I've been using the limma package often for analyzing microarray data. When I read in Affy CEL files using ReadAffy(), the resulting ExpressionSet won't contain any featureData annotation. Consequentially, when I run topTable to get a list of differentially expressed genes, there's no annotation information other than the Affymetrix probeset IDs or transcript cluster IDs.

Annotating limma Results with Gene Names for Affy Microarrays

I use this all the time, and the setup is dead simple. Follow the code below to load the RMySQL package, connect to a database (here the UCSC genome browser's public MySQL instance), set up a function to make querying easier, and query the database to return results as a data frame. Getting Genetics Done by Stephen Turner is licensed under a Creative Commons Attribution (CC BY) License.

Query a MySQL Database from R using RMySQL

The Galaxy Project started using CiteULike to organize papers that are about, use, or reference Galaxy. The Galaxy CiteULike group is open to any CUL user, and once you join, you can add papers to the group, assign tags, and rate papers.

Galaxy Project Group on CiteULike and Mendeley

BioMart recently got a facelift. I'm not sure if this was always available in the old BioMart, but there's now a link to a gene ID converter that worked pretty well for me for converting S. cerevisiae gene IDs to standard gene names. It looks like the tool will convert nearly any ID you could imagine. Looks like it will also map Affy probe IDs to gene, transcript, or protein IDs and names.

BioMart Gene ID Converter

James Taylor came to UVA last week and gave an excellent talk on how Galaxy enables transparent and reproducible research in genomics. I'm gearing up to take on several projects that involve next-generation sequencing, and I'm considering installing my own Galaxy framework on a local cluster or on the cloud. If you've used Galaxy in the past you're probably aware that it allows you to share data, workflows, and histories with other users.

Guide to RNA-seq Analysis in Galaxy

Daniel Cook in Jeff Murray's lab at the University of Iowa put together this handy Excel template for keeping track of how samples from four 96-well plates are interleaved to configure a single 384-well plate using robotic liquid handling systems, like the Hydra II. Paste in lists of samples on your 96-well plates: And you'll get out a map of how the 384-well plate layout: And a summary list: You can download the Excel file here.

Excel Template for Mapping Four 96-Well Plates to One 384-Well Plate

I’m a bit exhausted from a week of excellent science at ICHG. First, let me say that Montreal is a truly remarkable city with fantastic food and a fascinating blend of architectural styles, all making the meeting a fun place to be…. Now on to the genomics – I’ll recap a few of the most exciting sessions I attended. You can find a live-stream of tweets from the meeting by searching the #ICHG2011 and #ICHG hashtags.

My thoughts on ICHG 2011

I just accepted an offer for a faculty position at the University of Virginia in the Center for Public Health Genomics / Department of Public Health Sciences. Starting in October I will be developing and directing a new centralized bioinformatics core in the UVA School of Medicine. Over the next few weeks I'm taking a much-needed vacation next door in Kauai and then packing up for the move to Charlottesville.

I'm Starting a New Position at the University of Virginia

After evaluating an unnamed bioinformatics core facility, a group of bioinformaticians in Europe wrote up a short list of basic guidelines for organizing a bioinformatics core facility in large research institutes.

How to Run a Bioinformatics Core

I haven't posted much here recently, but here is a roundup of a few of the links I've shared on Twitter (@genetics_blog) over the last two weeks.

Here is a nice tutorial on

 accessing high-throughput public data

(from NCBI) using R and Bioconductor.

 Cloudnumbers.com

, a startup that allows you to run high-performance computing (HPC) applications in the cloud, now supports the previously mentioned R IDE, RStudio.

Friday Links: R, OpenHelix Bioinformatics Tips, 23andMe, Perl, Python, Next-Gen Sequencing

Sequencing company Complete Genomics recently made available 69 ethnically diverse complete human genome sequences: a Yoruba trio; a Puerto Rican trio; a 17-member, 3-generation pedigree; and a diversity panel representing 9 different populations. Some of the samples partially overlap with HapMap and the 1000 Genomes Project. The data can be downloaded directly from the FTP site.

Download 69 Complete Human Genomes

PLINK/SEQ is an open source C/C++ library for analyzing large-scale genome sequencing data. The library can be accessed via the pseq command line tool, or through an R interface. The project is developed independently of PLINK but it's syntax will be familiar to PLINK users. PLINK/SEQ boasts an impressive feature set for a project still in the beta testing phase.

PLINK/SEQ for Analyzing Large-Scale Genome Sequencing Data

This is a few months old but I just got around to reading this series of blog posts on next-generation sequencing (NGS) by Gabe Rudy, Golden Helix's VP of product development. This series gives a seriously useful overview of NGS technology, then delves into the analysis of NGS data at each step, right down to a description of the most commonly used file formats and tools for the job. Check it out now if you haven't already.

Golden Helix: A Hitchhiker's Guide to Next Generation Sequencing

Abhijit over at Stat Bandit posted some nice code for making forest plots using ggplot2 in R. You see these lots of times in meta-analyses, or as seen in the BioVU demonstration paper. The idea is simple - on the x-axis you have the odds ratio (or whatever stat you want to show), and each line is a different study, gene, SNP, phenotype, etc.

Forest plots using R and ggplot2

I recently started using RStudio, the amazing new IDE for R. You can view all of RStudio's keyboard shortcuts by going to the help menu, but I made this printable reference for myself and thought I'd share it. I only included the Windows shortcuts, and I cut out all the obvious ones (Ctrl-S for save, Ctrl-O for open, etc) so it would fit neatly on one page.

RStudio Keyboard Shortcut Reference PDF

Just saw the announcement of the availability of Rstudio, a new (free &amp; open source) integrated development environment for R that works on Windows, Mac, and Linux. Judging from the screenshots, it looks like Rstudio supports syntax highlighting for Sweave &amp;

RStudio: New free IDE for R

Had a mental block today trying to figure out how to get the indices of columns in a data frame given their names. Simple task but difficult to search Google for an answer. Thanks to jashapiro, Matt, and Vince for giving me a heads up on the which() function. The which() function returns the indices of TRUE values in a logical vector. If you're looking at the iris data: data(iris) head(iris)

R: Given column name in a Data Frame, Get the Index

A while back I asked you what reference management software you used, and how well you liked it. I received 180 responses, and here's what you said. Out of the choices on the poll, most of you used Mendeley (30%), followed by EndNote (23%) and Zotero (15%). Out of those of you who picked "other," it was mostly Papers or Qiqqa. There were even a few brave souls managing references caveman-style, manually.

Results from Reference Management Poll

After finishing the final revisions on my dissertation I was reminded of this spot-on graphical guide to what a Ph.D. is really all about. Now that I'm finished, I'm leaving Vanderbilt to start a postdoc in genetic epidemiology with Dr. Loic Le Marchand at the University of Hawaii Cancer Center. Posts may be sparse over the next few weeks, but I plan on blogging as usual once I'm set up at my postdoc.

So long Vanderbilt, and thanks for all the fish!

Lucila Ohno-Machado, Professor of Medicine and Chief of the Division of Biomedical Informatics at UC-San Diego, will be giving a talk on "Accuracy of Individualized Risk Estimates for Personalized Medicine" next week, August 18, noon-1pm in 202 Light Hall. This should be an interesting perspective from a scientist with medical training on the utility of personal genomics tools in making healthcare decisions.

Accuracy of Individualized Risk Estimates for Personalized Medicine

A live webcast of the House Committee on Energy and Commerce hearing on “Direct-to-Consumer Genetic Testing and the Consequences to the Public Health" is available at this link. I had trouble viewing the webcast in firefox - had to save the link and open it with VLC media player to get it working. You can also follow the #HouseDTC hastag on Twitter.

Webcast this Morning: House Committee on Energy and Commerce hearing on DTC Genetic Testing

There will be a (free) tutorial on the 1000 genomes project at this year's ASHG meeting on Wednesday, November 3, 7:00 – 9:30pm. You can register online at the link below.

Tutorial on the 1000 Genomes Project Data

A few weeks ago I suddenly reached the point that every graduate student once thought would never come - time to start writing my thesis. With a blank page and a blinking cursor staring me in the face it's time to compile all of my published and unpublished work I've accumulated over the last few years and wordsmith this pile of papers and results into a single cohesive unit.

Writing my Thesis - Follow me on Twitter

You can find two  nice  recaps of last week's personalized  medicine policy forum on Genomics   Law Report and 23andMe's blog, The   Spittoon.

23andMe GCPM recaps and FDA meeting on Laboratory Developed Tests

For those of you not following GGD on Twitter you may not have seen this - California State Senator Alex Padilla and 23andMe are hosting a policy forum entitled "Genomics and the Consumer: The Present and Future of Personalized Medicine" today in San Francisco.

Genomics and the Consumer: The Present and Future of Personalized Medicine

Update

Tuesday, September 14, 2010: Fixed the ylim issue, now it sets the y axis limit based on the smallest observed p-value. A while back Will showed you how to create QQ plots of p-values in Stata and in R using the now-deprecated

 sma

package. A bit later on I showed you how to do the same thing in R using ggplot2.

QQ plot of p-values in R using base graphics

Next week Brent Anderson with Illumina will be hosting a seminar series showcasing presentations from Vanderbilt scientists using Illumina technology to power their next-generation sequencing studies.

Illumina Sequencing Seminar Series

At the request of a commenter I just wanted to clarify that any code released here for R or anything else is free and open source unless specifically stated otherwise. The open source (GPLv2) license for any code on GGD can be found on this page.Getting Genetics Done by Stephen Turner is licensed under a Creative Commons Attribution (CC BY) License.

All code on GGD is Free and Open-Source (GPLv2)

Vanderbilt Center for Human Genetics Research faculty candidate Goncalo Abecasis will be interviewing for a faculty position here this week. Come check out his seminar - "Sequencing Thousands of Human Genomes" - Friday June 11th, 2-3pm in 512 Light Hall.Getting Genetics Done by Stephen Turner is licensed under a Creative Commons Attribution (CC BY) License.

Goncalo Abecasis "Sequencing 1000s of Human Genomes" Faculty Candidate Seminar

A District Court handed down a summary judgment invalidating most of Myriad's claims to both the BRCA1 DNA sequence and the method of testing for early-onset familial breast and ovarian cancer. See Genetic Future and Genomics Law Report for analysis.Getting Genetics Done by Stephen Turner is licensed under a Creative Commons Attribution (CC BY) License.

Federal Courts Invalidate Myriad's Breast Cancer Gene Patents

If you attended Frank Harrell's Regression Modeling Strategies course a few weeks ago, you got a chance to see the rms  package for R in action. Frank's rms package does regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit.

R Package 'rms' for Regression Modeling

A few weeks ago I covered an R package for efficient mixed model regression that is capable of simultaneously accounting for both population stratification and relatedness to compute unbiased estimates of standard errors and p-values for genetic association studies.

Mixed linear model approach adapted for genome-wide association studies

Definitely a seminar not to miss: Curtis Fideler, director of Sales at Pacific Biosciences, will be giving a special seminar here at Vanderbilt Thursday, May 20, 11:00a-noon in 202 Light Hall entitled "An Introduction to SMRT Sequencing: A description of Pacific Biosciences single molecule real time sequencing technology." Science: Real-Time DNA Sequencing from Single

Introduction to single molecule real time (SMRT) sequencing from Pac Bio's Curtis Fideler

The recent settlement between Arizona State University and the Havasupai Indian tribe is calling attention to (and perhaps challenging) the ideas of informed consent. While I'm sure there are arguments to be made supporting both sides of this case, regardless of your position this is an excellent reminder that there are people's lives behind the alleles in our spreadsheets and PED files.

Havasupai Indians and the Ethical Use of Data

This looks like a must-read for anyone starting out in computational biology without extensive experience at the command line.  The 135-page document linked at the bottom of the Google Group page looks like an excellent primer with lots of examples that could probably be completed in a day or two, and provides a great start for working in a Linux/Unix environment and programming with Perl.

Unix and Perl for Biologists

Check out this review essay in Cell: Genetic Heterogeneity in Human Disease, by Jon McClellan and Mary-Claire King. (King's lab, incidentally, was the group who discovered via linkage analysis that the gene for early-onset breast and ovarian cancer on chromosome 17q21, nearly 5 years before Myriad Genetics filed for patent protection on the BRCA1/2 genes). Anyhow, looks like a great review on genetic heterogeneity and GWAS. Thanks @JVJAI.

Cell: Genetic Heterogeneity in Human Disease Review

I've been using GenABEL for some time now for GWAS analysis using related individuals. It has an excellent set of functions for estimating a kinship matrix from a dense marker panel and then using this in a linear mixed effects model to allow for related individuals in the analysis of a quantitative trait.

ProbABEL - R package for GWAS data imputation

A short announcement - my friend, colleague, running partner, and GGD contributor Will Bush is now an assistant professor in the Department of Biomedical Informatics, and investigator in the Center for Human Genetics Research here at Vanderbilt.Getting Genetics Done by Stephen Turner is licensed under a Creative Commons Attribution (CC BY) License.

Professor Bush

A new paper in Nature by the Wellcome Trust Case Control Consortium examining 16,000 cases of 8 common diseases and 3000 shared controls finds that common CNVs probed on existing arrays are well tagged by SNPs and are unlikely to contribute much to common human disease.

Wellcome Trust: Common CNVs Unlikely to Influence Common Disease

VICC's retreat this year is Tuesday, May 11, 2010. The theme of the retreat looks interesting: "Genomic Approaches for Personalized Medicine." You can register free here. VICC retreat: Genomic Approaches for Personalized MedicineGetting Genetics Done by Stephen Turner is licensed under a Creative Commons Attribution (CC BY) License.

Vanderbilt-Ingram Cancer Center Retreat

Just caught this on the OpenHelix Blog. In an interview with Charlie Rose, NIH director Francis Collins said Computational biologists will be the "breakthrough" artists of the future. Getting Genetics Done by Stephen Turner is licensed under a Creative Commons Attribution (CC BY) License.

Getting Genetics Done

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