Getting Things Done in Genetics & Bioinformatics Research

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

Strict quality control procedures are extremely important for any genome-wide association study.  One of the first steps you should take when running QC on your GWAS is to look for related samples in your dataset.  This does two things for you.

Visualizing sample relatedness in a GWAS using PLINK and R

Background  This tutorial shows an example of RNA-seq data analysis with DESeq2, followed by KEGG pathway analysis using GAGE. Using data from GSE37704, with processed data available on Figshare DOI: 10.6084/m9.figshare.1601975.

Tutorial: RNA-seq differential expression &amp; pathway analysis with Sailfish, DESeq2, GAGE, and Pathview

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)

Stephen Turner

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

This is reposted from the original article:

Use R to prompt a local LLM with ollamar

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

 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/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

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

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?

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

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

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

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

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, 2011 Time: 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

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

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

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

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

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

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

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

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

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

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

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.

Francis Collins: Computational biologists are "breakthrough artists"

This Thursday's discovery lecture looks interesting. In case you missed last week's Nobel laureate, you can watch the recording from Cech's talk or any of the previous lectures in the discovery series here.

Kathy Giacomini: Personalizing Anti-diabetic Drug Therapy

In a previous tutorial I showed you how to create plots faceted by the level of a third variable using ggplot2. A commenter asked about using faceted plots and viewports and reminded me of this function I found in the ggplot2 Google group. The arrange function below is similar to using par(mfrow=c(r,c)) in base graphics to put more than one plot in the same image window.

Arrange multiple ggplot2 plots in the same image window

Department of Biostatistics Seminar/Workshop Series:

 Statistical Methods for DNA Resequencing Analysis in Disease-Gene Studies

Wenyi Wang, Ph.D., Faculty Candidate Stanford Genome Technology Center, UC Berkeley Statistics 2:00-3:00pm Monday, February 15, 2010 MRB III Room 1220 Intended Audience: Persons interested in applied statistics, statistical theory, epidemiology, health services research, clinical trials

Seminar: Statistical Methods for DNA Resequencing Analysis in Disease-Gene Studies

The Vanderbilt Genome Technology Core is hosting a seminar/workshop on next-generation sequencing where Vanderbilt investigators will share their experiences with several types of next-gen technologies.  You can visit the Genome Technology Core at

Seminar: Next-gen sequencing at VUMC: Our experience with RNA-, ChIP- and DNA-seq

The 2010 Cancer Biostatistics Workshop seminar series begins next Friday with a workshop by Ayumi Shintani on sample size estimation and power analysis. Here's the entire 2010 workshop schedule. Be sure to check out my previous post on software for power calculations in genetic analysis.

Seminar: Sample size estimation and power analysis

I've covered plyr once before, showing you how to get means and variances for two quantitative traits across multilocus genotypes. JD Long over at Cerebral Mastication recently posted a nice screencast illustrating how plyr "just works" as an alternative to R's family of apply commands.

Use plyr instead of _apply() in R

This looks interesting. Department of Biostatistics Seminar/Workshop Series: A Multivariate Methodology for Analyzing Genome-wide Association Studies, by Janice Brodsky, PhD, UCLA.

Seminar announcement: A Multivariate Methodology for Analyzing Genome-wide Association Studies

GGD is now on Twitter! I'll be linking to all of our posts on the Twitter page, and occasionally post something there that may not make its way into a full length post here on the blog. You can follow us on Twitter here @genetics_blog.Getting Genetics Done by Stephen Turner is licensed under a Creative Commons Attribution (CC BY) License.

Follow GGD on Twitter @genetics_blog

One of R's biggest strengths is its unparalleled graphing capabilities.  Just see any of our previous posts on ggplot2, visualization, or other posts tagged with R. R has several fundamentally different systems for plotting, including base graphics, lattice, and ggplot2.  Furthermore, many add-on packages come with their own functions for producing problem-domain specific graphics.

Browse R Graphics with the R Graph Gallery and the R Graphical Manual

Way back will wrote on this topic.  See his previous post for Stata code for doing this.  Unfortunately the R package that was used to create QQ-plots here has been removed from CRAN, so I wrote my own using ggplot2 and some code I received from Daniel Shriner at NHGRI. Of course you can use R's built-in qqplot() function, but I could never figure out a way to add the diagonal using base graphics.

QQ plots of p-values in R using ggplot2

Vanderbilt ACCRE users with PCs only...    If you use ACCRE to run multi-processor jobs you'll be glad to know that they now allow you to map your home directory to your local desktop using Samba (so you can access your files through My Computer as you normally would with local files).

Sync your home directories on ACCRE and the local Linux servers (a.k.a. "the cheeses")

I wish I would have discovered this long ago.  Loading data into R or MySQL requires you to specify the full path to the file.  If you do this on a Windows machine there are two annoyances.  First, if you save something to your desktop the path to your desktop is really long.  Second, windows by default uses backslashes "\" in the file path, while R or other software requires forward slashes "/".

ClipPath copies filename and path from windows for loading into R

A quick announcement about a formatting fix here - you can now print posts from GGD a little more cleanly.  When you print it should no longer include the title and sidebar, so most posts should now only use a page or two.Getting Genetics Done by Stephen Turner is licensed under a Creative Commons Attribution (CC BY) License.

GGD posts now printer-friendly

Logan recently emailed me an article in the New York Times about single-molecule DNA sequencing and I realized I knew next to nothing about the new and emerging technology that will change the way we do association studies (that is, if we're still even trying to find genetic associations in the first place).  The Wellcome Trust posted a news feature a few weeks back giving brief explanations and short videos on DNA sequencing, starting with the

Next-Gen Sequencing

The Systems Biology Graphical Notation (SBGN) project is an effort to standardize the graphical notation used in diagrams of pathways, biochemical processes, and cellular processes studied in systems biology. SBGN defines a comprehensive set of symbols with precise semantics, together with detailed syntactic rules defining their use and how diagrams are to be interpreted.

Systems Biology Graphical Notation

Here are citations for the articles discussed at our most recent meeting (June 24). Our next meeting is scheduled for July 17. ~Julia Choi Y, Wijsman EM, Weir BS. Case-control association testing in the presence of unknown relationships.

 Genet Epidemiol

. 2009 Mar 30. [Epub ahead of print] Gao X, Becker LC, Becker DM, Starmer JD, Province MA. Avoiding the high Bonferroni penalty in genome-wide association studies.

PCG Journal Club Articles, 6/24

Some of the best figure design ideas come from cartographers. If you've ever read a Tufte book you've seen lots of examples. Let's talk about using color effectively. Penn State geography professor Cindy Brewer's ColorBrewer tool for selecting color schemes for figures has been conveniently packaged into an R library called RColorBrewer.

Use meaningful color schemes in your figures

Writing from the previously mentioned intro to R course at the Kennedy Center. If you couldn't make it you can download all the course materials from Theresa Scott's website, under the "Current Teaching Material" heading. Here is a direct link to the PDF for the overview materials that we're going over today, along with the R code from the examples in this guide.

PDF tutorial from R course (Introduction to R)

Google's chief economist was recently quoted as saying "The sexy job in the next ten years will be statisticians… The ability to take data-to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it-that’s going to be a hugely important skill." I'll leave you for the weekend with this ego-boosting article relating how our skill set as statisticians is a hot commodity in the real world.

Statistics and sex appeal

The Vanderbilt Kennedy Center is offering a free (repeat, free) one-day introductory course to the R statistical computing language on June 23, taught by Theresa Scott from the department of Biostatistics. You can find contact/registration info at the link below. Vanderbilt Kennedy Center - An Introduction to the Fundamentals &amp;

Free one-day R course at Vanderbilt

Has anyone ever used Galaxy? I saw their presentation at last year's ASHG. Seems like a great way to do collaborate on and keep a record of analyses in an easy web-GUI interface without having to download any software.

Has anyone ever used Galaxy?

For those of you writing abstracts that will get you to Hawaii this fall for IGES (due June 1) and ASHG (due June 2), just follow this template from Piled Higher and Deeper. Getting Genetics Done by Stephen Turner is licensed under a Creative Commons Attribution (CC BY) License.

Friday fun: Abstract mad libs

If you're doing an analysis with variables that naturally vary on a continuous scale, like age or smoking pack-years, NEVER be tempted to categorize individuals into groups - there's nearly always a better approach that utilizes the full distribution of values. It may seem convenient for a particular analysis you're doing but you'll take an enormous hit in power and precision.

Don't categorize continuous variables!

AAAS and the Food and Drug Law Institute (FDLI) are holding three 2-day meetings in Washington DC on personalized medicine, addressing the scientific discoveries, business models, and policy changes that are necessary to develop personalized treatments and diagnostics. All three meetings will be available via a free live webcast where viewers can use response technology to interact with speakers and session chairs.

AAAS personalized medicine meeting webcast

Amidst the fallout of an academic discussion over the worth of GWA studies followed by several gloomy and scathing articles in the popular press, came this paper in Nature Genetics. In summary, the investigators sequenced all the coding DNA on the X-chromosome in families affected with an evidently X-linked mental retardation phenotype.

Sequencing is Not (Yet) the Silver Bullet

How often have you needed to extract a certain column from a file, or strip out only a few individuals from a ped file? This is often done with Excel, but that requires loading the data, manipulating it, then saving it back into a format that is acceptable, which may require converting tabs to spaces or other aggravating things.

Ditch Excel for AWK!

I just returned from the Genome Informatics meeting at Cold Spring Harbor. This was, hands down, the best scientific conference I've been to in years. The quality of the talks and posters was excellent, and it was great meeting in person many of the scientists and developers whose tools and software I use on a daily basis.

Software from CSHL Genome Informatics 2015

I recently reformatted my laptop and needed to reinstall R and all the packages that I regularly use. In a previous post I covered R Commander, a nice GUI for R that includes a decent data editor and menus for graphics and basic statistical analysis.

Install and load R package "Rcmdr" to quickly install lots of other packages

Here are citations for the articles discussed at our most recent meeting (July 31). Our next meeting is scheduled for August 14. ~Julia  Bogdanowicz W, Allen M, Branicki W, Lembring M, Gajewska M, Kupiec T. Genetic identification of putative remains of the famous astronomer Nicolaus Copernicus.

PCG Journal Club Articles, 7/31

I recently stumbled across this collection of computational biology primers in Nature Biotechnology. Many of these are old, but they're still great resources to get a fundamental understanding of the topic. Here they are in no particular order.

Primers in computational biology

Hi! I'm Julia Wall, the Technical Writer in Vanderbilt's CHGR. Every two weeks or so, I'll be posting citation links to the articles that the students in our Program for Computational Genomics (PCG) Journal Club discuss in their bimonthly meetings. Enjoy!

PCG Journal Club Articles, 5/13 &amp; 5/27

This is a guest post from VP Nagraj, a data scientist embedded within UVA’s Health Sciences Library, who runs our Data Analysis Support Hub (DASH) service.

The What GRUPO (Gauging Research University Publication Output) is a Shiny app that provides side-by-side benchmarking of American research university publication activity.

GRUPO: Shiny App For Benchmarking Pubmed Publication Output

I found the slides below on the education page from Bioinformatics &amp; Research Computing at the Whitehead Institute. The first set (PDF) gives an overview of the methods and software available for quality assessment of microarray and RNA-seq experiments using the FastX toolkit and FastQC. The second set (PDF)  gives an example RNA-seq workflow using TopHat, SAMtools, Python/HTseq, and R/DEseq.

An example RNA-Seq Quality Control and Analysis Workflow

This looks like an interesting and relevant webinar for anyone receiving federal funding for genetics projects.

Sharing Genomics Data: NIH Data Sharing Policies Past, Present, and Future

Something like this probably already exists in an R package somewhere out there, but I needed a function to summarize how much missing data I have in each variable of a data frame in R. Pass a data frame to this function and for each variable it'll give you the number of missing values, the total N, and the proportion missing.

Summarize Missing Data for all Variables in a Data Frame in R

Last year I linked to a series of perspectives in NEJM with contrasting views on the success or failure of GWAS - David Goldstein's paper and Nick Wade's synopsis that soon followed in the New York Times being particularly pessimistic. Earlier this year I was swayed by an essay in Cell by Jon McClellan and Mary-Claire King condemning the common disease common variant hypothesis and chalking up most GWAS hits to population stratification.

Success of GWAS Approach Demonstrated by Latest Lipids Meta-Analysis

Aviad Klein over at My ContRibution wrote a convenient R function to list the classes of all the vectors that make up a data.frame. You would think apply(kyphosis,2,class) would do the job but it doesn't - it calls every vector a character class. Aviad wrote an elegant little function that does the job perfectly without having to load any external package:

Quickly Find the Class of data.frame vectors in R

NYU PhD student Drew Conway has compiled a very nice list of 10 reasons why grad students should blog. I've been writing GGD for a little over a year now and it's been a great way to extend my own network past the Vanderbilt walls, participate in lively discussions with other scientists oceans away, and to write stuff that people actually read and find useful.

Ten Reasons Why Grad Students Should Blog

A few months ago I covered an algorithm called EMMA (Efficient Mixed-Model Association) implemented in R for simultaneously correct for both population stratification and relatedness in an association study.

Efficient Mixed-Model Association eXpedited (EMMAX) to Simutaneously Account for Relatedness and Stratification in Genome-Wide Association Studies

Here are the papers we talked about in yesterday's Journal Club:  Genome Biol. 2009; 10(11): R134 Searching for SNPs with cloud computing. Langmead B, Schatz MC, Lin J, Pop M, Salzberg SL.  Table of Contents, Nature Methods, Visualization Supplement. Am J Hum Genet. 2010 Feb 12; 86(2): 113-25 Functional gene group analysis reveals a role of synaptic heterotrimeric G proteins in cognitive ability.

Papers from March 8, 2010 Journal Club

The estout package for Stata is useful for quickly creating nicely formatted tables from a regression analysis for tables or papers. To install it, fire up Stata and type in this command:  ssc install estout, replace  Stata will automatically download and install the package. Run the regression as you normally would, then use the esttab command (part of the estout package) to create a table using those results.

Make Pretty Regression Tables in Stata

This week Illumina launched their own personal genome sequencing service. For $48,000 they'll send you the sequence of your entire genome on a Mac computer that you can keep. According to their website, all the sequencing is done in a CLIA-certified clinical lab.

Get your genome sequenced by Illumina for $48k

I tip my hat to Will for showing me this little command line trick. PLINK's output looks nice when you print it to the screen, but it can be a pain to load the output into excel or a MySQL database because all the fields are separated by a variable number of spaces. This little command line trick will convert a variable-space delimited PLINK output file to a comma delimited file. You need to be on a Linux/Unix machine to do this.

Convert PLINK output to CSV

I thought it would be trivial to extract the p-value on the F-test of a linear regression model (testing the null hypothesis R²=0). If I fit the linear model: fit&lt;-lm(y~x1+x2), I can't seem to find it in names(fit) or summary(fit). But summary(fit)$fstatistic does give you the F statistic, and both degrees of freedom, so I wrote this function to quickly pull out the p-value from this F-test on a lm object, and added it to my R profile.

R function for extracting F-test P-value from linear model object

I saw a demonstration of this tool at the workshop on network analysis I announced last week. Genes2Networks draws from a large background network consisting of several experimentally verified mammalian protein interaction databases. It will take a list of genes you provide as seed genes and identify all interacting genes that fall on paths through the background network between them.

Genes2Networks

Obtaining the probability that zero, one, or two alleles are shared identical by descent (IBD) is useful for many reasons in a GWAS analysis. A while back I showed you how to visualize sample relatedness using R and ggplot2, which requires IBD estimates. Using plink --genome uses IBS and allele frequencies to infer IBD.

Parallelize IBD estimation with PLINK

Last month I told you about Coursera's specializations in data science, systems biology, and computing. Today I was reading Jeff Leek's blog post defending p-values and found a link to HarvardX's Data Analysis for Genomics course, taught by Rafael Irizarry and Mike Love.

Data Analysis for Genomics MOOC

Last week I ran a one-day workshop on RNA-seq data analysis in the UVA Health Sciences Library. I set up an AWS public EC2 image with all the necessary software installed.

RNA-seq Data Analysis Course Materials

I direct the Bioinformatics Core at the University of Virginia, and I'm hiring. Visit this link on the UVA Jobs website for more information. Here's the description:  I'm Hiring - Bioinformatics Analyst in the UVA Bioinformatics CoreGetting Genetics Done by Stephen Turner is licensed under a Creative Commons Attribution (CC BY) License.

I'm Hiring!

Frank Harrell, chair of our Biostats department, will be giving a seminar entitled "Towards a More Rigorous Approach to Personalized Medicine." As a champion of methods and strategies for reproducible research, Dr. Harrell's lecture on personalized medicine should be interesting.

Getting Genetics Done

GGD Launches!