Bedtools tutorial from 2013 CSHL course

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. The tutorial uses data from the Maurano et al exploration of DnaseI hypersensitivity sites in hundreds of primary tissue types (Science 337:1190-1195).

The tutorial provides examples with pictures and code to do things like:

Intersections to find features that overlap (or do NOT overlap):

Merging features like {ChIP,MEDIP,DNAse,*}-seq peaks:

Examining coverage:

Advanced usage using the Jaccard statistic to measure similarity of all 20 tissue types to all other 20 20 tissues, and visualizing this with principal components analysis and ggplot2 in R:

See the full bedtools documentation for more.

2013 CSHL bedtools tutorial: http://quinlanlab.org/tutorials/bedtools/bedtools.html

An Annotated Online Bioinformatics / Computational Biology Curriculum

Two years ago David Searls published an article in PLoS Comp Bio describing a series of online courses in bioinformatics. Yesterday, the same author published an updated version, "A New Online Computational Biology Curriculum," (PLoS Comput Biol 10(6): e1003662. doi: 10.1371/journal.pcbi.1003662).

This updated curriculum has a supplemental PDF describing hundreds of video courses that are foundational to a good understanding of computational biology and bioinformatics. The table of contents embedded into the PDF's metadata (Adobe Reader: View>Navigation Panels>Bookmarks; Apple Preview: View>Table of Contents) breaks the curriculum down into 11 "departments" with links to online courses in each subject area:

1. Mathematics Department
2. Computer Science Department
3. Data Science Department
4. Chemistry Department
5. Biology Department
6. Computational Biology Department
7. Evolutionary Biology Department
8. Systems Biology Department
9. Neurosciences Department
10. Translational Sciences Department
11. Humanities Department

Listings in the catalog can take one of three forms: Courses, Current Topics, or Seminars. All listed courses are video-based and free of charge, many being MOOCs offered by Coursera or edX.

More than just a link dump, the author of this paper has "road tested" most of the courses, having enrolled in up to a dozen at a time. Under each course listing the author offers commentary on the importance of the subject to a computational biology education and an opinion on the quality of instruction. (The author ranks in the top 50 students on Coursera in terms of the number of completed courses on Coursera!). For the courses that the author completed, listings have an "evaluation" section, which ranks the course in difficulty, time requirements, lecture/homework effectiveness, assessment quality, and overall opinions. The author also injects autobiographical annotations as to why he thinks the courses are useful in a bioinformatics career. 

In summary, years of work went into creating this annotated course catalog, and is a great resource if you're looking to get started in a computational biology career, or if you're just looking to brush up on individual topics ranging from natural language processing to evolutionary theory.

Searls DB (2014) A New Online Computational Biology Curriculum. PLoS Comput Biol 10(6): e1003662. doi:10.1371/journal.pcbi.1003662

Collaborative lesson development with GitHub

If you're doing any kind of scientific computing and not using version control, you're doing it wrong. The git version control system and GitHub, a web-based service for hosting and collaborating on git-controlled projects, have both become wildly popular over the last few years. Late last year GitHub announced that the 10-millionth repository had been created, and Wired recently ran an article reporting on how git and GitHub were being used to version control everything from wedding invitations to Gregorian chants to legal documents. Version control and GitHub-enabled collaboration isn't just for software development anymore.

We recently held our second Software Carpentry bootcamp at UVA where I taught the UNIX shell and version control with git. Software Carpentry keeps all its bootcamp lesson material on GitHub, where anyone is free to use these materials and encouraged to contribute back new material. The typical way to contribute to any open-source project being hosted on GitHub is the fork and pull model. That is, if I wanted to contribute to the "bc" repository developed by user "swcarpentry" (swcarpentry/bc), I would first fork the project, which creates a copy for myself that I can work on. I make some changes and additions to my fork, then submit a pull request to the developer of the original "bc" repository, requesting that they review and merge in my changes.

GitHub makes this process extremely simple and effective, and preserves the entire history of changes that were submitted and the conversation that resulted from the pull request. I recently contributed a lesson on visualization with ggplot2 to the Software Carpentry bootcamp material repository. Take a look at this pull request and all the conversation that went with it here:

https://github.com/swcarpentry/bc/pull/395

On March 27 I forked swcarpentry/bc and started making a bunch of changes and additions, creating a new ggplot2 lesson. After submitting the pull request, I instantly received tons of helpful feedback from others reviewing my lesson material. This development-review cycle went back and forth a few times, and finally, when the Software Carpentry team was satisfied with all the changes to the lesson material, those changes were merged into the official bootcamp repository (the rendered lesson can be viewed here).

Git and GitHub are excellent tools for very effectively managing conflict resolution that inevitably results from merging work done asynchronously by both small and very large teams of contributors. As of this writing, the swcarpentry/bc repository has been forked 178 times, with pull requests merged from 71 different contributors, for a total of 1,464 committed changes and counting. Next time you try reconciling "tracked changes" and comments from 71 contributors in a M$ Word or Powerpoint file, please let me know how that goes.

In the meantime, if you're collaboratively developing code, lesson material, chord progressions, song lyrics, or anything else that involves text, consider using something like git and GitHub to make your life a bit easier. There are tons of resources for learning git. I'd start with Software Carpentry's material (or better yet, find an upcoming bootcamp near you). GitHub also offers courses online and in-person training classes, both free for-fee (cheap). You can also learn git right now by trying git commands in the browser at https://try.github.io.

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

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.

I've analyzed some data from GEO (GSE52202) using RNA-seq to study gene expression in motor neurons differentiated from induced pluripotent stem cells (iPSCs) derived from ALS patients carrying the C9ORF72 repeat expansion. I aligned the data, counted with featureCounts, and analyzed with DESeq2. I uploaded the results to this GitHub Gist.

Here's how you can use R to create a simple volcano plot. First, download the results file here and save it as a text file called results.txt.

After reading in the data from GitHub the next section creates a basic volcano plot. A few more lines color the points based on their fold change and statistical significance. Finally, if you have the calibrate package installed, the last line labels a few genes of interest.

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

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.

The qqman R package is on CRAN: http://cran.r-project.org/web/packages/qqman/

The source code is on GitHub: https://github.com/stephenturner/qqman

If you'd like to cite the qqman package (appreciated but not required), please cite this pre-print: Turner, S.D. qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots. biorXiv DOI: 10.1101/005165 (2014).

Something wrong? Please file bug reports, feature requests, or anything else related to the code as an issue on GitHub rather than commenting here. Also, please post the code you're using and some example data causing a failure in a publicly accessible place, such as a GitHub gist (no registration required). It's difficult to troubleshoot if I can't see the data where the code is failing. Want to contribute? Awesome! Send me a pull request.

Note: This release is substantially simplified for the sake of maintainability and creating an R package. The old code that allows confidence intervals on the Q-Q plot and allows more flexible annotation and highlighting is still available at the version 0.0.0 release on GitHub.

Here's a shout-out to all the blog commenters on the previous post for pointing out bugs and other issues, and a special thanks to Dan Capurso and Tim Knutsen for useful contributions and bugfixes.

qqman package tutorial

First things first. Install the package (do this only once), then load the package (every time you start a new R session)

# only once:
install.packages("qqman")

# each time:
library(qqman)

You can access this help any time from within R by accessing the vignette:

vignette("qqman")

The qqman package includes functions for creating manhattan plots and q-q plots from GWAS results. The gwasResults data.frame included with the package has simulated results for 16,470 SNPs on 22 chromosomes. Take a look at the data:

str(gwasResults)

'data.frame':   16470 obs. of  4 variables:
 $ SNP: chr  "rs1" "rs2" "rs3" "rs4" ...
 $ CHR: int  1 1 1 1 1 1 1 1 1 1 ...
 $ BP : int  1 2 3 4 5 6 7 8 9 10 ...
 $ P  : num  0.915 0.937 0.286 0.83 0.642 ...

head(gwasResults)

  SNP CHR BP      P
1 rs1   1  1 0.9148
2 rs2   1  2 0.9371
3 rs3   1  3 0.2861
4 rs4   1  4 0.8304
5 rs5   1  5 0.6417
6 rs6   1  6 0.5191

tail(gwasResults)

          SNP CHR  BP      P
16465 rs16465  22 530 0.5644
16466 rs16466  22 531 0.1383
16467 rs16467  22 532 0.3937
16468 rs16468  22 533 0.1779
16469 rs16469  22 534 0.2393
16470 rs16470  22 535 0.2630

How many SNPs on each chromosome?

as.data.frame(table(gwasResults$CHR))

   Var1 Freq
1     1 1500
2     2 1191
3     3 1040
4     4  945
5     5  877
6     6  825
7     7  784
8     8  750
9     9  721
10   10  696
11   11  674
12   12  655
13   13  638
14   14  622
15   15  608
16   16  595
17   17  583
18   18  572
19   19  562
20   20  553
21   21  544
22   22  535

Creating manhattan plots

Now, let's make a basic manhattan plot.

manhattan(gwasResults)

We can also pass in other graphical parameters. Let's add a title (main=), reduce the point size to 50%(cex=), and reduce the font size of the axis labels to 80% (cex.axis=):

manhattan(gwasResults, main = "Manhattan Plot", cex = 0.5, cex.axis = 0.8)

Let's change the colors and increase the maximum y-axis:

manhattan(gwasResults, col = c("blue4", "orange3"), ymax = 12)

Let's remove the suggestive and genome-wide significance lines:

manhattan(gwasResults, suggestiveline = F, genomewideline = F)

Let's look at a single chromosome:

manhattan(subset(gwasResults, CHR == 1))

Let's highlight some SNPs of interest on chromosome 3. The 100 SNPs we're highlighting here are in a character vector called snpsOfInterest. You'll get a warning if you try to highlight SNPs that don't exist.

str(snpsOfInterest)

 chr [1:100] "rs3001" "rs3002" "rs3003" "rs3004" "rs3005" ...

manhattan(gwasResults, highlight = snpsOfInterest)

We can combine highlighting and limiting to a single chromosome:

manhattan(subset(gwasResults, CHR == 3), highlight = snpsOfInterest, main = "Chr 3")

A few notes on creating manhattan plots:

• Run str(gwasResults). Notice that the gwasResults data.frame has SNP, chromosome, position, and p-value columns named SNP, CHR, BP, and P. If you're creating a manhattan plot and your column names are different, you'll have to pass the column names to the chr=, bp=, p=, and snp= arguments. See help(manhattan) for details.
• The chromosome column must be numeric. If you have “X,” “Y,” or “MT” chromosomes, you'll need to rename these 23, 24, 25, etc.
• If you'd like to change the color of the highlight or the suggestive/genomewide lines, you'll need to modify the source code. Search for col="blue", col="red", or col="green3" to modify the suggestive line, genomewide line, and highlight colors, respectively.

Creating Q-Q plots

Creating Q-Q plots is straightforward - simply supply a vector of p-values to the qq() function. You can optionally provide a title.

qq(gwasResults$P, main = "Q-Q plot of GWAS p-values")

Mycoplasma Contamination in Cell-Line Based Experiments

For a few years now, my EvoSTAR colleague, Bill Langdon, has been exploring the degree to which Mycoplasma bacteria have contaminated experimental systems and even "infected" online databases with the contents of their genomes.  He and his colleagues have previously shown that Mycoplasma genome sequences have previously been mislabeled as human sequences in several online resources (GenBank, dbEST, and RefSeq).

Early microarray designs were based largely on ESTs from these resources, and as a result, the Affymetrix HG-U133 plus 2.0 array contains probes for Mycoplasma sequences.  Details for these probes can be found here.  Exploiting these probes, Bill and colleagues have also examined the Gene Expression Omnibus for evidence of Mycoplasma contamination, and found around 30 studies (roughly 1% of GEO) that show high expression for this probe, the vast majority of which were from cell cultures.

By their proclivity to infect human experimental cell lines, Bill has playfully described Mycoplasma as having evolved the ability to transmit their genes into online databases.

Continuing this pursuit, Bill recently published an article in BMC BioData Mining illustrating Mycoplasma contamination of the 1000 Genomes Project.  It is unclear what the implications of this contamination are for the integrity of 1000 Genomes Data, as the majority of identified Mycoplasma reads to not map to the human reference genome.  This work should however serve as a bellwether to those performing experiments, or using experimental data from treated cell lines.  In these situations, any contamination might severely taint experimental results.

Russ Altman's Translational Bioinformatics Year in Review

A few weeks ago the 2014 AMIA Translational Bioinformatics Meeting (TBI) was held in beautiful San Francisco.  This meeting is full of great science that spans the divide between molecular and clinical research, but a true highlight of this meeting is the closing keynote, traditionally given by Russ Altman.  Each year, he highlights the most exciting articles/publications in translational bioinformatics, and presents them all in a tidy, entertaining talk.  You can find his blog here, along with a link to his slide deck.  

To make accessing the papers a bit easier, here is a list of links.

Controversies:

FDA and 23andMe

Lior Pachter's "Network Nonsense"

Inconsistency in Large Pharmacogenomic Studies

Clinical Genomics:

Warfarin Dosing with Pharmacogenomics

Clinically Actionable Genotypes in Patients with Premptive Pharmacogenomic Testing

Genic Intolerance to Functional Variation 

Estimating the Relative Pathogenicity of Human Genetic Variants

Analyzing the Incidentalome

Whole Genome Sequencing in Support of Wellness and Health Maintenance

Drugs:

Manual Curation of 88,000 Articles for Drug-Disease and Drug-Phenotype Interactions

Mining the Druggable Genome

Pathway-based Drug Screening Strategy

Identification of Proteins that Elicit Drug Side Effects

Network-Assisted Prediction of Potential Drugs for Addiction

Drug Repositioning of Antidepressants as Inhibitors of Small Cell Lung Cancer

Combinatorial Therapy Discovery Using Mixed Integer Linear Programming

The Druggable Genome

Identifying Drug Targets using Subtractive Genome Analyses

Genetic Basis of Disease:

Deleterious Variants in Mendelian Loci Contribute to Complex Disease

Comparison of PheWAS and GWAS data

Coherent Functional Modules Improve Transcription Factor Target Identification and Disease Association

A Disease-Phenotype Knowledge Base: Extracting Disease-Manifestation Relationships

A Common Rejection Module for Acute Rejection Across Multiple Organs

Network Models of GWAS Uncover Topological Centrality of Protein Interactions in Complex Disease

Emerging Data Sources:

A Network-Based Method for Analysis of lncRNA-Disease Associations

Lineage Structure of the Human Antibody Repertoire in Response to Influenza Vaccination

An Integrated Clinico-Metabolomic Model for Prediction of Death in Sepsis

Meta-Analyses of Studies of the Human Microbiota

PhenDisco: Phenotype Discovery in DBGaP

Comorbidity Clusters in Autism Spectrum Disorders

Network-based Analyses of Vaccine-Related Associations

Mice:

Knockouts Model the 100 Best-Selling Drugs

Mouse Model Phenotypes Provide Information About Human Drug Targets

Genomic Responses in Mouse Models Poorly Mimic Human Inflammatory Disease

The Scientific Process:

Atypical Combinations and Scientific Impact

Protein Interactions and Complex Disease

Quantifying Long-Term Scientific Impact

The Yale University Open Data Access Project

Evidence of Community Structure in Biomedical Research Grant Collaborations

Odds & Ends:

A Haplotype-Resolved Genome and Epigenome of the Aneuploid HeLa Cancer Cell Line

A Social Network of Hospital Acquired Infection Build from EMR Data

The Hidden Geometry of Complex, Network-Driven Contagion Phenomena

How Do You Feel?  Your Computer Knows

Simulation of Repetitive Diagnostic Blood Loss and Onset of Iatrogenic Anemia

Unsuck your writing

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.

When I pasted in some text from an abstract I submitted to ASHG years ago it showed me just how terrible and difficult to understand my scientific writing really is. My abstract text, which should have been hard-hitting and easy to understand at a glance, required a minimum grade 20 reading level. The majority of my 14 sentences were very hard to read and littered with too many adverbs, complicated words, and several uses of passive voice. (I still got a talk out of the submission, so maybe we as scientists enjoy reading tortuous verbiage...).

It looks like a desktop version is in the works, but the web app seemed to work fine, even for a 100,000-word manuscript I tried.

http://www.hemingwayapp.com/