Utility script for launching bare JAR files

Torsten Seemann compiled a list of minimum standards for bioinformatics command line tools, things like printing help when no commands are specified, including version info, avoid hardcoded paths, etc. These should be obvious to any seasoned software engineer, but many of these standards are not followed in bioinformatics.

#8 on the list was "Don't distribute bare JAR files." This is particularly annoying, requiring a user to invoke the software using something like: java -Xmx1000m -jar /path/on/my/system/to/software.jar . There are a few notable offenders in bioinformatics out there (I'm looking at you, Trimmomatic, snpEff, GATK...).

A very simple solution to the bare JAR file problem is distributing your java tool with a shell script wrapper that makes it easier for your users to invoke. E.g., if I have GATK installed in ~/bin/ngs/gatk/GenomeAnalysisTK.jar, I can create this shell script at ~/bin/ngs/gatk/gatk (replace GenomeAnalysisTK.jar with someOtherBioTool.jar):



Once I make that script executable and include that directory in my path, calling GATK is much simpler:


Yes, I'm fully aware that making my own JAR launcher utility scripts for existing software will make my code less reproducible, but for quick testing and development I don't think it matters. The tip has the best results when JAR files are distributed from the developer with utility scripts for invoking them.

See the post below for more standards that should be followed in bioinformatics software development.

Torsten Seeman: Minimum standards for bioinformatics command line tools

Understanding the ENSEMBL Schema

ENSEMBL is a frequently used resource for various genomics and transcriptomics tasks.  The ENSEMBL website and MART tools provide easy access to their rich database, but ENSEMBL also provides flat-file downloads of their entire database and a public MySQL portal.  You can access this using the MySQL Workbench using the following:

Host:     useastdb.ensembl.org
User:     anonymous

Once inside, you can get a sense for what the ENSEMBL schema (or data model) is like.  First, it’s important to understand the ENSEMBL ID system.  Most of the primary entities in the ENSEMBL database (genes, exons, transcripts, proteins) have a formal, stable identifier (beginning with ENSG, ENSE, ENST, and ENSP respectively) that does not change from build to build.  These entries can be found in the gene_stable_id tables.  All of these entities also have an internal identifier (an integer).  Once you have an internal ID for the entity of interest, details of the entity can be found in the genes, exons, transcripts, and translations (proteins) table. For example, the following query will retrieve a list of all transcripts and their exons for a given gene.

SELECT * FROM gene_stable_id a
inner join gene b on a.gene_id = b.gene_id
inner join transcript c on b.gene_id = c.gene_id
inner join exon_transcript d on c.transcript_id = d.transcript_id
inner join exon e on d.exon_id = e.exon_id
inner join transcript_stable_id f on c.transcript_id = f.transcript_id
inner join exon_stable_id g on e.exon_id = g.exon_id

The exon_transcript table contains a mapping of each exon to any transcripts containing it, and also contains a rank to indicate which exon it is relative to a given transcript.  To retrieve exons for a list of genes by their ENSEMBL IDs, these could be loaded into a table and joined to the gene_stable_id table in the query above.  To pull the build 37 chromosome and coordinates for an exon, use the following:

Select a.exon_id, b.name, a.seq_region_start, a.seq_region_end from exon a
inner join seq_region b on a.seq_region_id = b.seq_region_id
inner join coord_system c on b.coord_system_id = c.coord_system_id
where c.version = "GRCh37";

In this query, the seq_region table contains a field called name that identifies the contig to which the coordinates refer, in this case the chromosome number. 

There are also extensive cross-references in the ENSEMBL database.  To retrieve alternate identifiers for a set of transcripts, execute the following: 

select * from transcript_stable_id a
inner join transcript b on a.transcript_id = b.transcript_id
inner join object_xref c on b.transcript_id = c.ensembl_id
inner join xref d on c.xref_id = d.xref_id
inner join external_db e on d.external_db_id = e.external_db_id
where ensembl_object_type = "Transcript"
limit 20;

ENSEMBL organizes cross-references (xrefs) for all entity types into a single table object_xref.  This table contains an ensemble_object_type field that is a “Transcript”, “Gene”, or “Translation”, and an ensemble_id that matches either a gene_id, transcript_id, or a translation_id.  Replace “transcript” in the above query with “gene” or “translation” to retrieve gene or protein cross-references.  A list of all external cross-reference sources can be found by querying: 

Select db_name from external_db;

There is a great deal of information within the ENSEMBL database that can be accessed using SQL, which for some types of operations is easier than using the MART or web interface.  Full details of the ENSEMBL schema can be found here (http://useast.ensembl.org/info/docs/api/core/core_schema.html)

Google Developers R Programming Video Lectures

Google Developers recognized that most developers learn R in bits and pieces, which can leave significant knowledge gaps. To help fill these gaps, they created a series of introductory R programming videos. These videos provide a solid foundation for programming tools, data manipulation, and functions in the R language and software. The series of short videos is organized into four subsections: intro to R, loading data and more data formats, data processing and writing functions. Start watching the YouTube playlist here, or watch an individual lecture below:

1.1 - Initial Setup and Navigation
1.2 - Calculations and Variables
1.3 - Create and Work With Vectors
1.4 - Character and Boolean Vectors
1.5 - Vector Arithmetic
1.6 - Building and Subsetting Matrices
1.7 - Section 1 Review and Help Files
2.1 - Loading Data and Working With Data Frames
2.2 - Loading Data, Object Summaries, and Dates
2.3 - if() Statements, Logical Operators, and the which() Function
2.4 - for() Loops and Handling Missing Observations
2.5 - Lists
3.1 - Managing the Workspace and Variable Casting
3.2 - The apply() Family of Functions
3.3 - Access or Create Columns in Data Frames, or Simplify a Data Frame using aggregate()
4.1 - Basic Structure of a Function
4.2 - Returning a List and Providing Default Arguments
4.3 - Add a Warning or Stop the Function Execution
4.4 - Passing Additional Arguments Using an Ellipsis
4.5 - Make a Returned Result Invisible and Build Recursive Functions
4.6 - Custom Functions With apply()

Archival, Analysis, and Visualization of #ISMBECCB 2013 Tweets

As the 2013 ISMB/ECCB meeting is winding down, I archived and analyzed the 2000+ tweets from the meeting using a set of bash and R scripts I previously blogged about.

The archive of all the tweets tagged #ISMBECCB from July 19-24, 2013 is and will forever remain here on Github. You'll find some R code to parse through this text and run the analyses below in the same repository, explained in more detail in my previous blog post.

Number of tweets by date:

Number of tweets by hour:

Most popular hashtags, other than #ismbeccb. With separate hashtags for each session, this really shows which other SIGs and sessions were well-attended. It also shows the popularity of the unofficial ISMB BINGO card.

Most prolific users. I'm not sure who or what kind of account @sciencstream is - seems like spam to me.

And the obligatory word cloud:

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

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! 2013 R/Bioconductor for Analyzing High-Throughput Genomic Data

Customize your .Rprofile and Keep Your Workspace Clean

Like your .bashrc, .vimrc, or many other dotfiles you may have in your home directory, your .Rprofile is sourced every time you start an R session. On Mac and Linux, this file is usually located in ~/.Rprofile. On Windows it's buried somewhere in the R program files. Over the years I've grown and pruned my .Rprofile to set various options and define various "utility" functions I use frequently at the interactive prompt.

One of the dangers of defining too many functions in your .Rprofile is that your code becomes less portable, and less reproducible. For example, if I were to define adf() as a shortcut to as.data.frame(), code that I send to other folks using adf() would return errors that the adf object doesn't exist. This is a risk that I'm fully aware of in regards to setting the option stringsAsFactors=FALSE,  but it's a tradeoff I'm willing to accept for convenience. Most of the functions I define here are useful for exploring interactively. In particular, the n() function below is handy for getting a numbered list of all the columns in a data frame; lsp() and lsa() list all functions in a package, and list all objects and classes in the environment, respectively (and were taken from Karthik Ram's .Rprofile); and the o() function opens the current working directory in a new Finder window on my Mac. In addition to a few other functions that are self-explanatory, I also turn off those significance stars, set a default CRAN mirror so it doesn't ask me all the time, and source in the biocLite() function for installing Bioconductor packages (note: this makes R require web access, which might slow down your R initialization).

Finally, you'll notice that I'm creating a new hidden environment, and defining all the functions here as objects in this hidden environment. This allows me to keep my workspace clean, and remove all objects from that workspace without nuking any of these utility functions.

I used to keep my .Rprofile synced across multiple installations using Dropbox, but now I keep all my dotfiles in a single git-versioned directory, symlinked where they need to go (usually ~/). My .Rprofile is below: feel free to steal or adapt however you'd like.

ENCODE ChIP-Seq Significance Tool: Which TFs Regulate my Genes?

I collaborate with several investigators on gene expression projects using both microarray and RNA-seq. After I show a collaborator which genes are dysregulated in a particular condition or tissue, the most common question I get is "what are the transcription factors regulating these genes?"

This isn't the easiest question to answer. You could look at transcription factor binding site position weight matrices like those from TRANSFAC and come up with a list of all factors that potentially hit that site, then perform some kind of enrichment analysis on that. But this involves some programming, and is based solely on sequence motifs, not experimental data.

The ENCODE consortium spent over $100M and generated hundreds of ChIP-seq experiments for different transcription factors and histone modifications across many cell types (if you don't know much about ENCODE, go read the main ENCODE paper, and Sean Eddy's very fair commentary). Regardless of what you might consider "biologically functional", the ENCODE project generated a ton of data, and much of this data is publicly available. But that still doesn't help answer our question, because genes are often bound by multiple TFs, and TFs can bind many regions. We need to perform an enrichment (read: hypergeometric) test to assess an over-representation of experimentally bound transcription factors around our gene targets of interest ("around" also implies that some spatial boundary must be specified). To date, I haven't found a good tool to do this easily.

Raymond Auerbach and Bin Chen in Atul Butte's lab recently developed a resource to address this very common need, called the ENCODE ChIP-Seq Significance Tool.

The paper: Auerbach et al. Relating Genes to Function: Identifying Enriched Transcription Factors using the ENCODE ChIP-Seq Significance Tool. Bioinformatics (2013): 10.1093/bioinformatics/btt316.

The software: ENCODE ChIP-Seq Significance Tool (http://encodeqt.stanford.edu/).

This tool takes a list of "interesting" (significant, dysregulated, etc.) genes as input, and identifies ENCODE transcription factors from this list. Head over to http://encodeqt.stanford.edu/, select the ID type you're using (Ensembl, Symbol, etc), and paste in your list of genes. You can also specify your background set (this has big implications for the significance testing using the hypergeometric distribution). Scroll down some more to tell the tool how far up and downstream you want to look from the transcription start/end site or whole gene, select an ENCODE cell line (or ALL), and hit submit. 

You're then presented with a list of transcription factors that are most likely regulating your input genes (based on overrepresentation of ENCODE ChIP-seq binding sites). Your results can then be saved to CSV or PDF. You can also click on a number in the results table and get a list of genes that are regulated by a particular factor (the numbers do not appear as hyperlinks in my browser, but clicking the number still worked).

At the very bottom of the page, you can load example data that they used in the supplement of their paper, and run through the analysis presented therein. The lead author, Raymond Auerbach, even made a very informative screencast on how to use the tool:

Now, if I could only find a way to do something like this with mouse gene expression data.

Butte Lab: ENCODE ChIP-Seq Significance Tool  (Read the Bioinformatics paper here)

PLATO, an Alternative to PLINK

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.  Over the course of its development, numerous features and options were added to enhance its capabilities, but it is best known for the core functionality of performing quality control and standard association tests. 

Nearly 10 years ago (around the time PLINK was just getting started), the CHGR Computational Genomics Core (CGC) at Vanderbilt University started work on a similar framework for implementing genotype QC and association tests.  This project, called PLATO, has stayed active primarily to provide functionality and control that (for one reason or another) is unavailable in PLINK.  We have found it especially useful for processing ADME and DMET panel data – it supports QC and association tests of multi-allelic variants.    

PLATO runs via command line interface, but accepts a batch file that allows users to specify an order of operations for QC filtering steps.  When running multiple QC steps in a single run of PLINK, the order of application is hard-coded and not well documented.  As a result, users wanting this level of control must run a sequence of PLINK commands, generating new data files at each step leading to longer compute times and disk usage.  PLATO also has a variety of data reformatting options for other genetic analysis programs, making it easy to run EIGENSTRAT, for example.

The detail of QC output from each of the filtering steps is much greater in PLATO, allowing output per group (founders only, parents only, etc), and giving more details on why samples fail sex checks, Hardy-Weinberg checks, and Mendelian inconsistencies to facilitate deeper investigation of these errors.  And with family data, disabling samples due to poor genotype quality retains pedigree information useful for phasing and transmission tests. Full documentation and download links can be found here (https://chgr.mc.vanderbilt.edu/plato).  Special thanks to Yuki Bradford in the CGC for her thoughts on this post.  

Automated Archival and Visual Analysis of Tweets Mentioning #bog13, Bioinformatics, #rstats, and Others

Automatically Archiving Twitter Results

Ever since Twitter gamed its own API and killed off great services like IFTTT triggers, I've been looking for a way to automatically archive tweets containing certain search terms of interest to me. Twitter's built-in search is limited, and I wanted to archive interesting tweets for future reference and to start playing around with some basic text / trend analysis.

Enter t - the twitter command-line interface. t is a command-line power tool for doing all sorts of powerful Twitter queries using the command line. See t's documentation for examples.

I wrote this script that uses the t utility to search Twitter separately for a set of specified keywords, and append those results to a file. The comments at the end of the script also show you how to commit changes to a git repository, push to GitHub, and automate the entire process to run twice a day with a cron job. Here's the code as of May 14, 2013:



That script, and results for searching for "bioinformatics", "metagenomics", "#rstats", "rna-seq", and "#bog13" (the Biology of Genomes 2013 meeting) are all in the GitHub repository below. (Please note that these results update dynamically, and searching Twitter at any point could possibly result in returning some unsavory Tweets.)

https://github.com/stephenturner/twitterchive

Analyzing Tweets using R

You'll also find an analysis subdirectory, containing some R code to produce barplots showing the number of tweets per day over the last month, frequency of tweets by hour of the day, the most used hashtags within a search, the most prolific tweeters, and a ubiquitous word cloud. Much of this code is inspired by Neil Saunders's analysis of Tweets from ISMB 2012. Here's the code as of May 14, 2013:



Also in that analysis directory you'll see periodically updated plots for the results of the queries above.

Analyzing Tweets mentioning "bioinformatics"

Using the bioinformatics query, here are the number of tweets per day over the last month:

Here is the frequency of "bioinformatics" tweets by hour:

Here are the most used hashtags (other than #bioinformatics):

Here are the most prolific bioinformatics Tweeps:

Here's a wordcloud for all the bioinformatics Tweets since March:

Analyzing Tweets mentioning "#bog13"

The 2013 CSHL Biology of Genomes Meeting took place May 7-11, 2013. I searched and archived Tweets mentioning #bog13 from May 1 through May 14 using this script. You'll notice in the code above that I'm no longer archiving this hashtag. I probably need a better way to temporarily add keywords to the search, but I haven't gotten there yet.

Here are the number of Tweets per day during that period. Tweets clearly peaked a couple days into the meeting, with follow-up commentary trailing off quickly after the meeting ended.

Here is the frequency frequency of Tweets by hour, clearly bimodal:

Top hashtags (other than #bog13). Interestingly #bog14 was the most highly used hashtag, so I'm guessing lots of folks are looking forward to next years' meeting. Also, #ashg12 got lots of mentions, presumably because someone presented updated work from last years' ASHG meeting.

Here were the most prolific Tweeps - many of the usual suspects here, as well as a few new ones (new to me at least):

And finally, the requisite wordcloud:

More analysis

If you look in the analysis directory of the repo you'll find plots like these for other keywords (#rstats, metagenomics, rna-seq, and others to come). I would also like to do some sentiment analysis as Neil did in the ISMB post referenced above, but the sentiment package has since been removed from CRAN. I hear there are other packages for polarity analysis, but I haven't yet figured out how to use them. I've given you the code to do the mundane stuff (parsing the fixed-width files from t, for starters). I'd love to see someone take a stab at some further text mining / polarity / sentiment analysis!

twitterchive - archive and analyze results from a Twitter search

Three Metagenomics Papers for You

A handful of good metagenomics papers have come out over the last few months. Below I've linked to and copied my evaluation of each of these articles from F1000.

...

1. Willner, Dana, and Philip Hugenholtz. "From deep sequencing to viral tagging: Recent advances in viral metagenomics." BioEssays (2013). 

My evaluation: This review lays out some of the challenges and recent advances in viral metagenomic sequencing. There is a good discussion of library preparation and how that affects downstream sequencing. Alarmingly, they reference another paper that showed that different amplification methods resulted in detection of a completely different set of viruses (dsDNA viruses with LASL, ssDNA with MDA). The review also discusses many of the data management, analysis, and bioinformatics challenges associated with viral metagenomics.

...

2. Loman, Nicholas J., et al. "A Culture-Independent Sequence-Based Metagenomics Approach to the Investigation of an Outbreak of Shiga-Toxigenic Escherichia coli O104: H4Outbreak of Shiga-toxigenic Escherichia coli." JAMA 309.14 (2013): 1502-1510.

My evaluation: This paper is a groundbreaking exploration of the use of metagenomics to investigate and determine the causal organism of an infectious disease outbreak. The authors retrospectively collected fecal samples from symptomatic patients from the 2011 Escherichia coli O104:H4 outbreak in Germany and performed high-throughput shotgun sequencing, followed by a sophisticated analysis to determine the outbreak's causal organism. The analysis included comparing genetic markers from many symptomatic patients' metagenomes with those of healthy controls, followed by de novo assembly of the outbreak strain from the shotgun metagenomic data. This illustrates both the power, but the real limitations, of using metagenomic approaches for clinical diagnostics. Also see David Relman's synopsis of the study in the same JAMA issue

...

3. Shakya, Migun, et al. "Comparative metagenomic and rRNA microbial diversity characterization using archaeal and bacterial synthetic communities." Environmental microbiology (2013).

My evaluation: This study set out to compare shotgun metagenomic sequencing to 16S rRNA amplicon sequencing to determine the taxonomic and abundance profiles of mixed community metagenomic samples. Thus far, benchmarking metagenomic methodology has been difficult due to the lack of datasets where the underlying ground truth is known. In this study, the researchers constructed synthetic metagenomic communities consisting of 64 laboratory mixed genome DNAs of known sequence and polymerase chain reaction (PCR)-validated abundance. The researchers then compared metagenomic and 16S amplicon sequencing, using both 454 and Illumina technology, and found that metagenomic sequencing outperformed 16S sequencing in quantifying community composition. The synthetic metagenomes constructed here are publicly available (Gene Expression Omnibus [GEO] accession numbers are given in the manuscript), which represent a great asset to other researchers developing methods for amplicon-based or metagenomic approaches to sequence classification, diversity analysis, and abundance estimation.

List of Bioinformatics Workshops and Training Resources

I frequently get asked to recommend workshops or online learning resources for bioinformatics, genomics, statistics, and programming. I compiled a list of both online learning resources and in-person workshops (preferentially highlighting those where workshop materials are freely available online):

List of Bioinformatics Workshops and Training Resources

I hope to keep the page above as up-to-date as possible. Below is a snapshop of what I have listed as of today. Please leave a comment if you're aware of any egregious omissions, and I'll update the page above as appropriate.

From http://stephenturner.us/p/edu, April 4, 2013

In-Person Workshops:

Cold Spring Harbor Courses: meetings.cshl.edu/courses.html

Cold Spring Harbor has been offering advanced workshops and short courses in the life sciences for years. Relevant workshops include Advanced Sequencing Technologies & Applications, Computational & Comparative Genomics, Programming for Biology, Statistical Methods for Functional Genomics, the Genome Access Course, and others. Unlike most of the others below, you won't find material from past years' CSHL courses available online.

Canadian Bioinformatics Workshops: bioinformatics.ca/workshops
Bioinformatics.ca through its Canadian Bioinformatics Workshops (CBW) series began offering one and two week short courses in bioinformatics, genomics and proteomics in 1999. The more recent workshops focus on training researchers using advanced high-throughput technologies on the latest approaches being used in computational biology to deal with the new data. Course material from past workshops is freely available online, including both audio/video lectures and slideshows. Topics include microarray analysis, RNA-seq analysis, genome rearrangements, copy number alteration,network/pathway analysis, genome visualization, gene function prediction, functional annotation, data analysis using R, statistics for metabolomics, and much more.

UC Davis Bioinformatics Training Program: training.bioinformatics.ucdavis.edu
The UC Davis Bioinformatics Training program offers several intensive short bootcamp workshops on RNA-seq, data analysis and visualization, and cloud computing with a focus on Amazon's computing resources. They also offer a week-long Bioinformatics Short Course, covering in-depth the practical theory and application of cutting-edge next-generation sequencing techniques. Every course's documentation is freely available online, even if you didn't take the course.

MSU NGS Summer Course: bioinformatics.msu.edu/ngs-summer-course-2013
This intensive two week summer course will introduce attendees with a strong biology background to the practice of analyzing short-read sequencing data from Illumina and other next-gen platforms. The first week will introduce students to computational thinking and large-scale data analysis on UNIX platforms. The second week will focus on mapping, assembly, and analysis of short-read data for resequencing, ChIP-seq, and RNAseq. Materials from previous courses are freely available online under a CC-by-SA license.

Genetic Analysis of Complex Human Diseases: hihg.med.miami.edu/edu...
The Genetic Analysis of Complex Human Diseases is a comprehensive four-day course directed toward physician-scientists and other medical researchers. The course will introduce state-of-the-art approaches for the mapping and characterization of human inherited disorders with an emphasis on the mapping of genes involved in common and genetically complex disease phenotypes. The primary goal of this course is to provide participants with an overview of approaches to identifying genes involved in complex human diseases. At the end of the course, participants should be able to identify the key components of a study team, and communicate effectively with specialists in various areas to design and execute a study. The course is in Miami Beach, FL. (Full Disclosure: I teach a section in this course.) Most of the course material from previous years is not available online, but my RNA-seq & methylation lectures are on Figshare.

UAB Short Course on Statistical Genetics and Genomics: soph.uab.edu/ssg/...
Focusing on the state-of-art methodology to analyze complex traits, this five-day course will offer an interactive program to enhance researchers' ability to understand & use statistical genetic methods, as well as implement & interpret sophisticated genetic analyses. Topics include GWAS Design/Analysis/Imputation/Interpretation; Non-Mendelian Disorders Analysis; Pharmacogenetics/Pharmacogenomics; ELSI; Rare Variants & Exome Sequencing; Whole Genome Prediction; Analysis of DNA Methylation Microarray Data; Variant Calling from NGS Data; RNAseq: Experimental Design and Data Analysis; Analysis of ChIP-seq Data; Statistical Methods for NGS Data; Discovering new drugs & diagnostics from 300 billion points of data. Video recording from the 2012 course are available online.

MBL Molecular Evolution Workshop: hermes.mbl.edu/education/...
One of the longest-running courses listed here (est. 1988), the Workshop on Molecular Evolution at Woods Hole presents a series of lectures, discussions, and bioinformatic exercises that span contemporary topics in molecular evolution. The course addresses phylogenetic analysis, population genetics, database and sequence matching, molecular evolution and development, and comparative genomics, using software packages including AWTY, BEAST, BEST, Clustal W/X, FASTA, FigTree, GARLI, MIGRATE, LAMARC, MAFFT, MP-EST, MrBayes, PAML, PAUP*, PHYLIP, STEM, STEM-hy, and SeaView. Some of the course materials can be found by digging around the course wiki.


Online Material:

Canadian Bioinformatics Workshops: bioinformatics.ca/workshops
(In person workshop described above). Course material from past workshops is freely available online, including both audio/video lectures and slideshows. Topics include microarray analysis, RNA-seq analysis, genome rearrangements, copy number alteration, network/pathway analysis, genome visualization, gene function prediction, functional annotation, data analysis using R, statistics for metabolomics, andmuch more.

UC Davis Bioinformatics Training Program: training.bioinformatics.ucdavis.edu
(In person workshop described above). Every course's documentation is freely available online, even if you didn't take the course. Past topics include Galaxy, Bioinformatics for NGS, cloud computing, and RNA-seq.

MSU NGS Summer Course: bioinformatics.msu.edu/ngs-summer-course-2013
(In person workshop described above). Materials from previous courses are freely available online under a CC-by-SA license, which cover mapping, assembly, and analysis of short-read data for resequencing, ChIP-seq, and RNAseq.

EMBL-EBI Train Online: www.ebi.ac.uk/training/online
Train online provides free courses on Europe's most widely used data resources, created by experts at EMBL-EBI and collaborating institutes. Topics include Genes and Genomes, Gene Expression,Interactions, Pathways, and Networks, and others. Of particular interest may be the Practical Course on Analysis of High-Throughput Sequencing Data, which covers Bioconductor packages for short read analysis, ChIP-Seq, RNA-seq, and allele-specific expression & eQTLs.

UC Riverside Bioinformatics Manuals: manuals.bioinformatics.ucr.edu
This is an excellent collection of manuals and code snippets. Topics include Programming in R, R+Bioconductor, Sequence Analysis with R and Bioconductor, NGS analysis with Galaxy and IGV, basicLinux skills, and others.

Software Carpentry: software-carpentry.org
Software Carpentry helps researchers be more productive by teaching them basic computing skills. We recently ran a 2-day Software Carpentry Bootcamp here at UVA. Check out the online lectures for some introductory material on Unix, Python, Version Control, Databases, Automation, and many other topics.

Coursera: coursera.org/courses
Coursera partners with top universities to offer courses online for anytone to take, for free. Courses are usually 4-6 weeks, and consist of video lectures, quizzes, assignments, and exams. Joining a course gives you access to the course's forum where you can interact with the instructor and other participants. Relevant courses include Data Analysis, Computing for Data Analysis using R, and Bioinformatics Algorithms, among others. You can also view all of Jeff Leek's Data Analysis lectures on Youtube.
Rosalind: http://rosalind.info
Quite different from the others listed here, Rosalind is a platform for learning bioinformatics through gaming-like problem solving. Visit the Python Village to learn the basics of Python. Arm yourself at theBioinformatics Armory, equipping yourself with existing ready-to-use bioinformatics software tools. Or storm the Bioinformatics Stronghold, implementing your own algorithms for computational mass spectrometry, alignment, dynamic programming, genome assembly, genome rearrangements, phylogeny, probability, string algorithms and others.


Other Resources:

• Titus Brown's list bioinformatics courses: Includes a few others not listed here (also see the comments).
• GMOD Training and Outreach: GMOD is the Generic Model Organism Database project, a collection of open source software tools for creating and managing genome-scale biological databases. This page links out to tutorials on GMOD Components such as Apollo, BioMart, Galaxy, GBrowse, MAKER, and others.
• Seqanswers.com: A discussion forum for anything related to Bioinformatics, including Q&A, paper discussions, new software announcements, protocols, and more.
• Biostars.org: Similar to SEQanswers, but more strictly a Q&A site.
• BioConductor Mailing list: A very active mailing list for getting help with Bioconductor packages. Make sure you do some Google searching yourself first before posting to this list.
• Bioconductor Events: List of upcoming and prior Bioconductor training and events worldwide.
• Learn Galaxy: Screencasts and tutorials for learning to use Galaxy.
• Galaxy Event Horizon: Worldwide Galaxy-related events (workshops, training, user meetings) are listed here.
• Galaxy RNA-Seq Exercise: Run through a small RNA-seq study from start to finish using Galaxy.
• Rafael Irizarry's Youtube Channel: Several statistics and bioinformatics video lectures.
• PLoS Comp Bio Online Bioinformatics Curriculum: A perspective paper by David B Searls outlining a series of free online learning initiatives for beginning to advanced training in biology, biochemistry, genetics, computational biology, genomics, math, statistics, computer science, programming, web development, databases, parallel computing, image processing, AI, NLP, and more.
• Getting Genetics Done: Shameless plug – I write a blog highlighting literature of interest, new tools, and occasionally tutorials in genetics, statistics, and bioinformatics. I recently wrote this post about how to stay current in bioinformatics & genomics.

Evolutionary Computation and Data Mining in Biology

For over 15 years, members of the computer science, machine learning, and data mining communities have gathered in a beautiful European location each spring to share ideas about biologically-inspired computation.  Stemming from the work of John Holland who pioneered the field of genetic algorithms, multiple approaches have been developed that exploit the dynamics of natural systems to solve computational problems.  These algorithms have been applied in a wide variety of fields, and to celebrate and cross-pollinate ideas from these various disciplines the EvoStar event co-locates five conferences at the same venue, covering genetic programming (EuroGP), combinatorial optimization (EvoCOP), music, art, and design (EvoMUSART), multidisciplinary applications (EvoApplications), and computational biology (EvoBIO).  EvoStar 2013 will be held in Vienna, Austria on April 3-5, and is always expertly coordinated by the wonderful Jennifer Willies from Napier University, UK. Multiple research groups from the US and Europe will attend to present their exciting work in these areas.

Many problems in bioinformatics and statistical analysis use what are considered “greedy” algorithms to fit parameters to data – that is, they settle on a nearby collection of parameters as the solution and potentially miss a global best solution.  This problem is well-known in the computer science community for toy problems like bin packing or the knapsack problem.  In human genetics, related problems are partitioning complex pedigrees or selecting maximally unrelated individuals from a dataset, and can also appear when maximizing likelihood equations.

EvoBIO focuses on using biologically-inspired algorithms (like genetic algorithms) to improve performance for many bioinformatics tasks.  For example, Stephen and I have both applied these methods for analysis of genetic data using neural networks, and for forward-time genetic data simulation (additional details here).

EvoBIO is very pleased to be sponsored by BMC Biodata Mining, a natural partner for this conference.  I recently wrote a blog post for BioMed Central about EvoBIO as well.  Thanks to their sponsorship, the winner of the EvoBIO best paper award will receive free publication in Biodata Mining, and runners-up will receive 25% discount off the article processing charge.

So, if you are in the mood for a new conference and would like to see and influence some of these creative approaches to data analysis, consider attending EvoSTAR -- We'd love to see you there!

Software Carpentry Bootcamp at University of Virginia

A couple of weeks ago I, with the help of others here at UVA, organized a Software Carpentry bootcamp, instructed by Steve Crouch, Carlos Anderson, and Ben Morris. The day before the course started, Charlottesville was racked by nearly a foot of snow, widespread power outages, and many cancelled incoming flights. Luckily our instructors arrived just in time, and power was (mostly) restored shortly before the boot camp started. Despite the conditions, the course was very well-attended.

Software Carpentry's aim is to teach researchers (usually graduate students) basic computing concepts and skills so that they can get more done in less time, and with less pain. They're a volunteer organization funded by Mozilla and the Sloan foundation, and led this two-day bootcamp completely free of charge to us.

The course started out with a head-first dive into Unix and Bash scripting, followed by a tutorial on automation with Make, concluding the first day with an introduction to Python. The second day covered version control with git, Python code testing, and wrapped up with an introduction to databases and SQL. At the conclusion of the course, participants offered near-universal positive feedback, with the git and Make tutorials being exceptionally popular.

Software Carpentry's approach to teaching these topics is unlike many others that I've seen. Rather than lecturing on for hours, the instructors inject very short (~5 minute) partnered exercises between every ~15 minutes of instruction in 1.5 hour sessions. With two full days of intensive instruction and your computer in front of you, it's all too easy to get distracted by an email, get lost in your everyday responsibilities, and zone out for the rest of the session.  The exercises keep participants paying attention and accountable to their partner.

All of the bootcamp's materials are freely available:

Unix and Bash: https://github.com/redcurry/bash_tutorial
Python Introduction: https://github.com/redcurry/python_tutorial
Git tutorial: https://github.com/redcurry/git_tutorial
Databases & SQL: https://github.com/bendmorris/swc_databases
Everything else: http://users.ecs.soton.ac.uk/stc/SWC/tutorial-materials-virginia.zip

Perhaps more relevant to a broader audience are the online lectures and materials available on the Software Carpentry Website, which include all the above topics, as well as many others.

We capped the course at 50, and had 95 register within a day of opening registration, so we'll likely do this again in the future. I sit in countless meetings where faculty lament how nearly all basic science researchers enter grad school or their postdoc woefully unprepared for this brave new world of data-rich high-throughput science. Self-paced online learning works well for some, but if you're in a department or other organization that could benefit from a free, on-site, intensive introduction to the topics listed above, I highly recommend contacting Software Carpentry and organizing your own bootcamp.

Finally, when organizing an optional section of the course, we let participants vote whether they preferred learning number crunching with NumPy, or SQL/databases; SQL won by a small margin. However, Katherine Holcomb in UVACSE has graciously volunteered to teach a two-hour introduction to NumPy this week, regardless of whether you participated in the boot camp (although some basic Python knowledge is recommended). This (free) short course is this Thursday, March 21, 2-4pm, in the same place as the bootcamp (Brown Library Classroom in Clark Hall). Sign up here.

Comparing Sequence Classification Algorithms for Metagenomics

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 determine what's there.

A challenge in shotgun metagenomics analysis is the sequence classification problem: i.e., given a sequence, what's it's origin? I.e., did this sequence read come from E. coli or some other enteric bacteria? Note that sequence classification does not involve genome assembly - sequence classification is done on unassembled reads. If you could perfectly classify the origin of every sequence read in your sample, you would know exactly what organisms are in your environmental sample and how abundant each one is.

The solution to this problem isn't simply BLAST'ing every sequence read that comes off your HiSeq 2500 against NCBI nt/nr. The computational cost of this BLAST search would be many times more expensive than the sequencing itself. There are many algorithms for sequence classification. This paper examines a wide range of the available algorithms and software implementations for sequence classification as applied to metagenomic data:

Bazinet, Adam L., and Michael P. Cummings. "A comparative evaluation of sequence classification programs." BMC Bioinformatics 13.1 (2012): 92.

In this paper, the authors comprehensively evaluated the performance of over 25 programs that fall into three categories: alignment-based, composition-based, and phylogeny-based. For illustrative purposes, the authors constructed a "phylogenetic tree" that shows how each of the 25 methods they evaluated are related to each other:

Figure 1: Program clustering. A neighbor-joining tree that clusters the classification programs based on their similar attributes.

The performance evaluation was done on several different datasets where the composition was known, using a similar set of evaluation criteria (sensitivity = number of correct assignments / number of sequences in the data; precision = number of correct assignments/number of assignments made). They concluded that the performance of particular methods varied widely between datasets due to reasons like highly variable taxonomic composition and diversity, level of sequence representation in underlying databases, read lengths, and read quality. The authors specifically point out that just because some methods lack sensitivity (as they've defined it), they are still useful because they have high precision. For example, marker-based approaches (like Metaphyler) might only classify a small number of reads, but they're highly precise, and may still be enough to accurately recapitulate organismal distribution and abundance.

Importantly, the authors note that you can't ignore computational requirements, which varied by orders of magnitude between methods. Selection of the right method depends on the goals (is sensitivity or precision more important?) and the available resources (time and compute power are never infinite - these are tangible limitations that are imposed in the real world).

This paper was first received at BMC Bioinformatics a year ago, and since then many new methods for sequence classification have been published. Further, this paper only evaluates methods for classification of unassembled reads, and does not evaluate methods that rely on metagenome assembly (that's the subject of another much longer post, but check out Titus Brown's blog for lots more on this topic).

Overall, this paper was a great demonstration of how one might attempt to evaluate many different tools ostensibly aimed at solving the same problem but functioning in completely different ways.

Bazinet, Adam L., and Michael P. Cummings. "A comparative evaluation of sequence classification programs." BMC Bioinformatics 13.1 (2012): 92.

NetGestalt for Data Visualization in the Context of Pathways

Many of you may be familiar with WebGestalt, a wonderful web utility developed by Bing Zhang at Vanderbilt for doing basic gene-set enrichment analyses. Last year, we invited Bing to speak at our annual retreat for the Vanderbilt Graduate Program in Human Genetics, and he did not disappoint! Bing walked us through his new tool called NetGestalt.

NetGestalt provides users with the ability to overlay large-scale experimental data onto biological networks. Data are loaded using continuous and binary tracks that can contain either single or multiple lines of data (called composite tracks). Continuous tracks could be gene expression intensities from microarray data or any other quantitative measure that can be mapped to the genome.  Binary tracks are usually insertion/deletion regions, or called regions like ChIP peaks.  NetGestalt extends many of the features of WebGestalt, including enrichment analysis for modules within a biological network, and provides easy ways to visualize the overlay of multiple tracks with Venn diagrams.

Netgestalt provides a very nice interface for interacting with data. Extensive documentation on how to use it can be found here.  Bing and his colleagues also went the extra mile to create video tutorials on how to use their web tool, and walk you through an analysis of some tumor data.

http://www.netgestalt.org/

"Document Design and Purpose, Not Mechanics"

If you ever write code for scientific computing (chances are you do if you're here), stop what you're doing and spend 8 minutes reading this open-access paper:

Wilson et al. Best Practices for Scientific Computing. arXiv:1210.0530 (2012). (Direct link to PDF).

The paper makes a number of good points regarding software as a tool just like any other lab equipment: it should be built, validated, and used as carefully as any other physical instrumentation. Yet most scientists who write software are self-taught, and haven't been properly trained in fundamental software development skills. 

The paper outlines ten practices every computational biologist should adopt when writing code for research computing. Most of these are the usual suspects that you'd probably guess - using version control, workflow management, writing good documentation, modularizing code into functions, unit testing, agile development, etc. One that particularly jumped out at me was the recommendation to document design and purpose, not mechanics. 

We all know that good comments and documentation is critical for code reproducibility and maintenance, but inline documentation that recapitulates the code is hardly useful. Instead, we should aim to document the underlying ideas, interface, and reasons, not the implementation.

For example, the following commentary is hardly useful:

# Increment the variable "i" by one.

i = i+1

The real recommendation here is that if your code requires such substantial documentation of the actual implementation to be understandable, it's better to spend the time rewriting the code rather than writing a lengthy description of what it does. I'm very guilty of doing this with R code, nesting multiple levels of functions and vector operations:

# It would take a paragraph to explain what this is doing.

# Better to break up into multiple lines of code.

sapply(data.frame(n=sapply(x, function(d) sum(is.na(d)))), function(dd) mean(dd))

It would take much more time to properly document what this is doing than it would take to split the operation into manageable chunks over multiple lines such that the code no longer needs an explanation. We're not playing code golf here - using fewer lines doesn't make you a better programmer.

Best Practices for Scientific Computing

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

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. The power and sample size estimations are based on a t-test, which the authors claim, performs no worse than the negative binomial models implemented by popular RNA-seq methods such as DESeq, when there are three or more replicates present. Empirical distributions are taken from either (1) pilot data that the user can upload, or (2) built in publicly available data. The authors find that there is substantial heterogeneity between experiments (technical variation is larger than biological variation in many cases), and that power and sample size estimation will be more accurate when the user provides their own pilot data.

My only complaint, for all the reasons expressed in my previous blog post about why you shouldn't host things like this exclusively on your lab website, is that the code to run this analysis doesn't appear to be available to save, study, modify, maintain, or archive. When lead author Michele Busby leaves Gabor Marth's lab, hopefully the app doesn't fall into the graveyard of computational biology web apps.  Update 2/7/13: Michele Busby created a public Github repository for the Scotty code: https://github.com/mbusby/Scotty

tl;dr? There's a new web app that does power, sample size, and coverage calculations for RNA-seq, but it only works well if the pilot or public data you give it closely matches the actual data you'll collect. 

Paper: Busby, et al. "Scotty: A Web Tool For Designing RNA-Seq Experiments to Measure Differential Gene Expression." Bioinformatics (2013): 10.1093/bioinformatics/btt015.

Web app: http://euler.bc.edu/marthlab/scotty/scotty.php

Source code: https://github.com/mbusby/Scotty

The Pacific Symposium on Biocomputing 2013

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. This year, sessions included Computational Drug Repositioning, Epigenomics, Aberrant Pathway and Network Activity, Personalized Medicine, Phylogenomics and Population Genomics, Post-Next Generation Sequencing, and Text and Data Mining. The Proceedings are available online here, and a few of the highlights are:

Cheng et al. examine various analytical methods for processing data from the Connectivity Map, a dataset of gene expression changes due to small molecule treatment. They compare methods for identifying drug-induced gene expression profiles to a benchmark based on the Anatomical Theraputic Chemical (ATC) system with the hope of discovering additional mechanisms of action.

Huang et al. developed a recursive K-means spectral clustering algorithm and applied this method to gene expression data from the Cancer Genome Atlas. It provides better cluster separation than traditional hierarchical clustering, and better execution time than similar K-means approaches.

Schrider et al. used pooled paired-end sequence data from multiple Drosophila melanogaster species along the eastern US coast to identify copy number variants under selective pressure. Many of the CNVs identified contain CYP enzymes likely influencing insecticide resistance. Schrider also pointed out in his talk that human salivary amylase (AMY1) has copy numbers that are differentiated across human populations due to differences in dietary starch content. Cool!

Verspoor et al. presented an awesome application of text mining to identify catalytic protein residues from the biomedical literature. Text mining tasks are always wrought with difficulties such as identifier ambiguity and resolution, or simply identifying the corpus of text needed for the task. Using Literature-Enhanced Automated Prediction of Functional Sites (LEAP-FS) and the Protein Data Bank (with Pubmed references), they compare their text mining approach to the Catalytic Site Atlas as a ‘silver standard’. Despite the difficulty, a simple classifier gives an accuracy around 70% (measured by F-statistic).

Also, my colleague Ting Hu presented her excellent work on statistical epistasis networks which use entropy-based measures to identify high-order interactions in genetic data. And in case you are interested, I’ll end by shamelessly listing our own publications in complex data analysis and rare-variant population structure (with Marylyn Ritchie), and performance of the Illumina Metabochip in Hispanic samples and high-throughput epidemiology (with Dana Crawford).

PSB is always a fantastic meeting – hope to see you in 2014!

Stop Hosting Data and Code on your Lab Website

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:

Schultheiss, Sebastian J., et al. "Persistence and availability of web services in computational biology." PLoS one 6.9 (2011): e24914. 

Wren, Jonathan D. "404 not found: the stability and persistence of URLs published in MEDLINE." Bioinformatics 20.5 (2004): 668-672.

The first gives us some alarming statistics. In a survey of nearly 1000 web services published in the Nucleic Acids Web Server Issue between 2003 and 2009:

• Only 72% were still available at the published address.
• The authors could not test the functionality for 33% because there was no example data, and 13% no longer worked as expected.
• The authors could only confirm positive functionality for 45%.
• Only 274 of the 872 corresponding authors answered an email.
• Of these 78% said a service was developed by a student or temporary researcher, and many had no plan for maintenance after the researcher had moved on to a permanent position.

The Wren et al. paper found that of 1630 URLs identified in Pubmed abstracts, only 63% were consistently available. That rate was far worse for anonymous login FTP sites (33%).

OpenHelix recently started this thread on Biostar as an obituary section for bioinformatics tools and resources that have vanished.

It's a fact that most of us academics move around a fair amount. Often we may not deem a tool we developed or data we collected and released to be worth transporting and maintaining. After some grace period, the resource disappears without a trace. 

SOFTWARE

I won't spend much time here because most readers here are probably aware of source code repositories for hosting software projects. Unless you're not releasing the source code to your software (aside: starting an open-source project is a way to stake a claim in a field, not a real risk for getting yourself scooped), I can think of no benefit for hosting your code on your lab website when there are plenty of better alternatives available, such as Sourceforge, GitHub, Google Code, and others. In addition to free project hosting, tools like these provide version control, wikis, bug trackers, mailing lists and other services to enable transparent and open development with the end result of a better product and higher visibility. For more tips on open scientific software development, see this short editorial in PLoS Comp Bio:

Prlić A, Procter JB (2012) Ten Simple Rules for the Open Development of Scientific Software. PLoS Comput Biol 8(12): e1002802. 

Casey Bergman recently analyzed where bioinformaticians are hosting their code, where he finds that the growth rate of Github is outpacing both Google Code and Sourceforge. Indeed, Github hosts more repositories than there are articles in Wikipedia, and has an excellent tutorial and interactive learning modules to help you learn how to use it. However, Bergman also points out how easy it is to delete a repository from Github and Google Code, where repositories are published by individuals who hold the keys to preservation (as opposed to Sourceforge, where it is extremely difficult to remove a project once it's been released).

DATA, FIGURES, SLIDES, WEB SERVICES, OR ANYTHING ELSE

For everything else there's Figshare. Figshare lets you host and publicly share unlimited data (or store data privately up to 1GB). The name suggests a site for sharing figures, but Figshare allows you to permanently store and share any research object. That can be figures, slides, negative results, videos, datasets, or anything else. If you're running a database server or web service, you can package up the source code on one of the repositories mentioned above, and upload to Figshare a virtual machine image of the server running it, so that the service will be available to users long after you've lost the time, interest, or money to maintain it.

Research outputs stored at Figshare are archived in the CLOCKSS geographically and geopolitically distributed network of redundant archive nodes, located at 12 major research libraries around the world. This means that content will remain available indefinitely for everyone after a "trigger event," and ensures this work will be maximally accessible and useful over time. Figshare is hosted using Amazon Web Services to ensure the highest level of security and stability for research data. 

Upon uploading your data to Figshare, your data becomes discoverable, searchable, shareable, and instantly citable with its own DOI, allowing you to instantly take credit for the products of your research. 

To show you how easy this is, I recently uploaded a list of "consensus" genes generated by Will Bush where Ensembl refers to an Entrez-gene with the same coordinates, and that Entrez-gene entry refers back to the same Ensembl gene (discussed in more detail in this previous post).

Create an account, and hit the big upload link. You'll be given a screen to drag and drop anything you'd like here (there's also a desktop uploader for larger files).

Once I dropped in the data I downloaded from Vanderbilt's website linked from the original blog post, I enter some optional metadata, a description, a link back to the original post:

I then instantly receive a citeable DOI where the data is stored permanently, regardless of Will's future at Vanderbilt:

Ensembl/Entrez hg19/GRCh37 Consensus Genes. Stephen Turner. figshare. Retrieved 21:31, Dec 19, 2012 (GMT). http://dx.doi.org/10.6084/m9.figshare.103113

There are also links to the side that allow you to export that citation directly to your reference manager of choice.

Finally, as an experiment, I also uploaded this entire blog post to Figshare, which is now citeable and permanently archived at Figshare:

Stop Hosting Data and Code on your Lab Website. Stephen Turner. figshare. Retrieved 22:51, Dec 19, 2012 (GMT). http://dx.doi.org/10.6084/m9.figshare.105125.