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:

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

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.

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!