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.

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.

Twitter Roundup, January 4 2013

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

PLOS Computational Biology: Chapter 1: Biomedical Knowledge Integration

Assuring the quality of next-generation sequencing in clinical laboratory practice : Nature Biotechnology

De novo genome assembly: what every biologist should know : Nature Methods

How deep is deep enough for RNA-Seq profiling of bacterial transcriptomes?

Silence | Abstract | Strand-specific libraries for high throughput RNA sequencing (RNA-Seq) prepared without poly(A) selection

BMC Genomics | Abstract | Comparison of metagenomic samples using sequence signatures

Peak identification for ChIP-seq data with no controls.

TrueSight: a new algorithm for splice junction detection using RNA-seq

DiffCorr: An R package to analyze and visualize differential correlations in biological networks.

PLOS ONE: Reevaluating Assembly Evaluations with Feature Response Curves: GAGE and Assemblathons

Delivering the promise of public health genomics | Global Development Professionals Network

Metagenomics and Community Profiling: Culture-Independent Techniques in the Clinical Laboratory

PLOS ONE: A Model-Based Clustering Method for Genomic Structural Variant Prediction and Genotyping Using Paired-End Sequencing Data

InnoCentive - Metagenomics Challenge