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Maria Ciofani, Aviv Madar, Carolina Galan, MacLean Sellars, Kieran Mace, Florencia Pauli, Ashish Agarwal, Wendy Huang, Christopher N. Parkurst, Michael Muratet, Kim M. Newberry, Sarah Meadows, Alex Greenfield, Yi Yang, Preti Jain, Francis K. Kirigin, Carmen Birchmeier, Erwin F. Wagner, Kenneth M. Murphy, Richard M. Myers, Richard Bonneau, and Dan R. Littman (2012). A Validated Regulatory Network for Th17 Cell Specification, Cell 151:1-15.

There has been great interest in creating probabilistic programming languages to simplify the coding of statistical tasks; however, there still does not exist a formal language that simultaneously provides (1) continuous probability distributions, (2) the ability to naturally express custom probabilistic models, and (3) probability density functions (PDFs). This collection of features is necessary for mechanizing fundamental statistical techniques. We formalize the first probabilistic language that exhibits these features, and it serves as a foundational framework for extending the ideas to more general languages. Particularly novel are our type system for absolutely continuous (AC) distributions (those which permit PDFs) and our PDF calculation procedure, which calculates PDFs for a large class of AC distributions. Our formalization paves the way toward the rigorous encoding of powerful statistical reformulations.

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Sooraj Bhat, Ashish Agarwal, Richard Vuduc, Alexander Gray (2012). A Type Theory for Probability Density Functions, Proceedings of the 39th Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, POPL 2012. ACM SIGPLAN Notices 47(1):545-556.

Errata
In Figure 10, the P-PLUS rule should be:
\[
\frac{{\Upsilon;\Lambda} \vdash {\varepsilon_1} \perp {\varepsilon_2}
\qquad\{{\Upsilon;\Lambda} \vdash {\varepsilon_i} \leadsto {\delta_i}\}_{i=1,2}}
{{\Upsilon;\Lambda} \vdash {\varepsilon_1+\varepsilon_2} \leadsto {\lambda {x:\mathsf{R}}\centerdot
\int\lambda {t:\mathsf{R}}\centerdot\ \delta_1\ t * \delta_2\ (x – t)}}
\]
The \(t\) and \(x\) were accidentally transposed. Many thanks to Chung-chieh “Ken” Shan for finding this.

Biological data is often tabular but finding statistically valid connections between entities in a sequence of tables can be problematic – for example, connecting particular entities in a drug property table to gene properties in a second table, using a third table associating genes with drugs. Here we present an approach (CRIT) to find connections such as these and show how it can be applied in a variety of genomic contexts including chemogenomics data.

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Tara A Gianoulis, Ashish Agarwal, Michael Snyder, and Mark Gerstein (2011). The CRIT framework for identifying cross patterns in systems biology and application to chemogenomics, Genome Biology 12(R32):1-12.

We present an integrative machine learning method, incRNA, for whole-genome identification of noncoding RNAs (ncRNAs). It combines a large amount of expression data, RNA secondary-structure stability, and evolutionary conservation at the protein and nucleic-acid level. Using the incRNA model and data from the modENCODE consortium, we are able to separate known C. elegans ncRNAs from coding sequences and other genomic elements with a high level of accuracy (97% AUC on an independent validation set), and find more than 7000 novel ncRNA candidates, among which more than 1000 are located in the intergenic regions of C. elegans genome. Based on the validation set, we estimate that 91% of the approximately 7000 novel ncRNA candidates are true positives. We then analyze 15 novel ncRNA candidates by RT-PCR, detecting the expression for 14. In addition, we characterize the properties of all the novel ncRNA candidates and find that they have distinct expression patterns across developmental stages and tend to use novel RNA structural families. We also find that they are often targeted by specific transcription factors (∼59% of intergenic novel ncRNA candidates). Overall, our study identifies many new potential ncRNAs in C. elegans and provides a method that can be adapted to other organisms.

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Zhi John Lu, Kevin Y. Yip, Guilin Wang, Chong Shou, LaDeana W. Hillier, Ekta Khurana, Ashish Agarwal, Raymond Auerbach, Joel Rozowsky, Chao Cheng, Masaomi Kato, David M. Miller, Frank Slack, Michael Snyder, Robert H. Waterston, Valerie Reinke, and Mark B. Gerstein (2011). Prediction and characterization of noncoding RNAs in C. elegans by integrating conservation, secondary structure, and high-throughput sequencing and array data, Genome Research 21(2):276-85.

We systematically generated large-scale data sets to improve genome annotation for the nematode Caenorhabditis elegans, a key model organism. These data sets include transcriptome profiling across a developmental time course, genome-wide identification of transcription factor–binding sites, and maps of chromatin organization. From this, we created more complete and accurate gene models, including alternative splice forms and candidate noncoding RNAs. We constructed hierarchical networks of transcription factor–binding and microRNA interactions and discovered chromosomal locations bound by an unusually large number of transcription factors. Different patterns of chromatin composition and histone modification were revealed between chromosome arms and centers, with similarly prominent differences between autosomes and the X chromosome. Integrating data types, we built statistical models relating chromatin, transcription factor binding, and gene expression. Overall, our analyses ascribed putative functions to most of the conserved genome.

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Mark B. Gerstein, Zhi John Lu, Eric L. Van Nostrand, Chao Cheng, Bradley I. Arshinoff, Tao Liu, Kevin Y. Yip, Rebecca Robilotto, Andreas Rechtsteiner, Kohta Ikegami, Pedro Alves, Aurelien Chateigner, Marc Perry, Mitzi Morris, Raymond K. Auerbach, Xin Feng, Jing Leng, Anne Vielle, Wei Niu, Kahn Rhrissorrakrai, Ashish Agarwal, Roger P. Alexander, Galt Barber, Cathleen M. Brdlik, Jennifer Brennan, Jeremy Jean Brouillet, Adrian Carr, Ming-Sin Cheung, Hiram Clawson, Sergio Contrino, Luke O. Dannenberg, Abby F. Dernburg, Arshad Desai, Lindsay Dick, Andréa C. Dosé, Jiang Du, Thea Egelhofer, Sevinc Ercan, Ghia Euskirchen, Brent Ewing, Elise A. Feingold, Reto Gassmann, Peter J. Good, Phil Green, Francois Gullier, Michelle Gutwein, Mark S. Guyer, Lukas Habegger, Ting Han, Jorja G. Henikoff, Stefan R. Henz, Angie Hinrichs, Heather Holster, Tony Hyman, A. Leo Iniguez, Judith Janette, Morten Jensen, Masaomi Kato, W. James Kent, Ellen Kephart, Vishal Khivansara, Ekta Khurana, John K. Kim, Paulina Kolasinska-Zwierz, Eric C. Lai, Isabel Latorre, Amber Leahey, Suzanna Lewis, Paul Lloyd, Lucas Lochovsky, Rebecca F. Lowdon, Yaniv Lubling, Rachel Lyne, Michael MacCoss, Sebastian D. Mackowiak, Marco Mangone, Sheldon McKay, Desirea Mecenas, Gennifer Merrihew, David M. Miller III, Andrew Muroyama, John I. Murray, Siew-Loon Ooi, Hoang Pham, Taryn Phippen, Elicia A. Preston, Nikolaus Rajewsky, Gunnar Rätsch, Heidi Rosenbaum, Joel Rozowsky, Kim Rutherford, Peter Ruzanov, Mihail Sarov, Rajkumar Sasidharan, Andrea Sboner, Paul Scheid, Eran Segal, Hyunjin Shin, Chong Shou, Frank J. Slack, Cindie Slightam, Richard Smith, William C. Spencer, E. O. Stinson, Scott Taing, Teruaki Takasaki, Dionne Vafeados, Ksenia Voronina, Guilin Wang, Nicole L. Washington, Christina M. Whittle, Beijing Wu, Koon-Kiu Yan, Georg Zeller, Zheng Zha, Mei Zhong, Xingliang Zhou, modENCODE Consortium, Julie Ahringer, Susan Strome, Kristin C. Gunsalus, Gos Micklem, X. Shirley Liu, Valerie Reinke, Stuart K. Kim, LaDeana W. Hillier, Steven Henikoff, Fabio Piano, Michael Snyder, Lincoln Stein, Jason D. Lieb, and Robert H. Waterston (2010). Integrative Analysis of the Caenorhabditis elegans Genome by the modENCODE Project, Science 330(6012):1775-1787.

Summary: The advent of next-generation sequencing for functional genomics has given rise to quantities of sequence information that are often so large that they are difficult to handle. Moreover, sequence reads from a specific individual can contain sufficient information to potentially identify and genetically characterize that person, raising privacy concerns. In order to address these issues we have developed the Mapped Read Format (MRF), a compact data summary format for both short and long read alignments that enables the anonymization of confidential sequence information, while allowing one to still carry out many functional genomics studies. We have developed a suite of tools that use this format for the analysis of RNA-Seq experiments. RSEQtools consists of a set of modules that perform common tasks such as calculating gene expression values, generating signal tracks of mapped reads, and segmenting that signal into actively transcribed regions. Moreover, these tools can readily be used to build customizable RNA-Seq workflows. In addition to the anonymization afforded by this format it also facilitates the decoupling of the alignment of reads from downstream analyses.

Availability and implementation: RSEQtools is implemented in C and the source code is available at http://rseqtools.gersteinlab.org/

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Lukas Habegger, Andrea Sboner, Tara A. Gianoulis, Joel Rozowsky, Ashish Agarwal, Michael Snyder, Mark Gerstein (2011). RSEQtools: A modular framework to analyze RNA-Seq data using compact, anonymized data summaries, Bioinformatics 27(2):281-283.

Don’t Configure the Network, Program It! Domain-Specific Programming Languages for Network Systems

Nettle: Functional Reactive Programming for OpenFlow Networks

Background: Tiling arrays have been the tool of choice for probing an organism’s transcriptome without prior assumptions about the transcribed regions, but RNA-Seq is becoming a viable alternative as the costs of sequencing continue to decrease. Understanding the relative merits of these technologies will help researchers select the appropriate technology for their needs.

Results: Here, we compare these two platforms using a matched sample of poly(A)-enriched RNA isolated from the second larval stage of C. elegans. We find that the raw signals from these two technologies are reasonably well correlated but that RNA-Seq outperforms tiling arrays in several respects, notably in exon boundary detection and dynamic range of expression. By exploring the accuracy of sequencing as a function of depth of coverage, we found that about 4 million reads are required to match the sensitivity of two tiling array replicates. The effects of cross-hybridization were analyzed using a “nearest neighbor” classifier applied to array probes; we describe a method for determining potential “black list” regions whose signals are unreliable. Finally, we propose a strategy for using RNA-Seq data as a gold standard set to calibrate tiling array analysis. All tiling array and RNA-Seq data sets have been submitted to the modENCODE Data Coordinating Center.

Conclusions: Tiling arrays effectively detect transcript expression levels at a low cost for many species while RNA-Seq provides greater accuracy in several regards. Researchers will need to carefully select the technology appropriate to the biological investigations they are undertaking. It will also be important to reconsider a comparison such as ours as sequencing technologies continue to evolve.

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I made a presentation on this material at the ENCODE/modENCODE Meeting 2009

To my surprise, as of 26-Nov-2010:

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Ashish Agarwal, David Koppstein, Joel Rozowsky, Andrea Sboner, Lukas Habegger, LaDeana W. Hillier, Rajkumar Sasidharan, Valerie Reinke, Robert H. Waterston, Mark Gerstein (2010). Comparison and calibration of transcriptome data from RNA-Seq and tiling arrays, BMC Genomics 11(383):1-16.

When solving machine learning problems, there is currently little automated support for easily experimenting with alternative statistical models or solution strategies. This is because this activity often requires expertise from several different fields (e.g., statistics, optimization, linear algebra), and the level of formalism required for automation is much higher than for a human solving problems on paper. We present a system toward addressing these issues, which we achieve by (1) formalizing a type theory for probability and optimization, and (2) providing an interactive rewrite system for applying problem reformulation theorems. Automating solution strategies this way enables not only manual experimentation but also higher-level, automated activities, such as autotuning.

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Sooraj Bhat, Ashish Agarwal, Alexander Gray, Richard Vuduc (2010). Toward Interactive Statistical Modeling, In Procedia Computer Science, International Conference on Computational Science ICCS 2010, 1(1): 1892-1838.

Transcription factors are key components of regulatory networks that control development, as well as the response to environmental stimuli. We have established an experimental pipeline in Caenorhabditis elegans that permits global identification of the binding sites for transcription factors using chromatin immunoprecipitation and deep sequencing. We describe and validate this strategy, and apply it to the transcription factor PHA-4, which plays critical roles in organ development and other cellular processes. We identified thousands of binding sites for PHA-4 during formation of the embryonic pharynx, and also found a role for this factor during the starvation response. Many binding sites were found to shift dramatically between embryos and starved larvae, from developmentally regulated genes to genes involved in metabolism. These results indicate distinct roles for this regulator in two different biological processes and demonstrate the versatility of transcription factors in mediating diverse biological roles.

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Mei Zhong, Wei Niu, Zhi John Lu, Mihail Sarov, John I. Murray, Judith Janette, Debasish Raha, Karyn L. Sheaffer, Hugo Y. K. Lam, Elicia Preston, Cindie Slightham, LaDeana W. Hillier, Trisha Brock, Ashish Agarwal, Raymond Auerbach, Anthony A. Hyman, Mark Gerstein, Susan E. Mango, Stuart K. Kim, Robert H. Waterston, Valerie Reinke, Michael Snyder (2010). Genome-Wide Identification of Binding Sites Defines Distinct Functions for Caenorhabditis elegans PHA-4/FOXA in Development and Environmental Response, PLoS Genetics, 6(2): 1-13.