Bio

Bio


I am an Assistant Professor with a primary appointment in the Dept. of Genetics and a secondary appointment in the Dept. of Computer Science since Sept 2013. From 2003-2008, I was a PhD student in Christina Leslie's lab in the Computer Science Dept. at Columbia University in New York. I developed Machine Learning methods for modeling transcriptional gene regulation in yeast and worm. From 2008 - 2012, I was a Postdoctoral Research Associate with Serafim Batzoglou and Arend Sidow in the Computer Science Dept. at Stanford University. I served as one of the lead data coordinators and computational analysts for the ENCODE consortium. My primary focus was on deciphering heterogeneity of regulatory interactions in the human genome. I also developed ENCODE's ChIP-seq statistical data analysis pipeline. Before returning to Stanford as a faculty member, I was a Research Scientist in Manolis Kellis' lab (2012-2013) studying epigenomic and chromatin state dynamics across organisms, cell-types and individuals as part of the Roadmap Epigenomics Project and the mod/ENCODE (Encyclopedia for DNA elements) consortium.
My research focus is the development of machine learning and computational frameworks to learn integrative models of transcriptional and post-transcriptional gene regulation from massive amounts of diverse sequence, genetic and functional genomic datasets.

Academic Appointments


Honors & Awards


  • Alfred Sloan Foundation Research Fellowship, Alfred Sloan Foundation (2014-2016)

Research & Scholarship

Current Research and Scholarly Interests


Our research focusses on development of statistical and machine learning methods for integrative analysis of diverse functional genomic and genetic data to learn models of gene regulation. We have led the analysis efforts of the Encyclopedia of DNA Elements (ENCODE) and The Roadmap Epigenomics Projects with the development of novel methods for
1. Adaptive thresholding and normalization of massive collections of functional genomic data (e.g. ChIP-seq and DNase-seq)
2. Dissecting combinatorial transcription factor co-occupancy within and across cell-types
3. Predicting cell-type specific enhancers from chromatin state profiles
4. Exploiting expression and chromatin co-dynamics with to predict enhancer-target gene links
5. Jointly modeling sequence grammars at regulatory elements and their chromatin state dynamics, expression changes of regulators and functional interaction data to learn unified multi-scale gene regulation programs
6. Elucidating the heterogeneity of chromatin architecture at regulatory elements
Improving the detection and interpretation of potentially causal disease-associated variants from Genome-wide association studies
More recently, we have also been developing methods to
1. Decipher the functional heterogeneity of transcription factor binding
2. Infer causal regulatory mechansisms by integrating diverse functional genomic data from temporal (e.g. differentiation/reprogramming) and perturbation (e.g. drug response, knockdown, genome-editing) experiments
3. Model the complex relationships between genetic variation, regulatory chromatin variation and expression variation in healthy and diseased individuals

Projects


  • The Encyclopedia of DNA Elements (ENCODE) Project, Stanford University, MIT

    The project generates a resource of cell-type specific genome-wide regulatory maps in the human genome. We develop statistical processing methods for next-gen sequencing based functional genomic data and machine learning methods to predict regulatory events, learn combinatorial regulatory effects of transcription factors, cell-type specific regulatory networks

    Location

    Stanford, CA

    For More Information:

  • The Roadmap Epigenomics Project, MIT (February 2012 - Present)

    The project generates genome-wide epigenomic maps in 200 human cell types. We develop computational methods and analyses to infer cell-type specific regulatory elements (e.g. enhancers) and their activity states, learn cell-type specific regulatory networks and use these maps to interpret GWAS and disease studies.

    Location

    Boston, MA

Teaching

2013-14 Courses


Graduate and Fellowship Programs


Publications

Journal Articles


  • Extensive Variation in Chromatin States Across Humans SCIENCE Kasowski, M., Kyriazopoulou-Panagiotopoulou, S., Grubert, F., Zaugg, J. B., Kundaje, A., Liu, Y., Boyle, A. P., Zhang, Q. C., Zakharia, F., Spacek, D. V., Li, J., Xie, D., Olarerin-George, A., Steinmetz, L. M., Hogenesch, J. B., Kellis, M., Batzoglou, S., Snyder, M. 2013; 342 (6159): 750-752

    Abstract

    The majority of disease-associated variants lie outside protein-coding regions, suggesting a link between variation in regulatory regions and disease predisposition. We studied differences in chromatin states using five histone modifications, cohesin, and CTCF in lymphoblastoid lines from 19 individuals of diverse ancestry. We found extensive signal variation in regulatory regions, which often switch between active and repressed states across individuals. Enhancer activity is particularly diverse among individuals, whereas gene expression remains relatively stable. Chromatin variability shows genetic inheritance in trios, correlates with genetic variation and population divergence, and is associated with disruptions of transcription factor binding motifs. Overall, our results provide insights into chromatin variation among humans.

    View details for DOI 10.1126/science.1242510

    View details for Web of Science ID 000326647600047

    View details for PubMedID 24136358

  • Architecture of the human regulatory network derived from ENCODE data NATURE Gerstein, M. B., Kundaje, A., Hariharan, M., Landt, S. G., Yan, K., Cheng, C., Mu, X. J., Khurana, E., Rozowsky, J., Alexander, R., Min, R., Alves, P., Abyzov, A., Addleman, N., Bhardwaj, N., Boyle, A. P., Cayting, P., Charos, A., Chen, D. Z., Cheng, Y., Clarke, D., Eastman, C., Euskirchen, G., Frietze, S., Fu, Y., Gertz, J., Grubert, F., Harmanci, A., Jain, P., Kasowski, M., Lacroute, P., Leng, J., Lian, J., Monahan, H., O'Geen, H., Ouyang, Z., Partridge, E. C., Patacsil, D., Pauli, F., Raha, D., Ramirez, L., Reddy, T. E., Reed, B., Shi, M., Slifer, T., Wang, J., Wu, L., Yang, X., Yip, K. Y., Zilberman-Schapira, G., Batzoglou, S., Sidow, A., Farnham, P. J., Myers, R. M., Weissman, S. M., Snyder, M. 2012; 489 (7414): 91-100

    Abstract

    Transcription factors bind in a combinatorial fashion to specify the on-and-off states of genes; the ensemble of these binding events forms a regulatory network, constituting the wiring diagram for a cell. To examine the principles of the human transcriptional regulatory network, we determined the genomic binding information of 119 transcription-related factors in over 450 distinct experiments. We found the combinatorial, co-association of transcription factors to be highly context specific: distinct combinations of factors bind at specific genomic locations. In particular, there are significant differences in the binding proximal and distal to genes. We organized all the transcription factor binding into a hierarchy and integrated it with other genomic information (for example, microRNA regulation), forming a dense meta-network. Factors at different levels have different properties; for instance, top-level transcription factors more strongly influence expression and middle-level ones co-regulate targets to mitigate information-flow bottlenecks. Moreover, these co-regulations give rise to many enriched network motifs (for example, noise-buffering feed-forward loops). Finally, more connected network components are under stronger selection and exhibit a greater degree of allele-specific activity (that is, differential binding to the two parental alleles). The regulatory information obtained in this study will be crucial for interpreting personal genome sequences and understanding basic principles of human biology and disease.

    View details for DOI 10.1038/nature11245

    View details for Web of Science ID 000308347000042

    View details for PubMedID 22955619

  • An integrated encyclopedia of DNA elements in the human genome NATURE Dunham, I., Kundaje, A., Aldred, S. F., Collins, P. J., Davis, C., Doyle, F., Epstein, C. B., Frietze, S., Harrow, J., Kaul, R., Khatun, J., Lajoie, B. R., Landt, S. G., Lee, B., Pauli, F., Rosenbloom, K. R., Sabo, P., Safi, A., Sanyal, A., Shoresh, N., Simon, J. M., Song, L., Trinklein, N. D., Altshuler, R. C., Birney, E., Brown, J. B., Cheng, C., Djebali, S., Dong, X., Dunham, I., Ernst, J., Furey, T. S., Gerstein, M., Giardine, B., Greven, M., Hardison, R. C., Harris, R. S., Herrero, J., Hoffman, M. M., Iyer, S., Kellis, M., Khatun, J., Kheradpour, P., Kundaje, A., Lassmann, T., Li, Q., Lin, X., Marinov, G. K., Merkel, A., Mortazavi, A., Parker, S. C., Reddy, T. E., Rozowsky, J., Schlesinger, F., Thurman, R. E., Wang, J., Ward, L. D., Whitfield, T. W., Wilder, S. P., Wu, W., Xi, H. S., Yip, K. Y., Zhuang, J., Bernstein, B. E., Birney, E., Dunham, I., Green, E. D., Gunter, C., Snyder, M., Pazin, M. J., Lowdon, R. F., Dillon, L. A., Adams, L. B., Kelly, C. J., Zhang, J., Wexler, J. R., Green, E. D., Good, P. J., Feingold, E. A., Bernstein, B. E., Birney, E., Crawford, G. E., Dekker, J., Elnitski, L., Farnham, P. J., Gerstein, M., Giddings, M. C., Gingeras, T. R., Green, E. D., Guigo, R., Hardison, R. C., Hubbard, T. J., Kellis, M., Kent, W. J., Lieb, J. D., Margulies, E. H., Myers, R. M., Snyder, M., Stamatoyannopoulos, J. A., Tenenbaum, S. A., Weng, Z., White, K. P., Wold, B., Khatun, J., Yu, Y., Wrobel, J., Risk, B. A., Gunawardena, H. P., Kuiper, H. C., Maier, C. W., Xie, L., Chen, X., Giddings, M. C., Bernstein, B. E., Epstein, C. B., Shoresh, N., Ernst, J., Kheradpour, P., Mikkelsen, T. S., Gillespie, S., Goren, A., Ram, O., Zhang, X., Wang, L., Issner, R., Coyne, M. J., Durham, T., Ku, M., Truong, T., Ward, L. D., Altshuler, R. C., Eaton, M. L., Kellis, M., Djebali, S., Davis, C. A., Merkel, A., Dobin, A., Lassmann, T., Mortazavi, A., Tanzer, A., Lagarde, J., Lin, W., Schlesinger, F., Xue, C., Marinov, G. K., Khatun, J., Williams, B. A., Zaleski, C., Rozowsky, J., Roeder, M., Kokocinski, F., Abdelhamid, R. F., Alioto, T., Antoshechkin, I., Baer, M. T., Batut, P., Bell, I., Bell, K., Chakrabortty, S., Chen, X., Chrast, J., Curado, J., Derrien, T., Drenkow, J., Dumais, E., Dumais, J., Duttagupta, R., Fastuca, M., Fejes-Toth, K., Ferreira, P., Foissac, S., Fullwood, M. J., Gao, H., Gonzalez, D., Gordon, A., Gunawardena, H. P., Howald, C., Jha, S., Johnson, R., Kapranov, P., King, B., Kingswood, C., Li, G., Luo, O. J., Park, E., Preall, J. B., Presaud, K., Ribeca, P., Risk, B. A., Robyr, D., Ruan, X., Sammeth, M., Sandhu, K. S., Schaeffer, L., See, L., Shahab, A., Skancke, J., Suzuki, A. M., Takahashi, H., Tilgner, H., Trout, D., Walters, N., Wang, H., Wrobel, J., Yu, Y., Hayashizaki, Y., Harrow, J., Gerstein, M., Hubbard, T. J., Reymond, A., Antonarakis, S. E., Hannon, G. J., Giddings, M. C., Ruan, Y., Wold, B., Carninci, P., Guigo, R., Gingeras, T. R., Rosenbloom, K. R., Sloan, C. A., Learned, K., Malladi, V. S., Wong, M. C., Barber, G., Cline, M. S., Dreszer, T. R., Heitner, S. G., Karolchik, D., Kent, W. J., Kirkup, V. M., Meyer, L. R., Long, J. C., Maddren, M., Raney, B. J., Furey, T. S., Song, L., Grasfeder, L. L., Giresi, P. G., Lee, B., Battenhouse, A., Sheffield, N. C., Simon, J. M., Showers, K. A., Safi, A., London, D., Bhinge, A. A., Shestak, C., Schaner, M. R., Kim, S. K., Zhang, Z. Z., Mieczkowski, P. A., Mieczkowska, J. O., Liu, Z., McDaniell, R. M., Ni, Y., Rashid, N. U., Kim, M. J., Adar, S., Zhang, Z., Wang, T., Winter, D., Keefe, D., Birney, E., Iyer, V. R., Lieb, J. D., Crawford, G. E., Li, G., Sandhu, K. S., Zheng, M., Wang, P., Luo, O. J., Shahab, A., Fullwood, M. J., Ruan, X., Ruan, Y., Myers, R. M., Pauli, F., Williams, B. A., Gertz, J., Marinov, G. K., Reddy, T. E., Vielmetter, J., Partridge, E. C., Trout, D., Varley, K. E., Gasper, C., Bansal, A., Pepke, S., Jain, P., Amrhein, H., Bowling, K. M., Anaya, M., Cross, M. K., King, B., Muratet, M. A., Antoshechkin, I., Newberry, K. M., McCue, K., Nesmith, A. S., Fisher-Aylor, K. I., Pusey, B., DeSalvo, G., Parker, S. L., Balasubramanian, S., Davis, N. S., Meadows, S. K., Eggleston, T., Gunter, C., Newberry, J. S., Levy, S. E., Absher, D. M., Mortazavi, A., Wong, W. H., Wold, B., Blow, M. J., Visel, A., Pennachio, L. A., Elnitski, L., Margulies, E. H., Parker, S. C., Petrykowska, H. M., Abyzov, A., Aken, B., Barrell, D., Barson, G., Berry, A., Bignell, A., Boychenko, V., Bussotti, G., Chrast, J., Davidson, C., Derrien, T., Despacio-Reyes, G., Diekhans, M., Ezkurdia, I., Frankish, A., Gilbert, J., Gonzalez, J. M., Griffiths, E., Harte, R., Hendrix, D. A., Howald, C., Hunt, T., Jungreis, I., Kay, M., Khurana, E., Kokocinski, F., Leng, J., Lin, M. F., Loveland, J., Lu, Z., Manthravadi, D., Mariotti, M., Mudge, J., Mukherjee, G., Notredame, C., Pei, B., Rodriguez, J. M., Saunders, G., Sboner, A., Searle, S., Sisu, C., Snow, C., Steward, C., Tanzer, A., Tapanari, E., Tress, M. L., van Baren, M. J., Walters, N., Washietl, S., Wilming, L., Zadissa, A., Zhang, Z., Brent, M., Haussler, D., Kellis, M., Valencia, A., Gerstein, M., Reymond, A., Guigo, R., Harrow, J., Hubbard, T. J., Landt, S. G., Frietze, S., Abyzov, A., Addleman, N., Alexander, R. P., Auerbach, R. K., Balasubramanian, S., Bettinger, K., Bhardwaj, N., Boyle, A. P., Cao, A. R., Cayting, P., Charos, A., Cheng, Y., Cheng, C., Eastman, C., Euskirchen, G., Fleming, J. D., Grubert, F., Habegger, L., Hariharan, M., Harmanci, A., Iyengar, S., Jin, V. X., Karczewski, K. J., Kasowski, M., Lacroute, P., Lam, H., Lamarre-Vincent, N., Leng, J., Lian, J., Lindahl-Allen, M., Min, R., Miotto, B., Monahan, H., Moqtaderi, Z., Mu, X. J., O'Geen, H., Ouyang, Z., Patacsil, D., Pei, B., Raha, D., Ramirez, L., Reed, B., Rozowsky, J., Sboner, A., Shi, M., Sisu, C., Slifer, T., Witt, H., Wu, L., Xu, X., Yan, K., Yang, X., Yip, K. Y., Zhang, Z., Struhl, K., Weissman, S. M., Gerstein, M., Farnham, P. J., Snyder, M., Tenenbaum, S. A., Penalva, L. O., Doyle, F., Karmakar, S., Landt, S. G., Bhanvadia, R. R., Choudhury, A., Domanus, M., Ma, L., Moran, J., Patacsil, D., Slifer, T., Victorsen, A., Yang, X., Snyder, M., White, K. P., Auer, T., Centanin, L., Eichenlaub, M., Gruhl, F., Heermann, S., Hoeckendorf, B., Inoue, D., Kellner, T., Kirchmaier, S., Mueller, C., Reinhardt, R., Schertel, L., Schneider, S., Sinn, R., Wittbrodt, B., Wittbrodt, J., Weng, Z., Whitfield, T. W., Wang, J., Collins, P. J., Aldred, S. F., Trinklein, N. D., Partridge, E. C., Myers, R. M., Dekker, J., Jain, G., Lajoie, B. R., Sanyal, A., Balasundaram, G., Bates, D. L., Byron, R., Canfield, T. K., Diegel, M. J., Dunn, D., Ebersol, A. K., Frum, T., Garg, K., Gist, E., Hansen, R. S., Boatman, L., Haugen, E., Humbert, R., Jain, G., Johnson, A. K., Johnson, E. M., Kutyavin, T. V., Lajoie, B. R., Lee, K., Lotakis, D., Maurano, M. T., Neph, S. J., Neri, F. V., Nguyen, E. D., Qu, H., Reynolds, A. P., Roach, V., Rynes, E., Sabo, P., Sanchez, M. E., Sandstrom, R. S., Sanyal, A., Shafer, A. O., Stergachis, A. B., Thomas, S., Thurman, R. E., Vernot, B., Vierstra, J., Vong, S., Wang, H., Weaver, M. A., Yan, Y., Zhang, M., Akey, J. M., Bender, M., Dorschner, M. O., Groudine, M., MacCoss, M. J., Navas, P., Stamatoyannopoulos, G., Kaul, R., Dekker, J., Stamatoyannopoulos, J. A., Dunham, I., Beal, K., Brazma, A., Flicek, P., Herrero, J., Johnson, N., Keefe, D., Lukk, M., Luscombe, N. M., Sobral, D., Vaquerizas, J. M., Wilder, S. P., Batzoglou, S., Sidow, A., Hussami, N., Kyriazopoulou-Panagiotopoulou, S., Libbrecht, M. W., Schaub, M. A., Kundaje, A., Hardison, R. C., Miller, W., Giardine, B., Harris, R. S., Wu, W., Bickel, P. J., Banfai, B., Boley, N. P., Brown, J. B., Huang, H., Li, Q., Li, J. J., Noble, W. S., Bilmes, J. A., Buske, O. J., Hoffman, M. M., Sahu, A. D., Kharchenko, P. V., Park, P. J., Baker, D., Taylor, J., Weng, Z., Iyer, S., Dong, X., Greven, M., Lin, X., Wang, J., Xi, H. S., Zhuang, J., Gerstein, M., Alexander, R. P., Balasubramanian, S., Cheng, C., Harmanci, A., Lochovsky, L., Min, R., Mu, X. J., Rozowsky, J., Yan, K., Yip, K. Y., Birney, E. 2012; 489 (7414): 57-74

    Abstract

    The human genome encodes the blueprint of life, but the function of the vast majority of its nearly three billion bases is unknown. The Encyclopedia of DNA Elements (ENCODE) project has systematically mapped regions of transcription, transcription factor association, chromatin structure and histone modification. These data enabled us to assign biochemical functions for 80% of the genome, in particular outside of the well-studied protein-coding regions. Many discovered candidate regulatory elements are physically associated with one another and with expressed genes, providing new insights into the mechanisms of gene regulation. The newly identified elements also show a statistical correspondence to sequence variants linked to human disease, and can thereby guide interpretation of this variation. Overall, the project provides new insights into the organization and regulation of our genes and genome, and is an expansive resource of functional annotations for biomedical research.

    View details for DOI 10.1038/nature11247

    View details for Web of Science ID 000308347000039

    View details for PubMedID 22955616

  • Ubiquitous heterogeneity and asymmetry of the chromatin environment at regulatory elements GENOME RESEARCH Kundaje, A., Kyriazopoulou-Panagiotopoulou, S., Libbrecht, M., Smith, C. L., Raha, D., Winters, E. E., Johnson, S. M., Snyder, M., Batzoglou, S., Sidow, A. 2012; 22 (9): 1735-1747

    Abstract

    Gene regulation at functional elements (e.g., enhancers, promoters, insulators) is governed by an interplay of nucleosome remodeling, histone modifications, and transcription factor binding. To enhance our understanding of gene regulation, the ENCODE Consortium has generated a wealth of ChIP-seq data on DNA-binding proteins and histone modifications. We additionally generated nucleosome positioning data on two cell lines, K562 and GM12878, by MNase digestion and high-depth sequencing. Here we relate 14 chromatin signals (12 histone marks, DNase, and nucleosome positioning) to the binding sites of 119 DNA-binding proteins across a large number of cell lines. We developed a new method for unsupervised pattern discovery, the Clustered AGgregation Tool (CAGT), which accounts for the inherent heterogeneity in signal magnitude, shape, and implicit strand orientation of chromatin marks. We applied CAGT on a total of 5084 data set pairs to obtain an exhaustive catalog of high-resolution patterns of histone modifications and nucleosome positioning signals around bound transcription factors. Our analyses reveal extensive heterogeneity in how histone modifications are deposited, and how nucleosomes are positioned around binding sites. With the exception of the CTCF/cohesin complex, asymmetry of nucleosome positioning is predominant. Asymmetry of histone modifications is also widespread, for all types of chromatin marks examined, including promoter, enhancer, elongation, and repressive marks. The fine-resolution signal shapes discovered by CAGT unveiled novel correlation patterns between chromatin marks, nucleosome positioning, and sequence content. Meta-analyses of the signal profiles revealed a common vocabulary of chromatin signals shared across multiple cell lines and binding proteins.

    View details for DOI 10.1101/gr.136366.111

    View details for Web of Science ID 000308272800015

    View details for PubMedID 22955985

  • ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia GENOME RESEARCH Landt, S. G., Marinov, G. K., Kundaje, A., Kheradpour, P., Pauli, F., Batzoglou, S., Bernstein, B. E., Bickel, P., Brown, J. B., Cayting, P., Chen, Y., DeSalvo, G., Epstein, C., Fisher-Aylor, K. I., Euskirchen, G., Gerstein, M., Gertz, J., Hartemink, A. J., Hoffman, M. M., Iyer, V. R., Jung, Y. L., Karmakar, S., Kellis, M., Kharchenko, P. V., Li, Q., Liu, T., Liu, X. S., Ma, L., Milosavljevic, A., Myers, R. M., Park, P. J., Pazin, M. J., Perry, M. D., Raha, D., Reddy, T. E., Rozowsky, J., Shoresh, N., Sidow, A., Slattery, M., Stamatoyannopoulos, J. A., Tolstorukov, M. Y., White, K. P., Xi, S., Farnham, P. J., Lieb, J. D., Wold, B. J., Snyder, M. 2012; 22 (9): 1813-1831

    Abstract

    Chromatin immunoprecipitation (ChIP) followed by high-throughput DNA sequencing (ChIP-seq) has become a valuable and widely used approach for mapping the genomic location of transcription-factor binding and histone modifications in living cells. Despite its widespread use, there are considerable differences in how these experiments are conducted, how the results are scored and evaluated for quality, and how the data and metadata are archived for public use. These practices affect the quality and utility of any global ChIP experiment. Through our experience in performing ChIP-seq experiments, the ENCODE and modENCODE consortia have developed a set of working standards and guidelines for ChIP experiments that are updated routinely. The current guidelines address antibody validation, experimental replication, sequencing depth, data and metadata reporting, and data quality assessment. We discuss how ChIP quality, assessed in these ways, affects different uses of ChIP-seq data. All data sets used in the analysis have been deposited for public viewing and downloading at the ENCODE (http://encodeproject.org/ENCODE/) and modENCODE (http://www.modencode.org/) portals.

    View details for DOI 10.1101/gr.136184.111

    View details for Web of Science ID 000308272800021

    View details for PubMedID 22955991

  • Linking disease associations with regulatory information in the human genome GENOME RESEARCH Schaub, M. A., Boyle, A. P., Kundaje, A., Batzoglou, S., Snyder, M. 2012; 22 (9): 1748-1759

    Abstract

    Genome-wide association studies have been successful in identifying single nucleotide polymorphisms (SNPs) associated with a large number of phenotypes. However, an associated SNP is likely part of a larger region of linkage disequilibrium. This makes it difficult to precisely identify the SNPs that have a biological link with the phenotype. We have systematically investigated the association of multiple types of ENCODE data with disease-associated SNPs and show that there is significant enrichment for functional SNPs among the currently identified associations. This enrichment is strongest when integrating multiple sources of functional information and when highest confidence disease-associated SNPs are used. We propose an approach that integrates multiple types of functional data generated by the ENCODE Consortium to help identify "functional SNPs" that may be associated with the disease phenotype. Our approach generates putative functional annotations for up to 80% of all previously reported associations. We show that for most associations, the functional SNP most strongly supported by experimental evidence is a SNP in linkage disequilibrium with the reported association rather than the reported SNP itself. Our results show that the experimental data sets generated by the ENCODE Consortium can be successfully used to suggest functional hypotheses for variants associated with diseases and other phenotypes.

    View details for DOI 10.1101/gr.136127.111

    View details for Web of Science ID 000308272800016

    View details for PubMedID 22955986

  • Learning regulatory programs that accurately predict differential expression with MEDUSA REVERSE ENGINEERING BIOLOGICAL NETWORKS Kundaje, A., Lianoglou, S., Li, X., Quigley, D., Arias, M., Wiggins, C. H., Zhang, L., Leslie, C. 2007; 1115: 178-202

    Abstract

    Inferring gene regulatory networks from high-throughput genomic data is one of the central problems in computational biology. In this paper, we describe a predictive modeling approach for studying regulatory networks, based on a machine learning algorithm called MEDUSA. MEDUSA integrates promoter sequence, mRNA expression, and transcription factor occupancy data to learn gene regulatory programs that predict the differential expression of target genes. Instead of using clustering or correlation of expression profiles to infer regulatory relationships, MEDUSA determines condition-specific regulators and discovers regulatory motifs that mediate the regulation of target genes. In this way, MEDUSA meaningfully models biological mechanisms of transcriptional regulation. MEDUSA solves the problem of predicting the differential (up/down) expression of target genes by using boosting, a technique from statistical learning, which helps to avoid overfitting as the algorithm searches through the high-dimensional space of potential regulators and sequence motifs. Experimental results demonstrate that MEDUSA achieves high prediction accuracy on held-out experiments (test data), that is, data not seen in training. We also present context-specific analysis of MEDUSA regulatory programs for DNA damage and hypoxia, demonstrating that MEDUSA identifies key regulators and motifs in these processes. A central challenge in the field is the difficulty of validating reverse-engineered networks in the absence of a gold standard. Our approach of learning regulatory programs provides at least a partial solution for the problem: MEDUSA's prediction accuracy on held-out data gives a concrete and statistically sound way to validate how well the algorithm performs. With MEDUSA, statistical validation becomes a prerequisite for hypothesis generation and network building rather than a secondary consideration.

    View details for DOI 10.1196/annals.1407.020

    View details for Web of Science ID 000252037600013

    View details for PubMedID 17934055

  • Combining sequence and time series expression data to learn transcriptional modules IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS Kundaje, A., Middendorf, M., Gao, F., Wiggins, C., Leslie, C. 2005; 2 (3): 194-202

    Abstract

    Our goal is to cluster genes into transcriptional modules--sets of genes where similarity in expression is explained by common regulatory mechanisms at the transcriptional level. We want to learn modules from both time series gene expression data and genome-wide motif data that are now readily available for organisms such as S. cereviseae as a result of prior computational studies or experimental results. We present a generative probabilistic model for combining regulatory sequence and time series expression data to cluster genes into coherent transcriptional modules. Starting with a set of motifs representing known or putative regulatory elements (transcription factor binding sites) and the counts of occurrences of these motifs in each gene's promoter region, together with a time series expression profile for each gene, the learning algorithm uses expectation maximization to learn module assignments based on both types of data. We also present a technique based on the Jensen-Shannon entropy contributions of motifs in the learned model for associating the most significant motifs to each module. Thus, the algorithm gives a global approach for associating sets of regulatory elements to "modules" of genes with similar time series expression profiles. The model for expression data exploits our prior belief of smooth dependence on time by using statistical splines and is suitable for typical time course data sets with relatively few experiments. Moreover, the model is sufficiently interpretable that we can understand how both sequence data and expression data contribute to the cluster assignments, and how to interpolate between the two data sources. We present experimental results on the yeast cell cycle to validate our method and find that our combined expression and motif clustering algorithm discovers modules with both coherent expression and similar motif patterns, including binding motifs associated to known cell cycle transcription factors.

    View details for Web of Science ID 000235704200003

    View details for PubMedID 17044183

  • Large-Scale Quality Analysis of Published ChIP-seq Data. G3 (Bethesda, Md.) Marinov, G. K., Kundaje, A., Park, P. J., Wold, B. J. 2014; 4 (2): 209-223

    Abstract

    ChIP-seq has become the primary method for identifying in vivo protein-DNA interactions on a genome-wide scale, with nearly 800 publications involving the technique appearing in PubMed as of December 2012. Individually and in aggregate, these data are an important and information-rich resource. However, uncertainties about data quality confound their use by the wider research community. Recently, the Encyclopedia of DNA Elements (ENCODE) project developed and applied metrics to objectively measure ChIP-seq data quality. The ENCODE quality analysis was useful for flagging datasets for closer inspection, eliminating or replacing poor data, and for driving changes in experimental pipelines. There had been no similarly systematic quality analysis of the large and disparate body of published ChIP-seq profiles. Here, we report a uniform analysis of vertebrate transcription factor ChIP-seq datasets in the Gene Expression Omnibus (GEO) repository as of April 1, 2012. The majority (55%) of datasets scored as being highly successful, but a substantial minority (20%) were of apparently poor quality, and another ∼25% were of intermediate quality. We discuss how different uses of ChIP-seq data are affected by specific aspects of data quality, and we highlight exceptional instances for which the metric values should not be taken at face value. Unexpectedly, we discovered that a significant subset of control datasets (i.e., no immunoprecipitation and mock immunoprecipitation samples) display an enrichment structure similar to successful ChIP-seq data. This can, in turn, affect peak calling and data interpretation. Published datasets identified here as high-quality comprise a large group that users can draw on for large-scale integrated analysis. In the future, ChIP-seq quality assessment similar to that used here could guide experimentalists at early stages in a study, provide useful input in the publication process, and be used to stratify ChIP-seq data for different community-wide uses.

    View details for DOI 10.1534/g3.113.008680

    View details for PubMedID 24347632

  • STAT3 Targets Suggest Mechanisms of Aggressive Tumorigenesis in Diffuse Large B-Cell Lymphoma G3-GENES GENOMES GENETICS Hardee, J., Ouyang, Z., Zhang, Y., Kundaje, A., Lacroute, P., Snyder, M. 2013; 3 (12): 2173-2185

    Abstract

    The signal transducer and activator of transcription 3 (STAT3) is a transcription factor that, when dysregulated, becomes a powerful oncogene found in many human cancers, including diffuse large B-cell lymphoma. Diffuse large B-cell lymphoma is the most common form of non-Hodgkin's lymphoma and has two major subtypes: germinal center B-cell-like and activated B-cell-like. Compared with the germinal center B-cell-like form, activated B-cell-like lymphomas respond much more poorly to current therapies and often exhibit overexpression or overactivation of STAT3. To investigate how STAT3 might contribute to this aggressive phenotype, we have integrated genome-wide studies of STAT3 DNA binding using chromatin immunoprecipitation-sequencing with whole-transcriptome profiling using RNA-sequencing. STAT3 binding sites are present near almost a third of all genes that differ in expression between the two subtypes, and examination of the affected genes identified previously undetected and clinically significant pathways downstream of STAT3 that drive oncogenesis. Novel treatments aimed at these pathways may increase the survivability of activated B-cell-like diffuse large B-cell lymphoma.

    View details for DOI 10.1534/g3.113.007674

    View details for Web of Science ID 000328334500008

    View details for PubMedID 24142927

  • Integrative annotation of chromatin elements from ENCODE data NUCLEIC ACIDS RESEARCH Hoffman, M. M., Ernst, J., Wilder, S. P., Kundaje, A., Harris, R. S., Libbrecht, M., Giardine, B., Ellenbogen, P. M., Bilmes, J. A., Birney, E., Hardison, R. C., Dunham, I., Kellis, M., Noble, W. S. 2013; 41 (2): 827-841

    Abstract

    The ENCODE Project has generated a wealth of experimental information mapping diverse chromatin properties in several human cell lines. Although each such data track is independently informative toward the annotation of regulatory elements, their interrelations contain much richer information for the systematic annotation of regulatory elements. To uncover these interrelations and to generate an interpretable summary of the massive datasets of the ENCODE Project, we apply unsupervised learning methodologies, converting dozens of chromatin datasets into discrete annotation maps of regulatory regions and other chromatin elements across the human genome. These methods rediscover and summarize diverse aspects of chromatin architecture, elucidate the interplay between chromatin activity and RNA transcription, and reveal that a large proportion of the genome lies in a quiescent state, even across multiple cell types. The resulting annotation of non-coding regulatory elements correlate strongly with mammalian evolutionary constraint, and provide an unbiased approach for evaluating metrics of evolutionary constraint in human. Lastly, we use the regulatory annotations to revisit previously uncharacterized disease-associated loci, resulting in focused, testable hypotheses through the lens of the chromatin landscape.

    View details for DOI 10.1093/nar/gks1284

    View details for Web of Science ID 000314121100021

    View details for PubMedID 23221638

  • Sequence features and chromatin structure around the genomic regions bound by 119 human transcription factors GENOME RESEARCH Wang, J., Zhuang, J., Iyer, S., Lin, X., Whitfield, T. W., Greven, M. C., Pierce, B. G., Dong, X., Kundaje, A., Cheng, Y., Rando, O. J., Birney, E., Myers, R. M., Noble, W. S., Snyder, M., Weng, Z. 2012; 22 (9): 1798-1812

    Abstract

    Chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) has become the dominant technique for mapping transcription factor (TF) binding regions genome-wide. We performed an integrative analysis centered around 457 ChIP-seq data sets on 119 human TFs generated by the ENCODE Consortium. We identified highly enriched sequence motifs in most data sets, revealing new motifs and validating known ones. The motif sites (TF binding sites) are highly conserved evolutionarily and show distinct footprints upon DNase I digestion. We frequently detected secondary motifs in addition to the canonical motifs of the TFs, indicating tethered binding and cobinding between multiple TFs. We observed significant position and orientation preferences between many cobinding TFs. Genes specifically expressed in a cell line are often associated with a greater occurrence of nearby TF binding in that cell line. We observed cell-line-specific secondary motifs that mediate the binding of the histone deacetylase HDAC2 and the enhancer-binding protein EP300. TF binding sites are located in GC-rich, nucleosome-depleted, and DNase I sensitive regions, flanked by well-positioned nucleosomes, and many of these features show cell type specificity. The GC-richness may be beneficial for regulating TF binding because, when unoccupied by a TF, these regions are occupied by nucleosomes in vivo. We present the results of our analysis in a TF-centric web repository Factorbook (http://factorbook.org) and will continually update this repository as more ENCODE data are generated.

    View details for DOI 10.1101/gr.139105.112

    View details for Web of Science ID 000308272800020

    View details for PubMedID 22955990

  • Long noncoding RNAs are rarely translated in two human cell lines GENOME RESEARCH Banfai, B., Jia, H., Khatun, J., Wood, E., Risk, B., Gundling, W. E., Kundaje, A., Gunawardena, H. P., Yu, Y., Xie, L., Krajewski, K., Strahl, B. D., Chen, X., Bickel, P., Giddings, M. C., Brown, J. B., Lipovich, L. 2012; 22 (9): 1646-1657

    Abstract

    Data from the Encyclopedia of DNA Elements (ENCODE) project show over 9640 human genome loci classified as long noncoding RNAs (lncRNAs), yet only ~100 have been deeply characterized to determine their role in the cell. To measure the protein-coding output from these RNAs, we jointly analyzed two recent data sets produced in the ENCODE project: tandem mass spectrometry (MS/MS) data mapping expressed peptides to their encoding genomic loci, and RNA-seq data generated by ENCODE in long polyA+ and polyA- fractions in the cell lines K562 and GM12878. We used the machine-learning algorithm RuleFit3 to regress the peptide data against RNA expression data. The most important covariate for predicting translation was, surprisingly, the Cytosol polyA- fraction in both cell lines. LncRNAs are ~13-fold less likely to produce detectable peptides than similar mRNAs, indicating that ~92% of GENCODE v7 lncRNAs are not translated in these two ENCODE cell lines. Intersecting 9640 lncRNA loci with 79,333 peptides yielded 85 unique peptides matching 69 lncRNAs. Most cases were due to a coding transcript misannotated as lncRNA. Two exceptions were an unprocessed pseudogene and a bona fide lncRNA gene, both with open reading frames (ORFs) compromised by upstream stop codons. All potentially translatable lncRNA ORFs had only a single peptide match, indicating low protein abundance and/or false-positive peptide matches. We conclude that with very few exceptions, ribosomes are able to distinguish coding from noncoding transcripts and, hence, that ectopic translation and cryptic mRNAs are rare in the human lncRNAome.

    View details for DOI 10.1101/gr.134767.111

    View details for Web of Science ID 000308272800007

    View details for PubMedID 22955977

  • Modeling gene expression using chromatin features in various cellular contexts GENOME BIOLOGY Dong, X., Greven, M. C., Kundaje, A., Djebali, S., Brown, J. B., Cheng, C., Gingeras, T. R., Gerstein, M., Guigo, R., Birney, E., Weng, Z. 2012; 13 (9)

    Abstract

    Previous work has demonstrated that chromatin feature levels correlate with gene expression. The ENCODE project enables us to further explore this relationship using an unprecedented volume of data. Expression levels from more than 100,000 promoters were measured using a variety of high-throughput techniques applied to RNA extracted by different protocols from different cellular compartments of several human cell lines. ENCODE also generated the genome-wide mapping of eleven histone marks, one histone variant, and DNase I hypersensitivity sites in seven cell lines.We built a novel quantitative model to study the relationship between chromatin features and expression levels. Our study not only confirms that the general relationships found in previous studies hold across various cell lines, but also makes new suggestions about the relationship between chromatin features and gene expression levels. We found that expression status and expression levels can be predicted by different groups of chromatin features, both with high accuracy. We also found that expression levels measured by CAGE are better predicted than by RNA-PET or RNA-Seq, and different categories of chromatin features are the most predictive of expression for different RNA measurement methods. Additionally, PolyA+ RNA is overall more predictable than PolyA- RNA among different cell compartments, and PolyA+ cytosolic RNA measured with RNA-Seq is more predictable than PolyA+ nuclear RNA, while the opposite is true for PolyA- RNA.Our study provides new insights into transcriptional regulation by analyzing chromatin features in different cellular contexts.

    View details for DOI 10.1186/gb-2012-13-9-r53

    View details for Web of Science ID 000313182600006

    View details for PubMedID 22950368

  • Classification of human genomic regions based on experimentally determined binding sites of more than 100 transcription-related factors GENOME BIOLOGY Yip, K. Y., Cheng, C., Bhardwaj, N., Brown, J. B., Leng, J., Kundaje, A., Rozowsky, J., Birney, E., Bickel, P., Snyder, M., Gerstein, M. 2012; 13 (9)

    Abstract

    Transcription factors function by binding different classes of regulatory elements. The Encyclopedia of DNA Elements (ENCODE) project has recently produced binding data for more than 100 transcription factors from about 500 ChIP-seq experiments in multiple cell types. While this large amount of data creates a valuable resource, it is nonetheless overwhelmingly complex and simultaneously incomplete since it covers only a small fraction of all human transcription factors.As part of the consortium effort in providing a concise abstraction of the data for facilitating various types of downstream analyses, we constructed statistical models that capture the genomic features of three paired types of regions by machine-learning methods: firstly, regions with active or inactive binding; secondly, those with extremely high or low degrees of co-binding, termed HOT and LOT regions; and finally, regulatory modules proximal or distal to genes. From the distal regulatory modules, we developed computational pipelines to identify potential enhancers, many of which were validated experimentally. We further associated the predicted enhancers with potential target transcripts and the transcription factors involved. For HOT regions, we found a significant fraction of transcription factor binding without clear sequence motifs and showed that this observation could be related to strong DNA accessibility of these regions.Overall, the three pairs of regions exhibit intricate differences in chromosomal locations, chromatin features, factors that bind them, and cell-type specificity. Our machine learning approach enables us to identify features potentially general to all transcription factors, including those not included in the data.

    View details for DOI 10.1186/gb-2012-13-9-r48

    View details for Web of Science ID 000313182600001

    View details for PubMedID 22950945

  • A User's Guide to the Encyclopedia of DNA Elements (ENCODE) PLOS BIOLOGY Myers, R. M., Stamatoyannopoulos, J., Snyder, M., Dunham, I., Hardison, R. C., Bernstein, B. E., Gingeras, T. R., Kent, W. J., Birney, E., Wold, B., Crawford, G. E., Bernstein, B. E., Epstein, C. B., Shoresh, N., Ernst, J., Mikkelsen, T. S., Kheradpour, P., Zhang, X., Wang, L., Issner, R., Coyne, M. J., Durham, T., Ku, M., Thanh Truong, T., Ward, L. D., Altshuler, R. C., Lin, M. F., Kellis, M., Gingeras, T. R., Davis, C. A., Kapranov, P., Dobin, A., Zaleski, C., Schlesinger, F., Batut, P., Chakrabortty, S., Jha, S., Lin, W., Drenkow, J., Wang, H., Bell, K., Gao, H., Bell, I., Dumais, E., Dumais, J., Antonarakis, S. E., Ucla, C., Borel, C., Guigo, R., Djebali, S., Lagarde, J., Kingswood, C., Ribeca, P., Sammeth, M., Alioto, T., Merkel, A., Tilgner, H., Carninci, P., Hayashizaki, Y., Lassmann, T., Takahashi, H., Abdelhamid, R. F., Hannon, G., Fejes-Toth, K., Preall, J., Gordon, A., Sotirova, V., Reymond, A., Howald, C., Graison, E. A., Chrast, J., Ruan, Y., Ruan, X., Shahab, A., Poh, W. T., Wei, C., Crawford, G. E., Furey, T. S., Boyle, A. P., Sheffield, N. C., Song, L., Shibata, Y., Vales, T., Winter, D., Zhang, Z., London, D., Wang, T., Birney, E., Keefe, D., Iyer, V. R., Lee, B., McDaniell, R. M., Liu, Z., Battenhouse, A., Bhinge, A. A., Lieb, J. D., Grasfeder, L. L., Showers, K. A., Giresi, P. G., Kim, S. K., Shestak, C., Myers, R. M., Pauli, F., Reddy, T. E., Gertz, J., Partridge, E. C., Jain, P., Sprouse, R. O., Bansal, A., Pusey, B., Muratet, M. A., Varley, K. E., Bowling, K. M., Newberry, K. M., Nesmith, A. S., Dilocker, J. A., Parker, S. L., Waite, L. L., Thibeault, K., Roberts, K., Absher, D. M., Wold, B., Mortazavi, A., Williams, B., Marinov, G., Trout, D., Pepke, S., King, B., McCue, K., Kirilusha, A., DeSalvo, G., Fisher-Aylor, K., Amrhein, H., Vielmetter, J., Sherlock, G., Sidow, A., Batzoglou, S., Rauch, R., Kundaje, A., Libbrecht, M., Margulies, E. H., Parker, S. C., Elnitski, L., Green, E. D., Hubbard, T., Harrow, J., Searle, S., Kokocinski, F., Aken, B., Frankish, A., Hunt, T., Despacio-Reyes, G., Kay, M., Mukherjee, G., Bignell, A., Saunders, G., Boychenko, V., Brent, M., van Baren, M. J., Brown, R. H., Gerstein, M., Khurana, E., Balasubramanian, S., Zhang, Z., Lam, H., Cayting, P., Robilotto, R., Lu, Z., Guigo, R., Derrien, T., Tanzer, A., Knowles, D. G., Mariotti, M., Kent, W. J., Haussler, D., Harte, R., Diekhans, M., Kellis, M., Lin, M., Kheradpour, P., Ernst, J., Reymond, A., Howald, C., Graison, E. A., Chrast, J., Valencia, A., Tress, M., Manuel Rodriguez, J., Snyder, M., Landt, S. G., Raha, D., Shi, M., Euskirchen, G., Grubert, F., Kasowski, M., Lian, J., Cayting, P., Lacroute, P., Xu, Y., Monahan, H., Patacsil, D., Slifer, T., Yang, X., Charos, A., Reed, B., Wu, L., Auerbach, R. K., Habegger, L., Hariharan, M., Rozowsky, J., Abyzov, A., Weissman, S. M., Gerstein, M., Struhl, K., Lamarre-Vincent, N., Lindahl-Allen, M., Miotto, B., Moqtaderi, Z., Fleming, J. D., Newburger, P., Farnham, P. J., Frietze, S., O'Geen, H., Xu, X., Blahnik, K. R., Cao, A. R., Iyengar, S., Stamatoyannopoulos, J. A., Kaul, R., Thurman, R. E., Wang, H., Navas, P. A., Sandstrom, R., Sabo, P. J., Weaver, M., Canfield, T., Lee, K., Neph, S., Roach, V., Reynolds, A., Johnson, A., Rynes, E., Giste, E., Vong, S., Neri, J., Frum, T., Johnson, E. M., Nguyen, E. D., Ebersol, A. K., Sanchez, M. E., Sheffer, H. H., Lotakis, D., Haugen, E., Humbert, R., Kutyavin, T., Shafer, T., Dekker, J., Lajoie, B. R., Sanyal, A., Kent, W. J., Rosenbloom, K. R., Dreszer, T. R., Raney, B. J., Barber, G. P., Meyer, L. R., Sloan, C. A., Malladi, V. S., Cline, M. S., Learned, K., Swing, V. K., Zweig, A. S., Rhead, B., Fujita, P. A., Roskin, K., Karolchik, D., Kuhn, R. M., Haussler, D., Birney, E., Dunham, I., Wilder, S. P., Keefe, D., Sobral, D., Herrero, J., Beal, K., Lukk, M., Brazma, A., Vaquerizas, J. M., Luscombe, N. M., Bickel, P. J., Boley, N., Brown, J. B., Li, Q., Huang, H., Gerstein, M., Habegger, L., Sboner, A., Rozowsky, J., Auerbach, R. K., Yip, K. Y., Cheng, C., Yan, K., Bhardwaj, N., Wang, J., Lochovsky, L., Jee, J., Gibson, T., Leng, J., Du, J., Hardison, R. C., Harris, R. S., Song, G., Miller, W., Haussler, D., Roskin, K., Suh, B., Wang, T., Paten, B., Noble, W. S., Hoffman, M. M., Buske, O. J., Weng, Z., Dong, X., Wang, J., Xi, H., Tenenbaum, S. A., Doyle, F., Penalva, L. O., Chittur, S., Tullius, T. D., Parker, S. C., White, K. P., Karmakar, S., Victorsen, A., Jameel, N., Bild, N., Grossman, R. L., Snyder, M., Landt, S. G., Yang, X., Patacsil, D., Slifer, T., Dekker, J., Lajoie, B. R., Sanyal, A., Weng, Z., Whitfield, T. W., Wang, J., Collins, P. J., Trinklein, N. D., Partridge, E. C., Myers, R. M., Giddings, M. C., Chen, X., Khatun, J., Maier, C., Yu, Y., Gunawardena, H., Risk, B., Feingold, E. A., Lowdon, R. F., Dillon, L. A., Good, P. J. 2011; 9 (4)
  • A Predictive Model of the Oxygen and Heme Regulatory Network in Yeast PLOS COMPUTATIONAL BIOLOGY Kundaje, A., Xin, X., Lan, C., Lianoglou, S., Zhou, M., Zhang, L., Leslie, C. 2008; 4 (11)

    Abstract

    Deciphering gene regulatory mechanisms through the analysis of high-throughput expression data is a challenging computational problem. Previous computational studies have used large expression datasets in order to resolve fine patterns of coexpression, producing clusters or modules of potentially coregulated genes. These methods typically examine promoter sequence information, such as DNA motifs or transcription factor occupancy data, in a separate step after clustering. We needed an alternative and more integrative approach to study the oxygen regulatory network in Saccharomyces cerevisiae using a small dataset of perturbation experiments. Mechanisms of oxygen sensing and regulation underlie many physiological and pathological processes, and only a handful of oxygen regulators have been identified in previous studies. We used a new machine learning algorithm called MEDUSA to uncover detailed information about the oxygen regulatory network using genome-wide expression changes in response to perturbations in the levels of oxygen, heme, Hap1, and Co2+. MEDUSA integrates mRNA expression, promoter sequence, and ChIP-chip occupancy data to learn a model that accurately predicts the differential expression of target genes in held-out data. We used a novel margin-based score to extract significant condition-specific regulators and assemble a global map of the oxygen sensing and regulatory network. This network includes both known oxygen and heme regulators, such as Hap1, Mga2, Hap4, and Upc2, as well as many new candidate regulators. MEDUSA also identified many DNA motifs that are consistent with previous experimentally identified transcription factor binding sites. Because MEDUSA's regulatory program associates regulators to target genes through their promoter sequences, we directly tested the predicted regulators for OLE1, a gene specifically induced under hypoxia, by experimental analysis of the activity of its promoter. In each case, deletion of the candidate regulator resulted in the predicted effect on promoter activity, confirming that several novel regulators identified by MEDUSA are indeed involved in oxygen regulation. MEDUSA can reveal important information from a small dataset and generate testable hypotheses for further experimental analysis. Supplemental data are included.

    View details for DOI 10.1371/journal.pcbi.1000224

    View details for Web of Science ID 000261480800016

    View details for PubMedID 19008939

  • A classification-based framework for predicting and analyzing gene regulatory response BMC BIOINFORMATICS Kundaje, A., Middendorf, M., Shah, M., Wiggins, C. H., Freund, Y., Leslie, C. 2006; 7

    Abstract

    We have recently introduced a predictive framework for studying gene transcriptional regulation in simpler organisms using a novel supervised learning algorithm called GeneClass. GeneClass is motivated by the hypothesis that in model organisms such as Saccharomyces cerevisiae, we can learn a decision rule for predicting whether a gene is up- or down-regulated in a particular microarray experiment based on the presence of binding site subsequences ("motifs") in the gene's regulatory region and the expression levels of regulators such as transcription factors in the experiment ("parents"). GeneClass formulates the learning task as a classification problem--predicting +1 and -1 labels corresponding to up- and down-regulation beyond the levels of biological and measurement noise in microarray measurements. Using the Adaboost algorithm, GeneClass learns a prediction function in the form of an alternating decision tree, a margin-based generalization of a decision tree.In the current work, we introduce a new, robust version of the GeneClass algorithm that increases stability and computational efficiency, yielding a more scalable and reliable predictive model. The improved stability of the prediction tree enables us to introduce a detailed post-processing framework for biological interpretation, including individual and group target gene analysis to reveal condition-specific regulation programs and to suggest signaling pathways. Robust GeneClass uses a novel stabilized variant of boosting that allows a set of correlated features, rather than single features, to be included at nodes of the tree; in this way, biologically important features that are correlated with the single best feature are retained rather than decorrelated and lost in the next round of boosting. Other computational developments include fast matrix computation of the loss function for all features, allowing scalability to large datasets, and the use of abstaining weak rules, which results in a more shallow and interpretable tree. We also show how to incorporate genome-wide protein-DNA binding data from ChIP chip experiments into the GeneClass algorithm, and we use an improved noise model for gene expression data.Using the improved scalability of Robust GeneClass, we present larger scale experiments on a yeast environmental stress dataset, training and testing on all genes and using a comprehensive set of potential regulators. We demonstrate the improved stability of the features in the learned prediction tree, and we show the utility of the post-processing framework by analyzing two groups of genes in yeast--the protein chaperones and a set of putative targets of the Nrg1 and Nrg2 transcription factors--and suggesting novel hypotheses about their transcriptional and post-transcriptional regulation. Detailed results and Robust GeneClass source code is available for download from http://www.cs.columbia.edu/compbio/robust-geneclass.

    View details for DOI 10.1186/1471-2105-7-S1-S5

    View details for Web of Science ID 000236765200005

    View details for PubMedID 16723008

  • Statistical analysis of MPSS measurements: Application to the study of LPS-activated macrophage gene expression PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Stolovitzky, G. A., Kundaje, A., Held, G. A., Duggar, K. H., Haudenschild, C. D., Zhou, D., Vasicek, T. J., Smith, K. D., Aderem, A., Roach, J. C. 2005; 102 (5): 1402-1407

    Abstract

    Massively Parallel Signature Sequencing (MPSS), a recently developed high-throughput transcription profiling technology, has the ability to profile almost every transcript in a sample without requiring prior knowledge of the sequence of the transcribed genes. As is the case with DNA microarrays, effective data analysis depends crucially on understanding how noise affects measurements. We analyze the sources of noise in MPSS and present a quantitative model describing the variability between replicate MPSS assays. We use this model to construct statistical hypotheses that test whether an observed change in gene expression in a pair-wise comparison is significant. This analysis is then extended to the determination of the significance of changes in expression levels measured over the course of a time series of measurements. We apply these analytic techniques to the study of a time series of MPSS gene expression measurements on LPS-stimulated macrophages. To evaluate our statistical significance metrics, we compare our results with published data on macrophage activation measured by using Affymetrix GeneChips.

    View details for DOI 10.1073/pnas.0406555102

    View details for Web of Science ID 000226877300029

    View details for PubMedID 15668391

  • Motif discovery through predictive modeling of gene regulation RESEARCH IN COMPUTATIONAL MOLECULAR BIOLOGY, PROCEEDINGS Middendorf, M., Kundaje, A., Shah, M., Freund, Y., Wiggins, C. H., Leslie, C. 2005; 3500: 538-552
  • Predicting genetic regulatory response using classification: Yeast stress response REGULATORY GENOMICS Middendorf, M., Kundaje, A., Wiggins, C., Freund, Y., Leslie, C. 2005; 3318: 1-13
  • Predicting genetic regulatory response using classification BIOINFORMATICS Middendorf, M., Kundaje, A., Wiggins, C., Freund, Y., Leslie, C. 2004; 20: 232-240
  • Spectrogram analysis of genomes EURASIP JOURNAL ON APPLIED SIGNAL PROCESSING Sussillo, D., Kundaje, A., Anastassiou, D. 2004; 2004 (1): 29-42

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