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1.  Integrative Analysis of the Caenorhabditis elegans Genome by the modENCODE Project 
Gerstein, Mark B. | Lu, Zhi John | Van Nostrand, Eric L. | Cheng, Chao | Arshinoff, Bradley I. | Liu, Tao | Yip, Kevin Y. | Robilotto, Rebecca | Rechtsteiner, Andreas | Ikegami, Kohta | Alves, Pedro | Chateigner, Aurelien | Perry, Marc | Morris, Mitzi | Auerbach, Raymond K. | Feng, Xin | Leng, Jing | Vielle, Anne | Niu, Wei | Rhrissorrakrai, Kahn | Agarwal, Ashish | Alexander, Roger P. | Barber, Galt | Brdlik, Cathleen M. | Brennan, Jennifer | Brouillet, Jeremy Jean | Carr, Adrian | Cheung, Ming-Sin | Clawson, Hiram | Contrino, Sergio | Dannenberg, Luke O. | Dernburg, Abby F. | Desai, Arshad | Dick, Lindsay | Dosé, Andréa C. | Du, Jiang | Egelhofer, Thea | Ercan, Sevinc | Euskirchen, Ghia | Ewing, Brent | Feingold, Elise A. | Gassmann, Reto | Good, Peter J. | Green, Phil | Gullier, Francois | Gutwein, Michelle | Guyer, Mark S. | Habegger, Lukas | Han, Ting | Henikoff, Jorja G. | Henz, Stefan R. | Hinrichs, Angie | Holster, Heather | Hyman, Tony | Iniguez, A. Leo | Janette, Judith | Jensen, Morten | Kato, Masaomi | Kent, W. James | Kephart, Ellen | Khivansara, Vishal | Khurana, Ekta | Kim, John K. | Kolasinska-Zwierz, Paulina | Lai, Eric C. | Latorre, Isabel | Leahey, Amber | Lewis, Suzanna | Lloyd, Paul | Lochovsky, Lucas | Lowdon, Rebecca F. | Lubling, Yaniv | Lyne, Rachel | MacCoss, Michael | Mackowiak, Sebastian D. | Mangone, Marco | McKay, Sheldon | Mecenas, Desirea | Merrihew, Gennifer | Miller, David M. | Muroyama, Andrew | Murray, John I. | Ooi, Siew-Loon | Pham, Hoang | Phippen, Taryn | Preston, Elicia A. | Rajewsky, Nikolaus | Rätsch, Gunnar | Rosenbaum, Heidi | Rozowsky, Joel | Rutherford, Kim | Ruzanov, Peter | Sarov, Mihail | Sasidharan, Rajkumar | Sboner, Andrea | Scheid, Paul | Segal, Eran | Shin, Hyunjin | Shou, Chong | Slack, Frank J. | Slightam, Cindie | Smith, Richard | Spencer, William C. | Stinson, E. O. | Taing, Scott | Takasaki, Teruaki | Vafeados, Dionne | Voronina, Ksenia | Wang, Guilin | Washington, Nicole L. | Whittle, Christina M. | Wu, Beijing | Yan, Koon-Kiu | Zeller, Georg | Zha, Zheng | Zhong, Mei | Zhou, Xingliang | Ahringer, Julie | Strome, Susan | Gunsalus, Kristin C. | Micklem, Gos | Liu, X. Shirley | Reinke, Valerie | Kim, Stuart K. | Hillier, LaDeana W. | Henikoff, Steven | Piano, Fabio | Snyder, Michael | Stein, Lincoln | Lieb, Jason D. | Waterston, Robert H.
Science (New York, N.Y.)  2010;330(6012):1775-1787.
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.
doi:10.1126/science.1196914
PMCID: PMC3142569  PMID: 21177976
2.  A Negative Regulatory Loop between MicroRNA and Hox Gene Controls Posterior Identities in Caenorhabditis elegans 
PLoS Genetics  2010;6(9):e1001089.
MicroRNAs (miRNAs) have been found to regulate gene expression across eukaryotic species, but the function of most miRNA genes remains unknown. Here we describe how the analysis of the expression patterns of a well-conserved miRNA gene, mir-57, at cellular resolution for every minute during early development of Caenorhabditis elegans provided key insights in understanding its function. Remarkably, mir-57 expression shows strong positional bias but little tissue specificity, a pattern reminiscent of Hox gene function. Despite the minor defects produced by a loss of function mutation, overexpression of mir-57 causes dramatic posterior defects, which also mimic the phenotypes of mutant alleles of a posterior Hox gene, nob-1, an Abd homolog. More importantly, nob-1 expression is found in the same two posterior AB sublineages as those expressing mir-57 but with an earlier onset. Intriguingly, nob-1 functions as an activator for mir-57 expression; it is also a direct target of mir-57. In agreement with this, loss of mir-57 function partially rescues the nob-1 allele defects, indicating a negative feedback regulatory loop between the miRNA and Hox gene to provide positional cues. Given the conservation of the miRNA and Hox gene, the regulatory mechanism might be broadly used across species. The strategy used here to explore mir-57 function provides a path to dissect the regulatory relationship between genes.
Author Summary
miRNAs are small RNAs found in many multi-cellular species that inhibit gene expression. Many of them play important roles in cancer and cell fate determination, but the function of most miRNAs is uncertain. Using live cell imaging and automated expression analysis, we found a miRNA gene, mir-57, is expressed in a position rather than tissue dependent way. Hox genes also regulate cell fate patterning along anterior-posterior (a-p) axis across different tissues. By investigating interactions between genes of these classes expressed in mir-57 expressing cells, we demonstrated by both genetic analysis and gene expression assays that a negative feedback loop between a posterior Hox gene, nob-1, and mir-57 regulates posterior cell fate determination in C. elegans. On the one hand, the Hox gene is required for normal activation of mir-57 expression, and on the other, the Hox gene functions as a direct target of and is repressed by the miRNA. Given the conservation of the two genes, a negative feedback loop between Hox and miRNA genes might be broadly used across species to regulate cell fate along the a-p axis. Detailed expression analysis may provide a general way to dissect the regulatory role of miRNAs.
doi:10.1371/journal.pgen.1001089
PMCID: PMC2932687  PMID: 20824072
3.  Comparison and calibration of transcriptome data from RNA-Seq and tiling arrays 
BMC Genomics  2010;11:383.
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 data. 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.
doi:10.1186/1471-2164-11-383
PMCID: PMC3091629  PMID: 20565764
4.  Improved Discrimination of Visual Stimuli Following Repetitive Transcranial Magnetic Stimulation 
PLoS ONE  2010;5(4):e10354.
Background
Repetitive transcranial magnetic stimulation (rTMS) at certain frequencies increases thresholds for motor-evoked potentials and phosphenes following stimulation of cortex. Consequently rTMS is often assumed to introduce a “virtual lesion” in stimulated brain regions, with correspondingly diminished behavioral performance.
Methodology/Principal Findings
Here we investigated the effects of rTMS to visual cortex on subjects' ability to perform visual psychophysical tasks. Contrary to expectations of a visual deficit, we find that rTMS often improves the discrimination of visual features. For coarse orientation tasks, discrimination of a static stimulus improved consistently following theta-burst stimulation of the occipital lobe. Using a reaction-time task, we found that these improvements occurred throughout the visual field and lasted beyond one hour post-rTMS. Low-frequency (1 Hz) stimulation yielded similar improvements. In contrast, we did not find consistent effects of rTMS on performance in a fine orientation discrimination task.
Conclusions/Significance
Overall our results suggest that rTMS generally improves or has no effect on visual acuity, with the nature of the effect depending on the type of stimulation and the task. We interpret our results in the context of an ideal-observer model of visual perception.
doi:10.1371/journal.pone.0010354
PMCID: PMC2860988  PMID: 20442776
5.  Genome-Wide Identification of Binding Sites Defines Distinct Functions for Caenorhabditis elegans PHA-4/FOXA in Development and Environmental Response 
PLoS Genetics  2010;6(2):e1000848.
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.
Author Summary
The C. elegans transcription factor PHA-4 is a member of the highly conserved FOXA family of transcription factors. These factors act as master regulators of organ development by controlling how genes are turned off and on as tissues are formed. Additionally they regulate genes in response to nutrient levels and control both longevity and survival of the organism. However, the extent to which these factors control similar or distinct gene targets for each of these functions is unknown. For this reason, we have used the technique of chromatin immunoprecipitation followed by deep sequencing (ChIP–Seq), to define the target binding sites of PHA-4 on a genome-wide scale, when it is either functioning as an organ identity regulator or in response to environmental stress. Our data clearly demonstrate distinct sets of biologically relevant target genes for the transcription factor PHA-4 under these two different conditions. Not only have we defined PHA-4 targets, but we established an experimental ChIP–Seq pipeline to facilitate the identification of binding sites for many transcription factors in the future.
doi:10.1371/journal.pgen.1000848
PMCID: PMC2824807  PMID: 20174564
6.  Using machine learning to speed up manual image annotation: application to a 3D imaging protocol for measuring single cell gene expression in the developing C. elegans embryo 
BMC Bioinformatics  2010;11:84.
Background
Image analysis is an essential component in many biological experiments that study gene expression, cell cycle progression, and protein localization. A protocol for tracking the expression of individual C. elegans genes was developed that collects image samples of a developing embryo by 3-D time lapse microscopy. In this protocol, a program called StarryNite performs the automatic recognition of fluorescently labeled cells and traces their lineage. However, due to the amount of noise present in the data and due to the challenges introduced by increasing number of cells in later stages of development, this program is not error free. In the current version, the error correction (i.e., editing) is performed manually using a graphical interface tool named AceTree, which is specifically developed for this task. For a single experiment, this manual annotation task takes several hours.
Results
In this paper, we reduce the time required to correct errors made by StarryNite. We target one of the most frequent error types (movements annotated as divisions) and train a support vector machine (SVM) classifier to decide whether a division call made by StarryNite is correct or not. We show, via cross-validation experiments on several benchmark data sets, that the SVM successfully identifies this type of error significantly. A new version of StarryNite that includes the trained SVM classifier is available at http://starrynite.sourceforge.net.
Conclusions
We demonstrate the utility of a machine learning approach to error annotation for StarryNite. In the process, we also provide some general methodologies for developing and validating a classifier with respect to a given pattern recognition task.
doi:10.1186/1471-2105-11-84
PMCID: PMC2838868  PMID: 20146825

Results 1-6 (6)