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1.  Genome-wide analysis of chromatin features identifies histone modification sensitive and insensitive yeast transcription factors 
Genome Biology  2011;12(11):R111.
We propose a method to predict yeast transcription factor targets by integrating histone modification profiles with transcription factor binding motif information. It shows improved predictive power compared to a binding motif-only method. We find that transcription factors cluster into histone-sensitive and -insensitive classes. The target genes of histone-sensitive transcription factors have stronger histone modification signals than those of histone-insensitive ones. The two classes also differ in tendency to interact with histone modifiers, degree of connectivity in protein-protein interaction networks, position in the transcriptional regulation hierarchy, and in a number of additional features, indicating possible differences in their transcriptional regulation mechanisms.
PMCID: PMC3334597  PMID: 22060676
2.  A statistical framework for modeling gene expression using chromatin features and application to modENCODE datasets 
Genome Biology  2011;12(2):R15.
We develop a statistical framework to study the relationship between chromatin features and gene expression. This can be used to predict gene expression of protein coding genes, as well as microRNAs. We demonstrate the prediction in a variety of contexts, focusing particularly on the modENCODE worm datasets. Moreover, our framework reveals the positional contribution around genes (upstream or downstream) of distinct chromatin features to the overall prediction of expression levels.
PMCID: PMC3188797  PMID: 21324173
3.  Measuring the Evolutionary Rewiring of Biological Networks 
PLoS Computational Biology  2011;7(1):e1001050.
We have accumulated a large amount of biological network data and expect even more to come. Soon, we anticipate being able to compare many different biological networks as we commonly do for molecular sequences. It has long been believed that many of these networks change, or “rewire”, at different rates. It is therefore important to develop a framework to quantify the differences between networks in a unified fashion. We developed such a formalism based on analogy to simple models of sequence evolution, and used it to conduct a systematic study of network rewiring on all the currently available biological networks. We found that, similar to sequences, biological networks show a decreased rate of change at large time divergences, because of saturation in potential substitutions. However, different types of biological networks consistently rewire at different rates. Using comparative genomics and proteomics data, we found a consistent ordering of the rewiring rates: transcription regulatory, phosphorylation regulatory, genetic interaction, miRNA regulatory, protein interaction, and metabolic pathway network, from fast to slow. This ordering was found in all comparisons we did of matched networks between organisms. To gain further intuition on network rewiring, we compared our observed rewirings with those obtained from simulation. We also investigated how readily our formalism could be mapped to other network contexts; in particular, we showed how it could be applied to analyze changes in a range of “commonplace” networks such as family trees, co-authorships and linux-kernel function dependencies.
Author Summary
Biological networks represent various types of molecular organizations in a cell. During evolution, molecules have been shown to change at varying rates. Therefore, it is important to investigate the evolution of biological networks in terms of network rewiring. Understanding how biological networks evolve could eventually help explain the general mechanism of cellular system. In the past decade, a large amount of high-throughput experiments have helped to unravel the different types of networks in a number of species. Recent studies have provided evolutionary rate calculations on individual networks and observed different rewiring rates between them. We have chosen a systematic approach to compare rewiring rate differences among the common types of biological networks utilizing experimental data across species. Our analysis shows that regulatory networks generally evolve faster than non-regulatory collaborative networks. Our analysis also highlights future applications of the approach to address other interesting biological questions.
PMCID: PMC3017101  PMID: 21253555
4.  The Transcriptional Landscape of the Yeast Genome Defined by RNA Sequencing 
Science (New York, N.Y.)  2008;320(5881):1344-1349.
Although many genome sequences have been determined, identification of genes and their elements such as untranslated regions (UTRs), introns, and coding regions is still a significant challenge. We have developed a novel sequencing-based method called RNA-Seq in which cDNA fragments are subjected to high throughput sequencing using the Illumina platform, and short reads are computationally mapped to the genome to identify the transcribed regions. We have successfully applied RNA-Seq to generate a high-resolution transcriptome map of the yeast genome. We demonstrate that most (74.5%) of the unique sequence of the yeast genome is transcribed. We used this method to globally map 5′ UTR and 3′ UTR boundaries and confirmed many known and predicted introns and demonstrated that others are not actively used. Our results suggest an alternative initiation codon from that annotated for a number of known genes and demonstrate that many yeast genes contain upstream open reading frames (uORFs). We also found unexpected 3′ end heterogeneity and the presence of many overlapping genes. We also found many novel transcribed regions not identified by other methods. These results indicate that the yeast transcriptome is more complex than previously appreciated. Furthermore, RNA-Seq is demonstrated to be at least as accurate as DNA microarrays for quantifying RNA expression levels and has a much larger dynamic range. We expect that RNA-Seq will be a valuable general approach for high resolution mapping of transcriptomes in many organisms.
PMCID: PMC2951732  PMID: 18451266

Results 1-4 (4)