ORCID Profile
0000-0002-0599-1267
Current Organisation
Inserm U1220
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Publisher: Cold Spring Harbor Laboratory
Date: 09-2012
Abstract: Statistical models have been used to quantify the relationship between gene expression and transcription factor (TF) binding signals. Here we apply the models to the large-scale data generated by the ENCODE project to study transcriptional regulation by TFs. Our results reveal a notable difference in the prediction accuracy of expression levels of transcription start sites (TSSs) captured by different technologies and RNA extraction protocols. In general, the expression levels of TSSs with high CpG content are more predictable than those with low CpG content. For genes with alternative TSSs, the expression levels of downstream TSSs are more predictable than those of the upstream ones. Different TF categories and specific TFs vary substantially in their contributions to predicting expression. Between two cell lines, the differential expression of TSS can be precisely reflected by the difference of TF-binding signals in a quantitative manner, arguing against the conventional on-and-off model of TF binding. Finally, we explore the relationships between TF-binding signals and other chromatin features such as histone modifications and DNase hypersensitivity for determining expression. The models imply that these features regulate transcription in a highly coordinated manner.
Publisher: Cold Spring Harbor Laboratory
Date: 09-2012
Abstract: The human genome contains many thousands of long noncoding RNAs (lncRNAs). While several studies have demonstrated compelling biological and disease roles for in idual ex les, analytical and experimental approaches to investigate these genes have been h ered by the lack of comprehensive lncRNA annotation. Here, we present and analyze the most complete human lncRNA annotation to date, produced by the GENCODE consortium within the framework of the ENCODE project and comprising 9277 manually annotated genes producing 14,880 transcripts. Our analyses indicate that lncRNAs are generated through pathways similar to that of protein-coding genes, with similar histone-modification profiles, splicing signals, and exon/intron lengths. In contrast to protein-coding genes, however, lncRNAs display a striking bias toward two-exon transcripts, they are predominantly localized in the chromatin and nucleus, and a fraction appear to be preferentially processed into small RNAs. They are under stronger selective pressure than neutrally evolving sequences—particularly in their promoter regions, which display levels of selection comparable to protein-coding genes. Importantly, about one-third seem to have arisen within the primate lineage. Comprehensive analysis of their expression in multiple human organs and brain regions shows that lncRNAs are generally lower expressed than protein-coding genes, and display more tissue-specific expression patterns, with a large fraction of tissue-specific lncRNAs expressed in the brain. Expression correlation analysis indicates that lncRNAs show particularly striking positive correlation with the expression of antisense coding genes. This GENCODE annotation represents a valuable resource for future studies of lncRNAs.
Publisher: Springer Science and Business Media LLC
Date: 27-08-2014
DOI: 10.1038/NATURE13424
Publisher: Springer Science and Business Media LLC
Date: 09-2012
DOI: 10.1038/NATURE11247
Publisher: Springer Science and Business Media LLC
Date: 09-2012
DOI: 10.1038/NATURE11233
Publisher: Springer Science and Business Media LLC
Date: 03-11-2013
DOI: 10.1038/NMETH.2714
Publisher: Public Library of Science (PLoS)
Date: 19-04-2011
Location: France
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Sarah Djebali.