Dataset Information

Ab initio identification of transcription start sites in the Rhesus macaque genome by histone modification and RNA-Seq.

ABSTRACT: Rhesus macaque is a widely used primate model organism. Its genome annotations are however still largely comparative computational predictions derived mainly from human genes, which precludes studies on the macaque-specific genes, gene isoforms or their regulations. Here we took advantage of histone H3 lysine 4 trimethylation (H3K4me3)'s ability to mark transcription start sites (TSSs) and the recently developed ChIP-Seq and RNA-Seq technology to survey the transcript structures. We generated 14,013,757 sequence tags by H3K4me3 ChIP-Seq and obtained 17,322,358 paired end reads for mRNA, and 10,698,419 short reads for sRNA from the macaque brain. By integrating these data with genomic sequence features and extending and improving a state-of-the-art TSS prediction algorithm, we ab initio predicted and verified 17,933 of previously electronically annotated TSSs at 500-bp resolution. We also predicted approximately 10,000 novel TSSs. These provide an important rich resource for close examination of the species-specific transcript structures and transcription regulations in the Rhesus macaque genome. Our approach exemplifies a relatively inexpensive way to generate a reasonably reliable TSS map for a large genome. It may serve as a guiding example for similar genome annotation efforts targeted at other model organisms.

SUBMITTER: Liu Y

PROVIDER: S-EPMC3045608 | biostudies-literature | 2011 Mar

REPOSITORIES: biostudies-literature

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Ab initio identification of transcription start sites in the Rhesus macaque genome by histone modification and RNA-Seq.

Liu Yi Y Han Dali D Han Yixing Y Yan Zheng Z Xie Bin B Li Jing J Qiao Nan N Hu Haiyang H Khaitovich Philipp P Gao Yuan Y Han Jing-Dong J JD

Nucleic acids research 20101014 4

Rhesus macaque is a widely used primate model organism. Its genome annotations are however still largely comparative computational predictions derived mainly from human genes, which precludes studies on the macaque-specific genes, gene isoforms or their regulations. Here we took advantage of histone H3 lysine 4 trimethylation (H3K4me3)'s ability to mark transcription start sites (TSSs) and the recently developed ChIP-Seq and RNA-Seq technology to survey the transcript structures. We generated 14 ...[more]

PMID: 20952408

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Project description:BackgroundUnderstanding transcriptional regulation of gene expression is one of the greatest challenges of modern molecular biology. A central role in this mechanism is played by transcription factors, which typically bind to specific, short DNA sequence motifs usually located in the upstream region of the regulated genes. We discuss here a simple and powerful approach for the ab initio identification of these cis-regulatory motifs. The method we present integrates several elements: human-mouse comparison, statistical analysis of genomic sequences and the concept of coregulation. We apply it to a complete scan of the human genome.ResultsBy using the catalogue of conserved upstream sequences collected in the CORG database we construct sets of genes sharing the same overrepresented motif (short DNA sequence) in their upstream regions both in human and in mouse. We perform this construction for all possible motifs from 5 to 8 nucleotides in length and then filter the resulting sets looking for two types of evidence of coregulation: first, we analyze the Gene Ontology annotation of the genes in the set, searching for statistically significant common annotations; second, we analyze the expression profiles of the genes in the set as measured by microarray experiments, searching for evidence of coexpression. The sets which pass one or both filters are conjectured to contain a significant fraction of coregulated genes, and the upstream motifs characterizing the sets are thus good candidates to be the binding sites of the TF's involved in such regulation. In this way we find various known motifs and also some new candidate binding sites.ConclusionWe have discussed a new integrated algorithm for the "ab initio" identification of transcription factor binding sites in the human genome. The method is based on three ingredients: comparative genomics, overrepresentation, different types of coregulation. The method is applied to a full-scan of the human genome, giving satisfactory results.

Project description:BACKGROUND:Identifying splice sites is a necessary step to analyze the location and structure of genes. Two dinucleotides, GT and AG, are highly frequent on splice sites, and many other patterns are also on splice sites with important biological functions. Meanwhile, the dinucleotides occur frequently at the sequences without splice sites, which makes the prediction prone to generate false positives. Most existing tools select all the sequences with the two dimers and then focus on distinguishing the true splice sites from those pseudo ones. Such an approach will lead to a decrease in false positives; however, it will result in non-canonical splice sites missing. RESULT:We have designed SpliceFinder based on convolutional neural network (CNN) to predict splice sites. To achieve the ab initio prediction, we used human genomic data to train our neural network. An iterative approach is adopted to reconstruct the dataset, which tackles the data unbalance problem and forces the model to learn more features of splice sites. The proposed CNN obtains the classification accuracy of 90.25%, which is 10% higher than the existing algorithms. The method outperforms other existing methods in terms of area under receiver operating characteristics (AUC), recall, precision, and F1 score. Furthermore, SpliceFinder can find the exact position of splice sites on long genomic sequences with a sliding window. Compared with other state-of-the-art splice site prediction tools, SpliceFinder generates results in about half lower false positive while keeping recall higher than 0.8. Also, SpliceFinder captures the non-canonical splice sites. In addition, SpliceFinder performs well on the genomic sequences of Drosophila melanogaster, Mus musculus, Rattus, and Danio rerio without retraining. CONCLUSION:Based on CNN, we have proposed a new ab initio splice site prediction tool, SpliceFinder, which generates less false positives and can detect non-canonical splice sites. Additionally, SpliceFinder is transferable to other species without retraining. The source code and additional materials are available at https://gitlab.deepomics.org/wangruohan/SpliceFinder.

Dataset Information

Ab initio identification of transcription start sites in the Rhesus macaque genome by histone modification and RNA-Seq.

Publications

Ab initio identification of transcription start sites in the Rhesus macaque genome by histone modification and RNA-Seq.

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