Dataset Information

Transcription start site evolution in Drosophila.

ABSTRACT: Transcription start site (TSS) evolution remains largely undescribed in Drosophila, likely due to limited annotations in non-melanogaster species. In this study, we introduce a concise new method that selectively sequences from the 5'-end of mRNA and used it to identify TSS in four Drosophila species, including Drosophila melanogaster, D. simulans, D. sechellia, and D. pseudoobscura. For verification, we compared our results in D. melanogaster with known annotations, published 5'-rapid amplification of cDNA ends data, and with RNAseq from the same mRNA pool. Then, we paired 2,849 D. melanogaster TSS with its closest equivalent TSS in each species (likely to be its true ortholog) using the available multiple sequence alignments. Most of the D. melanogaster TSSs were successfully paired with an ortholog in each species (83%, 86%, and 55% for D. simulans, D. sechellia, and D. pseudoobscura, respectively). On the basis of the number and distribution of reads mapped at each TSS, we also estimated promoter-specific expression (PSE) and TSS peak shape, respectively. Among paired TSS orthologs, the location and promoter activity were largely conserved. TSS location appears important as PSE, and TSS peak shape was more frequently divergent among TSS that had moved. Unpaired TSS were surprisingly common in D. pseudoobscura. An increased mutation rate upstream of TSS might explain this pattern. We found an enrichment of ribosomal protein genes among diverged TSS, suggesting that TSS evolution is not uniform across the genome.

SUBMITTER: Main BJ

PROVIDER: S-EPMC3708499 | biostudies-literature | 2013 Aug

REPOSITORIES: biostudies-literature

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Transcription start site evolution in Drosophila.

Main Bradley J BJ Smith Andrew D AD Jang Hyosik H Nuzhdin Sergey V SV

Molecular biology and evolution 20130506 8

Transcription start site (TSS) evolution remains largely undescribed in Drosophila, likely due to limited annotations in non-melanogaster species. In this study, we introduce a concise new method that selectively sequences from the 5'-end of mRNA and used it to identify TSS in four Drosophila species, including Drosophila melanogaster, D. simulans, D. sechellia, and D. pseudoobscura. For verification, we compared our results in D. melanogaster with known annotations, published 5'-rapid amplifica ...[more]

PMID: 23649539

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Project description:Recognizing transcription start sites is key to gene identification. Several approaches have been employed in related problems such as detecting translation initiation sites or promoters, many of the most recent ones based on machine learning. Deep learning methods have been proven to be exceptionally effective for this task, but their use in transcription start site identification has not yet been explored in depth. Also, the very few existing works do not compare their methods to support vector machines (SVMs), the most established technique in this area of study, nor provide the curated dataset used in the study. The reduced amount of published papers in this specific problem could be explained by this lack of datasets. Given that both support vector machines and deep neural networks have been applied in related problems with remarkable results, we compared their performance in transcription start site predictions, concluding that SVMs are computationally much slower, and deep learning methods, specially long short-term memory neural networks (LSTMs), are best suited to work with sequences than SVMs. For such a purpose, we used the reference human genome GRCh38. Additionally, we studied two different aspects related to data processing: the proper way to generate training examples and the imbalanced nature of the data. Furthermore, the generalization performance of the models studied was also tested using the mouse genome, where the LSTM neural network stood out from the rest of the algorithms. To sum up, this article provides an analysis of the best architecture choices in transcription start site identification, as well as a method to generate transcription start site datasets including negative instances on any species available in Ensembl. We found that deep learning methods are better suited than SVMs to solve this problem, being more efficient and better adapted to long sequences and large amounts of data. We also create a transcription start site (TSS) dataset large enough to be used in deep learning experiments.

Dataset Information

Transcription start site evolution in Drosophila.

Publications

Transcription start site evolution in Drosophila.

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