Computational detection and location of transcription start sites in mammalian genomic DNA.

By Thomas A. Down, Tim J. P. Hubbard. Genome Research, 2002

298 citations recorded by the source corpus.

Transcription, the process whereby RNA copies are made from sections of the DNA genome, is directed by promoter regions. These define the transcription start site, and also the set of cellular conditions under which the promoter is active. At least in more complex species, it appears to be common for genes to have several different transcription start sites, which may be active under different conditions. Eukaryotic promoters are complex and fairly diffuse structures, which have proven hard to detect in silico. We show that a novel hybrid machine-learning method is able to build useful models of promoters for >50% of human transcription start sites. We estimate specificity to be >70%, and demonstrate good positional accuracy. Based on the structure of our learned models, we conclude that a signal resembling the well known TATA box, together with flanking regions of C-G enrichment, are the most important sequence-based signals marking sites of transcriptional initiation at a large class of typical promoters.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/be6aac2a13041c08a74ce81f36317bad64e8036d

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Citations in the corpus

Computational analysis of core promoters in the Drosophila genome (2002, methodology): “Therefore, promoter-finding methods based on sequence information successfully identify almost the exact same subset of promoters, owing to the high correlation with the subset of promoters located within CpG islands [8].”
A Review of Ensemble Methods in Bioinformatics (2010, methodology): “3 The application of ensemble methods to regulatory elements prediction Regulatory elements prediction is a general term that encompasses tasks such as promoter region recognition [5, 65], transcription start sites prediction [66], or glycosylation site and phosphorylation site prediction [67].”
Tandem duplication producing a novel oncogenic BRAF fusion gene defines the majority of pilocytic astrocytomas (2008, unknown): “This exon was located in an Ensembl-annotated CpG island with a transcriptional start site (TSS) predicted by the Eponine TSS finder (8), and contains two start AUG codons in frame with exon 2.”
The Ensembl gene annotation system (2016, methodology): “The ab initio algorithms include Genscan (64), for predicting gene models; Eponine (65), for finding transcription start sites; CpG (Gos Micklem, unpublished software), for finding CpG islands; tRNAscan-SE (66), for finding potential tRNA genes and FirstEF (67), for identifying the first exons of tr”
GENCODE: producing a reference annotation for ENCODE (2006, methodology): “We also run a number of ab initio prediction algorithms: Genescan [13] and Fgenesh [16] for genes, tRNAscan [39] to find tRNAgenes and Eponine TSS [40], to predict transcription start sites.”
POIMs: positional oligomer importance matrices—understanding support vector machine-based signal detectors (2008, unknown): “G,T , let x (cid:4) ∈ (cid:4) k length k .”
The Vertebrate Genome Annotation (Vega) database (2004, unknown): “andEponineTSS (29),which predicts transcription start sites.”
Current methods of gene prediction, their strengths and weaknesses. (2002, background): “Once more, improvements can be achieved by looking for a combination of several signals together with compositional biases (109).”
The State of the Art of Mammalian Promoter Recognition (2003, background): “The third program, Eponine, belongs half and half to both categories, since it can analyse anonymous sequences such as whole human chromosome 22 in principle, but was applied to a pseudo chromosome, which included only regions around known genes.(19) As their comparison shows, not only does Eponine ”
The Ensembl automatic gene annotation system. (2004, methodology): “The ESTs are useful in determining possible alternative splicing of the predicted genes (Eyras et al. 2004).”
DeePromoter: Robust Promoter Predictor Using Deep Learning (2019, methodology): “1 (Reese, 2001) which utilized initiator element (Inr) and TATA-Box for feature extraction and a time-delay neural network for classiﬁcation, and Down and Hubbard (2002) which used TATA-Box and utilized a relevance vector machines (RVM) as a classiﬁer.”
miRBase: tools for microRNA genomics (2007, methodology): “For human, mouse and rat genomes, TSSs are predicted using the Eponine-TSS software (25) at a threshold of 0.990.”