ArticleQuantitative biology (Beijing, China)2013
NPEST: a nonparametric method and a database for transcription start site prediction.
Article in Quantitative biology (Beijing, China), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 26 citations in OpenAlex.
- Deep learning and support vector machines for transcription start site identification.PeerJ. Computer science · 2023Article
- Critical assessment of computational tools for prokaryotic and eukaryotic promoter prediction.Briefings in bioinformatics · 2022Article
- Genome-Wide Prediction of Transcription Start Sites in Conifers.International journal of molecular sciences · 2022Article
- ReFeaFi: Genome-wide prediction of regulatory elements driving transcription initiation.PLoS computational biology · 2021Article
- Prediction of Rice Transcription Start Sites Using TransPrise: A Novel Machine Learning Approach.Methods in molecular biology (Clifton, N.J.) · 2021Article
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- Recognition of prokaryotic and eukaryotic promoters using convolutional deep learning neural networks.PloS one · 2017Article
- Integrated computational approach to the analysis of RNA-seq data reveals new transcriptional regulators of psoriasis.Experimental & molecular medicine · 2016Article
- Nucleotide diversity analysis highlights functionally important genomic regions.Scientific reports · 2016Article
- Toward high-resolution population genomics using archaeological samples.DNA research : an international journal for rapid publication of reports on genes and genomes · 2016Review
- A Rapid and Sensitive Next-Generation Sequencing Method to Detect RB1 Mutations Improves Care for Retinoblastoma Patients and Their Families.The Journal of molecular diagnostics : JMD · 2016Article
- Differential Evolution approach to detect recent admixture.BMC genomics · 2015Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
Abstract
In this paper we present NPEST, a novel tool for the analysis of expressed sequence tags (EST) distributions and transcription start site (TSS) prediction. This method estimates an unknown probability distribution of ESTs using a maximum likelihood (ML) approach, which is then used to predict positions of TSS. Accurate identification of TSS is an important genomics task, since the position of regulatory elements with respect to the TSS can have large effects on gene regulation, and performance of promoter motif-finding methods depends on correct identification of TSSs. Our probabilistic approach expands recognition capabilities to multiple TSS per locus that may be a useful tool to enhance the understanding of alternative splicing mechanisms. This paper presents analysis of simulated data as well as statistical analysis of promoter regions of a model dicot plant
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.