Gene prediction with a hidden Markov model and a new intron submodel

By Mario Stanke, Stephan Waack. European Conference on Computational Biology, 2003

1,588 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/69e71e169299f8e1b44ec665ba0311ae84081073

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In Molecular Biology

Citations in the corpus

Book Reviews: Data-Intensive Text Processing with MapReduce by Jimmy Lin and Chris Dyer (2010, methodology): “These simple but powerful models have been used in applications as diverse as speech recognition [54], information extraction [96], gene finding [100], part of speech tagging [30], stock market forecasting [48], and word alignment of parallel (translated) texts [106].”
Gene finding in novel genomes (2004, methodology): “I trained and evaluated SNAP in four genomes (see Meth-ods) and compared its performance to Genscan in all genomes, to HMMGene [12] and Genefinder [14] in C. elegans, and to Augustus [15] in D. melanogaster.”
Score-based prediction of genomic islands in prokaryotic genomes using hidden Markov models (2006, methodology): “In the case of predicting eukaryotic genes, for example, the programs GENSCAN [16,17], HMMGene [18,19], GenomeScan [20], AUGUSTUS [21], and AUGUSTUS+ [22] are HMM-based.”
AUGUSTUS: ab initio prediction of alternative transcripts (2006, background): “AUGUSTUS is a gene ﬁnder based on a Generalized Hidden Markov Model (GHMM) (2,3).”
Combining gene prediction methods to improve metagenomic gene annotation (2011, methodology): “First, the ab initio programs, which train model parameters on known annotations in order to predict unknown annotations, are widely used in gene prediction [4].”
AUGUSTUS at EGASP: using EST, protein and genomic alignments for improved gene prediction in the human genome (2006, methodology): “AUGUSTUS is a method for gene finding in eukaryotes [2].”
MAKER-P: A Tool Kit for the Rapid Creation, Management, and Quality Control of Plant Genome Annotations1[W][OPEN] (2013, methodology): “We ﬁ rst used the TAIR10 annotations as a gold standard with which to determine gene-level accuracies of the ab initio gene ﬁ nders Semi Hidden Markov model [HMM]-Based Nucleic Acid Parser (SNAP; Korf, 2004) and Augustus (Stanke and Waack, 2003; Stanke et al., 2008).”
AUGUSTUS: a web server for gene prediction in eukaryotes that allows user-defined constraints (2005, background): “The performance of AUGUSTUS has been extensively evaluated on sequence data from human and Drosophila (7,8) (http://webdoc.sub.gwdg.de/diss/2004/stanke/).”
A beginner's guide to eukaryotic genome annotation (2012, methodology): “Many ab initio tools, such as TwinScan 71 , FGENESH 72 , Augustus, Gnomon 73 , GAZE 74 and SNAP, can use external evidence to improve the accuracy of their predictions.”
Using Tablet for visual exploration of second-generation sequencing data (2013, methodology): “Once a satisfactory assembly has been reached, typically the next step is annotation with gene finding software such as Augustus [13] or Glimmer [14] to elucidate gene structure.”