GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions.

By John Besemer, Alexandre Lomsadze, Mark Borodovsky. Nucleic Acids Research, 2001

2,278 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d921dadec0e089891652ab6fc2c7f0abbf2e4b70

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Citations in the corpus

Prokaryotic Virus Orthologous Groups (pVOGs): a resource for comparative genomics and protein family annotation (2016, methodology): “In addition to the 286 242 protein-coding genes that were already present in the genome records, we conservatively added 10 353 de novo gene predictions made using the non-supervised heuristic GeneMarkS program (50).”
NCBI prokaryotic genome annotation pipeline (2016, background): “Phage and plasmid genes are frequent subjects of horizontal transfer and may be difficult to predict by gene finding tools that are tuned to identify native genes or foreign genes adapted to the genomic context in evolution to (1).”
Gene prediction in novel fungal genomes using an ab initio algorithm with unsupervised training. (2008, methodology): “…sequence) evolved into automatic self-training systems capable of adjusting to genome-specific properties in the process of estimating algorithm parameters from anonymous sequence (Audic and Claverie 1998; Hayes and Borodovsky 1998; Salzberg et al. 1998; Besemer et al. 2001; Larsen and Krogh 2003).”
Identification of protein coding regions in RNA transcripts (2014, methodology): “The method of inference of these functions was described earlier for short metagenomics sequences (7,15).”
Ab initio gene identification in metagenomic sequences (2010, methodology): “They were used in ab initio prokaryotic and eukaryotic gene finders GeneMarkS and GeneMark-ES to initiate unsupervised training for complete and nearly complete genomes (7,11–12).”
Handbook of Corynebacterium glutamicum (2005, methodology): “The National Center for Biotechnology Information has independently searched the deposited sequence with another gene-finding tool, GeneMarkS [4], and has predicted 2,993 genes.”
Discovery and functional characterization of diverse Class 2 CRISPR-Cas systems (2015, methodology): “[PubMed: 24766887] Besemer J, Lomsadze A, Borodovsky M. GeneMarkS: a self-training method for prediction of gene starts in microbial genomes.”
OPTIMOL: Automatic Online Picture Collection via Incremental Model Learning (2007, methodology): “In computational biology, self training is employed for gene prediction (Besemer et al. 2001).”
Combining gene prediction methods to improve metagenomic gene annotation (2011, methodology): “: GENIE [5], GENSCAN [6], GENEID [7], GLIMMER [8], and GeneMark [9].”
Choosing BLAST options for better detection of orthologs as reciprocal best hits (2008, background): “…as those within the RefSeq database (Maglott et al., 2000; Pruitt et al., 2005), the Genome Reviews (http://www.ebi.ac.uk/ GenomeReviews/), the HAMAP project (Boeckmann et al., 2003; Gattiker et al., 2003) or even those re-annotations produced by particular research groups (Besemer et al., 2001).”
A tale of two clades: monkeypox viruses. (2005, methodology): “Genes were predicted using GeneMarkS (Besemer et al., 2001) and Glimmer 2.02 (Delcher et al., 1999) and then tested for the presence of regulatory elements and assigned an initial annotation by comparison to other poxvirus gene databases, using BLASTP (Altschul et al., 1997).”
Current methods of gene prediction, their strengths and weaknesses. (2002, methodology): “This problem was addressed in the case of prokaryotic genomes by GeneMark-Genesis, which automatically clusters ORFs on the basis of their codon usage and derives Markov models for each cluster obtained (139), and more recently by GeneMarkS (140).”
Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation (2015, methodology): “Our pipeline combines a gene calling algorithm, GeneMarkS+ (49,50), with an alignment-based gene detection approach and is capable of annotating both complete and draft WGS genomes.”
Genome-scale models of bacterial metabolism: reconstruction and applications (2008, methodology): “Basically, coding sequences are ﬁrst identiﬁed using highly efﬁcient gene-ﬁnding algorithms [such as GENEMARK (Besemer et al ., 2001), GLIMMER (Delcher et al ., 1999), or AMIGENE (Bocs et al ., 2003)], which discard the ORFs that are not likely to be coding for a protein.”