ArticleNucleic acids research2014
Integration of mapped RNA-Seq reads into automatic training of eukaryotic gene finding algorithm.
Article in Nucleic acids research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 366 papers.
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366 citing papers in PubMed.
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- Post-polyploid chromosomal diploidization in plants is affected by clade divergence and constrained by shared genomic features.Nature communications · 2026Article
- Gene regulation in response to host sex and infection route in Brugia pahangi with new genome annotation.G3 (Bethesda, Md.) · 2026Article
- A chromosome-level genome assembly of the endangered rose Rosa anemoniflora.Scientific data · 2026Article
- Biological and genomic resources for the cosmopolitan phytoplankton Bathycoccus: insights into genetic diversity and function of outlier chromosomes.The Plant journal : for cell and molecular biology · 2026Article
306 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
Abstract
We present a new approach to automatic training of a eukaryotic ab initio gene finding algorithm. With the advent of Next-Generation Sequencing, automatic training has become paramount, allowing genome annotation pipelines to keep pace with the speed of genome sequencing. Earlier we developed GeneMark-ES, currently the only gene finding algorithm for eukaryotic genomes that performs automatic training in unsupervised ab initio mode. The new algorithm, GeneMark-ET augments GeneMark-ES with a novel method that integrates RNA-Seq read alignments into the self-training procedure. Use of 'assembled' RNA-Seq transcripts is far from trivial; significant error rate of assembly was revealed in recent assessments. We demonstrated in computational experiments that the proposed method of incorporation of 'unassembled' RNA-Seq reads improves the accuracy of gene prediction; particularly, for the 1.3 GB genome of Aedes aegypti the mean value of prediction Sensitivity and Specificity at the gene level increased over GeneMark-ES by 24.5%. In the current surge of genomic data when the need for accurate sequence annotation is higher than ever, GeneMark-ET will be a valuable addition to the narrow arsenal of automatic gene prediction tools.
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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.