Integration of mapped RNA-Seq reads into automatic training of eukaryotic gene finding algorithm

By Alexandre Lomsadze, Paul Burns, Mark Borodovsky. Nucleic Acids Research, 2014

518 citations recorded by the source corpus.

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.

Subfields: Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/22f6d60ab43fd6efbef0901834bbe142dcd117bc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Mark Borodovsky
2. Alexandre Lomsadze
3. Katharina Jasmin Hoff
4. Mario Stanke
5. Tomás Bruna
6. Mark Yandell
7. Gail L. Rosen
8. Non Yok

In Plant Science

1. Mario Stanke
2. Katharina Jasmin Hoff
3. Mark Borodovsky
4. Alexandre Lomsadze

Citations in the corpus

• BRAKER3: Fully automated genome annotation using RNA-seq and protein evidence with GeneMark-ETP, AUGUSTUS and TSEBRA (2023, unknown): “Therefore, more precise gene predictions are made when predictions based on statistical models are corrected by extrinsic evidence (Stanke et al., 2008; Lomsadze et al., 2014; Br˚una et al., 2020).”
• GeneMark-EP+: eukaryotic gene prediction with self-training in the space of genes and proteins (2020, unknown): “In addition, we compared GeneMark-EP with GeneMark-ET (2) that uses RNA-Seq short reads to provide external information (hints to intron coordinates) to select anchored gene elements for the GeneMark-ET algorithm parameter estimation.”
• Combining RNA-seq data and homology-based gene prediction for plants, animals and fungi (2017, unknown): “(2016) published BRAKER1 a pipeline for unsu65 pervised RNA-seq-based genome annotation that combines the advantages 66 of GeneMark-ET (Lomsadze et al., 2014) and AUGUSTUS (Stanke et al.”
• BRAKER2: automatic eukaryotic genome annotation with GeneMark-EP+ and AUGUSTUS supported by a protein database (2020, unknown): “A new self-training algorithm, GeneMark-ET (8), integrated data on spliced aligned RNA-seq reads into GeneMark-ES.”
• BRAKER1: Unsupervised RNA-Seq-Based Genome Annotation with GeneMark-ET and AUGUSTUS (2016, methodology): “We have developed BRAKER1, a pipeline that combines the complementary strengths of two gene prediction tools: GeneMark- ET (Lomsadze et al., 2014) incorporates unassembled RNA-Seq reads into unsupervised training and subsequently generates ab initio gene predictions.”