Sequencing

11th Annual Sequencing, Finishing, and Analysis in the Future Meeting
ALGORITHMIC SOLUTIONS FOR HIGH ACCURACY
GENE FINDING IN JUST ASSEMBLED NGS GENOMES
Thursday, 2nd June 9:30 La Fonda Ballroom Invited Speaker (IS‐2)
Mark Borodovsky
Georgia Institute of Technology
Gene prediction and annotation plays central role in genomics. However, in spite of much attention, open
problems still exist and stimulate a search for new algorithmic solutions in all categories of gene finding.
Prokaryotic genes can be identified with higher average accuracy than eukaryotic ones. Nevertheless,
the error rate is not negligible and largely species‐specific. Most errors are made in prediction of genes
located in genomic regions with atypical G+C composition. I will talk about our efforts to improve
GeneMarkS, a self‐training tool used in many genome projects. The new tool, GeneMarkS‐2 (Tang et al.,
submitted), uses local G+C‐specific heuristic models to make initial predictions of atypical genes that
serve as ‘external’ evidence in subsequent self‐training iterations. Unlike the current GeneMarkS the
new tool makes adjustments of the model structure within the self‐training process. In multiple tests we
have demonstrated that the new tool is favorably compared to the existing gene finders.
We also report progress in developing tools for structural annotation of eukaryotic genomes. We have
constantly updated the self‐training ab initio gene prediction tool, GeneMark‐ES. Recently, it was
extended to fully automated GeneMark‐ET (Lomsadze et al., 2014) that integrates information on
mapped RNA‐Seq reads as well as extension to GeneMark‐EP (Lomsadze et al., in preparation) that uses
initial self‐training and gene prediction to generate external evidence in terms of genomic footprints of
homologous proteins.
For ab initio gene prediction in fungal genomes we have developed fungi specific self‐training methods.
The constantly updated fungal version of GeneMark‐ES has been used in a number of DOE JGI and Broad
Institute fungal genome sequencing projects since 2007.
Our metagenomic gene finder, MetaGeneMark (Zhu et al., 2010) employed in IMG/M for metagenome
annotation and conventionally used for analysis of bacterial and arhaeal sequences was further
developed to predict genes in fungal metagenomes.
Finally, we describe BRAKER1 (Hoff et al., 2015), a pipeline for unsupervised RNA‐Seq‐based genome
annotation that combines advantages of GeneMark‐ET and AUGUSTUS. We observed that BRAKER1 was
more accurate than MAKER2 (Holt et al., 2011) when it is using assembled RNA‐Seq as sole source of
extrinsic evidence. BRAKER1 does not require pre‐trained parameters or a separate manually curated
training step.
All the tools described above can be applied for analysis of just assembled NGS genomes.
Speaker’s biographical sketch
Mark Borodovsky is a Regents' Professor at the Join Wallace H. Coulter Department of Biomedical
Engineering of Georgia Institute of Technology and Emory University and Director of the Center for
Bioinformatics and Computational Genomics at Georgia Tech. He is also a Chair of the Department of
Bioinformatics at the Moscow Institute of Physics and Technology in Moscow, Russia.
Mr. Borodovsky is interested in promoting bioinformatics education. He is a Founder of the Georgia
Tech Bioinformatics M.Sc. and Ph.D. Program, a Member of Educational Committee of the International
Society of Computational Biology as well as organizer of a series of International Conferences in
Bioinformatics at Georgia Tech started in 1997.
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