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11th Annual <strong>Sequencing</strong>, Finishing, and Analysis in the Future Meeting ANTIBIOTICS DISCOVERY: FROM GENOME SEQUENCING TO GENOME MINING TO SPECTRAL NETWORKS Thursday, 2nd June 8:45 La Fonda Ballroom Keynote Address (KN‐2) Sponsored by Kapa Biosystems Dr. Pavel Pevzner University of California, San Diego Genomics studies revealed numerous antibiotics‐encoding genes across a wide range of bacterial and fungal species, including various species in the human microbiome. However, little is known about the hundreds of secondary metabolites (including antibiotics) produced by microorganisms in the gut, despite the fact that humans are chronically exposed to them. Deep exploration of this meta‐antibiome critically depends on a transition from the current one‐off process of antibiotics analysis to a highthroughput antibiotics sequencing. I will discuss recent advances in computational antibiotics discovery that span bioinformatics techniques ranging from genome sequencing to genome mining to spectral networks. Speaker’s biographical sketch Dr. Pevzner is Ronald R. Taylor Distinguished Professor of Computer Science and Director of the NIH Technology Center for Computational Mass Spectrometry at University of California, San Diego. He holds Ph.D. (1988) from Moscow Institute of Physics and Technology, Russia. He was named Howard Hughes Medical Institute Professor in 2006. He was elected the ACM Fellow (2010) for "contribution to algorithms for genome rearrangements, DNA sequencing, and proteomics” and ISCB Fellow (2012). He was awarded a Honoris Causa (2011) from Simon Fraser University in Vancouver. In 2015, he founded the Center for Algorithmic Biotechnology at Saint Petersburg State University, Russia. Dr. Pevzner has authored textbooks "Computational Molecular Biology: An Algorithmic Approach" in 2000, "Introduction to Bioinformatics Algorithms" in 2004 (with Neal Jones), and “Bioinformatics Algorithms: An Active Learning Approach in 2014 (with Phillip Compeau). His latest textbook has become the basis of Bioinformatics Specialization at Coursera, a series of Massive Online Open Courses with over 230,000 students enrolled in the last 2 years. 101
11th Annual <strong>Sequencing</strong>, 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. 102
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Reliable solutions for focused NGS
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Sequencing

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