|Introduction & IMG Tutorials, Part 1: Microbial Genome Analysis|
|08.30–09.00||Welcome and Workshop Overview – Nikos Kyrpides|
|09.00–09.30||1. JGI’s Overview – Axel Visel
The powerful high-throughput DNA sequencing technologies catalyzed by the Human Genome Project, which have contributed to dramatic advances in biomedicine, are now being directed to characterizing the genomes of plants and microbes. Leading this effort is the US Department of Energy (DOE) Joint Genome Institute (JGI), a national user facility that unites the expertise of five national laboratories to advance genomics in support of the DOE mission areas of bioenergy, carbon cycling, and bioremediation.
|09.30–10.15||2. DNA Sequencing – Chris Daum
JGIʼs future depends on new sequencing technologies and applications developed based on these technologies. With multiple sequencing platforms available, JGIʼs R&D team has been aimed to develop sequencing applications based on the strength provided by different platforms. Our areas of development lie in de novo whole genome shotgun sequencing, transcriptome sequencing, and metagenomic sample diversity study. Examples of JGIʼs available sequencing applications in genomic research will be discussed.
|10.30-11.15||3. Sequence Assembly Overview – Brian Bushnell
While the ultimate goals for sequencing projects vary as much as the samples themselves, identifying gene content is a nearly universal goal. Recent work has shown that the lower limit for sequence lengths producing good annotation still exceeds read lengths achievable using next generation sequencing platforms. Therefore, assembly is a common step in analysis pipelines, since it can increase sequence length and reduce complexity via clustering. This talk will provide a high level overview of assembly, and discuss challenges and limitations, especially using next generation sequence data.
|11.15-12.00||4. Introduction to IMG – Nikos Kyrpides|
|13.00-13.45||5. Introduction to GOLD [Live Demo] – Supratim Mukherjee
The Genomes Online Database (GOLD) is data management system that catalogs sequencing projects and their associated metadata from around the world. There are three different sources of projects in GOLD: internal projects from the Department of Energy Joint Genome Institute (DOE JGI) that are entered automatically, external projects entered by GOLD users and projects from public databases such as NCBI. GOLD serves as the entry point for projects submitted for analysis to the IMG data management system and ensures that projects are correctly defined along with their necessary metadata. This presentation will provide an overview of the commonly used GOLD terminologies, a description of its four-level organization system and a tutorial on how to enter sequencing projects in GOLD.
|13.45-14.15||6. IMG Submission & Annotation Pipeline – Marcel Huntemann|
|14.30-16:00||7. IMG Navigation [Live Demo] – David Paez|
|16.00-16:30||IMG Hands On Exercises – Users|
|16.30-17:00||8. Working Group Formation & Initial Project Discussions – Natalia Ivanova|
|17.00-19.00||Poster Session & Dinner Reception|
|IMG Tutorials, Part 2: Microbial Genome Analysis|
|09.00-09.45||9. Introduction to Annotations, Terms and Definitions – Natalia Ivanova
Annotation of microbial genomes usually starts with finding the genes coding for stable RNAs (rRNA and tRNA) and protein-coding genes (CDSs). The principles underlying gene prediction in microbial genomes, as well as different implementations of these algorithms and most popular gene finding tools will be discussed.Genome analysis and gene function prediction depends on the comparison of sequences to the existing information stored in databases. They can either be simple repositories of nucleotide or protein sequence, or contain curated information related to the function of the genetic elements. Used in combination, bioinformatics databases constitute the most powerful method for gene function prediction. In this presentation, methods commonly used for functional annotation will be discussed.
|09.45-10.30||10. Introduction to Functional annotation and comparative genomics for gene discovery
[Live Demo] – Rekha Seshadri
Microbial genome data analysis in IMG is set in the comparative context of multiple microbial genomes. IMG allows navigating the microbial genome data space along three key dimensions: genomes (organisms), functions (terms and pathways), and genes. In this section, ways in which users can interact with protein families, function assignments, and pathways in IMG will be presented.
|10.45-11.30||Hands On Exercises I – Users|
|11.30-12.00||Exercise I solutions [Live Demo] – Rekha Seshadri|
|12.00–13.00||Lunch & Facility Tour|
|13.00–13.45||Hands On Exercises II – Users|
|13.45-14.15||Exercise II solutions [Live Demo] – Rekha Seshadri|
|14.15-15.00||11. Introduction to ANI – Neha Varghese
Increased sequencing of microbial genomes has revealed that prevailing prokaryotic species assignments can be inconsistent with whole genome information for a significant number of species. The long-standing need for a systematic and scalable species assignment technique can be met by the genome-wide Average Nucleotide Identity (ANI) metric, which is widely acknowledged as a robust measure of genomic relatedness. In this talk, you will be introduced to an efficient implementation for computation of ANI and generation of species-level clusters. Further, you will be walked through a demonstration of its application in IMG to allow you to explore central questions such as whether microorganisms form a continuum of genetic diversity or distinct species represented by distinct genetic signatures.
|15.15-16.15||ANI Exercises and solutions [Live Demo] – Neha Varghese|
|16.15-17.00||12. CASE STUDY: Hungate1000 – A catalog of genomes from the rumen microbiome – Rekha Seshadri
Productivity of ruminant livestock depends on the rumen microbiota, which ferment indigestible plant polysaccharides into nutrients used for growth. We present 410 cultured bacterial and archaeal genomes, and evaluate their role in polysaccharide degradation, short-chain fatty acid production and methanogenesis. Metagenome recrutiment and genome comparisons reveal new insights into rumen-specific adaptations. New tools in IMG to recapitulate some of these analyses will be presented.
|IMG Tutorials, Part 3: Metagenome Analysis|
|09.00-09:45||13. Introduction to Metagenomics Analysis and tools in IMG (IMG/M) – Natalia Ivanova
The main differences between genomes and metagenomes in terms of data and analysis tools will be reviewed.
A snapshot of microbial community structure can be derived from analysis of metagenomic data. IMG/M methods and tools for establishing the taxonomic identity of community members will be presented along with tools for determining the fine population structure, genetic variation and genome dynamics of the dominant populations. Methods for assessing the diversity and abundance of microbial communities will be discussed.
|09.45-10.30||Metagenome-based discovery [Discussion & Live Demo] – Natalia Ivanova|
|10.45-11.:30||14. IMG/M Hands-On Exercises I & Solutions I – Emiley Eloe-Fadrosh|
|11.30-12.:00||IMG/M Hands-On Exercises II – Users|
|13.00-13.30||IMG/M Hands-On Solutions II – Emiley Eloe-Fadrosh|
|13.30-14.00||15. Metagenome binning: a case study – Emiley Eloe-Fadrosh
Metagenome binning involves grouping assembled contigs from shotgun metagenomic sequences to deconvolute complex microbial communities. A case study will highlight the utility of binning population genomes from metagenomic data.
|14.00-14.45||16. CASE STUDY: Novel insights from uncultivated genomes of the human gut microbiome
– Stephen Nayfach
|15.00-17.00||Working Group Project Discussions – User Groups|
|Working Group Project Discussions|
|09.00-12.00||Working Group Project Discussions – User Groups|
|13.00-13.30||17. Viral EcoGenomics – Simon Roux|
|13.30-14.15||18. Viral Dark Matter [Live Demo] –David Paez|
|14.15-17.00||Working Group Project Discussions – User Groups|
|User Presentations, and JGI Technologies|
|09.00-11.30||Working Group Presentations – User Groups|
|11.30-12.00||19. JGI User Programs – Susannah Tringe
JGIʼs future depends on new sequencing technologies and applications developed based on these technologies. With multiple sequencing platforms available, JGIʼs &D team has been aimed to develop sequencing applications based on the strength provided by different platforms. Our areas of development lie in de novo whole genome shotgun sequencing, transcriptome sequencing, and metagenomic sample diversity study. Examples of JGIʼs available sequencing applications in genomic research will be discussed.
|13.30-13.45|| 20. Introduction to Single Cell Genomics – Tanja Woyke
The bulk of finished microbial genomes to date are derived from bacteria and archaea that can be readily grown in culture. However, the vast majority of microorganisms on this planet elude current culturing attempts, severely limiting access to their genomes. While various enrichment methods as well as metagenomic approaches have been successfully applied to aid the genome analysis of such uncultured environmental microbes, these methodologies are not suitable for countless community members of interest. Single-cell genomics is an approach that aims to access the genome from an individual microbial cell. The methodology as well as a range of JGI single cell projects will be discussed.
|13.45-14.30||21. Accelerating functional genomics using mass spectrometry – Trent Northen
Microorganisms exhibit complex metabolism and metabolic interactions with their environment, large parts of which remain unknown. Deficiencies in functional annotations of microbial genomes as well as incomplete knowledge of small molecule repertoires (metabolomes) of microorganisms limit the understanding of their metabolism. This talk will introduce mass spectrometry based metabolomics and approaches to link these to microbial genomics. This will include recent work connecting genes to the utilization of specific metabolites in bacteria by profiling metabolite utilization in libraries of mutant strains. Here, untargeted mass spectrometry-based metabolomics was used to identify metabolites utilized by soil microbes. Targeted high-throughput metabolite profiling of spent media of 8042 individual mutant strains was performed to link utilization to specific genes. Using this approach we identified genes of known function as well as those required for the metabolism of ‘novel’ metabolites.
|14.45-15.15||22.DNA Synthesis program – Yasuo Yoshikuni|
|15.15-16.00||23.Fungal Program, Science and Tools – Steven Ahrendt|