MGM-26 Agenda

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.

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.

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.

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.

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.

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.

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, IMG-based comparative analysis of genomes will be presented. Tools that will be discussed include phylogenetic profiles and occurrences, genome alignment, abundance profiles, and genome clustering.

Secondary metabolites are small naturally occurring bioactive molecules that are not necessary for the growth of an organism, but improve its survival chances. These molecules are usually produced by biosynthetic proteins that are encoded by genes found in clusters. The newly developed Atlas of Biosynthetic gene Clusters in IMG (IMG-ABC) contains information about predicted and experimentally verified biosynthetic gene clusters and, when available, the secondary metabolites that they produce. Additionally, IMG-ABC provides powerful search and analysis functions to help navigate this large dataset. During this presentation you will be introduced to the structure of the database, the different user interfaces and representative analysis workflows, thus providing you with the tools needed for an exploration of the secondary metabolism world and the search for novel chemical structures.

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.

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.

The methodology and steps to analyze a metagenome in IMG/M-ER will be presented through a use case scenario – study of microbial mat communities from two distinct hydrothermal vent sites within the Hellenic Volcanic Arc (HVA) located in the Aegean Sea. Hydrothermal vents represent a deep, hot, aphotic biosphere where chemosynthetic primary producers, fueled by chemicals from Earth’s subsurface, form the basis of life. Key questions about diversity and metabolic capabilities are explored.

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.

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.

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.