GMOD Evo Hackathon Proposal Supplemental Information

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Tools for Evolutionary Biology Hackathon
November 8-12, 2010
NESCent, Durham, North Carolina, USA

On this page you will find additional information related to the GMOD Evo Hackathon Proposal.

Other topics of Secondary Importance

Natural Diversity / Population Genetics / Multidimensional Data Visualization in a Genomic Context

The Barley1K project (Eyal Fridman group, The Hebrew University) is an example dataset that should be supportable by GMOD. They gathered a thousand wild samples of barley in a hierarchical mode of collection (51 sites that include 5 microsite on different slopes or niches within the site). They also recorded many local environmental conditions and collected detailed phenotype data on portion of this collection, including that of a diverse set of interspecifc hybrids derived from a genetically well-defined core collection (by Illumina Golden Gate platform, the Barley OPA array[BOPA1]). The Natural Diversity module will allow us to store this type of data including also accumulated allelic variation obtained from the microsatellites (SSRs) and BOPA1 array, as well as from next generation sequencing of cDNA libraries . However, we lack tools to visualize such multi-dimensional data in a genomic context (e.g., in GBrowse, JBrowse, and GBrowse_syn) including the association of genome-phenotype (phenome). This could be solved either with specific new glyphs and plugins, or with generic interfaces to statistical/geolocation/image based visualization packages.

There is also work currently under way to extend GFF3 to handle variant information. Several existing GMOD tools will need to be modified to recognize this data.

Web-based display of Chado Phenotype/Natural Diversity data

Tripal is a Drupal-based web interface to Chado databases. It supports interfaces for several popular data types, but does not currently support phylogenies, phenotypes, expression, or natural diversity data. We could extend it to evolutionary data types as part of the hackathon.

Support for pangenomes and core genomes

The concept of the pangenome and core genomes is becoming common in the analysis of bacterial genomes, but is more broadly applicable. The pangenome is the union of all genes found in all strains of a species, while the core genome is the intersection of those sets. In both cases, a gene or feature is a generalization of the instances of the feature in multiple genomes. The gene in a pangenome, like a gene in an inferred ancestor, does not have a physical location, but it may have one or more contextual locations in a syntenic block of sequence found in some or all of the strains.

Support for annotation tools based on phylogenetic analysis, such as PAINT

The RefGenome project of the GO consortium is working on PAINT, a system for doing inference of GO annotations based on the distribution of curated annotations within clades and outgroups. GMOD tools and schemas need to be prepared to handle this kind of annotation. For example, ancestor nodes in PANTHER trees will have accessions; these will require versioning to deal with changes in the analysis as annotations to descendants and the addition/placement of descendants changes with the addition of new genomes or revision of the orthology analysis.

Linking xrate (and other phylo-aware annotation tools) to jbrowse

The conservation track is a staple of the UCSC browser. Evofold predictions form another useful track. Tools like xrate ( allow automation and generalization of these kinds of phylogenetic HMM or SCFG model. Development would focus on linking these into existing GMOD browsers (e.g. JBrowse).

Discussion / Development Topics

This section contains early-stage ideas that merit discussion and serious consideration by the attendees of the hackathon, but are not yet developed enough for specific implementation objectives.

Post-Reference Genome Tools

This is a great example of how evolutionary biology can help lead the rest of the GMOD community.

The concept of a reference genome has been an extremely valuable tool in model organisms. The importance of a reference genome is not diminishing, but the need for an additional framework is on the rise.

To explain this, lets contrast evolutionary and developmental biologists. Developmental biologists embrace and strive for similarity as a means of controlling experimental conditions. Inbred lines of organisms do not usually occur in nature, but are usually preferred for developmental biology work. Developmental biology has anatomy ontologies and staging series based on stereotypic progression of anatomical development in single inbred lines. Developmental biologists strive to eliminate genetic and environmental diversity in order to create controlled experimental conditions. The concept of a reference genome historically has fit very well into this paradigm.

In contrast, evolutionary biologists embrace and study genetic and environmental diversity. Evolutionary biologists typically study populations rather than individual lines. They characterize and analyze differences, rather than eliminate them. For evolutionary biologists, a reference genome is much less of a central tool than it is for developmental biologists.

Second-generation sequencing now allows evolutionary biologists to exploit genomic data for populations or large numbers of individuals. It also allows every other kind of biologist to do the same. We currently have tools to show linkage disequilibrium, and genotype and allele frequencies, but these still typically show data in the context of a reference genome.

By some estimates, three years from now many projects will have thousands of full genomes. In such an environment, does the concept of a reference genome still remain relevant? How should GMOD tools change, grow, and adapt?

High-throughput Imaging / Phenotyping

Adoption of high-throughput imaging and phenotyping technologies is increasing. What software exists for working with this type of data, and how should the GMOD community participate?

Third-generation Sequencing

What challenges will the GMOD community face in handling third-generation (single-molecule) sequencing data, and how can we prepare for them? Second generation sequencing technologies typically produce many short reads at very high coverage. The high coverage is necessary to compensate for lower accuracy. Challenges with 2nd gen data include 1) dealing with the huge amount of data that comes with the high coverage, 2) distinguishing read and amplification errors from signal, and 3) assembling short reads.

Third generation technologies will have some commonalities and some key differences. First, they will continue to produce large volumes of data. The nature of the data will change significantly, though. Third generation technologies are expected to be significantly less error-prone, thus reducing the need for high coverage. This will also reduce cost and turnaround time. While the average depth of the data will decrease, the width of the data will greatly increase. The technology will enable more samples to be sequenced, and at greater accuracy. The improved accuracy and longer read length will also make assembly easier.

Areas of discussion include:

  • data modeling and storage
  • graphical visualization
  • online display and searching

Evo-Devo Support

Add the ability to compare the developmental programs between organisms at different levels: anatomy, genomics, expression patterns, gene regulatory networks and their architecture, and phenotypes. This objective, with the exception of gene regulatory networks, overlaps with several others.

Tree / Graph Visualization

GMOD's Chado database schema already includes strong support for storing trees and graphs. This capability has been in Chado since its beginning. GMOD, however, lacks visualization support for tree and graph based data. This includes phylogeny, gene orthology, lineage (anatomy and breeding), ontologies, and breeding data. Several GMOD users have developed their own visualization tools for this type of data. We could integrate one of those solutions, or an outside solution, for visualization.