XenoCluster: A Grid Computing Approach to Finding Ancient Evolutionary Genetic Anomalies

Walters, Jesse D.; Casavant, Thomas L.; Robinson, John P.; Bair, Thomas B.; Braun, Terry A.; Scheetz, Todd E.

doi:10.1007/11535294_31

Cited by 3 publications

(12 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [1], we presented performance results of the coarse-grained grid-based parallelization of the approach outlined above. Now we present results of fine-grained parallelization of the Tree Clustering (UIPTC) phase.…”

Section: Fine-grained Performance Resultsmentioning

confidence: 99%

“…The final phase performs an all-pairs distance analysis of phylogenetic trees for all gene groups, and then uses a clustering technique to identify maximal sets of trees, which represent sets of genes which share a common evolutionary history. Design details of the procedure outlined above are presented in our previous paper [1]. The highlights are repeated here for completeness only.…”

Section: Methods and Solutionmentioning

confidence: 98%

“…In our previous work [1], we discussed in detail the effects of deployment of XenoCluster on a large-scale grid of compute clusters. We utilized the figure of 588 seconds to estimate and bound the runtime of the entire dataset through the COE system.…”

Section: Grid/cluster Implementation and Coarse-grained Benchmarking mentioning

confidence: 99%

“…For the sake of completeness, an abbreviated overview of some background information as well as our general discovery method [1] is presented here.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Historically, evolutionary biological research has proceeded from painstaking taxonomical classification according to the physical characteristics of hundreds of organisms and species. In our previous paper [1], we described XenoCluster 1.0 which addressed one such setting known as horizontal gene transfer, or jumping genes. In addition, general issues facing the use of heterogeneous networks for such problems were addressed.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Validation of computational prediction of horizontal gene transfer events—XenoCluster

et al. 2010

Self Cite

View full text Add to dashboard Cite

In this paper, we assess the biological accuracy and relevance of XenoCluster results compared to a reported set of known xenologs in yeast (Hall et al. Eukaryot. Cell 4(6):1102-1115. We were able to assign a high-priority rank to all reported yeast xenologs for which we had sufficient genomic sequence data.Additionally, we now report on a fine-grained extension to our previous implementation (Walters et al. 8th International Parallel Computing Technologies Conference (PaCT), LNCS, vol. 3606, pp. 355-366, Springer, Berlin, 2005), in which we achieved 80% efficiency on 8 CPU cores.In the coarse-grained component, high degrees parallelism (up to 65x speedup on 4,096 processors) was reported. While an obvious candidate for coarse-grained implementation, this paper now describes a multiple granularity parallelism solution that includes exploitation of multicore shared memory nodes to address fine-grained aspects in the tree-clustering phase of our previous deployment of XenoCluster (Walters et al. 8th

show abstract

Section: Fine-grained Performance Resultsmentioning

confidence: 99%

Section: Methods and Solutionmentioning

confidence: 98%

Section: Grid/cluster Implementation and Coarse-grained Benchmarking mentioning

confidence: 99%

“…For the sake of completeness, an abbreviated overview of some background information as well as our general discovery method [1] is presented here.…”

Section: Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Validation of computational prediction of horizontal gene transfer events—XenoCluster

et al. 2010

Self Cite

View full text Add to dashboard Cite

show abstract

Multi-granularity Parallel Computing in a Genome-Scale Molecular Evolution Application

Walters

Bair

Braun

et al. 2009

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Previously [1], we reported a coarse-grained parallel computational approach to identifying rare molecular evolutionary events often referred to as horizontal gene transfers. Very high degrees of parallelism (up to 65x speedup on 4,096 processors) were reported, yet the overall execution time for a realistic problem size was still on the order of 12 days. With the availability of large numbers of compute clusters, as well as genomic sequence from more than 2,000 species containing as many as 35,000 genes each, and trillions of sequence nucleotides in all, we demonstrated the computational feasibility of a method to examine "clusters" of genes using phylogenetic tree similarity as a distance metric. A full serial solution to this problem requires years of CPU time, yet only makes modest IPC and memory demands; thus, it is an ideal candidate for a grid computing approach involving low-cost compute nodes. This paper now describes a multiple granularity parallelism solution that includes exploitation of multi-core shared memory nodes to address fine-grained aspects in the tree-clustering phase of our previous deployment of XenoCluster 1.0. In addition to benchmarking results that show up to 80% speedup efficiency on 8 CPU cores, we report on the biological accuracy and relevance of our results compared to a reported set of known xenologs in yeast.

show abstract

Construction and annotation of large phylogenetic trees

Sanderson¹

2007

Aust. Systematic Bot.

View full text Add to dashboard Cite

Broad availability of molecular sequence data allows construction of phylogenetic trees with 1000s or even 10 000s of taxa. This paper reviews methodological, technological and empirical issues raised in phylogenetic inference at this scale. Numerous algorithmic and computational challenges have been identified surrounding the core problem of reconstructing large trees accurately from sequence data, but many other obstacles, both upstream and downstream of this step, are less well understood. Before phylogenetic analysis, data must be generated de novo or extracted from existing databases, compiled into blocks of homologous data with controlled properties, aligned, examined for the presence of gene duplications or other kinds of complicating factors, and finally, combined with other evidence via supermatrix or supertree approaches. After phylogenetic analysis, confidence assessments are usually reported, along with other kinds of annotations, such as clade names, or annotations requiring additional inference procedures, such as trait evolution or divergence time estimates. Prospects for partial automation of large-tree construction are also discussed, as well as risks associated with ‘outsourcing’ phylogenetic inference beyond the systematics community.

show abstract

XenoCluster: A Grid Computing Approach to Finding Ancient Evolutionary Genetic Anomalies

Cited by 3 publications

References 10 publications

Validation of computational prediction of horizontal gene transfer events—XenoCluster

Validation of computational prediction of horizontal gene transfer events—XenoCluster

Multi-granularity Parallel Computing in a Genome-Scale Molecular Evolution Application

Construction and annotation of large phylogenetic trees

Contact Info

Product

Resources

About