Dataset Information

Mendel-GPU: haplotyping and genotype imputation on graphics processing units.

ABSTRACT:

Motivation

In modern sequencing studies, one can improve the confidence of genotype calls by phasing haplotypes using information from an external reference panel of fully typed unrelated individuals. However, the computational demands are so high that they prohibit researchers with limited computational resources from haplotyping large-scale sequence data.

Results

Our graphics processing unit based software delivers haplotyping and imputation accuracies comparable to competing programs at a fraction of the computational cost and peak memory demand.

Availability

Mendel-GPU, our OpenCL software, runs on Linux platforms and is portable across AMD and nVidia GPUs. Users can download both code and documentation at http://code.google.com/p/mendel-gpu/.

Contact

gary.k.chen@usc.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Chen GK

PROVIDER: S-EPMC3634317 | biostudies-literature | 2012 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Mendel-GPU: haplotyping and genotype imputation on graphics processing units.

Chen Gary K GK Wang Kai K Stram Alex H AH Sobel Eric M EM Lange Kenneth K

Bioinformatics (Oxford, England) 20120905 22

<h4>Motivation</h4>In modern sequencing studies, one can improve the confidence of genotype calls by phasing haplotypes using information from an external reference panel of fully typed unrelated individuals. However, the computational demands are so high that they prohibit researchers with limited computational resources from haplotyping large-scale sequence data.<h4>Results</h4>Our graphics processing unit based software delivers haplotyping and imputation accuracies comparable to competing pr ...[more]

PMID: 22954633

Similar Datasets

Project description:Large-scale simulation of detailed computational models of neuronal microcircuits plays a prominent role in reproducing and predicting the dynamics of the microcircuits. To reconstruct a microcircuit, one must choose neuron and synapse models, placements, connectivity, and numerical simulation methods according to anatomical and physiological constraints. For reconstruction and refinement, it is useful to be able to replace one module easily while leaving the others as they are. One way to achieve this is via a scaffolding approach, in which a simulation code is built on independent modules for placements, connections, and network simulations. Owing to the modularity of functions, this approach enables researchers to improve the performance of the entire simulation by simply replacing a problematic module with an improved one. Casali et al. (2019) developed a spiking network model of the cerebellar microcircuit using this approach, and while it reproduces electrophysiological properties of cerebellar neurons, it takes too much computational time. Here, we followed this scaffolding approach and replaced the simulation module with an accelerated version on graphics processing units (GPUs). Our cerebellar scaffold model ran roughly 100 times faster than the original version. In fact, our model is able to run faster than real time, with good weak and strong scaling properties. To demonstrate an application of real-time simulation, we implemented synaptic plasticity mechanisms at parallel fiber-Purkinje cell synapses, and carried out simulation of behavioral experiments known as gain adaptation of optokinetic response. We confirmed that the computer simulation reproduced experimental findings while being completed in real time. Actually, a computer simulation for 2 s of the biological time completed within 750 ms. These results suggest that the scaffolding approach is a promising concept for gradual development and refactoring of simulation codes for large-scale elaborate microcircuits. Moreover, a real-time version of the cerebellar scaffold model, which is enabled by parallel computing technology owing to GPUs, may be useful for large-scale simulations and engineering applications that require real-time signal processing and motor control.

Dataset Information

Mendel-GPU: haplotyping and genotype imputation on graphics processing units.

Motivation

Results

Availability

Contact

Supplementary information

Publications

Mendel-GPU: haplotyping and genotype imputation on graphics processing units.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets