Flexible, fast and accurate sequence alignment profiling on GPGPU with PaSWAS

Fast single individual haplotyping method using GPGPU

BACKGROUND

Most bioinformatic tools for next generation sequencing (NGS) data are computationally intensive, requiring a large amount of computational power for processing and analysis. Here the utility of graphic processing units (GPUs) for NGS data computation is assessed.

METHOD

In a previous study, we developed a probabilistic evolutionary algorithm with toggling for haplotyping (PEATH) method based on the estimation of distribution algorithm and toggling heuristic. Here, we parallelized the PEATH method (PEATH/G) using general-purpose computing on GPU (GPGPU).

RESULTS

The PEATH/G runs approximately 46.8 times and 25.4 times faster than PEATH on the NA12878 fosmid-sequencing dataset and the HuRef dataset, respectively, with an NVIDIA GeForce GTX 1660Ti. Moreover, the PEATH/G is approximately 13.3 times faster on the fosmid-sequencing dataset, even with an inexpensive conventional GPGPU (NVIDIA GeForce GTX 950).

CONCLUSIONS

PEATH/G can be a practical single individual haplotyping tool in terms of both its accuracy and speed. GPGPU can help reduce the running time of NGS analysis tools.

Correcting palindromes in long reads after whole-genome amplification

BACKGROUND

Next-generation sequencing requires sufficient DNA to be available. If limited, whole-genome amplification is applied to generate additional amounts of DNA. Such amplification often results in many chimeric DNA fragments, in particular artificial palindromic sequences, which limit the usefulness of long sequencing reads.

RESULTS

Here, we present Pacasus, a tool for correcting such errors. Two datasets show that it markedly improves read mapping and de novo assembly, yielding results similar to these that would be obtained with non-amplified DNA.

CONCLUSIONS

With Pacasus long-read technologies become available for sequencing targets with very small amounts of DNA, such as single cells or even single chromosomes.

GPU-DAEMON: GPU algorithm design, data management & optimization template for array based big omics data

In the age of ever increasing data, faster and more efficient data processing algorithms are needed. Graphics Processing Units (GPU) are emerging as a cost-effective alternative architecture for high-end computing. The optimal design of GPU algorithms is a challenging task which requires thorough understanding of the high performance computing architecture as well as the algorithmic design. The steep learning curve needed for effective GPU-centric algorithm design and implementation requires considerable expertise, time, and resources. In this paper, we present GPU-DAEMON, a GPU Data Management, Algorithm Design and Optimization technique suitable for processing array based big omics data. Our proposed GPU algorithm design template outlines and provides generic methods to tackle critical bottlenecks which can be followed to implement high performance, scalable GPU algorithms for given big data problem. We study the capability of GPU-DAEMON by reviewing the implementation of GPU-DAEMON based algorithms for three different big data problems. Speed up of as large as 386x (over the sequential version) and 50x (over naive GPU design methods) are observed using the proposed GPU-DAEMON. GPU-DAEMON template is available at https://github.com/pcdslab/GPU-DAEMON and the source codes for GPU-ArraySort, G-MSR and GPU-PCC are available at https://github.com/pcdslab.

pyPaSWAS: Python-based multi-core CPU and GPU sequence alignment

BACKGROUND

Our previously published CUDA-only application PaSWAS for Smith-Waterman (SW) sequence alignment of any type of sequence on NVIDIA-based GPUs is platform-specific and therefore adopted less than could be. The OpenCL language is supported more widely and allows use on a variety of hardware platforms. Moreover, there is a need to promote the adoption of parallel computing in bioinformatics by making its use and extension more simple through more and better application of high-level languages commonly used in bioinformatics, such as Python.

RESULTS

The novel application pyPaSWAS presents the parallel SW sequence alignment code fully packed in Python. It is a generic SW implementation running on several hardware platforms with multi-core systems and/or GPUs that provides accurate sequence alignments that also can be inspected for alignment details. Additionally, pyPaSWAS support the affine gap penalty. Python libraries are used for automated system configuration, I/O and logging. This way, the Python environment will stimulate further extension and use of pyPaSWAS.

CONCLUSIONS

pyPaSWAS presents an easy Python-based environment for accurate and retrievable parallel SW sequence alignments on GPUs and multi-core systems. The strategy of integrating Python with high-performance parallel compute languages to create a developer- and user-friendly environment should be considered for other computationally intensive bioinformatics algorithms.

Living apart together: crosstalk between the core and supernumerary genomes in a fungal plant pathogen

BACKGROUND

Eukaryotes display remarkable genome plasticity, which can include supernumerary chromosomes that differ markedly from the core chromosomes. Despite the widespread occurrence of supernumerary chromosomes in fungi, their origin, relation to the core genome and the reason for their divergent characteristics are still largely unknown. The complexity of genome assembly due to the presence of repetitive DNA partially accounts for this.

RESULTS

Here we use single-molecule real-time (SMRT) sequencing to assemble the genome of a prominent fungal wheat pathogen, Fusarium poae, including at least one supernumerary chromosome. The core genome contains limited transposable elements (TEs) and no gene duplications, while the supernumerary genome holds up to 25 % TEs and multiple gene duplications. The core genome shows all hallmarks of repeat-induced point mutation (RIP), a defense mechanism against TEs, specific for fungi. The absence of RIP on the supernumerary genome accounts for the differences between the two (sub)genomes, and results in a functional crosstalk between them. The supernumerary genome is a reservoir for TEs that migrate to the core genome, and even large blocks of supernumerary sequence (>200 kb) have recently translocated to the core. Vice versa, the supernumerary genome acts as a refuge for genes that are duplicated from the core genome.

CONCLUSIONS

For the first time, a mechanism was determined that explains the differences that exist between the core and supernumerary genome in fungi. Different biology rather than origin was shown to be responsible. A "living apart together" crosstalk exists between the core and supernumerary genome, accelerating chromosomal and organismal evolution.