G-PAS 2.0 – an improved version of protein alignment tool with an efficient backtracking routine on multiple GPUs

Frohmberg, Wojciech; Kierzynka, Michał; Błażewicz, Jacek; Wojciechowski, Pawel

doi:10.2478/v10175-012-0062-1

Cited by 6 publications

(3 citation statements)

References 29 publications

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“…analysis of DNA [4], protein [25] and RNA [38] as well as for virus analysis [40]. Problems based on graphs are often computationally efficient, easy to understand and can be solved by powerful GPU computing [14,15]. The key element in finding the solution to a particular biologically related graph problem is its correct transformation into a suited graph [39].…”

Section: Methods Descriptionmentioning

confidence: 99%

DomGen-Graph based method for protein domain delineation

Miłostan

Łukasiak

2016

RAIRO-Oper. Res.

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The role of a protein depends heavily on its 3D shape, which is composed of semiindependent three-dimensional blocks called domains. Domains fold independently and constitute units of evolution. Most proteins contain multiple domains that are associated with a particular functions; moreover, the same domain can be found in different proteins. Automated recognition of domains can make prediction of proteins function easier and can support the analysis of proteins. Here, we propose a novel algorithm designed for domain recognition by identification of domain boundaries in the protein structure. The proposed algorithm uses a contact graph and an iterative approach to find meaningful clusters corresponding to the protein domains. The distinctive feature of the method is its effective complexity, that improves over other well-known methods, while holding a comparable level of correct domain assignments.

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Section: Methods Descriptionmentioning

confidence: 99%

DomGen-Graph based method for protein domain delineation

Miłostan

Łukasiak

2016

RAIRO-Oper. Res.

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“…Other scenarios are possible as well, e.g. when all sequences are aligned with all the others [3,16], or when only selected pairs of sequences are aligned [5], or when the alignment concerns very long sequences [17], etc. Although the underlying algorithm may always be the same, e.g.…”

Section: Sequence Alignment Toolsmentioning

confidence: 99%

Energy efficiency of sequence alignment tools—Software and hardware perspectives

Kierzynka

Kosmann

Berge

et al. 2017

Future Generation Computer Systems

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Pairwise sequence alignment is ubiquitous in modern bioinformatics. It may be performed either explicitly, e.g. to find the most similar sequences in a database, or implicitly as a hidden building block of more complex methods, e.g. for reads mapping. The alignment algorithms have been widely investigated over the last few years, mainly with respect to their speed. However, no attention was given to their energy efficiency, which is becoming critical in high performance computing and cloud environment. We compare the energy efficiency of the most established software tools performing exact pairwise sequence alignment on various computational architectures: CPU, GPU and Intel Xeon Phi. The results show that the energy consumption may differ as much as nearly 5 times. Substantial differences are reported even for different implementations running on the same hardware. Moreover, we present an FPGA implementation of one of the tested tools -G-DNA, and show how it outperforms all the others on the energy efficiency front. Finally, some details regarding the special RECS R |Box servers used in our study are outlined. This hardware is designed and manufactured within the FiPS project by the Bielefeld University and Christmann Informationstechnik + Medien with a special purpose to deliver highly heterogeneous computational environment supporting energy efficiency and green ICT.

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“…This means that their performance drops dramatically if applied to the scheme described in the introductory section, in which only selected pairs of sequences are aligned. On the other hand, there are also some interesting GPU tools on the market that perform pairwise sequence alignment with the backtracking step [21,22], sequence mapping [23] or even multiple sequence alignment [24]. Yet, none of them has been suitable to be used effectively in the next-generation de-novo assembly problem.…”

Section: Related Workmentioning

confidence: 99%

G-DNA – a highly efficient multi-GPU/MPI tool for aligning nucleotide reads

Frohmberg¹,

Kierzynka²,

Błażewicz³

et al. 2013

Bulletin of the Polish Academy of Sciences: Technical Sciences

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Abstract. DNA/RNA sequencing has recently become a primary way researchers generate biological data for further analysis. Assembling algorithms are an integral part of this process. However, some of them require pairwise alignment to be applied to a great deal of reads. Although several efficient alignment tools have been released over the past few years, including those taking advantage of GPUs (Graphics Processing Units), none of them directly targets high-throughput sequencing data. As a result, a need arose to create software that could handle such data as effectively as possible. G-DNA (GPU-based DNA aligner) is the first highly parallel solution that has been optimized to process nucleotide reads (DNA/RNA) from modern sequencing machines. Results show that the software reaches up to 89 GCUPS (Giga Cell Updates Per Second) on a single GPU and as a result it is the fastest tool in its class. Moreover, it scales up well on multiple GPUs systems, including MPI-based computational clusters, where its performance is counted in TCUPS (Tera CUPS).

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G-PAS 2.0 – an improved version of protein alignment tool with an efficient backtracking routine on multiple GPUs

Cited by 6 publications

References 29 publications

DomGen-Graph based method for protein domain delineation

DomGen-Graph based method for protein domain delineation

Energy efficiency of sequence alignment tools—Software and hardware perspectives

G-DNA – a highly efficient multi-GPU/MPI tool for aligning nucleotide reads

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