Dna Sequence Assembly Involving an Acyclic Graph Model

Błażewicz, Jacek; Frohmberg, Wojciech; Gawron, Piotr; Kasprzak, Marta; Kierzynka, Michał; Świercz, Aleksandra; Wojciechowski, Pawel

doi:10.2478/v10209-011-0019-4

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…Graph theory has proved its effectiveness in modeling a large number of problems derived from life sciences (Gan et al, 2003;Giuliani et al, 2008;Blazewicz et al, 2013;2018;Szachniuk et al, 2014;2015;Wojciechowski et al, 2016;Radom et al, 2017). Graph-based models are often applied, e.g., to show a discrete view of biomolecular architecture, including an RNA secondary structure (Simon, 2005;Lai et al, 2012;Schlick, 2018).…”

Section: Graph-based Solutionmentioning

confidence: 99%

New models and algorithms for RNA pseudoknot order assignment

Żok

Badura

Swat

et al. 2020

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The pseudoknot is a specific motif of the RNA structure that highly influences the overall shape and stability of a molecule. It occurs when nucleotides of two disjoint single-stranded fragments of the same chain, separated by a helical fragment, interact with each other and form base pairs. Pseudoknots are characterized by great topological diversity, and their systematic description is still a challenge. In our previous work, we have introduced the pseudoknot order: a new coefficient representing the topological complexity of the pseudoknotted RNA structure. It is defined as the minimum number of base pair set decompositions, aimed to obtain the unknotted RNA structure. We have suggested how it can be useful in the interpretation and understanding of a hierarchy of RNA folding. However, it is not trivial to unambiguously identify pseudoknots and determine their orders in an RNA structure. Therefore, since the introduction of this coefficient, we have worked on the method to reliably assign pseudoknot orders in correspondence to the mechanisms that control the biological process leading to their formation in the molecule. Here, we introduce a novel graph coloring-based model for the problem of pseudoknot order assignment. We show a specialized heuristic operating on the proposed model and an alternative integer programming algorithm. The performance of both approaches is compared with that of state-of-the-art algorithms which so far have been most efficient in solving the problem in question. We summarize the results of computational experiments that evaluate our new methods in terms of classification quality on a representative data set originating from the non-redundant RNA 3D structure repository.

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Section: Graph-based Solutionmentioning

confidence: 99%

New models and algorithms for RNA pseudoknot order assignment

Żok

Badura

Swat

et al. 2020

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“…These kind of tools are often used for protein database scans, or constitute a common building block of more complex algorithms, e.g. for DNA de novo assembly [18], reads mapping [19], or multiple sequence alignment [20]. Furthermore, we chose to focus on the implementations based on the idea of dynamic programming, as they may be fairly compared with respect to power consumption and efficiency.…”

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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“…In fact, some of its unique properties are the cornerstone of a few established solutions, e.g. [1][2][3][4]. However, it is the high time consumption that often limits their applications, especially in the context of the constantly increasing number of biological sequences.…”

Section: Introductionmentioning

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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Dna Sequence Assembly Involving an Acyclic Graph Model

Cited by 6 publications

References 13 publications

New models and algorithms for RNA pseudoknot order assignment

New models and algorithms for RNA pseudoknot order assignment

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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