An algorithm of multiple sequence alignment based on consensus sequence searched by simulated annealing and star alignment

2015 ◽  
Author(s):  
Dengfeng Yao ◽  
Minghu Jiang ◽  
Xu You ◽  
Abudoukelimu Abulizi ◽  
Renkui Hou
Keyword(s):  
Simulated Annealing ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Consensus Sequence ◽  
Multiple Sequence

scholarly journals GPU accelerated partial order multiple sequence alignment for long reads self-correction

2020 ◽  
Author(s):  
Francesco Peverelli ◽  
Lorenzo Di Tucci ◽  
Marco D. Santambrogio ◽  
Nan Ding ◽  
Steven Hofmeyr ◽  
...  
Keyword(s):  
Error Correction ◽  
Partial Order ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Consensus Sequence ◽  
Pairwise Alignment ◽  
Multiple Sequence ◽  
Graph Alignment ◽  
Correction Process ◽  
Long Reads

AbstractAs third generation sequencing technologies become more reliable and widely used to solve several genome-related problems, self-correction of long reads is becoming the preferred method to reduce the error rate of Pacific Biosciences and Oxford Nanopore long reads, that is now around 10-12%. Several of these self-correction methods rely on some form of Multiple Sequence Alignment (MSA) to obtain a consensus sequence for the original reads. In particular, error-correction tools such as RACON and CONSENT use Partial Order (PO) graph alignment to accomplish this task. PO graph alignment, which is computationally more expensive than optimal global pairwise alignment between two sequences, needs to be performed several times for each read during the error correction process. GPUs have proven very effective in accelerating several compute-intensive tasks in different scientific fields. We harnessed the power of these architectures to accelerate the error correction process of existing self-correction tools, to improve the efficiency of this step of genome analysis.In this paper, we introduce a GPU-accelerated version of the PO alignment presented in the POA v2 software library, implemented on an NVIDIA Tesla V100 GPU. We obtain up to 6.5x speedup compared to 64 CPU threads run on two 2.3 GHz 16-core Intel Xeon Processors E5-2698 v3. In our implementation we focused on the alignment of smaller sequences, as the CONSENT segmentation strategy based on k-mer chaining provides an optimal opportunity to exploit the parallel-processing power of GPUs. To demonstrate this, we have integrated our kernel in the CONSENT software. This accelerated version of CONSENT provides a speedup for the whole error correction step that ranges from 1.95x to 8.5x depending on the input reads.

Multiple sequence alignment using simulated annealing

1994 ◽  
Vol 10 (4) ◽  
pp. 419-426 ◽  
Author(s):  
Jin Kim ◽  
Sakti Pramanik ◽  
Moon Jung Chung
Keyword(s):  
Simulated Annealing ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Multiple Sequence
Sign in / Sign up

Export Citation Format

Share Document