Hardware Acceleration of Approximate Tandem Repeat Detection

2010 ◽  
Author(s):  
Tomáš Martínek ◽  
Matej Lexa
Keyword(s):  
Tandem Repeat ◽  
Hardware Acceleration ◽  
Repeat Detection

scholarly journals Hybrid de novo tandem repeat detection using short and long reads

2015 ◽  
Vol 8 (S3) ◽  
Author(s):  
Guillaume Fertin ◽  
Géraldine Jean ◽  
Andreea Radulescu ◽  
Irena Rusu
Keyword(s):  
Tandem Repeat ◽  
De Novo ◽  
Long Reads ◽  
Repeat Detection

scholarly journals TRAL 2.0: Tandem Repeat Detection With Circular Profile Hidden Markov Models and Evolutionary Aligner

2021 ◽  
Vol 1 ◽  
Author(s):  
Matteo Delucchi ◽  
Paulina Näf ◽  
Spencer Bliven ◽  
Maria Anisimova
Keyword(s):  
Hidden Markov Models ◽  
Tandem Repeat ◽  
Markov Models ◽  
Hidden Markov ◽  
Alignment Method ◽  
Profile Hidden Markov Models ◽  
Repeat Units ◽  
Circular Profile ◽  
Version 2.0 ◽  
Repeat Detection

The Tandem Repeat Annotation Library (TRAL) focuses on analyzing tandem repeat units in genomic sequences. TRAL can integrate and harmonize tandem repeat annotations from a large number of external tools, and provides a statistical model for evaluating and filtering the detected repeats. TRAL version 2.0 includes new features such as a module for identifying repeats from circular profile hidden Markov models, a new repeat alignment method based on the progressive Poisson Indel Process, an improved installation procedure and a docker container. TRAL is an open-source Python 3 library and is available, together with documentation and tutorials viavital-it.ch/software/tral.

scholarly journals TideHunter: efficient and sensitive tandem repeat detection from noisy long-reads using seed-and-chain

2019 ◽  
Vol 35 (14) ◽  
pp. i200-i207 ◽  
Author(s):  
Yan Gao ◽  
Bo Liu ◽  
Yadong Wang ◽  
Yi Xing
Keyword(s):  
Tandem Repeat ◽  
Rolling Circle Amplification ◽  
Consensus Sequence ◽  
Error Rates ◽  
Sequencing Error ◽  
Template Molecule ◽  
Long Reads ◽  
Pattern Size ◽  
Repeat Pattern ◽  
Repeat Detection

Abstract Motivation Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) sequencing technologies can produce long-reads up to tens of kilobases, but with high error rates. In order to reduce sequencing error, Rolling Circle Amplification (RCA) has been used to improve library preparation by amplifying circularized template molecules. Linear products of the RCA contain multiple tandem copies of the template molecule. By integrating additional in silico processing steps, these tandem sequences can be collapsed into a consensus sequence with a higher accuracy than the original raw reads. Existing pipelines using alignment-based methods to discover the tandem repeat patterns from the long-reads are either inefficient or lack sensitivity. Results We present a novel tandem repeat detection and consensus calling tool, TideHunter, to efficiently discover tandem repeat patterns and generate high-quality consensus sequences from amplified tandemly repeated long-read sequencing data. TideHunter works with noisy long-reads (PacBio and ONT) at error rates of up to 20% and does not have any limitation of the maximal repeat pattern size. We benchmarked TideHunter using simulated and real datasets with varying error rates and repeat pattern sizes. TideHunter is tens of times faster than state-of-the-art methods and has a higher sensitivity and accuracy. Availability and implementation TideHunter is written in C, it is open source and is available at https://github.com/yangao07/TideHunter

Hardware Acceleration of Bilateral Filters

2013 ◽  
Vol 133 (2) ◽  
pp. 132-138
Author(s):  
Shuhei Isa ◽  
Chikatoshi Yamada ◽  
Yasunori Nagata
Keyword(s):  
Hardware Acceleration ◽  
Bilateral Filters

The real-time computing of the Stirling formula based on hardware acceleration

2013 ◽  
Author(s):  
Jiyang Yu ◽  
Dan Huang ◽  
Siyang Zhao ◽  
Nan Pei ◽  
Huixia Cheng ◽  
...  
Keyword(s):  
Real Time ◽  
Hardware Acceleration ◽  
Stirling Formula ◽  
The Real ◽  
Real Time Computing
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