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  1. Proceedings of the 5th international workshop on Bioinformatics (BIOKDD '05)
  2. Accelerating DNA sequencing-by-hybridization with noise
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Motif discovery for proteins using subsequence clustering
Predicting cancer susceptibility from single-nucleotide polymorphism data: a case study in multiple myeloma
Accelerating DNA sequencing-by-hybridization with noise
Analysis of protein-protein interaction networks using random walks
Graphical models of residue coupling in protein families
On discovery of maximal confident rules without support pruning in microarray data
Finding cliques in protein interaction networks via transitive closure of a weighted graph
A datamining approach to cell population deconvolution from gene expressions using particle filters
Boosting performance of bio-entity recognition by combining results from multiple systems
siRNA off-target search: a hybrid q-gram based filtering approach

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Accelerating DNA sequencing-by-hybridization with noise

Content Provider ACM Digital Library
Author Chen, Chen Xin, Dong Han, Jiawei
Abstract As a potential alternative to current wet-lab technologies, DNA sequencing-by-hybridization (SBH) has received much attention from different research communities. In order to deal with real applications, experiment environments should not be considered as error-free. Previously, under the assumption of random independent hybridization errors, Leong et al. [9] presented an algorithm for sequence reconstruction which exhibits graceful degradation of output accuracy as the error rate increases. However, as the authors also admitted, a notable downside of their method is its too high computational cost. In this paper, we show that the poor efficiency of [9] is due to its mixing-up of situations with widely different characteristics and treating everything in the safest but also slowest way. Our new algorithm addresses this problem and pushes analysis down to a finer level where a more effective solution is proposed. As demonstrated by experimentations on real human genome datasets, this new methodology yields significant performance improvements and at the same time guarantees almost the same degree of output accuracy.
Starting Page 29
Ending Page 36
Page Count 8
File Format PDF
ISBN 1595932135
DOI 10.1145/1134030.1134037
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2005-08-21
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Noise Algorithmic efficiency Clues from the genome Sequencing-by-hybridization
Content Type Text
Resource Type Article
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