PEACE: Parallel Environment for Assembly and Clustering of Gene Expression

Rao, Dhananjai M.; Ozden, Mufit; Zhang, Y.; Liang, Chun; Karro, John

doi:10.1093/nar/gkq470

Cited by 7 publications

(8 citation statements)

References 18 publications

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“…The similarity comparison based on word counting can be quantified using the well-known distance measure called d 2 . It was first proposed by [22] , and later it is widely accepted and embedded into the established EST clustering tools such as d2_cluster [17] , wcd [18] and PEACE [19] . We choose the same d 2 as the distance metric for our local feature, due to its reliability and acceptability.…”

Section: Methodsmentioning

confidence: 99%

“…This validity index can also be used to measure the similarity of the clustering results associated to two different methods. In EST clustering, this index is quite commonly used and has appeared in several papers [18] , [19] , [41] . The authors in these papers used it to make comparisons between their proposed algorithms with other EST clustering tools.…”

Section: Methodsmentioning

confidence: 99%

“…With the rapid growth in bioinformatics, there have been quite a number of published works focusing on the alignment-free distance measures for biological sequences. These distance measures can be generalized into several classes, and they are distance based on counting word frequencies [17] , [18] , [19] , [20] , [21] , [22] , distance based on compression [23] , [24] , and distance based on information theory [25] , [26] , [27] . Since EST is also classified as short biological sequence, therefore the above distance measures can be considered to be applied on ESTs.…”

Section: Problem Statementmentioning

confidence: 99%

“…Recently, Rao et al [19] proposed a tool called PEACE , to cluster ESTs. This tool also adopts the same distance measure and windowing like wcd and d2_cluster , but it uses the concept of minimum spanning tree to perform clustering.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Distance Measure for Clustering Expressed Sequence Tags Originating from the Same Gene Family

Phon-Amnuaisuk

2012

PLoS ONE

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BackgroundClustering is a key step in the processing of Expressed Sequence Tags (ESTs). The primary goal of clustering is to put ESTs from the same transcript of a single gene into a unique cluster. Recent EST clustering algorithms mostly adopt the alignment-free distance measures, where they tend to yield acceptable clustering accuracies with reasonable computational time. Despite the fact that these clustering methods work satisfactorily on a majority of the EST datasets, they have a common weakness. They are prone to deliver unsatisfactory clustering results when dealing with ESTs from the genes derived from the same family. The root cause is the distance measures applied on them are not sensitive enough to separate these closely related genes.Methodology/Principal FindingsWe propose a hybrid distance measure that combines the global and local features extracted from ESTs, with the aim to address the clustering problem faced by ESTs derived from the same gene family. The clustering process is implemented using the DBSCAN algorithm. We test the hybrid distance measure on the ten EST datasets, and the clustering results are compared with the two alignment-free EST clustering tools, i.e. wcd and PEACE. The clustering results indicate that the proposed hybrid distance measure performs relatively better (in terms of clustering accuracy) than both EST clustering tools.Conclusions/SignificanceThe clustering results provide support for the effectiveness of the proposed hybrid distance measure in solving the clustering problem for ESTs that originate from the same gene family. The improvement of clustering accuracies on the experimental datasets has supported the claim that the sensitivity of the hybrid distance measure is sufficient to solve the clustering problem.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Problem Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Hybrid Distance Measure for Clustering Expressed Sequence Tags Originating from the Same Gene Family

Phon-Amnuaisuk

2012

PLoS ONE

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“…The short list of tools capable of clustering data sizes in the range of at least several million sequences includes UCLUST and FreClu ( Edgar, 2010 ; Qu et al , 2009 ). Most other clustering tools in this area are designed to solve problems related to EST analysis, such as pre-clustering of ESTs to facilitate their downstream assemblies ( Hazelhurst et al , 2008 ; Huang and Madan, 1999 ; Picardi et al , 2009 ; Rao et al , 2010 ).…”

Section: Introductionmentioning

confidence: 99%

SEED: efficient clustering of next-generation sequences

et al. 2011

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Motivation: Similarity clustering of next-generation sequences (NGS) is an important computational problem to study the population sizes of DNA/RNA molecules and to reduce the redundancies in NGS data. Currently, most sequence clustering algorithms are limited by their speed and scalability, and thus cannot handle data with tens of millions of reads.Results: Here, we introduce SEED—an efficient algorithm for clustering very large NGS sets. It joins sequences into clusters that can differ by up to three mismatches and three overhanging residues from their virtual center. It is based on a modified spaced seed method, called block spaced seeds. Its clustering component operates on the hash tables by first identifying virtual center sequences and then finding all their neighboring sequences that meet the similarity parameters. SEED can cluster 100 million short read sequences in <4 h with a linear time and memory performance. When using SEED as a preprocessing tool on genome/transcriptome assembly data, it was able to reduce the time and memory requirements of the Velvet/Oasis assembler for the datasets used in this study by 60–85% and 21–41%, respectively. In addition, the assemblies contained longer contigs than non-preprocessed data as indicated by 12–27% larger N50 values. Compared with other clustering tools, SEED showed the best performance in generating clusters of NGS data similar to true cluster results with a 2- to 10-fold better time performance. While most of SEED's utilities fall into the preprocessing area of NGS data, our tests also demonstrate its efficiency as stand-alone tool for discovering clusters of small RNA sequences in NGS data from unsequenced organisms.Availability: The SEED software can be downloaded for free from this site: http://manuals.bioinformatics.ucr.edu/home/seed.Contact: thomas.girke@ucr.eduSupplementary information: Supplementary data are available at Bioinformatics online

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