A novel quasi-alignment-based method for discovering conserved regions in genetic sequences

Nagar, Anurag; Hahsler, Michael

doi:10.1109/bibmw.2012.6470216

Cited by 2 publications

(5 citation statements)

References 14 publications

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“…This approach has previously been used for phylogenetic classification [ 14 ]. This paper expands our preliminary investigation into discovering similar segments [ 15 ] by developing a more rigorous theory of quasi-alignment, improved visualization and an expansion to the species level. We show how quasi-alignment can be used to quickly and efficiently discover conserved regions across multiple sequences.…”

Section: Introductionmentioning

confidence: 92%

“…Several other useful functions, such as those for metagenomic classification, are also available. More details can be obtained from the package documentation [ 32 ].…”

Section: Quasi-alignment Via Position-sensitive P mentioning

confidence: 99%

“…This is a valuable asset for metagenomic analysis and also fits in nicely with the requirements of Next Generation Sequencing methods. All experiments in this paper can be reproduced using the QuasiAlign [ 32 ] package.…”

Section: Quasi-alignment Via Position-sensitive P mentioning

confidence: 99%

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Fast discovery and visualization of conserved regions in DNA sequences using quasi-alignment

Nagar

Hahsler

2013

BMC Bioinformatics

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BackgroundNext Generation Sequencing techniques are producing enormous amounts of biological sequence data and analysis becomes a major computational problem. Currently, most analysis, especially the identification of conserved regions, relies heavily on Multiple Sequence Alignment and its various heuristics such as progressive alignment, whose run time grows with the square of the number and the length of the aligned sequences and requires significant computational resources. In this work, we present a method to efficiently discover regions of high similarity across multiple sequences without performing expensive sequence alignment. The method is based on approximating edit distance between segments of sequences using p-mer frequency counts. Then, efficient high-throughput data stream clustering is used to group highly similar segments into so called quasi-alignments. Quasi-alignments have numerous applications such as identifying species and their taxonomic class from sequences, comparing sequences for similarities, and, as in this paper, discovering conserved regions across related sequences.ResultsIn this paper, we show that quasi-alignments can be used to discover highly similar segments across multiple sequences from related or different genomes efficiently and accurately. Experiments on a large number of unaligned 16S rRNA sequences obtained from the Greengenes database show that the method is able to identify conserved regions which agree with known hypervariable regions in 16S rRNA. Furthermore, the experiments show that the proposed method scales well for large data sets with a run time that grows only linearly with the number and length of sequences, whereas for existing multiple sequence alignment heuristics the run time grows super-linearly.ConclusionQuasi-alignment-based algorithms can detect highly similar regions and conserved areas across multiple sequences. Since the run time is linear and the sequences are converted into a compact clustering model, we are able to identify conserved regions fast or even interactively using a standard PC. Our method has many potential applications such as finding characteristic signature sequences for families of organisms and studying conserved and variable regions in, for example, 16S rRNA.

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Section: Introductionmentioning

confidence: 92%

“…Several other useful functions, such as those for metagenomic classification, are also available. More details can be obtained from the package documentation [ 32 ].…”

Section: Quasi-alignment Via Position-sensitive P mentioning

confidence: 99%

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Fast discovery and visualization of conserved regions in DNA sequences using quasi-alignment

Nagar

Hahsler

2013

BMC Bioinformatics

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“…In this paper we build on previous work on quasi-alignment [10,13], which applies computationally very e cient positionsensitive word frequency analysis and data stream clustering to create compact and lightweight profiles of related genetic sequences and defines scoring functions to calculate the similarity between sequences and profiles. The original method used the entire 16S rRNA sequence for finding similar regions and taxonomic classification.…”

Section: Introductionmentioning

confidence: 99%

“…QA requires no pre-processing of data either due to incomplete hierarchy or because of the multiple inheritance problem. It can be used locally and can also store vital meta information about the models[13].Results inTable 3show that at the phylum level QA is able to outperform RDP and also does not require any preprocessing or cleaning of the sequence data. It can be run locally and does not require extensive server resources.…”

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confidence: 97%

Genomic Sequence Fragment Identification using Quasi-Alignment

Nagar

Hahsler

2013

Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics

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Identification of organisms using their genetic sequences is a popular problem in molecular biology and is used in fields such as metagenomics, molecular phylogenetics and DNA Barcoding. These applications depend on searching large sequence databases for individual matching sequences (e.g., with BLAST) and comparing sequences using multiple sequence alignment (e.g., via Clustal), both of which are computationally expensive and require extensive server resources. We propose a novel method for sequence comparison, analysis, and classification which avoids the need to align sequences at the base level or search a database for similarity. Instead, our method uses alignment-free methods to find probabilistic quasi-alignments for longer (typically 100 base pairs) segments. Clustering is then used to create compact models that can be used to analyze a set of sequences and to score and classify unknown sequences against these models. In this paper we expand prior work in two ways. We show how quasi-alignments can be expanded into larger quasi-aligned sections and we develop a method to classify short sequence fragments. The latter is especially useful when working with Next-Generation Sequencing (NGS) techniques that generate output in the form of relatively short reads. We have conducted extensive experiments using fragments from bacterial 16S rRNA sequences obtained from the Greengenes project and our results show that the new quasi-alignment based approach can provide excellent results as well as overcome some of the restrictions of by the widely used Ribosomal Database Project (RDP) classifier.

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A novel quasi-alignment-based method for discovering conserved regions in genetic sequences

Cited by 2 publications

References 14 publications

Fast discovery and visualization of conserved regions in DNA sequences using quasi-alignment

Fast discovery and visualization of conserved regions in DNA sequences using quasi-alignment

Genomic Sequence Fragment Identification using Quasi-Alignment

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