Alignment-Free Sequence Analysis and Applications

Ren, Jie; Bai, Xin; Lu, Yang; Tang, Kujin; Wang, Ying; Reinert, Gesine; Sun, Fengzhu

doi:10.1146/annurev-biodatasci-080917-013431

Cited by 89 publications

(78 citation statements)

References 170 publications

(268 reference statements)

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“…Also the use of complete genome/gene sequences is computationally intensive and is practically infeasible for scaling up for matching a large number of genetic sequences. [5].…”

Section: Effect Of Data Length On Complexity Of Sequencesmentioning

confidence: 99%

“…Thus, there is a need for fast alignment-free techniques for sequence analysis [3,4]. Further, one may have only short segments and/or incomplete fragments of nucleotide sequences to analyze [5]. Information theory and data compression algorithms provide a rich set of mathematical and algorithmic/computational tools to capture essential patterns in data that could be used for matching nucleotide sequences.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Identification of SARS Coronavirus using Compression-Complexity Measures

Balasubramanian

Nagaraj

2020

Preprint

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Finding vaccine or specific antiviral treatment for global pandemic of virus diseases (such as the ongoing COVID-19) requires rapid analysis, annotation and evaluation of metagenomic libraries to enable a quick and efficient screening of nucleotide sequences. Traditional sequence alignment methods are not suitable and there is a need for fast alignment-free techniques for sequence analysis. Information theory and data compression algorithms provide a rich set of mathematical and computational tools to capture essential patterns in biological sequences. In 2013, our research group (Nagaraj et al., Eur. Phys. J. Special Topics 222(3-4), 2013) has proposed a novel measure known as Effort-To-Compress (ETC) based on the notion of compression-complexity to capture the information content of sequences. In this study, we propose a compression-complexity based distance measure for automatic identification of SARS coronavirus strains from a set of viruses using only short fragments of nucleotide sequences. We also demonstrate that our proposed method can correctly distinguish SARS-CoV-2 from SARS-CoV-1 viruses by analyzing very short segments of nucleotide sequences. This work could be extended further to enable medical practitioners in automatically identifying and characterizing SARS coronavirus strain in a fast and efficient fashion using short and/or incomplete segments of nucleotide sequences. Potentially, the need for sequence assembly can be circumvented.NoteThe main ideas and results of this research were first presented at the International Conference on Nonlinear Systems and Dynamics (CNSD-2013) held at Indian Institute of Technology, Indore, December 12, 2013. In this manuscript, we have extended our preliminary analysis to include SARS-CoV-2 virus as well.

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“…Also the use of complete genome/gene sequences is computationally intensive and is practically infeasible for scaling up for matching a large number of genetic sequences. [5].…”

Section: Effect Of Data Length On Complexity Of Sequencesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Identification of SARS Coronavirus using Compression-Complexity Measures

Balasubramanian

Nagaraj

2020

Preprint

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“…In recent years, a large number of alignment-free approaches to phylogeny reconstruction have been developed and applied, since these methods are much faster than traditional, alignment-based phylogenetic methods, see [51,39,3,25] for recent review papers and [50] for a systematic evaluation of alignment-free software tools. Most alignment-free approaches are based on k-mer statistics [21,44,7,48,17], but there are also approaches based on the length of common substrings [47,8,27,37,32,46], on word or spaced-word matches [38,33,35,34,1,41] or on so-called micro-alignments [49,20,29,28].…”

Section: Introductionmentioning

confidence: 99%

Read-SpaM: assembly-free and alignment-free comparison of bacterial genomes with low sequencing coverage

Lau

Leimeister

Dörrer

et al. 2019

Preprint

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In many fields of biomedical research, it is important to estimate phylogenetic distances between taxa based on low-coverage sequencing reads. Major applications are, for example, phylogeny reconstruction, species identification from small sequencing samples, or bacterial strain typing in medical diagnostics. Herein, we adapt our previously developed software program Filtered Spaced-Word Matches (FSWM) for alignment-free phylogeny reconstruction to work on unassembled reads; we call this implementation Read-SpaM. Test runs on simulated reads from bacterial genomes show that our approach can estimate phylogenetic distances with high accuracy, even for large evolutionary distances and for very low sequencing coverage.

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“…This approach has several limitations, including low sensitivity to detect remotely related species, slow computing, and low scalability (reviewed in (Zielezinski et al, 2017)). To overcome these limitations, several alignment-free methods were developed that are both computationally efficient and resistant to noise (Zielezinski et al, 2017;Ren et al, 2018;Jain et al, 2018). Based on their methodologies, there are three main categories of alignment-free genome similarity comparisons (reviewed in (Zielezinski et al, 2017)).…”

Section: Introductionmentioning

confidence: 99%

A new method for rapid genome classification, clustering, visualization, and novel taxa discovery from metagenome

Zhong

Egan

et al. 2019

Preprint

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Classifying taxa, including those that have not previously been identified, is a key task in characterizing the microbial communities of under-described habitats, including permanently ice-covered lakes in the dry valleys of the Antarctic. Current supervised phylogeny-based methods fall short on recognizing species assembled from metagenomic datasets from such habitats, as they are often incomplete or lack closely known relatives. Here, we report an efficient software suite, "Genome Constellation", that is capable of rapidly characterizing a large number of metagenome-assembled genomes. Genome Constellation estimates similarities between genomes based on their k-mer matches, and subsequently uses these similarities for classification, clustering, and visualization. The clusters of reference genomes formed by Genome Constellation closely resemble known phylogenetic relationships while simultaneously revealing unexpected connections. In a dataset containing 1,693 draft genomes assembled from the Antarctic lake communities where only 40% could be placed in a phylogenetic tree, Genome Constellation improves taxa assignment to 61%. The clustering-based analysis revealed several novel taxa groups, including six clusters that may represent new bacterial phyla. Remarkably, we discovered 63 new giant viruses, 3 of which could not be found by using the traditional marker-based approach. In summary, we demonstrate that Genome Constellation provides an unbiased option to rapidly analyze a large number of microbial genomes and visually explore their relatedness. The software is available under BSD license at: https://bitbucket.org/berkeleylab/jgi-genomeconstellation/.

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Alignment-Free Sequence Analysis and Applications

Cited by 89 publications

References 170 publications

Automatic Identification of SARS Coronavirus using Compression-Complexity Measures

Automatic Identification of SARS Coronavirus using Compression-Complexity Measures

Read-SpaM: assembly-free and alignment-free comparison of bacterial genomes with low sequencing coverage

A new method for rapid genome classification, clustering, visualization, and novel taxa discovery from metagenome

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