Alignment-free genetic sequence comparisons: a review of recent approaches by word analysis

Bonham-Carter, Oliver; Steele, Joe M.; Bastola, Dhundy

doi:10.1093/bib/bbt052

Cited by 118 publications

(84 citation statements)

References 69 publications

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“…Other software, such as Andi, computes rapid local alignments between each genome and defines the distance based on mismatches within each alignment (184). Haubold (185), as well as Bonham-Carter et al (186), has written excellent reviews of alignment-free methods that can be referred to for additional details.…”

Section: Phylogenetics To Phylogenomicsmentioning

confidence: 99%

A Primer on Infectious Disease Bacterial Genomics

et al. 2016

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SUMMARYThe number of large-scale genomics projects is increasing due to the availability of affordable high-throughput sequencing (HTS) technologies. The use of HTS for bacterial infectious disease research is attractive because one whole-genome sequencing (WGS) run can replace multiple assays for bacterial typing, molecular epidemiology investigations, and more in-depth pathogenomic studies. The computational resources and bioinformatics expertise required to accommodate and analyze the large amounts of data pose new challenges for researchers embarking on genomics projects for the first time. Here, we present a comprehensive overview of a bacterial genomics projects from beginning to end, with a particular focus on the planning and computational requirements for HTS data, and provide a general understanding of the analytical concepts to develop a workflow that will meet the objectives and goals of HTS projects.

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Section: Phylogenetics To Phylogenomicsmentioning

confidence: 99%

A Primer on Infectious Disease Bacterial Genomics

et al. 2016

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“…Furthermore, frequency analysis is especially well suited for comparing large data sets and discovery, as it embraces convenient techniques of network analysis to ascertain natural patterns [26,27]. Here, we discuss the collection of frequency information, which is later used to build networks to discover PTM and RS biases.…”

Section: Computing Frequenciesmentioning

confidence: 99%

A study of bias and increasing organismal complexity from their post-translational modifications and reaction site interplays

Bonham-Carter

Thapa²,

From

et al. 2016

Brief Bioinform

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Post-translational modifications (PTMs) are important steps in the biosynthesis of proteins. Aside from their integral contributions to protein development, i.e. perform specialized proteolytic cleavage of regulatory subunits, the covalent addition of functional groups of proteins or the degradation of entire proteins, PTMs are also involved in enabling proteins to withstand and recover from temporary environmental stresses (heat shock, microgravity and many others). The literature supports evidence of thousands of recently discovered PTMs, many of which may likely contribute similarly (perhaps, even, interchangeably) to protein stress response. Although there are many PTM actors on the biological stage, our study determines that these PTMs are generally cast into organism-specific, preferential roles. In this work, we study the PTM compositions across the mitochondrial (Mt) and non-Mt proteomes of 11 diverse organisms to illustrate that each organism appears to have a unique list of PTMs, and an equally unique list of PTM-associated residue reaction sites (RSs), where PTMs interact with protein.Despite the present limitation of available PTM data across different species, we apply existing and current protein data to illustrate particular organismal biases. We explore the relative frequencies of observed PTMs, the RSs and general amino-acid compositions of Mt and non-Mt proteomes. We apply these data to create networks and heatmaps to illustrate the evidence of bias. We show that the number of PTMs and RSs appears to grow along with organismal complexity, which may imply that environmental stress could play a role in this bias.

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“…Eisen (1998), Yi et al (2007), and Hatfull et al (2010) clustered genomes by various types of statistical information from DNA microarray data. Bonham-Carter et al (2014) summarized 14 alignment-free genetic sequence comparison methods. However, all these methods only utilize features from short string and probability information, without any features from the transformed space, such as frequency domain.…”

Section: Related Workmentioning

confidence: 99%

A wavelet-based feature vector model for DNA clustering

Jp¹,

Ry²

2015

Genet. Mol. Res.

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ABSTRACT. DNA data are important in the bioinformatic domain. To extract useful information from the enormous collection of DNA sequences, DNA clustering is often adopted to efficiently deal with DNA data. The alignmentfree method is a very popular way of creating feature vectors from DNA sequences, which are then used to compare DNA similarities. This paper proposes a wavelet-based feature vector (WFV) model, which is also an alignment-free method. From the perspective of signal processing, a DNA sequence is a sequence of digital signals. However, most traditional alignment-free models only extract features in the time domain. The WFV model uses discrete wavelet transform to adaptively yield feature vectors with a fixed dimension based on the features in both the time and frequency domains. The level of wavelet transform is adjusted according to the length of the DNA sequence rather than a fixed manually set value. The WFV model prefers a 32-dimension feature vector, which greatly promotes system performance. We compared the WFV model with the other five alignment-free models, i.e., k-tuple, DMK, TSM, AMI, and CV, on several large-scale DNA datasets on the DNA clustering application by means of the K-means algorithm. The experimental results showed that the WFV 19163-19172 (2015) model outperformed the other models in terms of both the clustering results and the running time.

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Alignment-free genetic sequence comparisons: a review of recent approaches by word analysis

Cited by 118 publications

References 69 publications

A Primer on Infectious Disease Bacterial Genomics

A Primer on Infectious Disease Bacterial Genomics

A study of bias and increasing organismal complexity from their post-translational modifications and reaction site interplays

A wavelet-based feature vector model for DNA clustering

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