On DNA numerical representations for genomic similarity computation

Mendizabal-Ruiz, Gerardo; Román-Godínez, Israel; Torres-Ramos, Sulema; Salido-Ruiz, Ricardo A.; Morales, J. Alejandro

doi:10.1371/journal.pone.0173288

Cited by 51 publications

(39 citation statements)

References 35 publications

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“…In this work, a similar algorithm is implemented to analyse nucleotide sequences: each nucleotide position in a sequence is represented as a four elements vector, the Voss representation (Voss, 1992), encoding the probability of each base according to previously aligned reads. This numerical representation of DNA sequence is appropriate for the comparison of DNA sequences (Mendizabal-Ruiz et al, 2017) and their classification (Mendizabal-Ruiz et al, 2018). In molecular biology, similar algorithm has been applied to the clustering of amino acid sequences (Olshen et al, 2005) where vector quantization is used to estimate the probability density of amino acids.…”

Section: Methodsmentioning

confidence: 99%

Effective Machine-Learning Assembly For Next-Generation Sequencing With Very Low Coverage

Ranjard

Wong

Rodrigo

2018

Preprint

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9In short-read DNA sequencing experiments, the read coverage is a key parameter to successfully assemble the reads and reconstruct the sequence of the input DNA. When coverage is very low, the original sequence reconstruction from the reads can be difficult because of the occurrence of uncovered gaps. Reference guided assembly can then improve these assemblies. However, when the available reference is phylogenetically distant from the sequencing reads, the mapping rate of the reads can be extremely low. Some recent improvements in read mapping approaches aim at modifying the reference according to the reads dynamically. Such approaches can significantly improve the alignment rate of the reads onto distant references but the processing of insertions and deletions remains challenging. Here, we introduce a dynamic programming algorithm to update the reference sequence according to previously aligned reads. Substitutions, insertions and deletions are performed in the reference sequence dynamically. We evaluate this approach to assemble a western-grey kangaroo mitochondrial amplicon. Our results show that more reads can be aligned and that this method produces assemblies of length comparable to the truth while limiting error rate when classic approaches fail to recover the correct length. Our method allows us to assemble the first full mitochondrial genome for the western-grey kangaroo. Finally, we discuss how the core algorithm of this method could be improved and combined with other approaches to analyse larger genomic sequences. 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26De novo assembly algorithms classically use graph, de Bruijn or overlap-layout-consensus, to join short 27 sequencing reads into longer contigs. However, when the short-reads coverage is very low, only short 28 contigs can be reconstructed because of the occurrence of uncovered gaps in the sequence (Miller et al., 29 2010). In this case, the availability of a reference sequence can be beneficial to connect and order these 30 contigs, an approach known as reference-guided assembly or homology-guided assembly (Rausch et al., 31 2009; Lischer and Shimizu, 2017). The reads are mapped onto this reference and a contig is constructed 32 by taking the consensus of the short-reads at each position. However, some gaps in the mapping of 33 the reads onto the reference may remain if the available reference is too distant phylogenetically from 34 the sequence the short-reads originate from. This is because the short-reads that cannot, or can only 35 partially, be mapped to the distant reference are discarded or trimmed. The information contained in the 36 discarded or trimmed sequences of the reads is therefore lost. Hence, improvements in the alignments of 37 the reads to the reference that are able to take advantage of this unexploited information should improve 38 the assemblies. 39Iterative referencing proposes to align all the reads to the reference and then update the reference 40 sequence by calling the consensus of the reads. Once the ref...

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Section: Methodsmentioning

confidence: 99%

Effective Machine-Learning Assembly For Next-Generation Sequencing With Very Low Coverage

Ranjard

Wong

Rodrigo

2018

Preprint

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“…This representation is one-dimensional [87,85]. This mapping can be obtained by substituting the four nucleotides (T, C, A, G) of a biological sequence for integers (0, 1, 2, 3), respectively, e.g., let s = (G, A, G, A, G, T, G, A, C, C, A), thus, d = (3, 2, 3, 2, 3, 0, 3, 2, 1, 1, 2), as exposed in Equation (5).…”

Section: Integer Representationmentioning

confidence: 99%

Feature Extraction Approaches for Biological Sequences: A Comparative Study of Mathematical Models

Bonidia

Sampaio

Domingues

et al. 2020

Preprint

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The number of available biological sequences has increased significantly in recent years due to various genomic sequencing projects, creating a huge volume of data. Consequently, new computational methods are needed to analyze and extract information from these sequences. Machine learning methods have shown broad applicability in computational biology and bioinformatics. The utilization of machine learning methods has helped to extract relevant information from various biological datasets. However, there are still several problems that motivate new algorithms and pipeline proposals, mainly involving feature extraction problems, in which extracting significant discriminatory information from a biological set is challenging. Considering this, our work proposes to study and analyze a feature extraction pipeline based on mathematical models (Numerical Mapping, Fourier, Entropy, and Complex Networks). As a case study, we analyze Long Non-Coding RNA sequences. Moreover, we divided this work into two studies, e.g., (I) we assessed our proposal with the most addressed problem in our review, e.g., lncRNA vs. mRNA; (II) we tested its generalization on different classification problems, e.g., circRNA vs. lncRNA. The experimental results demonstrated three main contributions: (1) An in-depth study of several mathematical models;(2) a new feature extraction pipeline and (3) its generalization and robustness

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“…In [31,32] three physio-chemical based representations of DNA sequences (atomic, molecular mass, and Electron-Ion Interaction Potential, EIIP) were considered for genomic analysis, and the authors concluded that the choice of numerical representation did not have any effect on the results. The latest study comparing different numerical representation techniques [33] concluded that multi-dimensional representations (such as Chaos Game Representation) yielded better genomic comparison results than one-dimensional representations. However, in general there is no agreement on whether or not the choice of numerical representation for DNA sequences makes a difference on the genome comparison results, or what are the numerical representations that are best suited for analyzing genomic data.…”

Section: Numerical Representations Of Dna Sequencesmentioning

confidence: 99%

ML-DSP: Machine Learning with Digital Signal Processing for ultrafast, accurate, and scalable genome classification at all taxonomic levels

Randhawa

Hill

Kari

2018

Preprint

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Background: Although methods and software tools abound for the comparison, analysis, identification, and taxonomic classification of the enormous amount of genomic sequences that are continuously being produced, taxonomic classification remains challenging. The difficulty lies within both the magnitude of the dataset and the intrinsic problems associated with classification. The need exists for an approach and software tool that addresses the limitations of existing alignment-based methods, as well as the challenges of recently proposed alignment-free methods. Results: We combine supervised Machine Learning with Digital Signal Processing to design ML-DSP, an alignment-free software tool for ultrafast, accurate, and scalable genome classification at all taxonomic levels.We test ML-DSP by classifying 7,396 full mitochondrial genomes from the kingdom to genus levels, with 98% classification accuracy. Compared with the alignment-based classification tool MEGA7 (with sequences aligned with either MUSCLE, or CLUSTALW), ML-DSP has similar accuracy scores while being significantly faster on two small benchmark datasets (2,250 to 67,600 times faster for 41 mammalian mitochondrial genomes). ML-DSP also successfully scales to accurately classify a large dataset of 4,322 complete vertebrate mtDNA genomes, a task which MEGA7 with MUSCLE or CLUSTALW did not complete after several hours, and had to be terminated. ML-DSP also outperforms the alignment-free tool FFP (Feature Frequency Profiles) in terms of both accuracy and time, being three times faster for the vertebrate mtDNA genomes dataset. Conclusions: We provide empirical evidence that ML-DSP distinguishes complete genome sequences at all taxonomic levels. Ultrafast and accurate taxonomic classification of genomic sequences is predicted to be highly relevant in the classification of newly discovered organisms, in distinguishing genomic signatures, in identifying mechanistic determinants of genomic signatures, and in evaluating genome integrity.

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On DNA numerical representations for genomic similarity computation

Cited by 51 publications

References 35 publications

Effective Machine-Learning Assembly For Next-Generation Sequencing With Very Low Coverage

Effective Machine-Learning Assembly For Next-Generation Sequencing With Very Low Coverage

Feature Extraction Approaches for Biological Sequences: A Comparative Study of Mathematical Models

ML-DSP: Machine Learning with Digital Signal Processing for ultrafast, accurate, and scalable genome classification at all taxonomic levels

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