Physicochemical property based computational scheme for classifying DNA sequence elements of Saccharomyces cerevisiae

Jaiswal, Atul Kumar; Krishnamachari, Annangarachari

doi:10.1016/j.compbiolchem.2018.12.014

Cited by 7 publications

(7 citation statements)

References 17 publications

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“…For binary classification, we used the same coding schemes as implemented in Orozco-Arias et al (2020) , such as DAX ( Yu et al, 2015 ), EIIP ( Nair & Sreenadhan, 2006 ), Complementary ( Akhtar, Epps & Ambikairajah, 2008 ), Enthalpy ( Kauer & Blöcker, 2003 ), and Galois (4) ( Rosen, 2006 ). Additionally, two techniques were applied to automatically extract features from the sequences: (i) k -mer frequencies were obtained for each element and (ii) three physical-chemical (PC) properties were extracted, such as average hydrogen bonding energy per base pair (bp), stacking energy (per bp), and solvation energy (per bp), which were calculated by taking the first di-nucleotide and moving in a sliding window of one base at a time ( Jaiswal & Krishnamachari, 2019 ). Moreover, we pre-processed the data by scaling, following the strategy implemented in Tabares-soto et al (2020) , and performed a dimensional reduction through a principal component analysis (PCA) ( Wold, Esbensen & Geladi, 1987 ) with a cumulative variance of 96% and tolerance of 1e−4.…”

Section: Methodsmentioning

confidence: 99%

K-mer-based machine learning method to classify LTR-retrotransposons in plant genomes

Orozco-Arias

Candamil-Cortés

Jaimes

et al. 2021

PeerJ

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Every day more plant genomes are available in public databases and additional massive sequencing projects (i.e., that aim to sequence thousands of individuals) are formulated and released. Nevertheless, there are not enough automatic tools to analyze this large amount of genomic information. LTR retrotransposons are the most frequent repetitive sequences in plant genomes; however, their detection and classification are commonly performed using semi-automatic and time-consuming programs. Despite the availability of several bioinformatic tools that follow different approaches to detect and classify them, none of these tools can individually obtain accurate results. Here, we used Machine Learning algorithms based on k-mer counts to classify LTR retrotransposons from other genomic sequences and into lineages/families with an F1-Score of 95%, contributing to develop a free-alignment and automatic method to analyze these sequences.

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Section: Methodsmentioning

confidence: 99%

K-mer-based machine learning method to classify LTR-retrotransposons in plant genomes

Orozco-Arias

Candamil-Cortés

Jaimes

et al. 2021

PeerJ

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“…Codebook References Recently, an innovative way to convert sequences into numerical representations was proposed by Jaiswal & Krishnamachari (2019). The authors considered three physicochemical properties, namely, average hydrogen bonding energy per base pair (bp), stacking energy (per bp), and solvation energy (per bp), which are calculated by taking the first di-nucleotide and then moving a sliding window, one base at a time.…”

Section: Encoding Schemesmentioning

confidence: 99%

A systematic review of the application of machine learning in the detection and classification of transposable elements

Orozco-Arias

Isaza

Guyot

et al. 2019

PeerJ

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Background Transposable elements (TEs) constitute the most common repeated sequences in eukaryotic genomes. Recent studies demonstrated their deep impact on species diversity, adaptation to the environment and diseases. Although there are many conventional bioinformatics algorithms for detecting and classifying TEs, none have achieved reliable results on different types of TEs. Machine learning (ML) techniques can automatically extract hidden patterns and novel information from labeled or non-labeled data and have been applied to solving several scientific problems. Methodology We followed the Systematic Literature Review (SLR) process, applying the six stages of the review protocol from it, but added a previous stage, which aims to detect the need for a review. Then search equations were formulated and executed in several literature databases. Relevant publications were scanned and used to extract evidence to answer research questions. Results Several ML approaches have already been tested on other bioinformatics problems with promising results, yet there are few algorithms and architectures available in literature focused specifically on TEs, despite representing the majority of the nuclear DNA of many organisms. Only 35 articles were found and categorized as relevant in TE or related fields. Conclusions ML is a powerful tool that can be used to address many problems. Although ML techniques have been used widely in other biological tasks, their utilization in TE analyses is still limited. Following the SLR, it was possible to notice that the use of ML for TE analyses (detection and classification) is an open problem, and this new field of research is growing in interest.

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“…Recently, an innovative way to convert sequences into numerical representations was proposed by (Jaiswal & Krishnamachari, 2019). The authors considered three physicochemical properties, namely, average hydrogen bonding energy per base pair (bp), stacking energy (per bp), and solvation energy (per bp), which are calculated by taking the first di-nucleotide and then moving a sliding window, one base at a time.…”

Section: Most Used Dna Coding Schemes (Q4)mentioning

confidence: 99%

“…PRISMA flow diagram Publication identifier Year Q1 Q2 Q3 Q4 Reference Publication identifier Year Q1 Q2 Q3 Q4 Reference P1 X X X X (Yu, Yu & Pan, 2017) P19 2013 X X X (Loureiro et al, 2013b) P2 X X X (Schietgat et al, 2018) P20 2014 X X (Ma, Zhang & Wang, 2014) P3 X X X (Arango-López et al, 2017) P21 2010 X X (Dashti & Masoudi-Nejad, 2010) P4 X X X (Loureiro et al, 2013a) P22 2010 X X (Ding, Zhou & Guan, 2010) P5 X X X (Tsafnat et al, 2011) P23 2019 X (Jaiswal & Krishnamachari, 2019) P6 X X X (Zhang et al, 2018) P24 2015 X X X X (Girgis, 2015) P7 X X X (Eraslan et al, 2019) P25 2018 X X X (Nakano et al, 2018a) P8 X X X (Douville et al, 2018) P26 2018 X X X (Zamith Santos et al, 2018) P9 X X (Chen et al, 2018) P27 2009 X (Abrusan et al, 2009) P10 X X X X (Ashlock & Datta, 2012) P28 2019 X X (Su, Gu & Peterson, 2019) P11 X X X (Smith et al, 2017) P29 2017 X X X X (Nakano et al, 2017) P12 X X X X (Kamath, De Jong & Shehu, 2014) P30 2014 X X X (Brayet et al, 2014) P13 X X X (Kim et al, 2016) P31 2013 X (Zamani et al, 2013) P14 X X X (Segal et al, 2018) P32 2019 X (Hubbard et al, 2019) P15 X X X (Rawal & Ramaswamy, 2011) P33 2014 X X (Ryvkin et al, 2014) P16 X X X (Tang et al, 2017) P34 2013 X X X X (Zhang et al, 2013) P17 X X X (Ventola et al, 2017) P35 2019 X X…”

Section: Figurementioning

confidence: 99%

Peer Review #1 of "A systematic review of the application of machine learning in the detection and classification of transposable elements (v0.1)"

2019

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Background. Transposable elements (TEs) constitute the most common repeated sequences in eukaryotic genomes. Recent studies demonstrated their deep impact on species diversity, adaptation to the environment, and diseases. Although there are many conventional bioinformatics algorithms for detecting and classifying TEs, none have achieved reliable results on different types of TEs. Machine learning (ML) techniques can automatically extract hidden patterns and novel information from labeled or non-labeled data and have been applied to solving several scientific problems. Methodology. We followed the Systematic Literature Review process, applying the six stages of the review protocol from it, but added a previous stage, which aims to detect the need for a review. Then search equations were formulated and executed in several literature databases. Relevant publications were scanned and used to extract evidence to answer research questions. Results. Several ML approaches have already been tested on other bioinformatics problems with promising results, yet there are few algorithms and architectures available in literature focused specifically on TEs, despite representing the majority of the nuclear DNA of many organisms. Only 35 papers were found and categorized as relevant in TE or related fields. Conclusions. ML is a powerful tool that can be used to address many problems. Although ML techniques have been used widely in other biological tasks, their utilization in TE analyses is still limited. Following the Systematic Literature Review process, it was possible to notice that the use of ML for TE analyses (detection and classification) is an open problem, and this new field of research is growing in interest.

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Physicochemical property based computational scheme for classifying DNA sequence elements of Saccharomyces cerevisiae

Cited by 7 publications

References 17 publications

K-mer-based machine learning method to classify LTR-retrotransposons in plant genomes

K-mer-based machine learning method to classify LTR-retrotransposons in plant genomes

A systematic review of the application of machine learning in the detection and classification of transposable elements

Peer Review #1 of "A systematic review of the application of machine learning in the detection and classification of transposable elements (v0.1)"

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