Application of Data Mining Algorithms to Classify Biological Data: The Coffea canephora Genome Case

Arango‐López, Jeferson; Orozco-Arias, Simón; Salazar, Johnny A.; Guyot, Romain

doi:10.1007/978-3-319-66562-7_12

Cited by 14 publications

(16 citation statements)

References 39 publications

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“…There is much literature about applications of ML in bioinformatics (e.g., reviewed in Larrañaga et al (2006)), showing improvements in many aspects such as genome annotation (Arango-López et al, 2017). In recent years, much bioinformatics software has been developed to detect TEs (Girgis, 2015) and, although they follow different strategies (such as homology-based, structure-based, de novo, and using comparative genomics), these lack sensitivity and specificity due to the polymorphic structures of TEs (Su, Gu & Table 2 Selected publications and their contribution to each research question.…”

Section: Benefits Of ML Over Bioinformatics (Q1)mentioning

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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Section: Benefits Of ML Over Bioinformatics (Q1)mentioning

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

Self Cite

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show abstract

“…There is much literature about applications of machine learning in bioinformatics (for example, reviewed in (Larrañaga et al, 2006)), showing improvements in many aspects such as genome annotation (Arango-López et al, 2017). In recent years, much bioinformatics software has been developed to detect TEs (Girgis, 2015) and, although they follow different strategies (such as homology-based, structure-based, de novo, and using comparative genomics), these lack sensitivity and specificity due to the polymorphic structures of TEs (Su, Gu & Peterson, 2019).…”

Section: Benefits Of ML Over Bioinformatics (Q1)mentioning

confidence: 99%

“…ML techniques also obtain better results than traditional methods regarding TEs classification. Using ML, it is possible to classify non-autonomous TEs (specifically derived from LTR retrotransposons) using features other than coding regions (which are commonly used in classification processes), including element length, LTR length, and ORF lengths (Arango-López et al, 2017). ML algorithms can distinguish between retroviral LTRs and SINEs (Short Interspersed Nuclear Elements) by combining a set of methods (Ashlock & Datta, 2012), which is a complicated procedure in bioinformatics.…”

Section: Benefits Of ML Over Bioinformatics (Q1)mentioning

confidence: 99%

“…For example, (Loureiro et al, 2013b), (Zamith Santos et al, 2018), and (Abrusan et al, 2009) used k-mer frequencies as features. On the contrary, (Schietgat et al, 2018), (Arango-López et al, 2017), (Loureiro et al, 2013a) used numerical and categorical features mainly based on structures. Other purposes of ML in TEs included detecting the boundaries of mobile elements (Tsafnat et al, 2011), identifying insertion sites of somatic LINEs insertions (Tang et al, 2017), and other mobile elements at the genome level (Rawal & Ramaswamy, 2011), detecting aneuploidy in patients with cancer through LINEs (Douville et al, 2018), detecting conserved motifs in TIR elements (Su, Gu & Peterson, 2019), distinguishing endogenous retroviral LTRs from SINEs (Ashlock & Datta, 2012), and comparing multiple studies on transposon insertion sequencing (Hubbard et al, 2019).…”

Section: Architectures and Algorithms Currently Used For Tes Or Simentioning

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%

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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.

show abstract

Transposable elements in plants: Recent advancements, tools and prospects

et al. 2022

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Application of Data Mining Algorithms to Classify Biological Data: The Coffea canephora Genome Case

Cited by 14 publications

References 39 publications

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

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)"

Transposable elements in plants: Recent advancements, tools and prospects

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