Survey on encoding schemes for genomic data representation and feature learning—from signal processing to machine learning

Yu, Ning; Li, Zhihua; Yu, Zeng

doi:10.26599/bdma.2018.9020018

Cited by 57 publications

(9 citation statements)

References 107 publications

(185 reference statements)

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“…It is assumed that proteins with more similar GO annotations are more functionally coherent [20]. We calculate and analyze such functional similarity by the fraction of aligned proteins that share same GO annotations.…”

Section: Resultsmentioning

confidence: 99%

ConnectedAlign: a PPI network alignment method for identifying conserved protein complexes across multiple species

Gao

Song

et al. 2018

BMC Bioinformatics

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BackgroundIn bioinformatics, network alignment algorithms have been applied to protein-protein interaction (PPI) networks to discover evolutionary conserved substructures at the system level. However, most previous methods aim to maximize the similarity of aligned proteins in pairwise networks, while concerning little about the feature of connectivity in these substructures, such as the protein complexes.ResultsIn this paper, we identify the problem of finding conserved protein complexes, which requires the aligned proteins in a PPI network to form a connected subnetwork. By taking the feature of connectivity into consideration, we propose ConnectedAlign, an efficient method to find conserved protein complexes from multiple PPI networks. The proposed method improves the coverage significantly without compromising of the consistency in the aligned results. In this way, the knowledge of protein complexes in well-studied species can be extended to that of poor-studied species.ConclusionsWe conducted extensive experiments on real PPI networks of four species, including human, yeast, fruit fly and worm. The experimental results demonstrate dominant benefits of the proposed method in finding protein complexes across multiple species.

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Section: Resultsmentioning

confidence: 99%

ConnectedAlign: a PPI network alignment method for identifying conserved protein complexes across multiple species

Gao

Song

et al. 2018

BMC Bioinformatics

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“…Triplet Encoding [6,42] 64 codons are encoded by weights [44] Each base is encoded with the value of the base distance between the next itself and the same base.…”

Section: X=[agctaccgtg]mentioning

confidence: 99%

“…Encoding [49] 1000 Chaos Game Representation (CGR) [6,50] A: (0, 0), T: (1, 0), G: (1, 1), C: (0, 1)…”

Section: Snp Ginmentioning

confidence: 99%

The Effect of Numerical Mapping Techniques on Performance in Genomic Research

GÜLOCAK

Daş

2022

Sakarya University Journal of Computer and Information Sciences

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In genomic signal processing applications, digitization of these signals is needed to process and analyze DNA signals. In the digitization process, the mapping technique to be chosen greatly affects the performance of the system for the genomic domain to be studied. The purpose of this review is to analyze how numerical mapping techniques used in digitizing DNA sequences affect performance in genomic studies. For this purpose, all digital coding techniques presented in the literature in the studies conducted in the last 10 years have been examined, and the numerical representations of these techniques are given in a sample DNA sequence. In addition, the frequency of use of these coding techniques in four popular genomic areas such as exon region identification, exon-intron classification, phylogenetic analysis, gene detection, and the min-max range of the performances obtained by using these techniques in that area are also given. This study is thought to be a guide for researchers who want to work in the field of bioinformatics.

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“…Another popular class of alignment-free representation schemes is one that is based on information theory principles such as entropy, although numerous other representations do not fall in either of these two categories [11] . A comprehensive review of the recent numerical encoding schemes can be found in [28] .…”

Section: Related Workmentioning

confidence: 99%

Classification of COVID-19 and Other Pathogenic Sequences: A Dinucleotide Frequency and Machine Learning Approach

et al. 2020

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The world is grappling with the COVID-19 pandemic caused by the 2019 novel SARS-CoV-2. To better understand this novel virus and its relationship with other pathogens, new methods for analyzing the genome are required. In this study, intrinsic dinucleotide genomic signatures were analyzed for whole genome sequence data of eight pathogenic species, including SARS-CoV-2. The genome sequences were transformed into dinucleotide relative frequencies and classified using the extreme gradient boosting (XGBoost) model. The classification models were trained to a) distinguish between the sequences of all eight species and b) distinguish between sequences of SARS-CoV-2 that originate from different geographic regions. Our method attained 100% in all performance metrics and for all tasks in the eight-species classification problem. Moreover, the models achieved 67% balanced accuracy for the task of classifying the SARS-CoV-2 sequences into the six continental regions and achieved 86% balanced accuracy for the task of classifying SARS-CoV-2 samples as either originating from Asia or not. Analysis of the dinucleotide genomic profiles of the eight species revealed a similarity between the SARS-CoV-2 and MERS-CoV viral sequences. Further analysis of SARS-CoV-2 viral sequences from the six continents revealed that samples from Oceania had the highest frequency of TT dinucleotides as well as the lowest CG frequency compared to the other continents. The dinucleotide signatures of AC, AG,CA, CT, GA, GT, TC, and TG were well conserved across most genomes, while the frequencies of other dinucleotide signatures varied considerably. Altogether, the results from this study demonstrate the utility of dinucleotide relative frequencies for discriminating and identifying similar species.INDEX TERMS alignment-free sequence analysis, COVID-19, dinucleotide frequencies, feature representations, genomic signatures, human pathogens, machine learning, XGBoost This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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Survey on encoding schemes for genomic data representation and feature learning—from signal processing to machine learning

Cited by 57 publications

References 107 publications

ConnectedAlign: a PPI network alignment method for identifying conserved protein complexes across multiple species

ConnectedAlign: a PPI network alignment method for identifying conserved protein complexes across multiple species

The Effect of Numerical Mapping Techniques on Performance in Genomic Research

Classification of COVID-19 and Other Pathogenic Sequences: A Dinucleotide Frequency and Machine Learning Approach

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