Identification of species based on DNA barcode using k-mer feature vector and Random forest classifier

Meher, Prabina Kumar; Sahu, Tanmaya Kumar; Rao, Atmakuri Ramakrishna

doi:10.1016/j.gene.2016.07.010

Cited by 21 publications

(15 citation statements)

References 23 publications

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“…Previous reports describe the relationship between the strength of the TCR signal and Treg development, showing that intensive TCR stimulation is a prerequisite for Treg development. 13 , 31 , 32 As the activation of AP-1 is a major consequence of the TCR signaling cascade, and as Batf3 is a transcriptional repressor of AP-1 due to lack of a transactivation domain, 33 Batf3 might negatively modulate the strength of the TCR signal in the nucleus during initial T-cell activation.…”

Section: Discussionmentioning

confidence: 99%

The transcription factor Batf3 inhibits the differentiation of regulatory T cells in the periphery

et al. 2017

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Naive CD4 T cells activated by antigen-presenting cells (APCs) undergo terminal differentiation in the periphery. Multiple mechanisms determine their fates, that is, whether they differentiate into conventional T (Tconv) cells or regulatory T (Treg) cells. The key event during Treg generation is expression of the transcription factor Foxp3, which is the lineage-determining regulator for Treg differentiation and function. Here we show that the transcription factor Batf3 acts as a fate-decision factor with respect to Tconv versus Tregs by restraining Treg differentiation. Batf3 was preferentially expressed in effector CD4 T cells but not in Treg cells, and ectopic expression of Batf3 inhibited Foxp3 induction. Batf3-deficient CD4 T cells favorably differentiated into Treg cells in vitro and in colonic lamina propria. Batf3 KO mice also showed enhanced Treg function in gut-associated immune disease models (for example, ovalbumin tolerance and inflammatory bowel disease models). Batf3 bound to the CNS1 region of the Foxp3 locus and reduced expression of the gene. Thus, Batf3 is a transcriptional suppressor of Treg differentiation.

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Section: Discussionmentioning

confidence: 99%

The transcription factor Batf3 inhibits the differentiation of regulatory T cells in the periphery

et al. 2017

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“…As far as barcode-based species identification using machine learning predictors is concerned, sparse encoding technique was adopted by Weitschek et al [15]. In another study, Meher et al [26] encoded the barcode sequences based on the composition of contiguous k -mer, for species identification using RF [22] machine learning technique. Specific to fungal species, k -mer features [26] were employed in RDP classfier, SINTAX algorithm and Mycofier for encoding barcode sequences into numeric vectors.…”

Section: Methodsmentioning

confidence: 99%

funbarRF: DNA barcode-based fungal species prediction using multiclass Random Forest supervised learning model

Meher¹,

Sahu²,

Gahoi³

et al. 2019

BMC Genet

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BackgroundIdentification of unknown fungal species aids to the conservation of fungal diversity. As many fungal species cannot be cultured, morphological identification of those species is almost impossible. But, DNA barcoding technique can be employed for identification of such species. For fungal taxonomy prediction, the ITS (internal transcribed spacer) region of rDNA (ribosomal DNA) is used as barcode. Though the computational prediction of fungal species has become feasible with the availability of huge volume of barcode sequences in public domain, prediction of fungal species is challenging due to high degree of variability among ITS regions within species.ResultsA Random Forest (RF)-based predictor was built for identification of unknown fungal species. The reference and query sequences were mapped onto numeric features based on gapped base pair compositions, and then used as training and test sets respectively for prediction of fungal species using RF. More than 85% accuracy was found when 4 sequences per species in the reference set were utilized; whereas it was seen to be stabilized at ~88% if ≥7 sequence per species in the reference set were used for training of the model. The proposed model achieved comparable accuracy, while evaluated against existing methods through cross-validation procedure. The proposed model also outperformed several existing models used for identification of different species other than fungi.ConclusionsAn online prediction server “funbarRF” is established at http://cabgrid.res.in:8080/funbarrf/ for fungal species identification. Besides, an R-package funbarRF (https://cran.r-project.org/web/packages/funbarRF/) is also available for prediction using high throughput sequence data. The effort put in this work will certainly supplement the future endeavors in the direction of fungal taxonomy assignments based on DNA barcode.

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“…The k-mer abundance analysis is widely used in genomics research [1][2][3][4][5][6][7][8][9][10]. The term k-mer refers to all possible substrings (in the 5′-3′ direction) of length k in a DNA sequence and, therefore, the k-mer frequency is a good variable for characterizing the composition of a genome's DNA sequence.…”

Section: Introductionmentioning

confidence: 99%

Methylation-driven model for analysis of dinucleotide evolution in genomes

Sun

Liu

2020

Theor Biol Med Model

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Background: CpGs, the major methylation sites in vertebrate genomes, exhibit a high mutation rate from the methylated form of CpG to TpG/CpA and, therefore, influence the evolution of genome composition. However, the quantitative effects of CpG to TpG/CpA mutations on the evolution of genome composition in terms of the dinucleotide frequencies/proportions remain poorly understood. Results: Based on the neutral theory of molecular evolution, we propose a methylation-driven model (MDM) that allows predicting the changes in frequencies/proportions of the 16 dinucleotides and in the GC content of a genome given the known number of CpG to TpG/CpA mutations. The application of MDM to the 10 published vertebrate genomes shows that, for most of the 16 dinucleotides and the GC content, a good consistency is achieved between the predicted and observed trends of changes in the frequencies and content relative to the assumed initial values, and that the model performs better on the mammalian genomes than it does on the lowervertebrate genomes. The model's performance depends on the genome composition characteristics, the assumed initial state of the genome, and the estimated parameters, one or more of which are responsible for the different application effects on the mammalian and lower-vertebrate genomes and for the large deviations of the predicted frequencies of a few dinucleotides from their observed frequencies. Conclusions: Despite certain limitations of the current model, the successful application to the higher-vertebrate (mammalian) genomes witnesses its potential for facilitating studies aimed at understanding the role of methylation in driving the evolution of genome dinucleotide composition.

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Identification of species based on DNA barcode using k-mer feature vector and Random forest classifier

Cited by 21 publications

References 23 publications

The transcription factor Batf3 inhibits the differentiation of regulatory T cells in the periphery

The transcription factor Batf3 inhibits the differentiation of regulatory T cells in the periphery

funbarRF: DNA barcode-based fungal species prediction using multiclass Random Forest supervised learning model

Methylation-driven model for analysis of dinucleotide evolution in genomes

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