SHARAKU: an algorithm for aligning and clustering read mapping profiles of deep sequencing in non-coding RNA processing

Tsuchiya, Masaharu; Amano, Kojiro; Abe, Masaya; Seki, Misato; Hase, Sumitaka; Sato, Kengo; Sakakibara, Yasubumi

doi:10.1093/bioinformatics/btw273

Cited by 4 publications

(6 citation statements)

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“…A pairwise alignment for a pair of read mapping profiles of ncRNAs with primary sequences and secondary structures was calculated in software called SHARAKU developed in our previous work ( Tsuchiya et al , 2016 ). When read mapping profiles for a pair of ncRNAs are obtained, SHARAKU fundamentally aligns two read mapping profiles by inserting gaps so that the sum of the differences of coverages at all positions between the two profiles is minimized.…”

Section: Resultsmentioning

confidence: 99%

“…Deep sequencing of transcripts of regulatory ncRNA sequences generates footprints of post-transcriptional processes ( Chen and Heard, 2013 ). After sequence reads are obtained, the short reads are mapped onto a reference genome and specific mapping patterns in the ncRNA sequences can be detected, which are called read mapping profiles ( Tsuchiya et al , 2016 ). These patterns reflect the maturation processes that produce shorter RNA sequences called derived RNAs ( Fig.…”

Section: Methodsmentioning

confidence: 99%

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Convolutional neural networks for classification of alignments of non-coding RNA sequences

Aoki

Sakakibara

2018

Bioinformatics

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MotivationThe convolutional neural network (CNN) has been applied to the classification problem of DNA sequences, with the additional purpose of motif discovery. The training of CNNs with distributed representations of four nucleotides has successfully derived position weight matrices on the learned kernels that corresponded to sequence motifs such as protein-binding sites.ResultsWe propose a novel application of CNNs to classification of pairwise alignments of sequences for accurate clustering of sequences and show the benefits of the CNN method of inputting pairwise alignments for clustering of non-coding RNA (ncRNA) sequences and for motif discovery. Classification of a pairwise alignment of two sequences into positive and negative classes corresponds to the clustering of the input sequences. After we combined the distributed representation of RNA nucleotides with the secondary-structure information specific to ncRNAs and furthermore with mapping profiles of next-generation sequence reads, the training of CNNs for classification of alignments of RNA sequences yielded accurate clustering in terms of ncRNA families and outperformed the existing clustering methods for ncRNA sequences. Several interesting sequence motifs and secondary-structure motifs known for the snoRNA family and specific to microRNA and tRNA families were identified.Availability and implementationThe source code of our CNN software in the deep-learning framework Chainer is available at http://www.dna.bio.keio.ac.jp/cnn/, and the dataset used for performance evaluation in this work is available at the same URL.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Convolutional neural networks for classification of alignments of non-coding RNA sequences

Aoki

Sakakibara

2018

Bioinformatics

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“…Contrarily deep sequencing has also been employed for ncRNA classification. For instance, Tsuchiya et al [49] presented an approach SHARAKU based on deep sequencing for ncRNA classification. SHARAKU incorporated an algorithm which aligned read mapping profiles of ncRNAs next generation data containing sequences.…”

Section: Related Workmentioning

confidence: 99%

A Robust and Precise ConvNet for Small Non-Coding RNA Classification (RPC-snRC)

et al. 2021

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Small non-coding RNAs (ncRNAs) are attracting increasing attention as they are now considered potentially valuable resources in the development of new drugs intended to cure several human diseases. A prerequisite for the development of drugs targeting ncRNAs or the related pathways is the identification and correct classification of such ncRNAs. State-of-the-art small ncRNA classification methodologies use secondary structural features as input. However, such feature extraction approaches only take global characteristics into account and completely ignore co-relative effects of local structures. Furthermore, secondary structure based approaches incorporate high dimensional feature space which is computationally expensive. The present paper proposes a novel Robust and Precise ConvNet (RPC-snRC) methodology which classifies small ncRNAs into relevant families by utilizing their primary sequence. RPC-snRC methodology learns hierarchical representation of features by utilizing positioning and information on the occurrence of nucleotides. To avoid exploding and vanishing gradient problems, we use an approach similar to DenseNet in which gradient can flow straight from subsequent layers to previous layers. In order to assess the effectiveness of deeper architectures for small ncRNA classification, we also adapted two ResNet architectures having a different number of layers. Experimental results on a benchmark small ncRNA dataset show that the proposed methodology does not only outperform existing small ncRNA classification approaches with a significant performance margin of 10% but it also gives better results than adapted ResNet architectures.

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“…The methods that implements deep sequencing for classification of non-coding RNA are provided in Table 6. Yasubumi Sakakibara et al [15] proposed a method SHARAKU which implements deep sequencing for classification of non-coding RNA. SHARAKU incorporates a new algorithm that aligns two read mapping profiles of non-coding RNA's next generation sequencing data.…”

Section: Deep Sequencingmentioning

confidence: 99%

“…This method uses multiple sequence alignment. An issue with SVM is that it can be used to find only two classes, it cannot be used to classify multiple types of non- SHARAKU is a new algorithm that uses next generation sequencing data and aligns two read mapping profiles of non-coding RNAs [15]. It uses sequence information and secondary structure information simultaneously for the detection of non-coding RNA.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2019

RJLBPCS

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Non-coding RNA (ncRNA) is the RNA that is not converted into protein rather produces functional RNA molecules. The type of non-coding RNAs include microRNA, snoRNA and many other small RNAs like siRNAs and microRNAs. Identifying non-coding RNA has emerged over the past decade as a hot trend in bioinformatics. Many techniques are developed for classification of non-coding RNA and it is appropriate to select a particular technique according to the situation and circumstances. In this article, several machine learning techniques on classification of non-coding RNA are discussed with their merits, limitations and application scope to aid people in selecting a suitable method and obtaining a reliable result. These techniques are compared on the basis of their performance and accuracy. The future scope is also provided.

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SHARAKU: an algorithm for aligning and clustering read mapping profiles of deep sequencing in non-coding RNA processing

Cited by 4 publications

References 30 publications

Convolutional neural networks for classification of alignments of non-coding RNA sequences

Convolutional neural networks for classification of alignments of non-coding RNA sequences

A Robust and Precise ConvNet for Small Non-Coding RNA Classification (RPC-snRC)

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