Long-Short Term Memory Network for RNA Structure Profiling Super-Resolution

Wong, Pak Kan; Wong, Man Leung; Leung, Kwong Sak

doi:10.1007/978-3-319-71069-3_20

Cited by 2 publications

(1 citation statement)

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“…CNN finds temporal features where RNN searches for consecutive features in a sequence. RNA [32] and protein [33] structure prediction and alternative splicing prediction are among direct sequence based applications. The only available example (by mid-June 2018 besides this study) of sequence-based DL application in metagenomics is [34] for OTUs clustering using CNN and PCA.…”

Section: E Word2vec In Bioinformaticsmentioning

confidence: 99%

SpeciesMLP: Sequence based Multi-layer Perceptron for Amplicon Read Classification Using Real-time Data Augmentation

Kishk¹,

El-Hadidi²

2018

Preprint

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Taxonomic assignment is the core of targeted metagenomics approaches that aims to assign sequencing reads to their corresponding taxonomy. Sequence similarity searching and machine learning (ML) are two commonly used approaches for taxonomic assignment based on the 16S rRNA. Similarity based approaches require high computation resources, while ML approaches don't need these resources in prediction. The majority of these ML approaches depend on k-mer frequency rather than direct sequence, which leads to low accuracy on short reads as k-mer frequency doesn't consider k-mer position. Moreover training ML taxonomic classifiers depend on a specific read length which may reduce the prediction performance by decreasing read length. In this study, we built a neural network classifier for 16S rRNA reads based on SILVA database (version 132). Modeling was performed on direct sequences using Convolutional neural network (CNN) and other neural network architectures such as Multi-layer Perceptron and Recurrent Neural Network. In order to reduce modeling time of the direct sequences, In-silico PCR was applied on SILVA database. Total number of 14 subset databases were generated by universal primers for each single or paired high variable region (HVR). Moreover, in this study, we illustrate the results for the V2 database model on 8443 classes on the species level and 1552 on the genus level. In order to simulate sequencing fragmentation, we trained variable length subsequences from 50 bases till the full length of the HVR that are randomly changing in each training iteration. Simple MLP model with global max pooling gives 0.71 & 0.93 test accuracy for the species and genus levels respectively (for reads of 100 base subsequences) and 0.75 & 0.96 accuracy for the species and genus levels respectively (on the full length V2 HVR). In this study, we present a novel method (SpeciesMLP https://github.com/ali-kishk/SpeciesMLP) to model the direct amplicon sequence using MLP over a sequence of k-mers faster 20 times than CNN in training and 10 times in prediction.

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Section: E Word2vec In Bioinformaticsmentioning

confidence: 99%