Locality Sensitive Hashing-based Sequence Alignment Using Deep Bidirectional LSTM Models

TL;DR

This paper introduces a novel approach using deep bidirectional LSTM models to learn locality-sensitive hashing features for sequence alignment, demonstrating improved accuracy in aligning short reads to a reference genome.

Contribution

The paper presents a new LSTM-based method for generating LSH features, enhancing sequence alignment accuracy over traditional methods.

Findings

Higher alignment accuracy with increased epochs

Effective sequence modeling with deep bidirectional LSTM

Feasibility demonstrated on human genome data

Abstract

Bidirectional Long Short-Term Memory (LSTM) is a special kind of Recurrent Neural Network (RNN) architecture which is designed to model sequences and their long-range dependencies more precisely than RNNs. This paper proposes to use deep bidirectional LSTM for sequence modeling as an approach to perform locality-sensitive hashing (LSH)-based sequence alignment. In particular, we use the deep bidirectional LSTM to learn features of LSH. The obtained LSH is then can be utilized to perform sequence alignment. We demonstrate the feasibility of the modeling sequences using the proposed LSTM-based model by aligning the short read queries over the reference genome. We use the human reference genome as our training dataset, in addition to a set of short reads generated using Illumina sequencing technology. The ultimate goal is to align query sequences into a reference genome. We first decompose the reference genome into multiple sequences. These sequences are then fed into the bidirectional LSTM model and then mapped into fixed-length vectors. These vectors are what we call the trained LSH, which can then be used for sequence alignment. The case study shows that using the introduced LSTM-based model, we achieve higher accuracy with the number of epochs.