A mapping-free natural language processing-based technique for sequence search in nanopore long-reads.

ABSTRACT:

Background

In unforeseen situations, such as nuclear power plant's or civilian radiation accidents, there is a need for effective and computationally inexpensive methods to determine the expression level of a selected gene panel, allowing for rough dose estimates in thousands of donors. The new generation in-situ mapper, fast and of low energy consumption, working at the level of single nanopore output, is in demand. We aim to create a sequence identification tool that utilizes natural language processing techniques and ensures a high level of negative predictive value (NPV) compared to the classical approach.

Results

The training dataset consisted of RNA sequencing data from 6 samples. Multiple natural language processing models were examined, differing in the type of dicti

SUBMITTER: Strzoda T 

PROVIDER: S-EPMC11562635 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature