  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 (NLP) techniques and ensures a high
level of negative predictive value (NPV) compared to the classical approach.
The training dataset consisted of RNASeq data from 6 samples. Having tested
multiple NLP models, the best configuration analyses the entire sequence and
uses a word length of 3 base pairs with one-word neighbor on each side. For the
considered FDXR gene, the achieved mean balanced accuracy (BACC) was 98.29% and
NPV 99.25%, compared to minimap2's performance in a cross-validation scenario.
Reducing the dictionary from 1024 to 145 changed BACC to 96.49% and the NPV to
98.15%. Obtained NLP model, validated on an external independent genome
sequencing dataset, gave NPV of 99.64% for complete and 95.87% for reduced
dictionary. The salmon-estimated read counts differed from the classical
approach on average by 3.48% for the complete dictionary and by 5.82% for the
reduced one. We conclude that for long Oxford Nanopore reads, an NLP-based
approach can successfully replace classical mapping in case of emergency. The
developed NLP model can be easily retrained to identify selected transcripts
and/or work with various long-read sequencing techniques. Our results of the
study clearly demonstrate the potential of applying techniques known from
classical text processing to nucleotide sequences and represent a significant
advancement in this field of science.
