Work

ESKEMAP: exact sketch-based read mapping

Year: 2024

Type: article

Abstract: Background Given a sequencing read, the broad goal of read mapping is to find the location(s) in the reference genome that have a “similar sequence”. Traditionally, “similar sequence” was defined as having a high alignment score and read mappers were viewed as heuristic solutions to this well-defined problem. For sketch-based mappers, however, there has not been a problem formulation to capture what problem an exact sketch-based mapping algorithm should solve. Moreover, there is no sketch-based method that can find all possible mapping positions for a read above a certain score threshold. Results In this paper, we formulate the problem of read mapping at the level of sequence sketches. We give an exact dynamic programming algorithm that finds all hits above a given similarity threshold. It runs in $$\mathcal {O} (|t| + |p| + \ell ^2)$$ O ( | t | + | p | + ℓ 2 ) time and $$\mathcal {O} (\ell \log \ell )$$ O ( ℓ log ℓ ) space, where | t | is the number of $$k$$ k -mers inside the sketch of the reference, | p | is the number of $$k$$ k -mers inside the read’s sketch and $$\ell$$ ℓ is the number of times that $$k$$ k -mers from the pattern sketch occur in the sketch of the text. We evaluate our algorithm’s performance in mapping long reads to the T2T assembly of human chromosome Y, where ampliconic regions make it desirable to find all good mapping positions. For an equivalent level of precision as minimap2, the recall of our algorithm is 0.88, compared to only 0.76 of minimap2.

Source: Algorithms for Molecular Biology

DOI: 10.1186/s13015-024-00261-7

Authors: Tizian Schulz, Paul Medvedev

Institutions: Bielefeld University, Pennsylvania State University

Language: English

Topic: Algorithms and Data Compression

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,968