Numeric Lyndon-based feature embedding of sequencing reads for machine learning approaches

• URL: http://arxiv.org/abs/2202.13884v1
• Date: Mon, 28 Feb 2022 15:33:37 GMT
• Title: Numeric Lyndon-based feature embedding of sequencing reads for machine learning approaches
• Abstract summary: We propose a novel feature embedding method for Next-Generation Sequencing (NGS) data using the notion of fingerprint. We use fingerprints to represent RNA-Seq reads and to assign them to the most likely gene from which they were originated. We provide an implementation of the proposed method in the tool lyn2vec, which produces Lyndon-based feature embeddings of sequencing reads.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Feature embedding methods have been proposed in literature to represent sequences as numeric vectors to be used in some bioinformatics investigations, such as family classification and protein structure prediction. Recent theoretical results showed that the well-known Lyndon factorization preserves common factors in overlapping strings. Surprisingly, the fingerprint of a sequencing read, which is the sequence of lengths of consecutive factors in variants of the Lyndon factorization of the read, is effective in preserving sequence similarities, suggesting it as basis for the definition of novels representations of sequencing reads. We propose a novel feature embedding method for Next-Generation Sequencing (NGS) data using the notion of fingerprint. We provide a theoretical and experimental framework to estimate the behaviour of fingerprints and of the k-mers extracted from it, called k-fingers, as possible feature embeddings for sequencing reads. As a case study to assess the effectiveness of such embeddings, we use fingerprints to represent RNA-Seq reads and to assign them to the most likely gene from which they were originated as fragments of transcripts of the gene. We provide an implementation of the proposed method in the tool lyn2vec, which produces Lyndon-based feature embeddings of sequencing reads.

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