MARIA: Multiple-alignment rr-index with aggregation

There now exist compact indexes that can efficiently list all the occurrences of a pattern in a dataset consisting of thousands of genomes, or even all the occurrences of all the pattern's maximal exact matches (MEMs) with respect to the dataset. Unless we are lucky and the pattern is specific to only a few genomes, however, we could be swamped by hundreds of matches -- or even hundreds per MEM -- only to discover that most or all of the matches are to substrings that occupy the same few columns in a multiple alignment. To address this issue, in this paper we present a simple and compact data index MARIA that stores a multiple alignment such that, given the position of one match of a pattern (or a MEM or other substring of a pattern) and its length, we can quickly list all the distinct columns of the multiple alignment where matches start

Wheeler Maps

Motivated by challenges in pangenomic read alignment, we propose a generalization of Wheeler graphs that we call Wheeler maps. A Wheeler map stores a text T[1..n] and an assignment of tags to the characters of T such that we can preprocess a pattern P[1..m] and then, given i and j, quickly return all the distinct tags labeling the first characters of the occurrences of P[i..j] in T. For the applications that most interest us, characters with long common contexts are likely to have the same tag, so we consider the number t of runs in the list of tags sorted by their characters’ positions in the Burrows-Wheeler Transform (BWT) of T. We show how, given a straight-line program with g rules for T, we can build an O(g+r+t)-space Wheeler map, where r is the number of runs in the BWT of T, with which we can preprocess a pattern P[1..m] in O(mlogn) time and then return the k distinct tags for P[i..j] in optimal O(k) time for any given i and j

Can We Replace Reads by Numeric Signatures? Lyndon Fingerprints as Representations of Sequencing Reads for Machine Learning

Bonizzoni P, De Felice C, Petescia A, et al. Can We Replace Reads by Numeric Signatures? Lyndon Fingerprints as Representations of Sequencing Reads for Machine Learning. In: Proceedings of AlCoB 2021. LNBI. Vol 12715. 2021: 16-28

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

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