Nicola Prezza

Composite Repetition-Aware Data Structures

In highly repetitive strings, like collections of genomes from the same species, distinct measures of repetition all grow sublinearly in the length of the text, and indexes targeted to such strings typically depend only on one of these measures. We describe two data structures whose size depends on multiple measures of repetition at once, and that provide competitive tradeoffs between the time for counting and reporting all the exact occurrences of a pattern, and the space taken by the structure. The key component of our constructions is the run-length encoded BWT (RLBWT), which takes space proportional to the number of BWT runs: rather than augmenting RLBWT with suffix array samples, we combine it with data structures from LZ77 indexes, which take space proportional to the number of LZ77 factors, and with the compact directed acyclic word graph (CDAWG), which takes space proportional to the number of extensions of maximal repeats. The combination of CDAWG and RLBWT enables also a new representation of the suffix tree, whose size depends again on the number of extensions of maximal repeats, and that is powerful enough to support matching statistics and constant-space traversal.

ERNE-BS5: aligning BS-treated sequences by multiple hits on a 5-letters alphabet

Cytosine methylation is a DNA modification that has great impact on the regulation of gene expression and important implications for the biology and health of several living beings, including humans. Bisulfite conversion followed by next generation sequencing (BS-seq) of DNA is the gold standard technique used to detect DNA methylation at single-base resolution on a genome scale through the identification of 5-methylcytosine (5-mC). However, by converting unmethylated cytosines into thymines, BS-seq poses computational challenges to read alignment and aggravates the issue of multiple hits due to the ambiguity raised by the reduced sequence complexity. Here we present ERNE-BS5 (Extended Randomized Numerical alignEr - BiSulfite 5), an aligning program developed to efficiently map BS-treated reads against large genomes (e.g., human). To achieve this goal we have implemented three different ideas: (i) we use a 5-letters alphabet for storing methylation information, (ii) we use a weighted context-aware Hamming distance to identify a T coming from an unmethylated C context, and (iii) we use an iterative process to position multiple-hit reads starting from a preliminary map built using single-hit alignments. The map is corrected and extended at each cycle using the alignments added in the previous iteration. ERNE-BS5 is based on a new improved version of the rNA [20] aligning software with a more efficient core. ERNE (Extended Randomized Numerical alignEr) is a short string alignment package whose goal is to provide an all-inclusive set of tools to handle short reads. ERNE comprises: ERNE-MAP, ERNE-DMAP, ERNE-FILTER, ERNE-VISUAL, and, from now on, ERNE-BS5. ERNE is free software and distributed with an Open Source License (GPL V3) and can be downloaded at: http://erne.sourceforge.net

Fast Online Lempel-Ziv Factorization in Compressed Space

Let T be a text of length n on an alphabet

Hashing and Indexing: Succinct DataStructures and Smoothed Analysis

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Fast randomized approximate string matching with succinct hash data structures

of size

At the roots of dictionary compression: string attractors

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