Fabio Cunial

Versatile Succinct Representations of the Bidirectional Burrows-Wheeler Transform

We describe succinct and compact representations of the bidirectional bwt of a string s ∈ Σ* which provide increasing navigation power and a number of space-time tradeoffs. One such representation allows to extend a substring of s by one character from the left and from the right in constant time, taking O(|s| log |Σ|) bits of space. We then match the functions supported by each representation to a number of algorithms that traverse the nodes of the suffix tree of s, exploiting connections between the bwt and the suffix-link tree. This results in near-linear time algorithms for many sequence analysis problems (e.g. maximal unique matches), for the first time in succinct space.

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.

LAF: Logic Alignment Free and its application to bacterial genomes classification.

Alignment-free algorithms can be used to estimate the similarity of biological sequences and hence are often applied to the phylogenetic reconstruction of genomes. Most of these algorithms rely on comparing the frequency of all the distinct substrings of fixed length (k-mers) that occur in the analyzed sequences.In this paper, we present Logic Alignment Free (LAF), a method that combines alignment-free techniques and rule-based classification algorithms in order to assign biological samples to their taxa. This method searches for a minimal subset of k-mers whose relative frequencies are used to build classification models as disjunctive-normal-form logic formulas (if-then rules).We apply LAF successfully to the classification of bacterial genomes to their corresponding taxonomy. In particular, we succeed in obtaining reliable classification at different taxonomic levels by extracting a handful of rules, each one based on the frequency of just few k-mers.State of the art methods to adjust the frequency of k-mers to the character distribution of the underlying genomes have negligible impact on classification performance, suggesting that the signal of each class is strong and that LAF is effective in identifying it.

Fast Label Extraction in the CDAWG

The compact directed acyclic word graph (CDAWG) of a string

Indexed Matching Statistics and Shortest Unique Substrings

T

Space-Efficient Detection of Unusual Words

of length

Classifying Bacterial Genomes with Compact Logic Formulas on k-Mer Frequencies

n

Flexible Indexing of Repetitive Collections

takes space proportional just to the number

Fast matching statistics in small space

e

A framework for space-efficient variable-order Markov models

of right extensions of the maximal repeats of