Browsing by Author "Kucherov, Gregory"

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    Efficient tree-structured categorical retrieval
    (Leibniz International Proceedings in Informatics (LIPIcs), 2020-06-09) Belazzougui, Djamal; Kucherov, Gregory
    We study a document retrieval problem in the new framework where D text documents are organized in a category tree with a predefined number h of categories. This situation occurs e.g. with taxomonic trees in biology or subject classification systems for scientific literature. Given a string pattern p and a category (level in the category tree), we wish to efficiently retrieve the t categorical units containing this pattern and belonging to the category. We propose several efficient solutions for this problem. One of them uses n(log σ(1+o(1))+log D + O(h)) + O(∆) bits of space and O(|p| + t) query time, where n is the total length of the documents, σ the size of the alphabet used in the documents and ∆ is the total number of nodes in the category tree. Another solution uses n(log σ(1+o(1))+O(log D))+O(∆)+O(D log n) bits of space and O(|p| + t log D) query time. We finally propose other solutions which are more space-efficient at the expense of a slight increase in query time.
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    Space-Efficient Representation of Genomic k-Mer Count Tables
    (Schloss Dagstuhl -- Leibniz-Zentrum für Informatik, 2021-07-22) Shibuya, Yoshihiro; Belazzougui, Djamal; Kucherov, Gregory
    k-mer counting is a common task in bioinformatic pipelines, with many dedicated tools available. Output formats could rely on quotienting to reduce the space of k-mers in hash tables, however counts are not usually stored in space-efficient formats. Overall, k-mer count tables for genomic data take a considerable space, easily reaching tens of GB. Furthermore, such tables do not support efficient random-access queries in general. In this work, we design an efficient representation of k-mer count tables supporting fast random-access queries. We propose to apply Compressed Static Functions (CSFs), with space proportional to the empirical zero-order entropy of the counts. For very skewed distributions, like those of k-mer counts in whole genomes, the only currently available implementation of CSFs does not provide a compact enough representation. By adding a Bloom Filter to a CSF we obtain a Bloom-enhanced CSF (BCSF) effectively overcoming this limitation. Furthermore, by combining BCSFs with minimizer-based bucketing of k-mers, we build even smaller representations breaking the empirical entropy lower bound, for large enough k. We also extend these representations to the approximate case, gaining additional space. We experimentally validate these techniques on k-mer count tables of whole genomes (E.Coli and C.Elegans) as well as on k-mer document frequency tables for 29 E.Coli genomes. In the case of exact counts, our representation takes about a half of the space of the empirical entropy, for large enough k’s.