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Compressed Weighted de Bruijn Graphs

Authors Giuseppe F. Italiano , Nicola Prezza , Blerina Sinaimeri , Rossano Venturini

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Subject Classification

ACM Subject Classification
  • Theory of computation → Data compression
  • Theory of computation → Data structures design and analysis
Keywords
  • weighted de Bruijn graphs
  • k-mer annotation
  • compressed data structures
  • partial sums

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Abstract

We propose a new compressed representation for weighted de Bruijn graphs, which is based on the idea of delta-encoding the variations of k-mer abundances on a spanning branching of the graph. Our new data structure is likely to be of practical value: to give an idea, when combined with the compressed BOSS de Bruijn graph representation, it encodes the weighted de Bruijn graph of a 16x-covered DNA read-set (60M distinct k-mers, k = 28) within 4.15 bits per distinct k-mer and can answer abundance queries in about 60 microseconds on a standard machine. In contrast, state of the art tools declare a space usage of at least 30 bits per distinct k-mer for the same task, which is confirmed by our experiments. As a by-product of our new data structure, we exhibit efficient compressed data structures for answering partial sums on edge-weighted trees, which might be of independent interest.

Cite As Get BibTex

Giuseppe F. Italiano, Nicola Prezza, Blerina Sinaimeri, and Rossano Venturini. Compressed Weighted de Bruijn Graphs. In 32nd Annual Symposium on Combinatorial Pattern Matching (CPM 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 191, pp. 16:1-16:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021) https://doi.org/10.4230/LIPIcs.CPM.2021.16

Author Details

Giuseppe F. Italiano
  • Luiss University, Rome, Italy
  • Erable, INRIA Grenoble Rhône-Alpes, France
Nicola Prezza
  • DAIS, Ca' Foscari University of Venice, Italy
Blerina Sinaimeri
  • Luiss University, Rome, Italy
  • Erable, INRIA Grenoble Rhône-Alpes, France
Rossano Venturini
  • Dipartimento di Informatica, Università di Pisa, Pisa, Italy

Funding

  • Italiano, Giuseppe F.: Partially supported by MUR, the Italian Ministry for University and Research, under PRIN Project AHeAD (Efficient Algorithms for HArnessing Networked Data).
  • Venturini, Rossano: Partially supported by MUR, the Italian Ministry for University and Research, under PRIN 2017 Project 2017K7XPAN Algorithms, Data Structures and Combinatorics for Machine Learning and Università di Pisa Project PRA_2020-2021_26.

Supplementary Materials

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