scholarly journals Variable-Order de Bruijn Graphs

Author(s):  
Christina Boucher ◽  
Alex Bowe ◽  
Travis Gagie ◽  
Simon J. Puglisi ◽  
Kunihiko Sadakane
2020 ◽  
Vol 21 (S8) ◽  
Author(s):  
Nicola Prezza ◽  
Nadia Pisanti ◽  
Marinella Sciortino ◽  
Giovanna Rosone

Abstract Background In [Prezza et al., AMB 2019], a new reference-free and alignment-free framework for the detection of SNPs was suggested and tested. The framework, based on the Burrows-Wheeler Transform (BWT), significantly improves sensitivity and precision of previous de Bruijn graphs based tools by overcoming several of their limitations, namely: (i) the need to establish a fixed value, usually small, for the order k, (ii) the loss of important information such as k-mer coverage and adjacency of k-mers within the same read, and (iii) bad performance in repeated regions longer than k bases. The preliminary tool, however, was able to identify only SNPs and it was too slow and memory consuming due to the use of additional heavy data structures (namely, the Suffix and LCP arrays), besides the BWT. Results In this paper, we introduce a new algorithm and the corresponding tool ebwt2InDel that (i) extend the framework of [Prezza et al., AMB 2019] to detect also INDELs, and (ii) implements recent algorithmic findings that allow to perform the whole analysis using just the BWT, thus reducing the working space by one order of magnitude and allowing the analysis of full genomes. Finally, we describe a simple strategy for effectively parallelizing our tool for SNP detection only. On a 24-cores machine, the parallel version of our tool is one order of magnitude faster than the sequential one. The tool ebwt2InDel is available at github.com/nicolaprezza/ebwt2InDel. Conclusions Results on a synthetic dataset covered at 30x (Human chromosome 1) show that our tool is indeed able to find up to 83% of the SNPs and 72% of the existing INDELs. These percentages considerably improve the 71% of SNPs and 51% of INDELs found by the state-of-the art tool based on de Bruijn graphs. We furthermore report results on larger (real) Human whole-genome sequencing experiments. Also in these cases, our tool exhibits a much higher sensitivity than the state-of-the art tool.


Author(s):  
Djamal Belazzougui ◽  
Travis Gagie ◽  
Veli Mäkinen ◽  
Marco Previtali ◽  
Simon J. Puglisi

2018 ◽  
Vol 29 (08) ◽  
pp. 1279-1295 ◽  
Author(s):  
Djamal Belazzougui ◽  
Travis Gagie ◽  
Veli Mäkinen ◽  
Marco Previtali ◽  
Simon J. Puglisi

Compressed suffix trees and bidirectional FM-indexes can store a set of strings and support queries that let us explore the set of substrings they contain, adding and deleting characters on both the left and right, but they can use much more space than a de Bruijn graph for the strings. Bowe et al.’s BWT-based de Bruijn graph representation (Proc. Workshop on Algorithms for Bioinformatics, pp. 225–235, 2012) can be made bidirectional as well, at the cost of increasing its space usage by a small constant, but it fixes the length of the substrings. Boucher et al. (Proc. Data Compression Conference, pp. 383–392, 2015) generalized Bowe et al.’s representation to support queries about variable-length substrings, but at the cost of bidirectionality. In this paper we show how to make Boucher et al.’s variable-order implementation of de Bruijn graphs bidirectional.


Author(s):  
Jarno Alanko ◽  
Bahar Alipanahi ◽  
Jonathen Settle ◽  
Christina Boucher ◽  
Travis Gagie
Keyword(s):  

1997 ◽  
Vol 79 (1-3) ◽  
pp. 3-34 ◽  
Author(s):  
Thomas Andreae ◽  
Martin Hintz ◽  
Michael Nölle ◽  
Gerald Schreiber ◽  
Gerald W. Schuster ◽  
...  

2018 ◽  
Vol 34 (15) ◽  
pp. 2556-2565 ◽  
Author(s):  
Isaac Turner ◽  
Kiran V Garimella ◽  
Zamin Iqbal ◽  
Gil McVean

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