Toward perfect reads: short reads correction via mapping on compacted de Bruijn graphs

Limasset, Antoine; Flot, Jean‐François; Peterlongo, Pierre

doi:10.1101/558395

Cited by 5 publications

(12 citation statements)

References 32 publications

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“…Optionally, we can apply a filter on this coverage, removing low coverage unitigs (useful in case (5), Figure 2). While this kind of filtering has been shown to be efficient in genomics [17], such a threshold could suppress poorly represented but relevant k-mers. Unless said otherwise we did not applied this filter.…”

Section: Graph Construction and Hard Filtersmentioning

confidence: 99%

“…The mapping procedure follows a seed and extend paradigm as described in [16,17]. During the mapping of a read, the anchors shared between the read and the graph are detected and the alignment is greedily extended from each anchor using a simple linear scoring function, and the best alignments are used for correction.…”

Section: Global Strategymentioning

confidence: 99%

“…The reads were generated from RefSeq reference genome v19 and represent 107 millions reads, and 19 billions bases and were simulated with a one percent error rate. We selected rnaSPAdes [3] as it shares similarities with the presented approach (graph topology and hamming-based cleaning), the graph correction module of Bcool [17] to benchmark our own BCT implementation. We also included how a simple k-mer spectrum technique would perform for comparison.…”

Section: Transcriptomic De Bruijn Graph Cleaningmentioning

confidence: 99%

“…The rationale is that a set of k-mers can efficiently be represented in such a graph, and that a read can be corrected by its alignment on the graph that contain a low amount of errors. Following this idea, the two methods [22] [24] were specifically designed to correct long reads from PacBio or Oxford Nanopore technologies (therefore, they are out of the scope of short read correction, as shown in [17]). Lastly, Bcool [17] introduced a new approach dedicated to short reads.…”

Section: Introduction Motivationsmentioning

confidence: 99%

“…Following this idea, the two methods [22] [24] were specifically designed to correct long reads from PacBio or Oxford Nanopore technologies (therefore, they are out of the scope of short read correction, as shown in [17]). Lastly, Bcool [17] introduced a new approach dedicated to short reads. The approach first constructs a De Bruijn graph and applies heuristics to remove most of the erroneous kmers from the graph.…”

Section: Introduction Motivationsmentioning

confidence: 99%

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Read correction for non-uniform coverages

Marchet

Dufresne

Limasset

2019

Preprint

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Next generation sequencing produces large volumes of short sequences with broad applications. The noise due to sequencing errors led to the development of several correction methods. The main correction paradigm expects a high (from 30-40X) uniform coverage to correctly infer a reference set of subsequences from the reads, that are used for correction. In practice, most accurate methods use k-mer spectrum techniques to obtain a set of reference k-mers. However, when correcting NGS datasets that present an uneven coverage, such as RNA-seq data, this paradigm tends to mistake rare variants for errors. It may therefore discard or alter them using highly covered sequences, which leads to an information loss and may introduce bias. In this paper we present two new contributions in order to cope with this situation.First, we show that starting from non-uniform sequencing coverages, a De Bruijn graph can be cleaned from most errors while preserving biological variability. Second, we demonstrate that reads can be efficiently corrected via local alignment on the cleaned De Bruijn graph paths. We implemented the described method in a tool dubbed BCT and evaluated its results on RNA-seq and metagenomic data. We show that the graph cleaning strategy combined with the mapping strategy leads to save more rare k-mers, resulting in a more conservative correction than previous methods. BCT is also capable to better take advantage of the signal of high depth datasets. We suggest that BCT, being scalable to large metagenomic datasets as well as correcting shallow single cell RNA-seq data, can be a general corrector for non-uniform data. Availability: BCT is open source and available at github.com/Malfoy/BCT under the Affero GPL License.

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Section: Graph Construction and Hard Filtersmentioning

confidence: 99%

Section: Global Strategymentioning

confidence: 99%

Section: Transcriptomic De Bruijn Graph Cleaningmentioning

confidence: 99%

Section: Introduction Motivationsmentioning

confidence: 99%

Section: Introduction Motivationsmentioning

confidence: 99%

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Read correction for non-uniform coverages

Marchet

Dufresne

Limasset

2019

Preprint

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On the Complexity of Sequence-to-Graph Alignment

Jain

Zhang

Gao

et al. 2020

Journal of Computational Biology

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Availability of extensive genetics data across multiple individuals and populations is driving the growing importance of graph based reference representations. Aligning sequences to graphs is a fundamental operation on several types of sequence graphs (variation graphs, assembly graphs, pan-genomes, etc.) and their biological applications. Though research on sequence to graph alignments is nascent, it can draw from related work on pattern matching in hypertext. In this paper, we study sequence to graph alignment problems under Hamming and edit distance models, and linear and affine gap penalty functions, for multiple variants of the problem that allow changes in query alone, graph alone, or in both. We prove that when changes are permitted in graphs either standalone or in conjunction with changes in the query, the sequence to graph alignment problem is N P-complete under both Hamming and edit distance models for alphabets of size ≥ 2. For the case where only changes to the sequence are permitted, we present an O(|V | + m|E|) time algorithm, where m denotes the query size, and V and E denote the vertex and edge sets of the graph, respectively. Our result is generalizable to both linear and affine gap penalty functions, and improves upon the run-time complexity of existing algorithms.

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AStarix: Fast and Optimal Sequence-to-Graph Alignment

Ivanov

Bichsel

Mustafa

et al. 2020

Preprint

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We present an algorithm for the optimal alignment of sequences to genome graphs. It works by phrasing the edit distance minimization task as finding a shortest path on an implicit alignment graph. To find a shortest path, we instantiate the A paradigm with a novel domain-specific heuristic function that accounts for the upcoming subsequence in the query to be aligned, resulting in a provably optimal alignment algorithm called AStarix. Experimental evaluation of AStarix shows that it is 1-2 orders of magnitude faster than state-of-the-art optimal algorithms on the task of aligning Illumina reads to reference genome graphs. Implementations and evaluations are available at https://github.com/eth-sri/astarix.

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Toward perfect reads: short reads correction via mapping on compacted de Bruijn graphs

Cited by 5 publications

References 32 publications

Read correction for non-uniform coverages

Read correction for non-uniform coverages

On the Complexity of Sequence-to-Graph Alignment

AStarix: Fast and Optimal Sequence-to-Graph Alignment

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