File Name: de novo assembly and genotyping of variants using colored de bruijn graphs .zip
Metrics details. The increasing amount of available genome sequence data enables large-scale comparative studies. A common task is the inference of phylogenies—a challenging task if close reference sequences are not available, genome sequences are incompletely assembled, or the high number of genomes precludes multiple sequence alignment in reasonable time. We present a new whole-genome based approach to infer phylogenies that is alignment- and reference-free. In contrast to other methods, it does not rely on pairwise comparisons to determine distances to infer edges in a tree.
There exist several large genomic and metagenomic data collection efforts, including GenomeTrakr and MetaSub, which are routinely updated with new data. To analyze such datasets, memory-efficient methods to construct and store the colored de Bruijn graph were developed. Yet, a problem that has not been considered is constructing the colored de Bruijn graph in a scalable manner that allows new data to be added without reconstruction. This problem is important for large public datasets as scalability is needed but also the ability to update the construction is also needed. We create a method for constructing the colored de Bruijn graph for large datasets that is based on partitioning the data into smaller datasets, building the colored de Bruijn graph using a FM-index based representation, and succinctly merging these representations to build a single graph. The last step, merging succinctly, is the algorithmic challenge which we solve in this article. This construction method also allows the graph to be updated with new data.
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Turner and P. Flicek and G.
Request PDF | De novo assembly and genotyping of variants using colored De Bruijn graphs | Detecting genetic variants that are highly.
The de Bruijn graph plays an important role in bioinformatics, especially in the context of de novo assembly. However, the representation of the de Bruijn graph in memory is a computational bottleneck for many assemblers. Recent papers proposed a navigational data structure approach in order to improve memory usage. We prove several theoretical space lower bounds to show the limitations of these types of approaches. We further design and implement a general data structure dbgfm and demonstrate its use on a human whole-genome dataset, achieving space usage of 1. As part of dbgfm , we develop the notion of frequency-based minimizers and show how it can be used to enumerate all maximal simple paths of the de Bruijn graph using only 43 MB of memory. Finally, we demonstrate that our approach can be integrated into an existing assembler by modifying the ABySS software to use dbgfm.
Detecting genetic variants that are highly divergent from a reference sequence remains a major challenge in genome sequencing.
In graph theory , an n -dimensional De Bruijn graph of m symbols is a directed graph representing overlaps between sequences of symbols. It has m n vertices , consisting of all possible length- n sequences of the given symbols; the same symbol may appear multiple times in a sequence. If one of the vertices can be expressed as another vertex by shifting all its symbols by one place to the left and adding a new symbol at the end of this vertex, then the latter has a directed edge to the former vertex.
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