Integrative Comparison of Burrows-Wheeler Transform-Based Mapping algorithm with de Bruijn graph for Identification of Lung/Liver Cancer-specific Gene

2021 ◽  
Author(s):  
Atul Ajaykumar ◽  
Jung Jin Yang
Keyword(s):  
Liver Cancer ◽  
De Bruijn Graph ◽  
Specific Gene ◽  
Mapping Algorithm ◽  
De Bruijn ◽  
Burrows Wheeler Transform

scholarly journals Validating Paired-end Read Alignments in Sequence Graphs

2019 ◽  
Author(s):  
Chirag Jain ◽  
Haowen Zhang ◽  
Alexander Dilthey ◽  
Srinivas Aluru
Keyword(s):  
Sparse Matrix ◽  
Mathematical Formulation ◽  
Genomic Diversity ◽  
De Bruijn Graph ◽  
Distance Constraints ◽  
De Bruijn Graphs ◽  
Sequencing Platform ◽  
Mapping Algorithm ◽  
End Distance ◽  
De Bruijn

AbstractGraph based non-linear reference structures such as variation graphs and colored de Bruijn graphs enable incorporation of full genomic diversity within a population. However, transitioning from a simple string-based reference to graphs requires addressing many computational challenges, one of which concerns accurately mapping sequencing read sets to graphs. Paired-end Illumina sequencing is a commonly used sequencing platform in genomics, where the paired-end distance constraints allow disambiguation of repeats. Many recent works have explored provably good index-based and alignment-based strategies for mapping individual reads to graphs. However, validating distance constraints efficiently over graphs is not trivial, and existing sequence to graph mappers rely on heuristics. We introduce a mathematical formulation of the problem, and provide a new algorithm to solve it exactly. We take advantage of the high sparsity of reference graphs, and use sparse matrix-matrix multiplications (SpGEMM) to build an index which can be queried efficiently by a mapping algorithm for validating the distance constraints. Effectiveness of the algorithm is demonstrated using real reference graphs, including a human MHC variation graph, and a pan-genome de-Bruijn graph built using genomes of 20 B. anthracis strains. While the one-time indexing time can vary from a few minutes to a few hours using our algorithm, answering a million distance queries takes less than a second.2012 ACM Subject ClassificationMathematics of computing → Paths and connectivity problems; Applied computing → Computational genomics

scholarly journals Fast and efficient Rmap assembly using the Bi-labelled de Bruijn graph

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Kingshuk Mukherjee ◽  
Massimiliano Rossi ◽  
Leena Salmela ◽  
Christina Boucher
Keyword(s):  
Single Molecule ◽  
De Bruijn Graph ◽  
Anabas Testudineus ◽  
E Coli ◽  
Genome Wide ◽  
A Genome ◽  
De Bruijn ◽  
Optical Maps ◽  
Definition Of ◽  
Numeric Representation

AbstractGenome wide optical maps are high resolution restriction maps that give a unique numeric representation to a genome. They are produced by assembling hundreds of thousands of single molecule optical maps, which are called Rmaps. Unfortunately, there are very few choices for assembling Rmap data. There exists only one publicly-available non-proprietary method for assembly and one proprietary software that is available via an executable. Furthermore, the publicly-available method, by Valouev et al. (Proc Natl Acad Sci USA 103(43):15770–15775, 2006), follows the overlap-layout-consensus (OLC) paradigm, and therefore, is unable to scale for relatively large genomes. The algorithm behind the proprietary method, Bionano Genomics’ Solve, is largely unknown. In this paper, we extend the definition of bi-labels in the paired de Bruijn graph to the context of optical mapping data, and present the first de Bruijn graph based method for Rmap assembly. We implement our approach, which we refer to as rmapper, and compare its performance against the assembler of Valouev et al. (Proc Natl Acad Sci USA 103(43):15770–15775, 2006) and Solve by Bionano Genomics on data from three genomes: E. coli, human, and climbing perch fish (Anabas Testudineus). Our method was able to successfully run on all three genomes. The method of Valouev et al. (Proc Natl Acad Sci USA 103(43):15770–15775, 2006) only successfully ran on E. coli. Moreover, on the human genome rmapper was at least 130 times faster than Bionano Solve, used five times less memory and produced the highest genome fraction with zero mis-assemblies. Our software, rmapper is written in C++ and is publicly available under GNU General Public License at https://github.com/kingufl/Rmapper.

A novel 3D NoC architecture based on De Bruijn graph

2012 ◽  
Vol 38 (3) ◽  
pp. 801-810 ◽  
Author(s):  
Yiou Chen ◽  
Jianhao Hu ◽  
Xiang Ling ◽  
Tingting Huang
Keyword(s):  
De Bruijn Graph ◽  
De Bruijn

scholarly journals SOME EXPERIMENTS ON THE CONSTRUCTION AND ANALYSIS OF THE DE BRUIJN GRAPH

2020 ◽  
Author(s):  
Alexander G. Marchuk ◽  
◽  
Sergey Nikolaevich Troshkov ◽  
Keyword(s):  
Data Storage ◽  
Parallel Computations ◽  
De Bruijn Graph ◽  
Distributed Data ◽  
Distributed Data Storage ◽  
De Bruijn

This paper describes the experience of solving the problem of finding chains in the De Bruijn graph using parallel computations and distributed data storage.

On the connectivity of the De Bruijn graph

1988 ◽  
Vol 27 (6) ◽  
pp. 315-318 ◽  
Author(s):  
M.A. Sridhar
Keyword(s):  
De Bruijn Graph ◽  
De Bruijn
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