MARAGAP: A modular approach to reference assisted genome assembly pipeline

Bilal Wajid; Erchin Serpedin; Mohamed Nounou; Hazem Nounou

doi:10.1504/IJCBDD.2015.072073

MARAGAP: A modular approach to reference assisted genome assembly pipeline

Bilal Wajid^*, Erchin Serpedin, Mohamed Nounou, Hazem Nounou

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

Abstract

This paper presents MARAGAP, a modular approach to reference assisted genome assembly pipeline. MARAGAP uses the principle of Minimum Description Length to determine the optimal reference sequence for the assembly. The optimal reference sequence is used as a template to infer inversions, insertions, deletions and SNPs in the target genome. MARAGAP uses an algorithmic approach to detect and correct inversions and deletions, a De-Bruijn graph based approach to infer the insertions, an affine-match affine-gap local alignment tool to estimate the locations of insertions and a Bayesian estimation framework for detecting SNPs.

Original language	English
Pages (from-to)	226-250
Number of pages	25
Journal	International Journal of Computational Biology and Drug Design
Volume	8
Issue number	3
DOIs	https://doi.org/10.1504/IJCBDD.2015.072073
Publication status	Published - 2015
Externally published	Yes

Keywords

Bayesian statistics
De-Bruijn graph
Genome assembly
Graph theory
Local alignment
Minimum description length principle
Mutations
Next generation sequencing
Reference assisted assembly
SNPs
Single nucleotide polymorphisms

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title = "MARAGAP: A modular approach to reference assisted genome assembly pipeline",

abstract = "This paper presents MARAGAP, a modular approach to reference assisted genome assembly pipeline. MARAGAP uses the principle of Minimum Description Length to determine the optimal reference sequence for the assembly. The optimal reference sequence is used as a template to infer inversions, insertions, deletions and SNPs in the target genome. MARAGAP uses an algorithmic approach to detect and correct inversions and deletions, a De-Bruijn graph based approach to infer the insertions, an affine-match affine-gap local alignment tool to estimate the locations of insertions and a Bayesian estimation framework for detecting SNPs.",

keywords = "Bayesian statistics, De-Bruijn graph, Genome assembly, Graph theory, Local alignment, Minimum description length principle, Mutations, Next generation sequencing, Reference assisted assembly, SNPs, Single nucleotide polymorphisms",

author = "Bilal Wajid and Erchin Serpedin and Mohamed Nounou and Hazem Nounou",

note = "Publisher Copyright: Copyright {\textcopyright} 2015 Inderscience Enterprises Ltd.",

year = "2015",

doi = "10.1504/IJCBDD.2015.072073",

language = "English",

volume = "8",

pages = "226--250",

journal = "International Journal of Computational Biology and Drug Design",

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TY - JOUR

T1 - MARAGAP

T2 - A modular approach to reference assisted genome assembly pipeline

AU - Wajid, Bilal

AU - Serpedin, Erchin

AU - Nounou, Mohamed

AU - Nounou, Hazem

PY - 2015

Y1 - 2015

N2 - This paper presents MARAGAP, a modular approach to reference assisted genome assembly pipeline. MARAGAP uses the principle of Minimum Description Length to determine the optimal reference sequence for the assembly. The optimal reference sequence is used as a template to infer inversions, insertions, deletions and SNPs in the target genome. MARAGAP uses an algorithmic approach to detect and correct inversions and deletions, a De-Bruijn graph based approach to infer the insertions, an affine-match affine-gap local alignment tool to estimate the locations of insertions and a Bayesian estimation framework for detecting SNPs.

AB - This paper presents MARAGAP, a modular approach to reference assisted genome assembly pipeline. MARAGAP uses the principle of Minimum Description Length to determine the optimal reference sequence for the assembly. The optimal reference sequence is used as a template to infer inversions, insertions, deletions and SNPs in the target genome. MARAGAP uses an algorithmic approach to detect and correct inversions and deletions, a De-Bruijn graph based approach to infer the insertions, an affine-match affine-gap local alignment tool to estimate the locations of insertions and a Bayesian estimation framework for detecting SNPs.

KW - Bayesian statistics

KW - De-Bruijn graph

KW - Genome assembly

KW - Graph theory

KW - Local alignment

KW - Minimum description length principle

KW - Mutations

KW - Next generation sequencing

KW - Reference assisted assembly

KW - SNPs

KW - Single nucleotide polymorphisms

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DO - 10.1504/IJCBDD.2015.072073

M3 - Article

AN - SCOPUS:84943398658

SN - 1756-0756

VL - 8

SP - 226

EP - 250

JO - International Journal of Computational Biology and Drug Design

JF - International Journal of Computational Biology and Drug Design

IS - 3

ER -

MARAGAP: A modular approach to reference assisted genome assembly pipeline

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