Sorry, you need to enable JavaScript to visit this website.

facebooktwittermailshare

Sparse Signal Recovery Methods for Variant Detection in Next-Generation Sequencing Data

Abstract: 

Abstract:
Recent advances in high-throughput sequencing technologies have led to the collection of vast quantities of genomic data. These sequencing data have the potential to answer questions about the evolutionary history of a species and the genomic basis of hereditary diseases. Structural variants (SVs) -- rearrangements of the genome larger than one letter such as inversions, insertions, deletions, and duplications -- are an important source of genetic variation and have been implicated in some genetic diseases. However, inferring SVs from sequencing data has proven to be challenging because true SVs are rare and are prone to low-coverage noise. We attempt to mitigate the deleterious effects of low-coverage sequences by following a maximum likelihood approach to SV prediction. Specifically, we model the noise using Poisson statistics and constrain the solution with a sparsity-promoting penalty since SV instances should be rare. In addition, because offspring SVs inherit SVs from their parents, we incorporate familial relationships in the optimization problem formulation to increase the likelihood of detecting true SV occurrences. Numerical results are presented to validate our proposed approach.

up
0 users have voted:

Paper Details

Authors:
Mario Banuelos, Rubi Almanza, Lasith Adhikari, Suzanne Sindi, Roummel Marcia
Submitted On:
19 March 2016 - 4:15am
Short Link:
Type:
Poster
Event:
Presenter's Name:
Mario Banuelos
Document Year:
2016
Cite

Document Files

ICASSP2016_2589.pdf

(206 downloads)

Keywords

Additional Categories

Subscribe

[1] Mario Banuelos, Rubi Almanza, Lasith Adhikari, Suzanne Sindi, Roummel Marcia, "Sparse Signal Recovery Methods for Variant Detection in Next-Generation Sequencing Data", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/785. Accessed: Jun. 24, 2017.
@article{785-16,
url = {http://sigport.org/785},
author = { Mario Banuelos; Rubi Almanza; Lasith Adhikari; Suzanne Sindi; Roummel Marcia },
publisher = {IEEE SigPort},
title = {Sparse Signal Recovery Methods for Variant Detection in Next-Generation Sequencing Data},
year = {2016} }
TY - EJOUR
T1 - Sparse Signal Recovery Methods for Variant Detection in Next-Generation Sequencing Data
AU - Mario Banuelos; Rubi Almanza; Lasith Adhikari; Suzanne Sindi; Roummel Marcia
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/785
ER -
Mario Banuelos, Rubi Almanza, Lasith Adhikari, Suzanne Sindi, Roummel Marcia. (2016). Sparse Signal Recovery Methods for Variant Detection in Next-Generation Sequencing Data. IEEE SigPort. http://sigport.org/785
Mario Banuelos, Rubi Almanza, Lasith Adhikari, Suzanne Sindi, Roummel Marcia, 2016. Sparse Signal Recovery Methods for Variant Detection in Next-Generation Sequencing Data. Available at: http://sigport.org/785.
Mario Banuelos, Rubi Almanza, Lasith Adhikari, Suzanne Sindi, Roummel Marcia. (2016). "Sparse Signal Recovery Methods for Variant Detection in Next-Generation Sequencing Data." Web.
1. Mario Banuelos, Rubi Almanza, Lasith Adhikari, Suzanne Sindi, Roummel Marcia. Sparse Signal Recovery Methods for Variant Detection in Next-Generation Sequencing Data [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/785