GenomeGems: evaluation of genetic variability from deep sequencing data
<p>Abstract</p> <p>Background</p> <p>Detection of disease-causing mutations using Deep Sequencing technologies possesses great challenges. In particular, organizing the great amount of sequences generated so that mutations, which might possibly be biologically relevant,...
Main Authors: | Ben-Zvi Sharon, Givati Adi, Shomron Noam |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2012-07-01
|
Series: | BMC Research Notes |
Subjects: | |
Online Access: | http://www.biomedcentral.com/1756-0500/5/338 |
Similar Items
-
Improving high-resolution copy number variation analysis from next generation sequencing using unique molecular identifiers
by: Pierre-Julien Viailly, et al.
Published: (2021-03-01) -
A study on fast calling variants from next-generation sequencing data using decision tree
by: Zhentang Li, et al.
Published: (2018-04-01) -
Identification of single nucleotide variants using position-specific error estimation in deep sequencing data
by: Dimitrios Kleftogiannis, et al.
Published: (2019-08-01) -
Best practices for variant calling in clinical sequencing
by: Daniel C. Koboldt
Published: (2020-10-01) -
Application of deep learning technique in next generation sequence experiments
by: Su Özgür, et al.
Published: (2023-10-01)