Open AccessSequana Coverage: Detection and Characterization of Genomic Variations using Running Median and Mixture Models
Author(s) -
Dimitri Desvillechabrol,
Christiane Bouchier,
Sean Kennedy,
Thomas Cokelaer
Publication year - 2018
Publication title -
gigascience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.947
H-Index - 54
ISSN - 2047-217X
DOI - 10.1093/gigascience/giy110
Subject(s) - computer science , statistic , genome , mixture model , copy number variation , computational biology , gaussian , data mining , biology , genetics , statistics , artificial intelligence , mathematics , gene , physics , quantum mechanics
In addition to mapping quality information, the Genome coverage contains valuable biological information such as the presence of repetitive regions, deleted genes, or copy number variations (CNVs). It is essential to take into consideration atypical regions, trends (e.g., origin of replication), or known and unknown biases that influence coverage. It is also important that reported events have robust statistics (e.g. z-score) associated with their detections as well as precise location.