Abstract:
Structural variants (SVs) are changes in genome like deletions, insertions, inversions, duplications
and translocations. These changes are in part of genome greater than 1kb of size. SVs that are
rearrangements in the genome are responsible for various disorders. SVs not only affect genes but
also change the expression of the genes and cana lead to diseases like autism, schizophrenia,
rheumatoid arthritis and cancer. To determine these variants Next Generation Sequencing (NGS)
is the most promising approach. To identify SVs commonly four different approaches; read pair,
read depth, split read and assembly are used. Read pair approach is based on evaluating the
distance and orientation of read pairs it uses discordant read pairs to identify SVs. Read depth
approach make use of some random distribution in mapping depth to detect variants. Split read
approach use single base pair resolution to determine SVs. Assembly based approach work by first
reconstructing DNA fragments contigs and then assembles to reference genome to find discordant
pairs. Different tools are available to identify SVs these tools use only one or two approaches to
identify SVs. The evaluation of most of the tools suggests that the tools that use multiple
approaches can identify the variants more accurately. Therefore, SV identifier (SVI), a new
computational method based on multiple approaches, to identify SVs is designed using NGS data.
SVI is a python based method. This method is designed to detect the SVs like deletions, insertions,
inversions and duplications from paired end NGS data. It can analyze multiple samples, it is not
platform dependent, detect deletions in reference genome, annotate the variants and also provide
visualization of results.
