Genetic variation is the genetic difference both within and among populations, ranging from single nucleotide changes to large-scale karyotypic alterations, which is the genetic basis of phenotypic variation. Extensive studies have shown that genetic variants are involved in various genetic disorders, including cancer. With the advancement of high-throughput sequencing technologies (HTS), genetic variation detection has been proven to be one of the most efficient approaches to screen candidate genes related to diseases. However, the high volumes of short-read data generated by HTS technologies bring many computational challenges, including filtering and correcting low-quality reads, mapping a vast amount of short reads to reference genomes, and building a de novo assembly from short reads, etc. SNP calling in HTS data is pretty feasible with both vendor-provided and community-developed tools. Detection and annotation of structural variants (SVs) including indels, duplications, translocations and inversions, however, is much less straightforward due to the complex structure of certain types of SVs and the repetitive nature of eukaryotic genomes. SV detection has gained more attention recently and motivated rapid developments of new computational methods and applications. This research topic will host recent progress of bioinformatics methods for genetic variation detection and also encourage the submission of reviews of recent methodological developments and applications.
The topics in this area include but not limited to:
(1) New algorithms and softwares for genetic variation detection, including SNV, Indel, CNV, etc; integrated pipelines and visualization tools;
(2) Statistical models for genome-wide or targeted association studies; parametric and non-parametric linkage; gene-gene and gene-environment interaction; algorithms and statistical models for SNP and SV detection in genetically heterogeneous samples (e.g. tumor);
(3) Performance evaluation of existing or novel methods using simulated and experimental data sets; reviews of recent methodological developments in genetic variation exploration;
(4) Statistical models and tools for genetic variation detection in non-model organisms; functional annotation and pathway modeling; computational tools developed for various sequencing platforms.
Genetic variation is the genetic difference both within and among populations, ranging from single nucleotide changes to large-scale karyotypic alterations, which is the genetic basis of phenotypic variation. Extensive studies have shown that genetic variants are involved in various genetic disorders, including cancer. With the advancement of high-throughput sequencing technologies (HTS), genetic variation detection has been proven to be one of the most efficient approaches to screen candidate genes related to diseases. However, the high volumes of short-read data generated by HTS technologies bring many computational challenges, including filtering and correcting low-quality reads, mapping a vast amount of short reads to reference genomes, and building a de novo assembly from short reads, etc. SNP calling in HTS data is pretty feasible with both vendor-provided and community-developed tools. Detection and annotation of structural variants (SVs) including indels, duplications, translocations and inversions, however, is much less straightforward due to the complex structure of certain types of SVs and the repetitive nature of eukaryotic genomes. SV detection has gained more attention recently and motivated rapid developments of new computational methods and applications. This research topic will host recent progress of bioinformatics methods for genetic variation detection and also encourage the submission of reviews of recent methodological developments and applications.
The topics in this area include but not limited to:
(1) New algorithms and softwares for genetic variation detection, including SNV, Indel, CNV, etc; integrated pipelines and visualization tools;
(2) Statistical models for genome-wide or targeted association studies; parametric and non-parametric linkage; gene-gene and gene-environment interaction; algorithms and statistical models for SNP and SV detection in genetically heterogeneous samples (e.g. tumor);
(3) Performance evaluation of existing or novel methods using simulated and experimental data sets; reviews of recent methodological developments in genetic variation exploration;
(4) Statistical models and tools for genetic variation detection in non-model organisms; functional annotation and pathway modeling; computational tools developed for various sequencing platforms.
