Category Archives: Best practices in genetic analysis

WEBCAST: CNV Analysis with VarSeq

      Cheryl Rogers    November 22, 2016    No Comments on WEBCAST: CNV Analysis with VarSeq

December’s webcast will provide the Golden Helix community with a more in-depth look at CNV analysis in VarSeq. On December 7th, Dr. Nathan Fortier will discuss the challenges and metrics surrounding CNV detection and then demonstrate VarSeq’s new capability from VCF to clinical report.  Wednesday, December 7th @ 12:00 PM, EST Numerous studies have documented the role of Copy Number Variations (CNVs)… Read more »

Genotype Imputation and Phasing Coming to SNP & Variation Suite

Genotype Imputation

One of the tools at the top of the toolbox for researchers working with microarray data is genotype imputation. Genotype imputation is the process of inferring the genotype of one or more markers based on the correlation pattern (aka linkage disequilibrium or LD) of the surrounding markers for which genotypes are known. In tomorrow’s webcast, we will announce the coming integration of a… Read more »

Using GWAS to investigate neurodevelopmental disorders

Sergey Kornilov

Dr. Sergey Kornilov, a Duncan Scholar in Molecular and Human Genetics at Baylor College of Medicine, combines his broad psychology background with genetics to research the genetic basis of neurodevelopmental disorders with a unique dual perspective. Neuro-developmental disorders, for example, those of the spoken and written language, affect many worldwide – up to 10% of preschool children. In most cases, these… Read more »

Why Call CNVs: Getting More from your NGS Data

CNV Call

Copy Number Variants have been important to clinical genetics for quite a while now. So, what has made now the right time to be looking at calling CNVs from NGS data? Well, there are a number of good reasons. The dominant one is simply that the NGS data you are already creating for calling variants can be used in many cases… Read more »

NGS-Based Clinical Testing (Part V)

Quality Managment

Quality Management in Clinical Testing Any validated bioinformatics pipeline must be continuously monitored. Quality management in clinical testing labs ensures that any divergence from predefined quality metrics during the analysis of clinical samples is investigated. For example: There is an insufficient number of sequence reads that passed the predefined base quality score threshold The number of variants identified in a… Read more »