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[Array Studio Video Tutorial] DNA-Seq Analysis: Sequence Variation, Copy Number Variation And More

Vivian Zhang

With mapped DNA-Seq data, Array Studio allows users to identify, visualize and annotate sequence, mutations and copy number variations. In this article, we walk you through a few important DNA-Seq analysis modules.

 

 

1. Identify DNA Sequence Variation and Generate and Annotate VCF Variant Data

Users can run Summarize Variant Data module to identify SNPs, insertions and deletions. It also automatically runs in DNA-Seq pipeline. The output Variant Report can be annotated with Mutation Annotator databases.

This module returns a report table showing the gene name for each annotated mutation, chromosome, position, reference allele, mutation allele, Annotation type (intron, non-synonymous, 5’ UTR, synonymous, 3’UTR, etc.), AAPosition (amino acid position of change), AAChange (amino acid change—if there is one), transcript ID, transcript name, transcript strand, distance to 3’ end, and distance to 5’ end.

This module returns a report table showing the gene name for each annotated mutation, chromosome, position, reference allele, mutation allele, Annotation type (intron, non-synonymous, 5’ UTR, synonymous, 3’UTR, etc.), AAPosition (amino acid position of change), AAChange (amino acid change—if there is one), transcript ID, transcript name, transcript strand, distance to 3’ end, and distance to 5’ end.

Variant Call Format (VCF) data is the most common format for reporting sequence variation. Array Studio variant detection can output merged or individual VCF files, and can organize and annotate these data for efficient filtering in Array Studio. and public database-based annotators, please check out our wiki page on Annotate Variant Files. Array Studio provides a large number of classifiers and annotators to improve the identification of interesting variants. Examples include:  1000GenomesClinVarGERP++dbNSFP (database for nonsynonymous SNPs' functional predictions), GRASP : Genome-Wide Repository of Associations between SNPs and Phenotypes, GWAVAHaploregRegulomeDB.

 

2. Identify Somatic and Germline Mutations in Matched-Pair Data

If you have matched pair DNA-Seq data between tumor and normal from the same subject, you can run the Var Scan 2 matched pair analysis. this annotates somatic mutations that compare two samples from the same subject such as tumor or normal for differences in genotype. It will generate calls for genotype of the samples and flag germline versus somatic mutations. 

For each variant, the result reports "Normal coverage/frequency", "Tumor coverage/frequency", "Somatic/variant p-value", "Call", "NormalGenotype", "TumorGenotype" and "FilteringStatus", allowing the user to filter the result and identify somatic mutations of interest.

For each variant, the result reports "Normal coverage/frequency", "Tumor coverage/frequency", "Somatic/variant p-value", "Call", "NormalGenotype", "TumorGenotype" and "FilteringStatus", allowing the user to filter the result and identify somatic mutations of interest.

 

 

3. Summarize NGS Coverage Data to Detect Copy Number Variants and Visualize NGS Copy Number Variations

DNAseq Whole-Genome and Whole-Exome data can be processed in Array Studio to detect amplification and deletion events. By comparing the relative signal between samples from the same subject, regions with unusually high or low signal in the disease sample will be flagged as a potential Copy Number Variation (CNV) event. The result can be visualized in a few ways, including scatter plot, segment chromosome view, or in genome browser. 

Summary Copy Number report provides "Observation", "Log2Ratio", "Copy Number", "Normal Coverage", "Tumor Coverage", and segment information.

Summary Copy Number report provides "Observation", "Log2Ratio", "Copy Number", "Normal Coverage", "Tumor Coverage", and segment information.

Genome browser view of coverage data. The highlighted example clearly has an increased coverage for this genomic region, while the coverage is comparable in adjacent region.  

Genome browser view of coverage data. The highlighted example clearly has an increased coverage for this genomic region, while the coverage is comparable in adjacent region.  

 

For more details and additional DNA-Seq analysis function, please check out our video tutorial Getting Started with DNAseq Analysis or search on our wiki page about your specific topic of interest.