Automated Software for Variant Identification and Analysis
VARIANT IDENTIFICATION AND ANALYSIS
The path from DNA sequencing to genetic variant interpretation can be long and complex. With a myriad of software tools and pipelines available, analyzing variants from raw sequence data can require a mastery of up to a dozen bioinformatics applications and online databases. And each additional step in the pipeline can affect the accuracy and completeness of the results.
Enter Lasergene Genomics, a fully integrated variant analysis and annotation pipeline with an intuitive, easy-to-use interface. Lasergene Genomics and its Variant Annotation Database greatly simplify variant analysis so that you can get accurate answers without needing a PhD in bioinformatics.
Use Lasergene Genomics to accurately identify significant variants between multiple data sets
Lasergene Genomics is an automated pipeline that assembles your reads to a template, performs variant calling, and then compares the variants across multiple NGS or long read data sets, all without human intervention.
Our variant calling tools have been proven to be more accurate than both our commercial and open source competitors, so you can rest easy in knowing that you can trust the results.
Easily identify significant variants between multiple experiments through a powerful filtering tools, rich graphical views, and integrated access to large variant databases, including Mastermind, dbSNP, GERP, dbNSFP, and the 1000 Genomes Project. Lasergene Genomics even makes it easy for you to compare and analyze multiple VCF files that come from other NGS software pipelines and annotate them with information from our custom genome template packages for enriched variant analysis.
Variant analysis software features
- Automated, point-and-click pipeline for assembly, variant calling, and variant annotation
- Advanced gene filtering based on the level of disruption to each gene caused by variations
- Comparison of groups of variants using text filters, tabular data, or graphical representations that include Venn diagrams, scatter plots and heat maps
- The ability to view the read alignment at a specific variant or gene in SeqMan Ultra or the assembly coverage for all data sets simultaneously in GenVision Pro
Variant analysis in 4 simple steps
Step 1
Set up and run assembly
Step 2
Compare variants across multiple data sets
Step 3
Filter to find variants of interest
Step 4
View variants within alignment
Resources
Please see our resources below for more information on variant calling, variant annotation, and variant analysis.
Comprehensive Variant Analysis: from Raw Sequence Data to Aligned Genomes
Working with Variant Call Format Files in Lasergene Genomics
Identifying Candidate Variants and Their Effects on Protein Structure Starting from NGS Data or VCF Files
DNASTAR’s SeqMan NGen vs. Four Alternative Pipelines: Variant Detection Comparison Using Illumina Data from NA12878
Tutorials
Watch one of our videos or check out one of our written tutorials to learn more about variant identification and analysis.
Exome Analysis Tutorial
See how to align exome resequencing data from all major NGS platforms against a reference sequence with unsurpassed ease and speed in Lasergene Genomics. Comprehensive post-assembly analysis options make it easy to identify and compare genetic variants as well as structural and non-coding variants. Advanced gene filtering offers the ability to determine the level of disruption to each gene caused by variations.
Sanger Validation for NGS Assemblies
If you are working with next-gen sequencing, you may wish to use Sanger sequencing data to validate the results of the assembly or variant calls. Lasergene Genomics supports this validation, allowing you to combine both data types into a single project in SeqMan NGen.
The Human Variant Analysis pipeline can be complex and time-consuming.
To learn how to simplify the process, sign up and get our free guide.
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FAQs
Why study variants?
A common workflow in the study of human genetic variation involves the analysis and identification of deleterious variants or of variants associated with a particular population or trait…
A common workflow in the study of human genetic variation involves the analysis and identification of deleterious variants or of variants associated with a particular population or trait. There are thousands of known variants that cause Mendelian disorders, and thousands more whose molecular basis is yet unknown.
In a typical human variant analysis study, the researcher’s goal is to identify which single-nucleotide polymorphisms (SNPs), small insertions and deletions (INDELs), copy number variations (CNVs), or other types of structural variations and rearrangements (SVs) have functional significance. Functionally significant variants are those that cause amino acid changes, abnormal exon splicing, or other protein structure changes that contribute to a diseased state.
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How long does it take to assemble and call variants?
This depends whether you are doing the assembly on your local computer or are using DNASTAR Cloud Assembly. Depending on hardware and depth of sequences, local whole-genome…
This depends whether you are doing the assembly on your local computer or are using DNASTAR Cloud Assembly. Depending on hardware and depth of sequences, local whole-genome resequencing can take as little as 5 minutes for a bacterial assembly to 24+ hours for a mammalian genome. A typical whole exome sequencing assembly takes between 30 and 90 minutes. Regardless of workflow, cloud assemblies are much faster and multiple assemblies can also be run simultaneously.
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How do I compare variants between assembled NGS experiments?
Comparing and analyzing variants between multiple NGS or long read data sets is done in ArrayStar, part of Lasergene Genomics. NGS experiments can be compared and filtered into sets of interest using customizable tables, a filter wizard, Venn diagram, heat map or scatter plot.
How do I analyze variants for a multiple sequence alignment?
The variant analysis capabilities in Lasergene Genomics are designed for sequence assemblies starting with raw sequence data. To analyze variants between already assembled sequences, such as multiple genomic strains, you should instead use the variant analysis features in MegAlign Pro.
What supplemental variant data is available in the Variant Annotation Database?
For human resequencing data, DNASTAR provides access to the Variant Annotation Database, which contains variant information using coordinates from GRCh37 (hg19) and GRCh38. Annotations…
For human resequencing data, DNASTAR provides access to the Variant Annotation Database, which contains variant information using coordinates from GRCh37 (hg19) and GRCh38. Annotations include information about the frequency of the variant in the general population, in specific populations, and in publications, as well as information concerning the variant’s impact on functionality. The variant annotation information comes from several sources, including Mastermind, the 1000 Genomes Project and the Exome Sequencing Project (ESP).
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Can I import other annotation data into my variant analysis project?
Yes, you can import most any annotation data in text format, including gene and SNP level annotation data.
Can Lasergene Genomics detect structural variations in genomic resequencing data?
Yes, Lasergene Genomics can detect copy number variation (CNV) and other structural variants as part of gene panel, whole exome and whole genome sequence analysis. Simply check the box for Calculate Copy Number Variation during assembly setup in SeqMan NGen. You can then view, filter and analyze CNVs and other structural variants using ArrayStar and SeqMan Pro.
Does Lasergene Genomics support BED or VCF files for targeted sequencing?
Yes. SeqMan NGen can read and utilize BED and VCF files in the assembly and can also create and export a VCF file during assembly. ArrayStar can export BED and VCF files.
To learn more, see our blog post, Working with Variant Call Format Files in Lasergene Genomics.
Benchmarks
In most studies, especially when looking for rare mutations, having a reliable reference set with known variations isn’t feasible. To test the accuracy of NGS alignment and variant calling in Lasergene Genomics, we used SeqMan NGen to align whole human exome data from the Genome in a Bottle Consortium (GIAB) to the human genome. Because this is a well curated data set, we were able to compare the variant calls to the “answer” provided by GIAB. We also performed alignment and variant calling in several other software packages using the same data and comparable settings. We then looked at three metrics:
- Sensitivity – This is also known as the true positive rate, and is the ratio of correctly identified variants to the total known variants in the reference set. The higher the sensitivity, the greater the likelihood that a variant in the sample will be identified by the software.
- Specificity – Also known as the true negative rate, this is the ratio of non-variant calls to the total number of positions in the reference set that are known to be homozygous with the reference sequence. Specificity is inversely related to the number of false positives.
- False Discovery Rate (FDR) – This is the ratio of false positives to all variant calls made by the software. The FDR value for a variant caller allows you to understand how many variants in your project are likely to be false positives.
Because an accurate alignment is a necessary precursor to accurate variant detection, these metrics also help you understand the alignment accuracy from various software pipelines.
Results Summary:
Citations
Analysis of genome-wide variants through bulked segregant RNA sequencing reveals a major gene for resistance to Plasmodiophora brassicae in Brassica oleracea.
Dakouri, A., Zhang, X., Peng, G. et al. Sci Rep 8, 17657 (2018) doi:10.1038/s41598-018-36187-5.
Whole genome sequencing data for two individuals of Pakistani descent.
Khan, S., Kabir, F., M’Hamdi, O. et al. Sci Data 5, 180174 (2018) doi:10.1038/sdata.2018.174.
Novel variants in PAX6 gene caused congenital aniridia in two Chinese families.
Zhang, R., Linpeng, S., Wei, X. et al. Eye 31, 956–961 (2017) doi:10.1038/eye.2016.326.
Molecular characterization of Portuguese patients with dilated cardiomyopathy.
Sousa, Alexandra, et al. Portuguese Society of Cardiology, Volume 38, Issue 2, February 2019, Pages 129-139.
Detailed Characteristics of Tonsillar Tumors with Extrachromosomal or Integrated Form of Human Papillomavirus.
Pokrývková, B.; Saláková, M.; Šmahelová, J.; Vojtěchová, Z.; Novosadová, V.; Tachezy, R. Viruses 2020, 12, 42.
Expansion of phenotypic spectrum of MYO15A pathogenic variants to include postlingual onset of progressive partial deafness.
Chang, M.Y., Lee, C., Han, J.H. et al. BMC Med Genet 19, 29 (2018) doi:10.1186/s12881-018-0541-9.
A novel co-segregating DCTN1 splice site variant in a family with Bipolar Disorder may hold the key to understanding the etiology.
André Hallen, Arthur J.L. Cooper. bioRxiv 354100; doi: https://doi.org/10.1101/354100.
A mutation in the major autophagy gene, WIPI2, associated with global developmental abnormalities.
Musharraf Jelani, Hannah C. Dooley, Andrea Gubas, Hussein Sheikh Ali Mohamoud, Muhammad Tariq Masood Khan, Zahir Ali, Changsoo Kang, Fazal Rahim, Amin Jan, Nirmal Vadgama, Muhammad Ismail Khan, Jumana Yousuf Al-Aama, Asifullah Khan, Sharon A Tooze, Jamal Nasir. Brain, Volume 142, Issue 5, May 2019, Pages 1242–1254, https://doi.org/10.1093/brain/awz075.
