Calling known variants and identifying new variants while rapidly aligning sequence data | JoVE Visualize

Area of Science:

Genomics and Bioinformatics
Computational Biology
Molecular Genetics

Background:

Whole-genome sequencing (WGS) is crucial for identifying causative mutations in genomic evaluations.
Existing WGS analysis pipelines often involve post-alignment variant calling, which can be time-consuming and less accurate.
Improving the speed and accuracy of sequence alignment and variant identification is essential for large-scale genomic studies.

Purpose of the Study:

To develop and evaluate novel programs, Findmap and Findvar, for enhanced whole-genome sequencing data analysis.
To compare the performance of Findmap and Findvar against established alignment (BWA, SNAP) and variant identification (GATK, SAMtools) tools.
To assess the efficiency, accuracy, and utility of incorporating known variant locations during the sequence alignment phase.

Main Methods:

Developed Findmap for sequence alignment, utilizing known variant locations to guide the process and count alleles.
Developed Findvar for variant identification, distinguishing true variants from sequencing errors and outputting genotype probabilities.
Tested Findmap and Findvar using simulated and actual data from cattle and human populations, including large variant datasets.

Main Results:

Findmap and Findvar demonstrated significantly faster processing times compared to BWA, GATK, and SAMtools, with Findvar completing in ~1 hour for 100x data.
Findmap achieved high read mapping accuracy (92.9%) and accurate allele calling for known variants.
Findvar showed superior accuracy in identifying new variants, especially single nucleotide variants (99.8%), and lower false positive rates compared to GATK and SAMtools.

Conclusions:

Findmap and Findvar offer substantial advantages in processing speed, alignment precision, data summarization, and output compactness.
Integrating known variant information during alignment significantly improves the efficiency and accuracy of sequence-based genotyping.
These new programs represent a more efficient and accurate approach for whole-genome sequencing analysis in genomic evaluations.