Genomic variants, such as single nucleotide polymorphisms (SNPs) and DNA insertions and deletions (also known as indels), are identified by various variant analysis pipelines with combinations between short read aligners and variant callers. The low concordance of variant-calling data analysis pipelines also prompts the clinical genomics community to seek for standardization of performance bench marking of the pipelines. Rasa’s variant-calling data analysis services consists a systematic comparison of variant calling performance is based on gold standard data set of reference variant calls for variant annotations which is the final stage of our variant-calling data analysis services .
Rasa’s variant-calling data analysis services along with simple variant analysis; compares multiple variant calling pipelines based on positive predictive value; (PPV; also known as precision) and sensitivity (also known as recall) for a single sequence data set.
Importantly, results from the variant-calling data analysis services can indicate significant variation across the pipelines, suggesting the need for a further detailed analysis. Rasa’s variant-calling data analysis services consists of two phase: First, previous studies are used for analyzing only a single data set. Thus, data-specific effects will not be excluded. Second, these studies specifically measures PPV and sensitivity, separately, to benchmark performance. Thus, a difference in false positive rate between high score variants and low score variants is not reflected in a single bench marking score, such as, the area under a precision-recall curve (APR), which reflects the intrinsic trade-off between precision (i.e., PPV) and recall (i.e., sensitivity), providing a more informative performance score.
SNP calling is a bit of a misnomer, as it implies finding “SNPs” in NGS data. Without information about population frequency or function, it is premature to call a single nucleotide change a “polymorphism“. With that caveat in mind, “SNP calling” in the context of NGS data analysis can be defined as the process of finding bases in the NGS data that differ from the reference genome, typically including an associated confidence score or statistical evidence metric. The details of this analysis vary somewhat by application, but an early and still applicable description can be found in many research papers as well describing the MAQ alignment and variant calling algorithm which is the major consideration in our variant-calling data analysis services
Copy Number Variation (CNV) and balanced rearrangements such as inversions and translocations are types of the large structural variations in the human genome and other organisms. In Copy Number Variation, a gene or a genomic region appears in different number of copies in different individuals or even in different cells of the same individual. CNVs are generally referred to as a duplication or deletion of a genomic region with at least 1kb in length. However, several clinically important CNVs are shorter than 1 kb in length. CNV results in having variations in the gene expressions and abnormalities in the human phenotypes. Moreover, in variant-calling data analysis services CNV identified is envisaged to be associated with many human diseases such as autoimmune disease, autismand developmental disabilities, diabetes, schizophrenia, cancer, obesity and many more.
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