Monovar is a complex and elegant algorithm to detect single nucleotide variants (SVNs) in the cancer cells, which was invented by the researchers at The University of Texas MD Anderson Cancer Centre. This program is written on the Python platform, providing a more adaptable treatment to cancer patients by pointing out important changes and variation regularly in a single cancer cell.
Current variant callers are not appropriate for single-cell DNA sequencing, as they do not predict for allelic dropout, most of them gives false-positive errors and coverage non-uniformity. This programming idea is being used to detect and determine the DNA mutation in cancer cells which analyses millions of cells to state a resolute conclusion.
On the other hand, this program can be very helpful to the doctors to give a more detailed and sure approach to cancer treatment before going through the painful treatments such as Chemotherapy.
How the program gets implemented?
Monovar method inspects data based on multiple single cells which allows the doctors to spot out the essential issues that typically go undetected in the noise.
The newly developed technology used in Monovar is known as Single Cell Sequencing (SCS) which also has vast applications in the fields like microbiology, neurobiology and immunology. Monovar enables the doctors to detect single nucleotide variants (SNVs), which as very minute and tiny DNA variation and cannot be determined by any other means. SNVs play a vital role in treatment of cancer as they can accurately instigate how the patient develops the disease, what stage the patient is in, and how is he/she responding to the medication.
Bitbucket description about Monovar program states that – “Monovar is a single nucleotide variant (SNV) detection and genotyping algorithm for single-cell DNA sequencing data. It takes a list of bam files as input and outputs a vcf file containing the detected SNVs.”
You can easily take a look at the Monovar program and see how does it works and gets implemented. Intelligent and precise algorithms and big data applications have already been used for the statistical analysis of the growth and deterioration of the cancer cells.
Nevertheless, Monovar exhibits very superior performance over a range of standard algorithms on benchmarks and in identifying and demonstrating driver mutations and delineating clonal sub-structure in three distinct human tumour sets of data.
Now all we can do is hope that Monovar method makes the cancer detection and cure easier and more successful as well.
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