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  • HAVoC: A database to track SARS-CoV-2 variants

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HAVoC: A database to track SARS-CoV-2 variants
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HAVoC: A database to track SARS-CoV-2 variants

BioTech Today July 20, 2021July 19, 2021

Shrestha Dutta, Amity University Kolkata

Coronavirus

Coronaviruses (family Coronaviridae) are covered with single-abandoned RNA infections, which cause respiratory, enteric, hepatic, and neurological illnesses with a wide range of severity among various organisms and people. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel transformative infection that is responsible for the current pandemic, has damaged societies and economies universally. The SARS-CoV-2 pandemic has effectively infected more than 100 million individuals in 221 nations, causing over 2.2 million deaths worldwide. 

The mutations occur in the receptor-binding domain (RBD) of the spike protein at position 501, where the amino acid asparagine (N) has been substituted with tyrosine (Y) enabling explicit PCR to distinguish the N501Y transformation

Purpose of creation of HAVoC:

The observation of infection variations by sequencing the SARS-CoV-2 genomes would give a quick method to screen variations and their spread, but there are few openly accessible strategies for speedy reference-based consensus assembly and hereditary tasks for SARS-CoV-2 samples. For this reason, Truong Nguyen, Phuoc Thien, et al. have developed a straightforward pipeline, called HAVoC (Helsinki University Analyzer for Variants of Concern), for fast reference-based consensus assembly from short read sequencing with Illumina and linear task for SARS-CoV-2 samples. This will give the end client a speedy and available strategy for identification and checking. The pipeline was created to be run on Unix/Linux working frameworks, and consequently, can be utilized in distant servers (For example CSC—IT Center for Science, Finland).

Composition and use of HAVoC:

HAVoC comprises a solitary shell script, which performs reference-based consensus assemblies to question SARS-CoV-2 FASTQ sequence libraries and appoints lineages to them exclusively in progression. It does this utilizing a few bioinformatic techniques which are openly accessible in Bioconda on Unix/Linux platforms. For HAVoC to be used, the client is needed to introduce these conditions.

HAVoC is comparable to different blends of tools, for example, Jovian and pangolin, in both speed and exactness. These devices nonetheless work independently, and as of distributing, there are no single public tools that can both play a reference-based consensus assembly and a lineage identification in an effectively open way.

Utility of HAVoC:

With the rising number of variations recognized worldwide and with a lot of them related to increased contagiousness and lower antibody viability, there is an arising need for quick, proficient, and dependable pipelines to help distinguish, recognize and follow SARS-CoV-2 heredities. These pipelines ought to also be open to analysts who may not be acquainted with using complex bioinformatic devices or prearranging pipelines.

Because of these difficulties, the scientists have created HAVoC, a basic, dependable, and easy-to-use pipeline, which can be essentially downloaded from the store and run without being introduced. The pipeline performs reference-based congregations and lineage tasks from SARS-CoV-2 examples sequenced with Illumina using a blend of various well-established bioinformatic devices in current use. Every condition can be introduced by means of existing package directors, of which the scientists suggest Bioconda. HAVoC is at present being created and refreshed as well as being used for recognizing and monitoring SARS-CoV-2 variations of concern, chiefly B.1.1.7, B1.351 and P.1, in Finland.

Also read: New bioluminescent tool to quantify disease resistance in plants

Reference:
  1. Truong Nguyen, P. T., Plyusnin, I., Sironen, T., Vapalahti, O., Kant, R., & Smura, T. (2021). HAVoC,  bioinformatic pipeline for reference-based consensus assembly and lineage assignment for SARS-CoV-2 sequences. BMC Bioinformatics, 22(1), 373. https://doi.org/10.1186/s12859-021-04294-2
  • The Corrosion Prediction from the Corrosion Product Performance
  • Nitrogen Resilience in Waterlogged Soybean plants
  • Cell Senescence in Type II Diabetes: Therapeutic Potential
  • Transgene-Free Canker-Resistant Citrus sinensis with Cas12/RNP
  • AI Literacy in Early Childhood Education: Challenges and Opportunities

Author info:

Shrestha Dutta is a 4th-year Biotechnology Engineering Student with a great interest in Genetics, Recombinant DNA Technology, and Immunology. She is a creative scientific writer in Bioxone with an inclination towards gaining knowledge regarding various sections of Biotechnology and engaging herself in various wet lab skills. She also has a review paper published in the journal IJSER.

Reference links:

  1. https://www.ijser.org/researchpaper/Unfaltering-boon-of-Nanotechnology-on-Plant-Growth.pdf
  2. https://bioxone.in/news/worldnews/therapy-for-congenital-myasthenia-a-destructive-neuromuscular-disorder/
  3. https://bioxone.in/news/indianews/first-cadaveric-liver-transplantation-in-india-by-hope-pump/
  4. https://bioxone.in/news/worldnews/nanodecoys-from-special-lung-cells-can-kill-sars-cov2/

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