Analyzing hCov Genome Sequences: Predicting Virulence and Mutation
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Abstract
Background
Covid-19 pandemic, caused by the SARS-CoV-2 genome sequence of coronavirus, has affected millions of people all over the world and taken thousands of lives. It is of utmost importance that the character of this deadly virus be studied and its nature be analyzed.
Methods
We present here an analysis pipeline comprising a classification exercise to identify the virulence of the genome sequences and extraction of important features from its genetic material that are used subsequently to predict mutation at those interesting sites using deep learning techniques.
Results
We have classified the SARS-CoV-2 genome sequences with high accuracy and predicted the mutations in the sites of Interest.
Conclusions
In a nutshell, we have prepared an analysis pipeline for hCov genome sequences leveraging the power of machine intelligence and uncovered what remained apparently shrouded by raw data.
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SciScore for 10.1101/2020.06.03.131987: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources For deep learning models, scikit-learn, tensorflow and keras neural network libraries are used and for LightGBM classifier, python LightGBM framework has been used. scikit-learnsuggested: (scikit-learn, RRID:SCR_002577)The phylogenetic trees are constructed using the Dendropy library of python [57] keeping default parameters. pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the …SciScore for 10.1101/2020.06.03.131987: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources For deep learning models, scikit-learn, tensorflow and keras neural network libraries are used and for LightGBM classifier, python LightGBM framework has been used. scikit-learnsuggested: (scikit-learn, RRID:SCR_002577)The phylogenetic trees are constructed using the Dendropy library of python [57] keeping default parameters. pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- No funding statement was detected.
- No protocol registration statement was detected.
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