journal of biomedical informatics
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Alan R. Henry*
School of Medicine, Queen’s University Belfast, UK, Email:
*Correspondence: Alan R. Henry, School of Medicine, Queen’s University Belfast, UK, Email:

Received: 03-Dec-2021 Accepted Date: Dec 20, 2021 ; Published: 27-Dec-2021

Citation: Henry AR (2021). Computational Approaches to assess the Impact of Non-Synonymous Single Nucleotide Variations on Diseases. EJBI. 17(12): 92-93.

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Nonsynonymous Single-Nucleotide Variants (nsSNVs) and mutations can disrupt protein stability by affecting genotype and phenotype. Changes in protein stability can lead to illnesses like cancer. The discovery of nsSNVs and mutations can be a useful tool for early diagnosis of the disease. Many researches have described various solitary and consensus prediction methods based on various Machine Learning Techniques (MLTs) and distinct datasets. Many researchers are developing novel techniques to anticipate pathogenic variants, as well as Meta-tools that combine many of them to improve their predictive potential. In addition, the protein stability predictors were screened for various types of computational techniques in the state-of-the-art, as well as methods for predicting the influence of both coding and noncoding variations. Targeted at bioinformaticians interested in identifying regulatory variants, geneticists, molecular biologists interested in learning more about the nature and functional role of such variants from a functional standpoint, and clinicians interested in learning about variants in humans linked to a specific disease and figuring out what to do next to figure out how they affect the underlying mechanisms.


Human genetic illnesses are linked to a variety of factors, one of which is non-synonymous Single Nucleotide Variants (nsSNVs), which cause a single amino acid to be replaced by another, resulting in protein function changes and disease. Many computational strategies for predicting the effects of amino acid changes on protein function and classifying mutations as pathogenic or neutral have been revealed [1].

Cancer is one of the most serious diseases caused by genetic abnormalities. Every minute, one person dies from cancer. The number of deaths per day is likely to rise. To control and eventually overcome cancer, it is vital to recognise it. Many scientists are working hard to figure out where cancer begins in the DNA. DNA is a molecule that contains instructions for cells to follow. Cells may not function appropriately if their instructions are incorrect, including proliferating out of control and causing cancer. Mutations in DNA that cause cancer can be inherited, but the majority are acquired. Environmental causes, such as pollutants, or lifestyle decisions, such as smoking, can cause them. Some sequence changes have an impact on critical protein function or disease potential, whereas others have no effect. One key to the flow is separating the mutations that modify protein function from those that are neutral. Currently, a large quantity of data has been created from numerous genome sequencing initiatives, which has been used to create libraries and databases. Although the majority of gene or protein variants have no negative repercussions, recognising genetic abnormalities is usually a must. Protein structure, as well as stability and function, are influenced by variations in the protein [2]. Nonsynonymous Single-Nucleotide Variants (nsSNVs) and mutations can have a number of negative impacts on proteins, such as affecting the genotype and phenotype of any protein, which can lead to disorders such as cancer. Researchers will frequently concentrate on nsSNVs, which account for about maximum no. of all known sequence differences in the human genome. Each individual has more than three million nsSNVs on average. As more unique variants are discovered to distinguish harmful mutations from neutral mutations, the functional effect of amino acid substitution (AAS) produced by nsSNVs is becoming increasingly important. The most common sort of mutation in the human genome is single nucleotide variations. Recognizing the pathogenicity of nsSNVs is one of the most difficult tasks in the human genome. The effect of a large number of variants cannot be proven since experimental characterisation of all reported variants is impractical and often difficult in terms of cost and time. Although certain gene variations have been tested in the lab, many others have not been investigated for their potential impact on protein structure and function [3].

The capacity to distinguish harmful variants from other neutral alterations is one of the most difficult tasks in whole exome sequencing (WES) investigations. Recent advancements in next-generation sequencing technology have made it possible to generate massive amounts of genomic sequence data quickly. A major difficulty in genetics understands the relationship between phenotype and genotype (mutations). There has been a lot of effort put into identifying linkages between human genetic variants and their phenotypic impacts. A thorough examination of various machine learning algorithms for predicting nsSNVs based on sequence homology, protein structure, integrated characteristics (structure and sequence), or consensus decision (scores of individual techniques). Some of the approaches used in prior studies will never be available, while others will be available for a limited time [4]. As a result, the availability of the evaluated tools is linked to their selection from modern and most popular servers. There are a variety of computational strategies that may be used to find missense variations that are more likely to have a pathogenic effect and contribute to a disease like cancer. It is critical to rank these strategies according to their efficiency in predicting outcomes and to discover whether one methodically outperforms all others in the overall expectation precision dataset. It remains the finest tool for any unknown datasets to this day [5].


As a result, it‘s critical to focus research on this subject on developing the best algorithms for predicting pathogenic nsSNVs from other neutral nsSNVs across all datasets. To encourage students and researchers to combine new approaches like PON-P2 and iFish with older models like SIFT, Provean, PANTHER, SNAP2, Mutation Assessor, and FATHMM to create new techniques like PON-P2 and iFish The evaluation of classifiers enables them to be ranked according to performance and the best one in the deleterious prediction to be determined.