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Germline BRCA1/2 alteration has been linked to an increased risk of hereditary breast and ovarian cancer syndromes. As a result, genetic testing, based on NGS, allows us to identify a high number of variants of uncertain significance (VUS) or conflicting interpretation of pathogenicity (CIP) variants. The identification of CIP/VUS is often considered inconclusive and clinically not actionable for the patients' and unaffected carriers' management. In this context, their assessment and classification remain a significant challenge. The aim of the study was to investigate whether the in silico prediction tools (PolyPhen-2, SIFT, Mutation Taster and PROVEAN) could predict the potential clinical impact and significance of BRCA1/2 CIP/VUS alterations, eventually impacting the clinical management of Breast Cancer subjects. In a cohort of 860 BC patients, 10.6% harbored BRCA1 or BRCA2 CIP/VUS alterations, mostly observed in BRCA2 sequences (85%). Among them, forty-two out of fifty-five alterations were predicted as damaging, with at least one in silico that used tools. Prediction agreement of the four tools was achieved in 45.5% of patients. Moreover, the highest consensus was obtained in twelve out of forty-two (28.6%) mutations by considering three out of four in silico algorithms. The use of prediction tools may help to identify variants with a potentially damaging effect. The lack of substantial agreement between the different algorithms suggests that the bioinformatic approaches should be combined with the personal and family history of the cancer patients.

Background: The genetic diversity in HIV-1 genes affects viral pathogenesis in HIV-1 positive patients. Accessory genes of HIV-1, including vpu , are reported to play a critical role in HIV pathogenesis and disease progression. Vpu has a crucial role in CD4 degradation and virus release. The sequence heterogeneity in the vpu gene may affect disease progression in patients, therefore, the current study was undertaken to identify the role of vpu in patients defined as rapid progressors., Objective: The objective of the study was to identify the viral determinants present on vpu that may be important in disease progression in rapid progressors., Methods: Blood samples were collected from 13 rapid progressors. DNA was isolated from PBMCs and vpu was amplified using nested PCR. Both strands of the gene were sequenced using an automated DNA Sequencer. The characterization and analysis of vpu was done using various bioinformatics tools., Results: The analysis revealed that all sequences had intact ORF and sequence heterogeneity was present across all sequences and distributed all over the gene. The synonymous substitutions, however, were higher than nonsynonymous substitutions. The phylogenetic tree analysis showed an evolutionary relationship with previously published Indian subtype C sequences. Comparatively, the cytoplasmic tail (77 - 86) showed the highest degree of variability in these sequences as determined by Entropy- one tool., Conclusion: The study showed that due to the robust nature of the protein, the biological activity of the protein was intact and sequence heterogeneity may promote disease progression in the study population., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Cyclin-dependent kinase-like 5 (CDKL5) is a serine/threonine protein kinase whose pathological mutations cause CDKL5 deficiency disorder. Most missense mutations are concentrated in the catalytic domain. Therefore, anticipating whether mutations in this region affect CDKL5 function is informative for clinical diagnosis. This study comprehensively predicted the pathogenicity of all 5700 missense substitutions in the catalytic domain of CDKL5 using in silico analysis and evaluating their accuracy. Each missense substitution was evaluated as "pathogenic" or "benign". In silico tools PolyPhen-2 HumDiv mode/HumVar mode, PROVEAN, and SIFT were selected individually or in combination with one another to determine their performance using 36 previously reported mutations as a reference. Substitutions predicted as pathogenic were over 88.0% accurate using each of the three tools. The best performance score (accuracy, 97.2%; sensitivity, 100%; specificity, 66.7%; and Matthew's correlation coefficient (MCC), 0.804) was achieved by combining PolyPhen-2 HumDiv, PolyPhen-2 HumVar, and PROVEAN. This provided comprehensive information that could accurately predict the pathogenicity of the disease, which might be used as an aid for clinical diagnosis.

Colonization of new habitats often reduces population sizes and may result in the accumulation of deleterious mutations by genetic drift. Compared with the genomic basis for adaptation to new environments, genome-wide analysis of deleterious mutations in isolated populations remains limited. In the present study, we investigated the accumulation of deleterious mutations in five endangered freshwater populations of threespine stickleback (Gasterosteus aculeatus) in the central part of the mainland of Japan. Using whole-genome resequencing data, we first conducted phylogenomic analysis and confirmed at least two independent freshwater colonization events in the central mainland from ancestral marine ecotypes. Next, analyses of single nucleotide polymorphisms showed a substantial reduction of heterozygosity in freshwater populations compared with marine populations. Reduction in heterozygosity was more apparent at the center of each chromosome than the peripheries and on X chromosomes compared with autosomes. Third, bioinformatic analysis of deleterious mutations showed increased accumulation of putatively deleterious mutations in the landlocked freshwater populations compared with marine populations. For the majority of populations examined, the frequencies of putatively deleterious mutations were higher on X chromosomes than on autosomes. The interpopulation comparison indicated that the majority of putatively deleterious mutations may have accumulated independently. Thus, whole-genome resequencing of endangered populations can help to estimate the accumulation of deleterious mutations and inform us of which populations are the most severely endangered. Furthermore, analysis of variation among chromosomes can give insights into whether any particular chromosomes are likely to accumulate deleterious mutations., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

Human granulocyte colony stimulating factor (hG-CSF), known as CSF3, plays an important role in the growth, differentiation, proliferation, survival, and activation of the granulocyte cell lineage such as neutrophils and their precursors. Functional reduction in native CSF3 protein results in reduced proliferation and activation of neutrophils and leads to neutropenia. Single nucleotide polymorphisms (SNPs) in the CSF3 gene may have deleterious effects on the CSF3 protein function. This study was undertaken to find the functional SNPs in the human CSF3 gene. Results suggest that 18.9% of all the SNPs in the dbSNP database for CSF3 gene were present in the coding region. Out of 59 non-synonymous SNPs (nsSNPs), 26 nsSNPs were predicted to be non-tolerable by SIFT whereas 18 and 7 nsSNPs were predicted as probably damaging and possibly damaging, respectively by PolyPhen. Among this 31 nsSNPs, 16 nsSNPs were identified to be potentially deleterious by PANTHER server, and 4 nsSNPs were found to be neutral by PROVEAN. SNPAnalyzer predicted 7 nsSNPs to be neutral phenotype and the remaining 24 nsSNPs to be associated with diseases. Among the predicted nsSNPs, rs144408123, rs144408123, rs145136406, rs145311241, rs373191696, rs762945096, rs763688260, rs767572172, rs775326370, rs777777864, rs777983866, rs781596455, rs139072004, rs757612684, rs772911210, rs139072004, rs746634544, rs749993200, rs763426127, rs772466210 were identified as deleterious and potentially damaging. I-Mutant analysis revealed that th 20 nsSNPs were important for protein stability of CSF3. Therefore, th 20 nsSNPs may be used for the wider population-based genetic studies and also should be taken into account while engineering the recombinant CSF3 protein for clinical use., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

The human genetic diseases associated with many factors, one of these factors is the non-synonymous Single Nucleotide Variants (nsSNVs) cause single amino acid change with another resulting in protein function change leading to disease. Many computational techniques have been released to expect the impacts of amino acid alteration on protein function and classify mutations as pathogenic or neutral. Here in this article, we assessed the performance of eight techniques; FATHMM, SIFT, Provean, iFish, Mutation Assessor, PANTHER, SNAP2, and PON- P2 using a VaribenchSelectedPure dataset of 2144 pathogenic variants and 3777 neutral variants extracted from the free standard database "Varibench." The first five techniques achieve (45.60-83.75) % specificity, (52.64-94.13) % sensitivity, (51.00-88.90) % AUC, and (49.76-88.24) % ACC on whole dataset, while all eight techniques achieve (36.54-77.88) % specificity, (50.00-75.00) % sensitivity, (51.00-76.40) % AUC, and (25.00-77.78) % ACC on random sample dataset. We also created a Meta classifier (CSTJ48) that combines FATHMM, iFish, and Mutation Assessor. It registers 96.33% specificity, 86.07% sensitivity, 91.20% AUC, and 91.89 ACC. By comparing the results, it's clear that FATHMM gives the highest performance over the seven individual techniques, where it achieves 83.75% and 77.88% specificity, 94.13%, and 75.00% sensitivity, 88.90% and 76.40% AUC, and 88.24% and 77.78% ACC on whole and random sample dataset, respectively. Also, the launched Meta classifier (CSTJ48) is outperforming over all the eight individual tools that compared here., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Background: Duchenne muscular dystrophy (DMD) and Becker muscular dystrophy (BMD) are X-linked recessive disorders caused by mutations in the DMD gene. The aim of this study was to predict the effect of gene mutations on the dystrophin protein and study its impact on clinical phenotype., Methods: In this study, 415 clinically diagnosed patients were tested for mutations by Multiplex ligation dependent probe amplification (MLPA). Muscle biopsy was performed in 34 patients with negative MLPA. Phenotype-genotype correlation was done using PROVEAN, hydrophobicity and eDystrophin analysis. We have utilized bioinformatics tools in order to evaluate the observed mutations both at the level of primary as well as secondary structure., Results: Mutations were identified in 75.42% cases, of which there were deletions in 91.6% and duplications in 8.30%. As per the reading frame rule, 84.6% out-of frame and 15.3% in-frame mutations were noted. Exon 50 was the most frequently deleted exon and the exon 45-52 region was the hot-spot for deletions in this cohort. There was no correlation noted between age of onset or creatine kinase (CK) values with extent of gene mutation. The PROVEAN analysis showed a deleterious effect in 94.5% cases and a neutral effect in 5.09% cases. Mutations in exon 45-54 (out of frame) and exon 46-54 (in-frame) regions in the central rod domain of dystrophin showed more negative scores compared to other domains in the present study. Hydrophobicity profile analysis showed that the hydrophobic regions I & III were equally affected. Analysis of deletions in hinge III hydrophobic region by the eDystrophin programme also predicted a hybrid repeat seen to be associated with a BMD like disease progression, thus making the hinge III region relatively tolerant to mutations., Conclusions: We found that, while the predictions made by the software utilized might have overall significance, the results were not convincing on a case by case basis. This reflects the inadequacy of the currently available tools and also underlines the possible inadequacy of MLPA to detect other minor mutations that might enhance or suppress the effect of the primary mutation in this large gene. Next Generation Sequencing or targeted Sanger sequencing on a case by case basis might improve phenotype- genotype correlation.

Gaucher's disease (GD) is an autosomal recessive disorder caused by the deficiency of glucocerebrosidase, a lysosomal enzyme that catalyses the hydrolysis of the glycolipid glucocerebroside to ceramide and glucose. Polymorphisms in GBA gene have been associated with the development of Gaucher disease. We hypothesize that prediction of SNPs using multiple state of the art software tools will help in increasing the confidence in identification of SNPs involved in GD. Enzyme replacement therapy is the only option for GD. Our goal is to use several state of art SNP algorithms to predict/address harmful SNPs using comparative studies. In this study seven different algorithms (SIFT, MutPred, nsSNP Analyzer, PANTHER, PMUT, PROVEAN, and SNPs&GO) were used to predict the harmful polymorphisms. Among the seven programs, SIFT found 47 nsSNPs as deleterious, MutPred found 46 nsSNPs as harmful. nsSNP Analyzer program found 43 out of 47 nsSNPs are disease causing SNPs whereas PANTHER found 32 out of 47 as highly deleterious, 22 out of 47 are classified as pathological mutations by PMUT, 44 out of 47 were predicted to be deleterious by PROVEAN server, all 47 shows the disease related mutations by SNPs&GO. Twenty two nsSNPs were commonly predicted by all the seven different algorithms. The common 22 targeted mutations are F251L, C342G, W312C, P415R, R463C, D127V, A309V, G46E, G202E, P391L, Y363C, Y205C, W378C, I402T, S366R, F397S, Y418C, P401L, G195E, W184R, R48W, and T43R.

Crigler-Najjar syndrome (CNS) type I and type II are usually inherited as autosomal recessive conditions that result from mutations in the UGT1A1 gene. The main objective of the present review is to summarize results of all available evidence on the accuracy of SNP-based pathogenicity detection tools compared to published clinical result for the prediction of in nsSNPs that leads to disease using prediction performance method. A comprehensive search was performed to find all mutations related to CNS. Database searches included dbSNP, SNPdbe, HGMD, Swissvar, ensemble, and OMIM. All the mutation related to CNS was extracted. The pathogenicity prediction was done using SNP-based pathogenicity detection tools include SIFT, PHD-SNP, PolyPhen2, fathmm, Provean, and Mutpred. Overall, 59 different SNPs related to missense mutations in the UGT1A1 gene, were reviewed. Comparing the diagnostic OR, PolyPhen2 and Mutpred have the highest detection 4.983 (95% CI: 1.24 - 20.02) in both, following by SIFT (diagnostic OR: 3.25, 95% CI: 1.07 - 9.83). The highest MCC of SNP-based pathogenicity detection tools, was belong to SIFT (34.19%) followed by Provean, PolyPhen2, and Mutpred (29.99%, 29.89%, and 29.89%, respectively). Hence the highest SNP-based pathogenicity detection tools ACC, was fit to SIFT (62.71%) followed by PolyPhen2, and Mutpred (61.02%, in both). Our results suggest that some of the well-established SNP-based pathogenicity detection tools can appropriately reflect the role of a disease-associated SNP in both local and global structures.