Encore -- d:(Mutation (Biology))

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Population biology and criticality : from critical birth-death processes to self-organized criticality in mutation pathogen systems / Nico Stollenwerk, Vincent Jansen.

Stollenwerk, Nico.

Book | 2011

Available at Available Merrill-Cazier Books (3rd Floor North) (Call number: QH460 .S76 2011)

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Merrill-Cazier Books (3rd Floor North)	QH460 .S76 2011	Available

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Fast and accurate variant identification tool for sequencing-based studies.

Gaston JM;Alm EJ;Zhang AN

Academic Journal | Publisher: BioMed Central Country of Publication: England NLM ID: 101190720 Publication Model: Electronic Cited Medium: Internet ISSN: 1741-7007 (Electronic) Linking ISSN: 17417007 NLM ISO Abbreviation: BMC Biol Subsets: MEDLINE Please log in to see more details

Background: Accurate identification of genetic variants, such as point mutations and i... more

Fast and accurate variant identification tool for sequencing-based studies.

Publisher: BioMed Central Country of Publication: England NLM ID: 101190720 Publication Model: Electronic Cited Medium: Internet ISSN: 1741-7007 (Electronic) Linking ISSN: 17417007 NLM ISO Abbreviation: BMC Biol Subsets: MEDLINE

Background: Accurate identification of genetic variants, such as point mutations and insertions/deletions (indels), is crucial for various genetic studies into epidemic tracking, population genetics, and disease diagnosis. Genetic studies into microbiomes often require processing numerous sequencing datasets, necessitating variant identifiers with high speed, accuracy, and robustness.
Results: We present QuickVariants, a bioinformatics tool that effectively summarizes variant information from read alignments and identifies variants. When tested on diverse bacterial sequencing data, QuickVariants demonstrates a ninefold higher median speed than bcftools, a widely used variant identifier, with higher accuracy in identifying both point mutations and indels. This accuracy extends to variant identification in virus samples, including SARS-CoV-2, particularly with significantly fewer false negative indels than bcftools. The high accuracy of QuickVariants is further demonstrated by its detection of a greater number of Omicron-specific indels (5 versus 0) and point mutations (61 versus 48-54) than bcftools in sewage metagenomes predominated by Omicron variants. Much of the reduced accuracy of bcftools was attributable to its misinterpretation of indels, often producing false negative indels and false positive point mutations at the same locations.
Conclusions: We introduce QuickVariants, a fast, accurate, and robust bioinformatics tool designed for identifying genetic variants for microbial studies. QuickVariants is available at https://github.com/caozhichongchong/QuickVariants .
(© 2024. The Author(s).)

Subject terms:

Computational Biology methods - Humans - Software - COVID-19 virology - High-Throughput Nucleotide Sequencing methods - Point Mutation - Genetic Variation - Sequence Analysis, DNA methods - SARS-CoV-2 genetics - INDEL Mutation

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MEDLINE

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The mutant project : inside the global race to genetically modify humans / Eben Kirksey.

Kirksey, Eben, 1976- author.

Ebook | 2020

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CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods.

Academic Journal | Publisher: BioMed Central Ltd Country of Publication: England NLM ID: 100960660 Publication Model: Electronic Cited Medium: Internet ISSN: 1474-760X (Electronic) Linking ISSN: 14747596 NLM ISO Abbreviation: Genome Biol Subsets: MEDLINE Please log in to see more details

Background: The Critical Assessment of Genome Interpretation (CAGI) aims to advance th... more

CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods.

Publisher: BioMed Central Ltd Country of Publication: England NLM ID: 100960660 Publication Model: Electronic Cited Medium: Internet ISSN: 1474-760X (Electronic) Linking ISSN: 14747596 NLM ISO Abbreviation: Genome Biol Subsets: MEDLINE

Background: The Critical Assessment of Genome Interpretation (CAGI) aims to advance the state-of-the-art for computational prediction of genetic variant impact, particularly where relevant to disease. The five complete editions of the CAGI community experiment comprised 50 challenges, in which participants made blind predictions of phenotypes from genetic data, and these were evaluated by independent assessors.
Results: Performance was particularly strong for clinical pathogenic variants, including some difficult-to-diagnose cases, and extends to interpretation of cancer-related variants. Missense variant interpretation methods were able to estimate biochemical effects with increasing accuracy. Assessment of methods for regulatory variants and complex trait disease risk was less definitive and indicates performance potentially suitable for auxiliary use in the clinic.
Conclusions: Results show that while current methods are imperfect, they have major utility for research and clinical applications. Emerging methods and increasingly large, robust datasets for training and assessment promise further progress ahead.
(© 2024. The Author(s).)

Subject terms:

Humans - Phenotype - Computational Biology methods - Mutation, Missense

Content provider:

MEDLINE

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Learn how gene therapy seeks to repair genetic mutations through the introduction of healthy genes / [Kurt Heintz, narrator] ; [Melinda Leonard, director].

Streaming video | 2020

Available at Available Online Academic Video Online (USU and USU Eastern) (Call number: Streaming Video)

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Online Academic Video Online (USU and USU Eastern)	Streaming Video	Available

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Assessing computational tools for predicting protein stability changes upon missense mutations using a new dataset.

Zheng F;Liu Y;Yang Y;Wen Y;Li M

Academic Journal | Publisher: Cold Spring Harbor Laboratory Press Country of Publication: United States NLM ID: 9211750 Publication Model: Print Cited Medium: Internet ISSN: 1469-896X (Electronic) Linking ISSN: 09618368 NLM ISO Abbreviation: Protein Sci Subsets: MEDLINE Please log in to see more details

Insight into how mutations affect protein stability is crucial for protein engineering... more

Assessing computational tools for predicting protein stability changes upon missense mutations using a new dataset.

Publisher: Cold Spring Harbor Laboratory Press Country of Publication: United States NLM ID: 9211750 Publication Model: Print Cited Medium: Internet ISSN: 1469-896X (Electronic) Linking ISSN: 09618368 NLM ISO Abbreviation: Protein Sci Subsets: MEDLINE

Insight into how mutations affect protein stability is crucial for protein engineering, understanding genetic diseases, and exploring protein evolution. Numerous computational methods have been developed to predict the impact of amino acid substitutions on protein stability. Nevertheless, comparing these methods poses challenges due to variations in their training data. Moreover, it is observed that they tend to perform better at predicting destabilizing mutations than stabilizing ones. Here, we meticulously compiled a new dataset from three recently published databases: ThermoMutDB, FireProtDB, and ProThermDB. This dataset, which does not overlap with the well-established S2648 dataset, consists of 4038 single-point mutations, including over 1000 stabilizing mutations. We assessed these mutations using 27 computational methods, including the latest ones utilizing mega-scale stability datasets and transfer learning. We excluded entries with overlap or similarity to training datasets to ensure fairness. Pearson correlation coefficients for the tested tools ranged from 0.20 to 0.53 on unseen data, and none of the methods could accurately predict stabilizing mutations, even those performing well in anti-symmetric property analysis. While most methods present consistent trends for predicting destabilizing mutations across various properties such as solvent exposure and secondary conformation, stabilizing mutations do not exhibit a clear pattern. Our study also suggests that solely addressing training dataset bias may not significantly enhance accuracy of predicting stabilizing mutations. These findings emphasize the importance of developing precise predictive methods for stabilizing mutations.
(© 2023 The Protein Society.)

Subject terms:

Computational Biology methods - Mutation - Point Mutation - Protein Stability - Datasets as Topic - Mutation, Missense - Proteins genetics - Proteins chemistry

Content provider:

MEDLINE

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Gene editing, law, and the environment : life beyond the human / edited by Irus Braverman.

Ebook | 2018

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3DVizSNP: a tool for rapidly visualizing missense mutations identified in high throughput experiments in iCn3D.

Sierk M;Ratnayake S;Wagle MM;Chen B;Park B;Wang J;Youkharibache P;Meerzaman...

Academic Journal | Publisher: BioMed Central Country of Publication: England NLM ID: 100965194 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2105 (Electronic) Linking ISSN: 14712105 NLM ISO Abbreviation: BMC Bioinformatics Subsets: MEDLINE Please log in to see more details

Background: High throughput experiments in cancer and other areas of genomic research ... more

3DVizSNP: a tool for rapidly visualizing missense mutations identified in high throughput experiments in iCn3D.

Publisher: BioMed Central Country of Publication: England NLM ID: 100965194 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2105 (Electronic) Linking ISSN: 14712105 NLM ISO Abbreviation: BMC Bioinformatics Subsets: MEDLINE

Background: High throughput experiments in cancer and other areas of genomic research identify large numbers of sequence variants that need to be evaluated for phenotypic impact. While many tools exist to score the likely impact of single nucleotide polymorphisms (SNPs) based on sequence alone, the three-dimensional structural environment is essential for understanding the biological impact of a nonsynonymous mutation.
Results: We present a program, 3DVizSNP, that enables the rapid visualization of nonsynonymous missense mutations extracted from a variant caller format file using the web-based iCn3D visualization platform. The program, written in Python, leverages REST APIs and can be run locally without installing any other software or databases, or from a webserver hosted by the National Cancer Institute. It automatically selects the appropriate experimental structure from the Protein Data Bank, if available, or the predicted structure from the AlphaFold database, enabling users to rapidly screen SNPs based on their local structural environment. 3DVizSNP leverages iCn3D annotations and its structural analysis functions to assess changes in structural contacts associated with mutations.
Conclusions: This tool enables researchers to efficiently make use of 3D structural information to prioritize mutations for further computational and experimental impact assessment. The program is available as a webserver at https://analysistools.cancer.gov/3dvizsnp or as a standalone python program at https://github.com/CBIIT-CGBB/3DVizSNP .
(© 2023. The Author(s).)

Subject terms:

Genomics methods - Software - Mutation - Mutation, Missense - Computational Biology methods

Content provider:

MEDLINE

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Crumbling genome / Alexey S. Kondrashov.

Kondrashov, Alexey S., 1957- author.

Ebook | 2017

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Crumbling genome / Alexey S. Kondrashov.

Kondrashov, Alexey S., 1957- author.

Ebook | 2017

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Influence of Model Structures on Predictors of Protein Stability Changes from Single-Point Mutations.

Rollo C;Pancotti C;Birolo G;Rossi I;Sanavia T;Fariselli P

Academic Journal | Publisher: MDPI Country of Publication: Switzerland NLM ID: 101551097 Publication Model: Electronic Cited Medium: Internet ISSN: 2073-4425 (Electronic) Linking ISSN: 20734425 NLM ISO Abbreviation: Genes (Basel) Subsets: MEDLINE Please log in to see more details

Missense variation in genomes can affect protein structure stability and, in turn, the... more

Influence of Model Structures on Predictors of Protein Stability Changes from Single-Point Mutations.

Publisher: MDPI Country of Publication: Switzerland NLM ID: 101551097 Publication Model: Electronic Cited Medium: Internet ISSN: 2073-4425 (Electronic) Linking ISSN: 20734425 NLM ISO Abbreviation: Genes (Basel) Subsets: MEDLINE

Missense variation in genomes can affect protein structure stability and, in turn, the cell physiology behavior. Predicting the impact of those variations is relevant, and the best-performing computational tools exploit the protein structure information. However, most of the current protein sequence variants are unresolved, and comparative or ab initio tools can provide a structure. Here, we evaluate the impact of model structures, compared to experimental structures, on the predictors of protein stability changes upon single-point mutations, where no significant changes are expected between the original and the mutated structures. We show that there are substantial differences among the computational tools. Methods that rely on coarse-grained representation are less sensitive to the underlying protein structures. In contrast, tools that exploit more detailed molecular representations are sensible to structures generated from comparative modeling, even on single-residue substitutions.

Subject terms:

Proteins metabolism - Protein Stability - Amino Acid Sequence - Point Mutation - Computational Biology methods

Content provider:

MEDLINE

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Translational oncogenomics [electronic resource].

Electronic Journal | 2006 - 2017

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MMPatho: Leveraging Multilevel Consensus and Evolutionary Information for Enhanced Missense Mutation Pathogenic Prediction.

Ge F;Arif M;Yan Z;Alahmadi H;Worachartcheewan A;Yu DJ;Shoombuatong W

Academic Journal | Publisher: American Chemical Society Country of Publication: United States NLM ID: 101230060 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1549-960X (Electronic) Linking ISSN: 15499596 NLM ISO Abbreviation: J Chem Inf Model Subsets: MEDLINE Please log in to see more details

Understanding the pathogenicity of missense mutation (MM) is essential for shed light ... more

MMPatho: Leveraging Multilevel Consensus and Evolutionary Information for Enhanced Missense Mutation Pathogenic Prediction.

Publisher: American Chemical Society Country of Publication: United States NLM ID: 101230060 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1549-960X (Electronic) Linking ISSN: 15499596 NLM ISO Abbreviation: J Chem Inf Model Subsets: MEDLINE

Understanding the pathogenicity of missense mutation (MM) is essential for shed light on genetic diseases, gene functions, and individual variations. In this study, we propose a novel computational approach, called MMPatho, for enhancing missense mutation pathogenic prediction. First, we established a large-scale nonredundant MM benchmark data set based on the entire Ensembl database, complemented by a focused blind test set specifically for pathogenic GOF/LOF MM. Based on this data set, for each mutation, we utilized Ensembl VEP v104 and dbNSFP v4.1a to extract variant-level, amino acid-level, individuals' outputs, and genome-level features. Additionally, protein sequences were generated using ENSP identifiers with the Ensembl API, and then encoded. The mutant sites' ESM-1b and ProtTrans-T5 embeddings were subsequently extracted. Then, our model group (MMPatho) was developed by leveraging upon these efforts, which comprised ConsMM and EvoIndMM. To be specific, ConsMM employs individuals' outputs and XGBoost with SHAP explanation analysis, while EvoIndMM investigates the potential enhancement of predictive capability by incorporating evolutionary information from ESM-1b and ProtT5-XL-U50, large protein language embeddings. Through rigorous comparative experiments, both ConsMM and EvoIndMM were capable of achieving remarkable AUROC (0.9836 and 0.9854) and AUPR (0.9852 and 0.9902) values on the blind test set devoid of overlapping variations and proteins from the training data, thus highlighting the superiority of our computational approach in the prediction of MM pathogenicity. Our Web server, available at http://csbio.njust.edu.cn/bioinf/mmpatho/, allows researchers to predict the pathogenicity (alongside the reliability index score) of MMs using the ConsMM and EvoIndMM models and provides extensive annotations for user input. Additionally, the newly constructed benchmark data set and blind test set can be accessed via the data page of our web server.

Subject terms:

Humans - Reproducibility of Results - Consensus - Proteins - Computational Biology - Mutation, Missense

Content provider:

MEDLINE

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Things that make you go yuck! : mystifying mutants / Jenn Dlugos & Charlie Hatton.

Dlugos, Jenn, author.

Ebook | 2017

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Adaptation of a mutual exclusivity framework to identify driver mutations within oncogenic pathways.

Wang X;Kostrzewa C;Reiner A;Shen R;Begg C

Academic Journal | Publisher: Cell Press Country of Publication: United States NLM ID: 0370475 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1537-6605 (Electronic) Linking ISSN: 00029297 NLM ISO Abbreviation: Am J Hum Genet Subsets: MEDLINE Please log in to see more details

Distinguishing genomic alterations in cancer-associated genes that have functional imp... more

Adaptation of a mutual exclusivity framework to identify driver mutations within oncogenic pathways.

Publisher: Cell Press Country of Publication: United States NLM ID: 0370475 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1537-6605 (Electronic) Linking ISSN: 00029297 NLM ISO Abbreviation: Am J Hum Genet Subsets: MEDLINE

Distinguishing genomic alterations in cancer-associated genes that have functional impact on tumor growth and disease progression from the ones that are passengers and confer no fitness advantage have important clinical implications. Evidence-based methods for nominating drivers are limited by existing knowledge on the oncogenic effects and therapeutic benefits of specific variants from clinical trials or experimental settings. As clinical sequencing becomes a mainstay of patient care, applying computational methods to mine the rapidly growing clinical genomic data holds promise in uncovering functional candidates beyond the existing knowledge base and expanding the patient population that could potentially benefit from genetically targeted therapies. We propose a statistical and computational method (MAGPIE) that builds on a likelihood approach leveraging the mutual exclusivity pattern within an oncogenic pathway for identifying probabilistically both the specific genes within a pathway and the individual mutations within such genes that are truly the drivers. Alterations in a cancer-associated gene are assumed to be a mixture of driver and passenger mutations with the passenger rates modeled in relationship to tumor mutational burden. We use simulations to study the operating characteristics of the method and assess false-positive and false-negative rates in driver nomination. When applied to a large study of primary melanomas, the method accurately identifies the known driver genes within the RTK-RAS pathway and nominates several rare variants as prime candidates for functional validation. A comprehensive evaluation of MAGPIE against existing tools has also been conducted leveraging the Cancer Genome Atlas data.
Competing Interests: Declaration of interests The authors declare no competing interests.
(Copyright © 2023 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

Subject terms:

Humans - Likelihood Functions - Genomics methods - Mutation genetics - Algorithms - Computational Biology methods - Neoplasms genetics

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MEDLINE

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Biology. Mutations.

Streaming video | 2015

Available at Available Online Academic Video Online (USU and USU Eastern) (Call number: Streaming Video)

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Mutation of neurotrophic tyrosine receptor kinase can promote pan-cancer immunity and the efficacy of immunotherapy.

Wang C;Li Y;Huang J;Yan H;Zhao B

Academic Journal | Publisher: BioMed Central Country of Publication: England NLM ID: 101147698 Publication Model: Electronic Cited Medium: Internet ISSN: 1476-4598 (Electronic) Linking ISSN: 14764598 NLM ISO Abbreviation: Mol Cancer Subsets: MEDLINE Please log in to see more details

The Neurotrophic tyrosine receptor kinase (NTRK) family plays important roles in tumor... more

Mutation of neurotrophic tyrosine receptor kinase can promote pan-cancer immunity and the efficacy of immunotherapy.

Publisher: BioMed Central Country of Publication: England NLM ID: 101147698 Publication Model: Electronic Cited Medium: Internet ISSN: 1476-4598 (Electronic) Linking ISSN: 14764598 NLM ISO Abbreviation: Mol Cancer Subsets: MEDLINE

The Neurotrophic tyrosine receptor kinase (NTRK) family plays important roles in tumor progression and is involved in tumor immunogenicity. Here, we conducted a comprehensive bioinformatic and clinical analysis to investigate the characteristics of NTRK mutations and their association with the outcomes in pan-cancer immunotherapy. In 3888 patients across 12 cancer types, patients with NTRK-mutant tumors showed more benefit from immunotherapy in terms of objective response rate (ORR; 41.7% vs. 27.5%; P < 0.001), progress-free survival (PFS; HR = 0.80; 95% CI, 0.68-0.96; P = 0.01), and overall survival (OS; HR = 0.71; 95% CI, 0.61-0.82; P < 0.001). We further constructed and validated a nomogram to estimate survival probabilities after the initiation of immunotherapy. Multi-omics analysis on intrinsic and extrinsic immune landscapes indicated that NTRK mutation was associated with enhanced tumor immunogenicity, enriched infiltration of immune cells, and improved immune responses. In summary, NTRK mutation may promote cancer immunity and indicate favorable outcomes in immunotherapy. Our results have implications for treatment decision-making and developing immunotherapy for personalized care.
(© 2024. The Author(s).)

Subject terms:

Humans - Biomarkers, Tumor genetics - Prognosis - Tumor Microenvironment immunology - Tumor Microenvironment genetics - Nomograms - Computational Biology methods - Mutation - Immunotherapy methods - Neoplasms genetics - Neoplasms therapy - Neoplasms immunology - Neoplasms mortality

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MEDLINE

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A mutation-selection model with recombination for general genotypes / Steven N. Evans, David Steinsaltz, Kenneth W. Wachter.

Evans, Steven N. (Steven Neil)

Book | 2013

Available at Available Merrill-Cazier Books (3rd Floor North) (Call number: QH390 .E92 2013)

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Merrill-Cazier Books (3rd Floor North)	QH390 .E92 2013	Available

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Unravelling driver genes as potential therapeutic targets in ovarian cancer via integrated bioinformatics approach.

Beg A;Parveen R;Fouad H;Yahia ME;Hassanein AS

Academic Journal | Publisher: BioMed Central Country of Publication: England NLM ID: 101474849 Publication Model: Electronic Cited Medium: Internet ISSN: 1757-2215 (Electronic) Linking ISSN: 17572215 NLM ISO Abbreviation: J Ovarian Res Subsets: MEDLINE Please log in to see more details

Target-driven cancer therapy is a notable advancement in precision oncology that has b... more

Unravelling driver genes as potential therapeutic targets in ovarian cancer via integrated bioinformatics approach.

Publisher: BioMed Central Country of Publication: England NLM ID: 101474849 Publication Model: Electronic Cited Medium: Internet ISSN: 1757-2215 (Electronic) Linking ISSN: 17572215 NLM ISO Abbreviation: J Ovarian Res Subsets: MEDLINE

Target-driven cancer therapy is a notable advancement in precision oncology that has been accompanied by substantial medical accomplishments. Ovarian cancer is a highly frequent neoplasm in women and exhibits significant genomic and clinical heterogeneity. In a previous publication, we presented an extensive bioinformatics study aimed at identifying specific biomarkers associated with ovarian cancer. The findings of the network analysis indicate the presence of a cluster of nine dysregulated hub genes that exhibited significance in the underlying biological processes and contributed to the initiation of ovarian cancer. Here in this research article, we are proceeding our previous research by taking all hub genes into consideration for further analysis. GEPIA2 was used to identify patterns in the expression of critical genes. The KM plotter analysis indicated that the out of all genes 5 genes are statistically significant. The cBioPortal platform was further used to investigate the frequency of genetic mutations across the board and how they affected the survival of the patients. Maximum mutation was reported by ELAVL2. In order to discover viable therapeutic candidates after competitive inhibition of ELAVL2 with small molecular drug complex, high throughput screening and docking studies were used. Five compounds were identified. Overall, our results suggest that the ELAV-like protein 2-ZINC03830554 complex was relatively stable during the molecular dynamic simulation. The five compounds that have been found can also be further examined as potential therapeutic possibilities. The combined findings suggest that ELAVL2, together with their genetic changes, can be investigated in therapeutic interventions for precision oncology, leveraging early diagnostics and target-driven therapy.
(© 2024. The Author(s).)

Subject terms:

Humans - Female - Mutation - Biomarkers, Tumor genetics - Gene Expression Regulation, Neoplastic - Gene Regulatory Networks - Molecular Targeted Therapy - Molecular Docking Simulation - ELAV-Like Protein 2 genetics - Ovarian Neoplasms genetics - Ovarian Neoplasms drug therapy - Computational Biology methods

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MEDLINE

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Next pandemic [electronic resource] / produced by Ian Watson.

Streaming video | 2013

Available at Available Online Academic Video Online (USU and USU Eastern) (Call number: Streaming Video)

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Enhancing missense variant pathogenicity prediction with protein language models using VariPred.

Lin W;Wells J;Wang Z;Orengo C;Martin ACR

Academic Journal | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE Please log in to see more details

Computational approaches for predicting the pathogenicity of genetic variants have adv... more

Enhancing missense variant pathogenicity prediction with protein language models using VariPred.

Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE

Computational approaches for predicting the pathogenicity of genetic variants have advanced in recent years. These methods enable researchers to determine the possible clinical impact of rare and novel variants. Historically these prediction methods used hand-crafted features based on structural, evolutionary, or physiochemical properties of the variant. In this study we propose a novel framework that leverages the power of pre-trained protein language models to predict variant pathogenicity. We show that our approach VariPred (Variant impact Predictor) outperforms current state-of-the-art methods by using an end-to-end model that only requires the protein sequence as input. Using one of the best-performing protein language models (ESM-1b), we establish a robust classifier that requires no calculation of structural features or multiple sequence alignments. We compare the performance of VariPred with other representative models including 3Cnet, Polyphen-2, REVEL, MetaLR, FATHMM and ESM variant. VariPred performs as well as, or in most cases better than these other predictors using six variant impact prediction benchmarks despite requiring only sequence data and no pre-processing of the data.
(© 2024. Crown.)

Subject terms:

Virulence - Amino Acid Sequence - Computational Biology methods - Proteins genetics - Mutation, Missense

Content provider:

MEDLINE

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Infection control in healthcare. Multi drug resistant organisms. Germ mutation and multi-drug resistant organisms / [produced by Medcom, Inc.].

Streaming video | 2013

Available at Available Online Academic Video Online (USU and USU Eastern) (Call number: Streaming Video)

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[Is hematological cytology outdated at the age of molecular biology and flow cytometry ? Report of a VEXAS syndrome clinical case].

Rulmont J;Tassin F;Keutgens A

Review | Publisher: Hopital De Baviere Country of Publication: Belgium NLM ID: 0404317 Publication Model: Print Cited Medium: Print ISSN: 0370-629X (Print) Linking ISSN: 0370629X NLM ISO Abbreviation: Rev Med Liege Subsets: MEDLINE Please log in to see more details

VEXAS syndrome is a new entity, described as the first one of a new class of hemato-in... more

[Is hematological cytology outdated at the age of molecular biology and flow cytometry ? Report of a VEXAS syndrome clinical case].

Publisher: Hopital De Baviere Country of Publication: Belgium NLM ID: 0404317 Publication Model: Print Cited Medium: Print ISSN: 0370-629X (Print) Linking ISSN: 0370629X NLM ISO Abbreviation: Rev Med Liege Subsets: MEDLINE

VEXAS syndrome is a new entity, described as the first one of a new class of hemato-inflammatory diseases. Through this article and based on the first case highlighted at the CHU of Liege, we offer you a review of the literature as well as an overview of different laboratory techniques used for the diagnosis of this syndrome.

Subject terms:

Humans - Flow Cytometry - Syndrome - Mutation - Molecular Biology - Myelodysplastic Syndromes - Skin Diseases, Genetic

Content provider:

MEDLINE

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A mutation-selection model with recombination for general genotypes / Steven N. Evans, David Steinsaltz, Kenneth W. Wachter.

Evans, Steven N. (Steven Neil), author.

Ebook | 2012