Partial Loss of USP9X Function Leads to a Male Neurodevelopmental and Behavioral Disorder Converging on Transforming Growth Factor β Signaling DOI Creative Commons
Brett V. Johnson,

Raman Kumar,

Sabrina Oishi

и другие.

Biological Psychiatry, Год журнала: 2019, Номер 87(2), С. 100 - 112

Опубликована: Июнь 29, 2019

Язык: Английский

Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria DOI Creative Commons
Vikas Pejaver, Alicia B. Byrne, Bing Feng

и другие.

The American Journal of Human Genetics, Год журнала: 2022, Номер 109(12), С. 2163 - 2177

Опубликована: Ноя. 21, 2022

Recommendations from the American College of Medical Genetics and Genomics Association for Molecular Pathology (ACMG/AMP) interpreting sequence variants specify use computational predictors as "supporting" level evidence pathogenicity or benignity using criteria PP3 BP4, respectively. However, score intervals defined by tool developers, ACMG/AMP recommendations that require consensus multiple predictors, lack quantitative support. Previously, we described a probabilistic framework quantified strengths (supporting, moderate, strong, very strong) within recommendations. We have extended this to introduce new standard converts tool's scores BP4 strengths. Our approach is based on estimating local positive predictive value can calibrate any other continuous-scale variant type. estimate thresholds (score intervals) corresponding each strength thirteen missense interpretation tools, carefully assembled independent data sets. Most tools achieved supporting both pathogenic benign classification newly established thresholds. Multiple reached justifying moderate several strong levels. One some variants. Based these findings, provide evidence-based revisions individual future assessment methods clinical interpretation.

Язык: Английский

Процитировано

287

Big Data in Public Health: Terminology, Machine Learning, and Privacy DOI Creative Commons
Stephen J. Mooney, Vikas Pejaver

Annual Review of Public Health, Год журнала: 2017, Номер 39(1), С. 95 - 112

Опубликована: Дек. 20, 2017

The digital world is generating data at a staggering and still increasing rate. While these “big data” have unlocked novel opportunities to understand public health, they hold greater potential for research practice. This review explores several key issues that arisen around big data. First, we propose taxonomy of sources clarify terminology identify threads common across some subtypes Next, consider health practice uses data, including surveillance, hypothesis-generating research, causal inference, while exploring the role machine learning may play in each use. We then ethical implications revolution with particular emphasis on maintaining appropriate care privacy which technology rapidly changing social norms regarding need (and even meaning of) privacy. Finally, make suggestions structuring teams training succeed working

Язык: Английский

Процитировано

285

MetaRNN: differentiating rare pathogenic and rare benign missense SNVs and InDels using deep learning DOI Creative Commons
Chang Li, Degui Zhi, Kai Wang

и другие.

Genome Medicine, Год журнала: 2022, Номер 14(1)

Опубликована: Окт. 8, 2022

Multiple computational approaches have been developed to improve our understanding of genetic variants. However, their ability identify rare pathogenic variants from benign ones is still lacking. Using context annotations and deep learning methods, we present pathogenicity prediction models, MetaRNN MetaRNN-indel, help prioritize nonsynonymous single nucleotide (nsSNVs) non-frameshift insertion/deletions (nfINDELs). We use independent test sets demonstrate that these new models outperform state-of-the-art competitors achieve a more interpretable score distribution. Importantly, scores both are comparable, enabling easy adoption integrated genotype-phenotype association analysis methods. All pre-computed nsSNV available at http://www.liulab.science/MetaRNN . The stand-alone program also https://github.com/Chang-Li2019/MetaRNN

Язык: Английский

Процитировано

100

How chromosomal inversions reorient the evolutionary process DOI Creative Commons
Emma L. Berdan, Nick Barton, Roger K. Butlin

и другие.

Journal of Evolutionary Biology, Год журнала: 2023, Номер 36(12), С. 1761 - 1782

Опубликована: Ноя. 9, 2023

Abstract Inversions are structural mutations that reverse the sequence of a chromosome segment and reduce effective rate recombination in heterozygous state. They play major role adaptation, as well other evolutionary processes such speciation. Although inversions have been studied since 1920s, they remain difficult to investigate because reduced conferred by them strengthens effects drift hitchhiking, which turn can obscure signatures selection. Nonetheless, numerous found be under Given recent advances population genetic theory empirical study, here we review how different mechanisms selection affect evolution inversions. A key difference between mutations, single nucleotide variants, is fitness an inversion may affected larger number frequently interacting processes. This considerably complicates analysis causes underlying We discuss extent these disentangled, approach. often roles adaptation speciation, but direct their obscured characteristic makes so unique (reduced arrangements). In this review, examine impact evolution, weaving together both theoretical studies. emphasize most patterns overdetermined (i.e. caused multiple processes), highlight new technologies provide path forward towards disentangling mechanisms.

Язык: Английский

Процитировано

48

CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods DOI Creative Commons
Shantanu Jain, Constantina Bakolitsa, Steven E. Brenner

и другие.

Genome biology, Год журнала: 2024, Номер 25(1)

Опубликована: Фев. 22, 2024

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

Язык: Английский

Процитировано

37

VarSite: Disease variants and protein structure DOI Creative Commons
Roman A. Laskowski, James Stephenson, Ian Sillitoe

и другие.

Protein Science, Год журнала: 2019, Номер 29(1), С. 111 - 119

Опубликована: Окт. 13, 2019

VarSite is a web server mapping known disease-associated variants from UniProt and ClinVar, together with natural gnomAD, onto protein 3D structures in the Protein Data Bank. The analyses are primarily image-based provide both an overview for each human protein, as well report any specific variant of interest. information can be useful assessing whether given might pathogenic or benign. structural annotations position include secondary structure, interactions ligand, metal, DNA/RNA, other various measures variant's possible impact on protein's function. locations viewed interactively via 3dmol.js JavaScript viewer, RasMol PyMOL. Users search variants, sets by providing DNA coordinates base change(s) Additionally, agglomerative given, such disease Pfam CATH domains. freely accessible to all at: https://www.ebi.ac.uk/thornton-srv/databases/VarSite.

Язык: Английский

Процитировано

103

Autism risk in offspring can be assessed through quantification of male sperm mosaicism DOI
Martin W. Breuss, Danny Antaki, Renee D. George

и другие.

Nature Medicine, Год журнала: 2019, Номер 26(1), С. 143 - 150

Опубликована: Дек. 23, 2019

Язык: Английский

Процитировано

93

BioAider: An efficient tool for viral genome analysis and its application in tracing SARS-CoV-2 transmission DOI Open Access
Zhijian Zhou, Ye Qiu, Ying Pu

и другие.

Sustainable Cities and Society, Год журнала: 2020, Номер 63, С. 102466 - 102466

Опубликована: Авг. 28, 2020

Язык: Английский

Процитировано

92

Unsupervised and semi‐supervised learning: the next frontier in machine learning for plant systems biology DOI
Jun Yan, Xiangfeng Wang

The Plant Journal, Год журнала: 2022, Номер 111(6), С. 1527 - 1538

Опубликована: Июль 13, 2022

SUMMARY Advances in high‐throughput omics technologies are leading plant biology research into the era of big data. Machine learning (ML) performs an important role systems because its excellent performance and wide application analysis However, to achieve ideal performance, supervised ML algorithms require large numbers labeled samples as training In some cases, it is impossible or prohibitively expensive obtain enough data; here, paradigms unsupervised (UL) semi‐supervised (SSL) play indispensable role. this review, we first introduce basic concepts techniques, well representative UL SSL algorithms, including clustering, dimensionality reduction, self‐supervised (self‐SL), positive‐unlabeled (PU) transfer learning. We then review recent advances applications both phenotyping research. Finally, discuss limitations highlight significance challenges strategies biology.

Язык: Английский

Процитировано

61

Insights on variant analysis in silico tools for pathogenicity prediction DOI Creative Commons
Felipe Antônio de Oliveira Garcia, Edilene Santos de Andrade, Edenir Inêz Palmero

и другие.

Frontiers in Genetics, Год журнала: 2022, Номер 13

Опубликована: Ноя. 29, 2022

Molecular biology is currently a fast-advancing science. Sequencing techniques are getting cheaper, but the interpretation of genetic variants requires expertise and computational power, therefore still challenge. Next-generation sequencing releases thousands to classify them, researchers propose protocols with several parameters. Here we present review in silico pathogenicity prediction tools involved variant prioritization/classification process used by some international for analysis studies evaluating their efficiency.

Язык: Английский

Процитировано

57