Feasibility of predicting allele specific expression from DNA sequencing using machine learning

[1]  S. Galea,et al.  The impact of psychopathology, social adversity and stress-relevant DNA methylation on prospective risk for post-traumatic stress: A machine learning approach. , 2021, Journal of affective disorders.

[2]  Chuan-Yu Chang,et al.  Machine Learning Based Computational Gene Selection Models: A Survey, Performance Evaluation, Open Issues, and Future Research Directions , 2020, Frontiers in Genetics.

[3]  M. Swertz,et al.  Imbalanced expression for predicted high-impact, autosomal-dominant variants in a cohort of 3,818 healthy samples , 2020, bioRxiv.

[4]  D. A. Stein,et al.  TMPRSS2 and furin are both essential for proteolytic activation of SARS-CoV-2 in human airway cells , 2020, Life Science Alliance.

[5]  A. Akalin,et al.  Deep learning for genomics using Janggu , 2020, Nature Communications.

[6]  J. Uitto,et al.  Research Techniques Made Simple: Whole-Transcriptome Sequencing by RNA-Seq for Diagnosis of Monogenic Disorders. , 2020, The Journal of investigative dermatology.

[7]  Namita Srivastava,et al.  The Machine‐Learning Approach , 2020, Machine Learning for iOS Developers.

[8]  M. Tarailo-Graovac,et al.  Genetic Modifiers and Rare Mendelian Disease , 2020, Genes.

[9]  Xue Zhang,et al.  DeepHE: Accurately predicting human essential genes based on deep learning , 2020, bioRxiv.

[10]  Karsten M. Borgwardt,et al.  Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping , 2020, Nature Communications.

[11]  Cleo C. van Diemen,et al.  CAPICE: a computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations , 2019, Genome Medicine.

[12]  O. Lund,et al.  Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with machine learning , 2019, Scientific Reports.

[13]  V. Verendel,et al.  Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure , 2019, Nature Communications.

[14]  Ryan L. Collins,et al.  The mutational constraint spectrum quantified from variation in 141,456 humans , 2020, Nature.

[15]  Guillermo Marco-Puche,et al.  RNA-Seq Perspectives to Improve Clinical Diagnosis , 2019, Front. Genet..

[16]  Beryl B. Cummings,et al.  Genetic regulatory variation in populations informs transcriptome analysis in rare disease , 2019, Science.

[17]  Andy B. Chen,et al.  Allele-specific expression and high-throughput reporter assay reveal functional genetic variants associated with alcohol use disorders , 2019, Molecular Psychiatry.

[18]  Eun Yong Kang,et al.  Profiling allele specific gene expression in brains from individuals with autism spectrum disorder reveals preferential minor allele usage , 2019, Nature Neuroscience.

[19]  Fabian J Theis,et al.  Deep learning: new computational modelling techniques for genomics , 2019, Nature Reviews Genetics.

[20]  Gill Bejerano,et al.  S-CAP extends pathogenicity prediction to genetic variants that affect RNA splicing , 2019, Nature Genetics.

[21]  Morris A. Swertz,et al.  Deconvolution of bulk blood eQTL effects into immune cell subpopulations , 2019, BMC Bioinformatics.

[22]  A. Uitterlinden,et al.  Skewed X-inactivation is common in the general female population , 2018, European Journal of Human Genetics.

[23]  A. Takata,et al.  RNA sequencing solved the most common but unrecognized NEB pathogenic variant in Japanese nemaline myopathy , 2018, Genetics in Medicine.

[24]  Christine M Eng,et al.  Next-Generation Sequencing to Diagnose Suspected Genetic Disorders. , 2018, The New England journal of medicine.

[25]  Alec A K Nielsen,et al.  Deep learning to predict the lab-of-origin of engineered DNA , 2018, Nature Communications.

[26]  Gary A. Churchill,et al.  Hierarchical analysis of RNA‐seq reads improves the accuracy of allele‐specific expression , 2018, Bioinform..

[27]  Stefano Nembrini,et al.  The revival of the Gini importance? , 2018, Bioinform..

[28]  H. Hakonarson,et al.  Increasing diagnostic yield by RNA-Sequencing in rare disease-bypass hurdles of interpreting intronic or splice-altering variants. , 2018, Annals of Translational Medicine.

[29]  K. Yuan,et al.  Genome-wide comparison of allele-specific gene expression between African and European populations , 2018, Human molecular genetics.

[30]  Cristina Has,et al.  Faculty of 1000 evaluation for Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. , 2018 .

[31]  V Kishore Ayyadevara,et al.  Gradient Boosting Machine , 2018 .

[32]  Nicola J. Rinaldi,et al.  Genetic effects on gene expression across human tissues , 2017, Nature.

[33]  C. Kubisch,et al.  The rapid evolution of molecular genetic diagnostics in neuromuscular diseases. , 2017, Current opinion in neurology.

[34]  S. Patel,et al.  A critical review on serine protease: Key immune manipulator and pathology mediator , 2017, Allergologia et Immunopathologia.

[35]  Thomas Meitinger,et al.  Genetic diagnosis of Mendelian disorders via RNA sequencing , 2017, Nature Communications.

[36]  A. Hofman,et al.  Identification of context-dependent expression quantitative trait loci in whole blood , 2016, Nature Genetics.

[37]  Beryl B. Cummings,et al.  Landscape of X chromosome inactivation across human tissues , 2016, Nature.

[38]  Andrew D. Rouillard,et al.  Enrichr: a comprehensive gene set enrichment analysis web server 2016 update , 2016, Nucleic Acids Res..

[39]  J. Carpten,et al.  Translating RNA sequencing into clinical diagnostics: opportunities and challenges , 2016, Nature Reviews Genetics.

[40]  James Y. Zou.  Analysis of protein-coding genetic variation in 60,706 humans , 2015, Nature.

[41]  Bale,et al.  Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology , 2015, Genetics in Medicine.

[42]  D. Pearce,et al.  Nonsense-mediated decay in genetic disease: friend or foe? , 2014, Mutation research. Reviews in mutation research.

[43]  David A. Knowles,et al.  Allelic Expression of Deleterious Protein-Coding Variants across Human Tissues , 2014, PLoS genetics.

[44]  J. Shendure,et al.  A general framework for estimating the relative pathogenicity of human genetic variants , 2014, Nature Genetics.

[45]  Deanna M. Church,et al.  ClinVar: public archive of relationships among sequence variation and human phenotype , 2013, Nucleic Acids Res..

[46]  Amna Z. Shah,et al.  Dominant PRPF31 Mutations Are Hypostatic to a Recessive CNOT3 Polymorphism in Retinitis Pigmentosa: A Novel Phenomenon of “Linked Trans-Acting Epistasis” , 2013, Annals of human genetics.

[47]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[48]  Anh-Dao Nguyen,et al.  Clinical Genomic Database , 2013, Proceedings of the National Academy of Sciences.

[49]  Ellen T. Gelfand,et al.  The Genotype-Tissue Expression (GTEx) project , 2013, Nature Genetics.

[50]  E. Chen,et al.  Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool , 2013, BMC Bioinformatics.

[51]  V. Narayanan,et al.  Skewed Allele-Specific Expression of the NF1 Gene in Normal Subjects , 2012, Journal of child neurology.

[52]  Tomas Babak,et al.  Critical Evaluation of Imprinted Gene Expression by RNA–Seq: A New Perspective , 2012, PLoS genetics.

[53]  T. Pastinen,et al.  Interaction between genetic and epigenetic variation defines gene expression patterns at the asthma-associated locus 17q12-q21 in lymphoblastoid cell lines , 2012, Human Genetics.

[54]  F. Couch,et al.  Effects of BRCA2 cis-regulation in normal breast and cancer risk amongst BRCA2 mutation carriers , 2012, Breast Cancer Research.

[55]  Caroline Lee,et al.  Deterministic and Stochastic Allele Specific Gene Expression in Single Mouse Blastomeres , 2011, PloS one.

[56]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[57]  D. Geschwind,et al.  TMEM106B regulates progranulin levels and the penetrance of FTLD in GRN mutation carriers , 2010, Neurology.

[58]  Craig H. Bassing,et al.  Antigen Receptor Allelic Exclusion: An Update and Reappraisal , 2010, The Journal of Immunology.

[59]  M. Schlissel,et al.  Allelic exclusion of immunoglobulin genes: models and mechanisms , 2010, Immunological reviews.

[60]  E. Grice,et al.  Differential contributions of rare and common, coding and noncoding Ret mutations to multifactorial Hirschsprung disease liability. , 2010, American journal of human genetics.

[61]  Matthew E. Ritchie,et al.  High-throughput analysis of candidate imprinted genes and allele-specific gene expression in the human term placenta , 2010, BMC Genetics.

[62]  Jehyuk Lee,et al.  A Robust Approach to Identifying Tissue-Specific Gene Expression Regulatory Variants Using Personalized Human Induced Pluripotent Stem Cells , 2009, PLoS genetics.

[63]  A. Chapelle,et al.  Genetic predisposition to human disease: allele-specific expression and low-penetrance regulatory loci , 2009, Oncogene.

[64]  S. Liyanarachchi,et al.  Germline Allele-Specific Expression of TGFBR1 Confers an Increased Risk of Colorectal Cancer , 2008, Science.

[65]  John D. Hunter,et al.  Matplotlib: A 2D Graphics Environment , 2007, Computing in Science & Engineering.

[66]  A. Alavi,et al.  Opportunities and Challenges , 1998, In Vitro Diagnostic Industry in China.

[67]  C. Gicquel,et al.  Epimutation of the telomeric imprinting center region on chromosome 11p15 in Silver-Russell syndrome , 2005, Nature Genetics.

[68]  J. Knight,et al.  Allele-specific gene expression uncovered. , 2004, Trends in genetics : TIG.

[69]  K. Buetow,et al.  Allelic variation in gene expression is common in the human genome. , 2003, Genome research.

[70]  Bert Vogelstein,et al.  Allelic Variation in Human Gene Expression , 2002, Science.

[71]  Dmitri A. Nusinow,et al.  Xist RNA and the mechanism of X chromosome inactivation. , 2002, Annual review of genetics.

[72]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[73]  W. Uttal.  On models and mechanisms , 1992, Behavioral and Brain Sciences.

[74]  H. Farmer.  A new perspective. , 1988, The Journal of the Florida Medical Association.

[75]  D. Pressey.  Friend or FoE? , 1983, Nature.

[76]  Arthur D. Riggs,et al.  X inactivation, differentiation, and DNA methylation. , 1975, Cytogenetics and cell genetics.