Genome-wide, integrative analysis implicates microRNA dysregulation in autism spectrum disorder
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T Grant Belgard | D. Geschwind | T. G. Belgard | Neelroop N. Parikshak | Y. E. Wu | Daniel H Geschwind | Ye E Wu | Neelroop N Parikshak | N. Parikshak | Ye E. Wu
[1] ScienceOpen Admin. Advances in Autism , 2017 .
[2] Zhen-Yu Zhou,et al. SV2 Acts via Presynaptic Calcium to Regulate Neurotransmitter Release , 2010, Neuron.
[3] Z. Lasiecka,et al. Mechanisms of polarized membrane trafficking in neurons — Focusing in on endosomes , 2011, Molecular and Cellular Neuroscience.
[4] Margaret A. Pericak-Vance,et al. Individual common variants exert weak effects on the risk for autism spectrum disorders , 2012, Human molecular genetics.
[5] R. Wong,et al. The role of epidermal growth factor and its receptors in mammalian CNS. , 2004, Cytokine & growth factor reviews.
[6] S. Horvath,et al. Integrative Functional Genomic Analyses Implicate Specific Molecular Pathways and Circuits in Autism , 2013, Cell.
[7] M. Gill,et al. Development of Strategies for SNP Detection in RNA-Seq Data: Application to Lymphoblastoid Cell Lines and Evaluation Using 1000 Genomes Data , 2013, PloS one.
[8] D. Licatalosi,et al. FMRP Stalls Ribosomal Translocation on mRNAs Linked to Synaptic Function and Autism , 2011, Cell.
[9] Colm O'Dushlaine,et al. INRICH: interval-based enrichment analysis for genome-wide association studies , 2012, Bioinform..
[10] Daniel H. Geschwind,et al. Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders , 2015, Nature Reviews Genetics.
[11] Boris Yamrom,et al. The contribution of de novo coding mutations to autism spectrum disorder , 2014, Nature.
[12] Manuel A. R. Ferreira,et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.
[13] D. Geschwind,et al. Gene hunting in autism spectrum disorder: on the path to precision medicine , 2015, The Lancet Neurology.
[14] Ana M. Aransay,et al. miRanalyzer: an update on the detection and analysis of microRNAs in high-throughput sequencing experiments , 2011, Nucleic Acids Res..
[15] H. Akil,et al. The Fibroblast Growth Factor System and Mood Disorders , 2006, Biological Psychiatry.
[16] Robert T. Schultz,et al. Common genetic variants on 5p14.1 associate with autism spectrum disorders , 2009, Nature.
[17] Hsien-Da Huang,et al. miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions , 2013, Nucleic Acids Res..
[18] Andrew E. Jaffe,et al. Bioinformatics Applications Note Gene Expression the Sva Package for Removing Batch Effects and Other Unwanted Variation in High-throughput Experiments , 2022 .
[19] M. Gerstein,et al. FOXG1-Dependent Dysregulation of GABA/Glutamate Neuron Differentiation in Autism Spectrum Disorders , 2015, Cell.
[20] Bin Zhang,et al. Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R , 2008, Bioinform..
[21] Yosuke Tanaka,et al. Molecular Motors in Neurons: Transport Mechanisms and Roles in Brain Function, Development, and Disease , 2010, Neuron.
[22] L. Lim,et al. MicroRNA targeting specificity in mammals: determinants beyond seed pairing. , 2007, Molecular cell.
[23] M. Mor,et al. Hypomethylation of miR-142 promoter and upregulation of microRNAs that target the oxytocin receptor gene in the autism prefrontal cortex , 2015, Molecular Autism.
[24] N. Rajewsky,et al. Discovering microRNAs from deep sequencing data using miRDeep , 2008, Nature Biotechnology.
[25] Sebastian D. Mackowiak,et al. miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades , 2011, Nucleic acids research.
[26] V. Kim,et al. Regulation of microRNA biogenesis , 2014, Nature Reviews Molecular Cell Biology.
[27] Kathryn Roeder,et al. Most genetic risk for autism resides with common variation , 2014, Nature Genetics.
[28] W. Koh,et al. Single-cell genome sequencing: current state of the science , 2016, Nature Reviews Genetics.
[29] Sharmila Banerjee-Basu,et al. AutDB: a gene reference resource for autism research , 2008, Nucleic Acids Res..
[30] Steve Horvath,et al. WGCNA: an R package for weighted correlation network analysis , 2008, BMC Bioinformatics.
[31] D. Amaral,et al. Neuroanatomy of autism , 2008, Trends in Neurosciences.
[32] D. Bartel,et al. Weak Seed-Pairing Stability and High Target-Site Abundance Decrease the Proficiency of lsy-6 and Other miRNAs , 2011, Nature Structural &Molecular Biology.
[33] S. Horvath,et al. Functional organization of the transcriptome in human brain , 2008, Nature Neuroscience.
[34] S. Horvath,et al. Statistical Applications in Genetics and Molecular Biology , 2011 .
[35] S. Horvath,et al. Transcriptomic Analysis of Autistic Brain Reveals Convergent Molecular Pathology , 2011, Nature.
[36] Ilan Gronau,et al. Genome-wide inference of natural selection on human transcription factor binding sites , 2013, Nature Genetics.
[37] C. Spencer,et al. Biological Insights From 108 Schizophrenia-Associated Genetic Loci , 2014, Nature.
[38] Pall I. Olason,et al. A human phenome-interactome network of protein complexes implicated in genetic disorders , 2007, Nature Biotechnology.
[39] Wei Wu,et al. From neural development to cognition: unexpected roles for chromatin , 2013, Nature Reviews Genetics.
[40] C. Redies,et al. Cadherins and neuropsychiatric disorders , 2012, Brain Research.
[41] K. Hansen,et al. Removing technical variability in RNA-seq data using conditional quantile normalization , 2012, Biostatistics.
[42] P. Kenny,et al. MicroRNAs in neuronal function and dysfunction , 2012, Trends in Neurosciences.
[43] Xavier Estivill,et al. Evidence for the biogenesis of more than 1,000 novel human microRNAs , 2014, Genome Biology.
[44] D. Geschwind,et al. Cortical Evolution: Judge the Brain by Its Cover , 2013, Neuron.
[45] M. Sur,et al. The Emerging Role of microRNAs in Schizophrenia and Autism Spectrum Disorders , 2012, Front. Psychiatry.
[46] Yuan Tian,et al. A Quantitative Framework to Evaluate Modeling of Cortical Development by Neural Stem Cells , 2014, Neuron.
[47] Ana M. Aransay,et al. miRanalyzer: a microRNA detection and analysis tool for next-generation sequencing experiments , 2009, Nucleic Acids Res..
[48] Nick C Fox,et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease , 2013, Nature Genetics.
[49] J. Kleinman,et al. Spatiotemporal transcriptome of the human brain , 2011, Nature.
[50] Paul Theodor Pyl,et al. HTSeq—a Python framework to work with high-throughput sequencing data , 2014, bioRxiv.
[51] W. Huber,et al. which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. MAnorm: a robust model for quantitative comparison of ChIP-Seq data sets , 2011 .
[52] A. Kriegstein,et al. A Primate lncRNA Mediates Notch Signaling during Neuronal Development by Sequestering miRNA , 2016, Neuron.
[53] M. Daly,et al. Proteins Encoded in Genomic Regions Associated with Immune-Mediated Disease Physically Interact and Suggest Underlying Biology , 2011, PLoS genetics.
[54] C. Burge,et al. Conserved Seed Pairing, Often Flanked by Adenosines, Indicates that Thousands of Human Genes are MicroRNA Targets , 2005, Cell.
[55] Daniel H. Geschwind,et al. Genetics and genomics of psychiatric disease , 2015, Science.
[56] N. Mori,et al. Serum microRNA profiles in children with autism , 2014, Molecular Autism.
[57] Rui Luo,et al. Is My Network Module Preserved and Reproducible? , 2011, PLoS Comput. Biol..
[58] Cole Trapnell,et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions , 2013, Genome Biology.
[59] Christopher S. Poultney,et al. Insights into Autism Spectrum Disorder Genomic Architecture and Biology from 71 Risk Loci , 2015, Neuron.
[60] Gonçalo R. Abecasis,et al. The Sequence Alignment/Map format and SAMtools , 2009, Bioinform..
[61] C. Burge,et al. Most mammalian mRNAs are conserved targets of microRNAs. , 2008, Genome research.
[62] M. Daly,et al. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis , 2013, The Lancet.
[63] S. Grant,et al. Characterization of the proteome, diseases and evolution of the human postsynaptic density , 2011, Nature Neuroscience.
[64] Gábor Csárdi,et al. The igraph software package for complex network research , 2006 .
[65] Aaron R. Quinlan,et al. Bioinformatics Applications Note Genome Analysis Bedtools: a Flexible Suite of Utilities for Comparing Genomic Features , 2022 .
[66] T. Bourgeron. From the genetic architecture to synaptic plasticity in autism spectrum disorder , 2015, Nature Reviews Neuroscience.
[67] Chris T. A. Evelo,et al. Bioinformatics Applications Note Databases and Ontologies Go-elite: a Flexible Solution for Pathway and Ontology Over-representation , 2022 .
[68] S. Horvath,et al. Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways , 2010, Proceedings of the National Academy of Sciences.