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Nat Methods ; 2019 Sep 09.
Artigo em Inglês | MEDLINE | ID: mdl-31501547


Spatial and molecular characteristics determine tissue function, yet high-resolution methods to capture both concurrently are lacking. Here, we developed high-definition spatial transcriptomics, which captures RNA from histological tissue sections on a dense, spatially barcoded bead array. Each experiment recovers several hundred thousand transcript-coupled spatial barcodes at 2-µm resolution, as demonstrated in mouse brain and primary breast cancer. This opens the way to high-resolution spatial analysis of cells and tissues.

Nat Commun ; 10(1): 2548, 2019 06 11.
Artigo em Inglês | MEDLINE | ID: mdl-31186427


Epigenetic processes, including DNA methylation (DNAm), are among the mechanisms allowing integration of genetic and environmental factors to shape cellular function. While many studies have investigated either environmental or genetic contributions to DNAm, few have assessed their integrated effects. Here we examine the relative contributions of prenatal environmental factors and genotype on DNA methylation in neonatal blood at variably methylated regions (VMRs) in 4 independent cohorts (overall n = 2365). We use Akaike's information criterion to test which factors best explain variability of methylation in the cohort-specific VMRs: several prenatal environmental factors (E), genotypes in cis (G), or their additive (G + E) or interaction (GxE) effects. Genetic and environmental factors in combination best explain DNAm at the majority of VMRs. The CpGs best explained by either G, G + E or GxE are functionally distinct. The enrichment of genetic variants from GxE models in GWAS for complex disorders supports their importance for disease risk.

Metilação de DNA/genética , DNA/sangue , Interação Gene-Ambiente , Estudos de Coortes , Epigênese Genética , Feminino , Sangue Fetal , Genótipo , Humanos , Recém-Nascido , Masculino , Gravidez , Fatores de Risco
Nat Rev Genet ; 20(7): 389-403, 2019 07.
Artigo em Inglês | MEDLINE | ID: mdl-30971806


As a data-driven science, genomics largely utilizes machine learning to capture dependencies in data and derive novel biological hypotheses. However, the ability to extract new insights from the exponentially increasing volume of genomics data requires more expressive machine learning models. By effectively leveraging large data sets, deep learning has transformed fields such as computer vision and natural language processing. Now, it is becoming the method of choice for many genomics modelling tasks, including predicting the impact of genetic variation on gene regulatory mechanisms such as DNA accessibility and splicing.

Aprendizado Profundo , Genômica/métodos , Modelos Genéticos , Redes Neurais (Computação) , Sequência de Bases , Simulação por Computador , Humanos , Aprendizado de Máquina Supervisionado , Aprendizado de Máquina não Supervisionado
Nat Commun ; 10(1): 390, 2019 01 23.
Artigo em Inglês | MEDLINE | ID: mdl-30674886


Single-cell RNA sequencing (scRNA-seq) has enabled researchers to study gene expression at a cellular resolution. However, noise due to amplification and dropout may obstruct analyses, so scalable denoising methods for increasingly large but sparse scRNA-seq data are needed. We propose a deep count autoencoder network (DCA) to denoise scRNA-seq datasets. DCA takes the count distribution, overdispersion and sparsity of the data into account using a negative binomial noise model with or without zero-inflation, and nonlinear gene-gene dependencies are captured. Our method scales linearly with the number of cells and can, therefore, be applied to datasets of millions of cells. We demonstrate that DCA denoising improves a diverse set of typical scRNA-seq data analyses using simulated and real datasets. DCA outperforms existing methods for data imputation in quality and speed, enhancing biological discovery.

Biologia Computacional/métodos , Perfilação da Expressão Gênica/métodos , RNA/genética , Análise de Sequência de RNA/métodos , Animais , Células Sanguíneas , Caenorhabditis elegans/genética , Regulação da Expressão Gênica/genética , Leucócitos Mononucleares , Modelos Estatísticos , Fenótipo , RNA/análise , RNA Citoplasmático Pequeno/genética , Análise de Célula Única/métodos
Hum Mutat ; 38(9): 1240-1250, 2017 09.
Artigo em Inglês | MEDLINE | ID: mdl-28220625


In many human diseases, associated genetic changes tend to occur within noncoding regions, whose effect might be related to transcriptional control. A central goal in human genetics is to understand the function of such noncoding regions: given a region that is statistically associated with changes in gene expression (expression quantitative trait locus [eQTL]), does it in fact play a regulatory role? And if so, how is this role "coded" in its sequence? These questions were the subject of the Critical Assessment of Genome Interpretation eQTL challenge. Participants were given a set of sequences that flank eQTLs in humans and were asked to predict whether these are capable of regulating transcription (as evaluated by massively parallel reporter assays), and whether this capability changes between alternative alleles. Here, we report lessons learned from this community effort. By inspecting predictive properties in isolation, and conducting meta-analysis over the competing methods, we find that using chromatin accessibility and transcription factor binding as features in an ensemble of classifiers or regression models leads to the most accurate results. We then characterize the loci that are harder to predict, putting the spotlight on areas of weakness, which we expect to be the subject of future studies.

Biologia Computacional/métodos , Expressão Gênica , Regulação da Expressão Gênica , Predisposição Genética para Doença , Humanos , Locos de Características Quantitativas