Modular combinatorial binding among human trans-acting factors reveals direct and indirect factor binding

Guo, Yuchun; Gifford, David K.

doi:10.1186/s12864-016-3434-3

Cited by 32 publications

(29 citation statements)

References 62 publications

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“…For example, GABPA perturbation represses mitochondrial functions ( P < 10 −8 , Figure S6C, Table S6), consistent with its known role (Yang et al, 2014). YY1 perturbation is correctly (Goffart and Wiesner, 2003) predicted to represses oxidative phosphorylation (P<10 −10 ) and induce an innate immune response (P<10 −10 ), and is enriched for its ChIP-seq targets (Guo and Gifford, 2015)).…”

Section: Resultsmentioning

confidence: 99%

Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens

Dixit

Parnas

et al. 2016

Cell

1,281

1,260

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SUMMARY Genetic screens help infer gene function in mammalian cells, but it has remained difficult to assay complex phenotypes – such as transcriptional profiles – at scale. Here, we develop Perturb-seq, combining single cell RNA-seq and CRISPR based perturbations to perform many such assays in a pool. We demonstrate Perturb-seq by analyzing 200,000 cells in immune cells and cell lines, focusing on transcription factors regulating the response of dendritic cells to lipopolysaccharide (LPS). Perturb-seq accurately identifies individual gene targets, gene signatures, and cell states affected by individual perturbations and their genetic interactions. We posit new functions for regulators of differentiation, the anti-viral response, and mitochondrial function during immune activation. By decomposing many high content measurements into the effects of perturbations, their interactions, and diverse cell metadata, Perturb-seq dramatically increases the scope of pooled genomic assays.

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Section: Resultsmentioning

confidence: 99%

Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens

Dixit

Parnas

et al. 2016

Cell

1,281

1,260

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“…Reference genomes are improved by the Genome Reference Consortium and these improvements should lead to more accurate genomic analyses. 106 The previous human reference genome GRCh37/hg19 is still widely used.…”

Section: Whole-genome Sequencingmentioning

confidence: 99%

Clinical sequencing: From raw data to diagnosis with lifetime value

et al. 2018

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High-throughput sequencing (HTS) has revolutionized genetics by enabling the detection of sequence variants at hitherto unprecedented large scale. Despite these advances, however, there are still remaining challenges in the complete coverage of targeted regions (genes, exome or genome) as well as in HTS data analysis and interpretation. Moreover, it is easy to get overwhelmed by the plethora of available methods and tools for HTS. Here, we review the step-bystep process from the generation of sequence data to molecular diagnosis of Mendelian diseases. Highlighting advantages and limitations, this review addresses the current state of (1) HTS technologies, considering targeted, whole-exome, and whole-genome sequencing on short-and long-read platforms; (2) read alignment, variant calling and interpretation; as well as (3) regulatory issues related to genetic counseling, reimbursement, and data storage.

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“…We determined T=50 as an optimal number of topics using several metrics (Arun et al, 2010a, Cao et al, 2009, Griffiths and Steyvers, 2004. We then tested how similarities in the original data can be preserved compared with two other algorithms, non-negative matrix factorization (NMF) and K-means, using methods presented by Guo et al, 2017 (Guo andGifford, 2017). Each method gives a 50-dimensional representation of the samples.…”

Section: Validation Of Lda Modelmentioning

confidence: 99%

TopicNet: a framework for measuring transcriptional regulatory network change

Lou

Kong

et al. 2019

Preprint

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Next generation sequencing data highlights comprehensive and dynamic changes in the human gene regulatory network. Moreover, changes in regulatory network connectivity (network "rewiring") manifest different regulatory programs in multiple cellular states. However, due to the dense and noisy nature of the connectivity in regulatory networks, directly comparing the gains and losses of targets of key TFs is not that informative. Thus, here, we seek a abstracted lowerdimensional representation to understand the main features of network change. In particular, we propose a method called TopicNet that applies latent Dirichlet allocation (LDA) to extract meaningful functional topics for a collection of genes regulated by a TF. We then define a rewiring score to quantify the large-scale changes in the regulatory network in terms of topic change for a TF. Using this framework, we can pinpoint particular TFs that change greatly in network connectivity between different cellular states. This is particularly relevant in oncogenesis. Also, incorporating gene-expression data, we define a topic activity score that gives the degree that a topic is active in a particular cellular state. Furthermore, we show how activity differences can highlight differential survival in certain cancers.

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Modular combinatorial binding among human trans-acting factors reveals direct and indirect factor binding

Cited by 32 publications

References 62 publications

Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens

Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens

Clinical sequencing: From raw data to diagnosis with lifetime value

TopicNet: a framework for measuring transcriptional regulatory network change

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