Comprehensive analysis of high-throughput screens with HiTSeekR

List, Markus; Schmidt, Steffen; Christiansen, Helle; Rehmsmeier, Marc; Tan, Qihua; Mollenhauer, Jan; Baumbach, Jan

doi:10.1093/nar/gkw554

Cited by 17 publications

(26 citation statements)

References 46 publications

(57 reference statements)

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“…De novo pathways identification [48][49][50] ; BioNet, 54 KeyPathwayMiner app in Cytoscape 55 and as a web app 56 Identifying drug targets in high-throughput screening 51 ; Robust disease subtyping 52 ; For phospho-proteomics analysis. 53…”

Section: Network Modelingmentioning

confidence: 99%

“…Examples: BioNet was used for identifying de novo pathways implicated in Alzheimer's disease 48 and in diabetes. 49 KeyPathwayMiner was used for identifying potential drug targets in high-throughput screening, 51 for robust disease subtyping in breast cancer, 52 for phospho-proteomics analysis in epithelial-tomesenchymal transition, 53 and for detecting de novo pathways implicated in liver fibrosis. 50 Bayesian modeling.…”

Section: Stochastic Modelingmentioning

confidence: 99%

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Mechanistic Modeling and Multiscale Applications for Precision Medicine: Theory and Practice

Stalidzāns

Zanin

Tieri

et al. 2020

Network and Systems Medicine

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Drug research, therapy development, and other areas of pharmacology and medicine can benefit from simulations and optimization of mathematical models that contain a mathematical description of interactions between systems elements at the cellular, tissue, organ, body, and population level. This approach is the foundation of systems medicine and precision medicine. Here, simulated experiments are performed with computers (in silico) first, and they are then replicated through lab experiments (in vivo or in vitro) or clinical studies. In turn, these experiments and studies can be used to validate or improve the models. This iterative loop of dry and wet lab work is successful when biomedical researchers tightly collaborate with data scientists and modelers. From an educational point of view, the interdisciplinary research in systems biology can be sustained most effectively when specialists have been trained to have both a strong background in the disciplines of biology or modeling and strong communication skills, which make them able to communicate with other specialists. This overview addresses possible interdisciplinary communication gaps. Focusing our attention on biomedical researchers, we describe the reasons for using modeling and ways to collaborate with modelers, including their needs for specific biological expertise and data. This review includes an introduction to the principles of several widely used mechanistic modeling methods, focusing on their areas of applicability as well as their limitations. A potential complementary role of machine-learning methods in the development of mechanistic models is also discussed. The descriptions of the methods also include links to corresponding modeling software tools as well as practical examples of their application. Finally, we also explicitly address different aspects of multiscale modeling approaches that allow a more complete and holistic perspective of the human body.

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Section: Network Modelingmentioning

confidence: 99%

Section: Stochastic Modelingmentioning

confidence: 99%

Mechanistic Modeling and Multiscale Applications for Precision Medicine: Theory and Practice

Stalidzāns

Zanin

Tieri

et al. 2020

Network and Systems Medicine

Self Cite

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“…Although most pooled CRISPR screens have compared gRNA representation between two samples, it is also possible to have a multi-dimensional phenotypic readout, such as FACS-based sorting of multiple populations to determine relevant effect size or single-cell full transcriptome readout (Adamson et al, 2016; Datlinger et al, 2017; Dixit et al, 2016; Jaitin et al, 2016; Xie et al, 2017). Many different tools have been developed for analysis of gene-targeted screens and offer such features as automated determination of positively/negatively selected genes, pathway analysis, quality control analysis, and data visualization (Jeong et al, 2017; Li et al, 2014, 2015; List et al, 2016; Winter et al, 2016). …”

Section: Analysis Of Pooled Crispr Screensmentioning

confidence: 99%

High-Throughput Approaches to Pinpoint Function within the Noncoding Genome

2017

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The clustered regularly interspaced short palindromic repeats (CRISPR)-Cas nuclease system is a powerful tool for genome editing, and its simple programmability has enabled high-throughput genetic and epigenetic studies. These high-throughput approaches offer investigators a toolkit for functional interrogation of not only protein-coding genes but also noncoding DNA. Historically, noncoding DNA has lacked the detailed characterization that has been applied to protein-coding genes in large part because there has not been a robust set of methodologies for perturbing these regions. Although the majority of high-throughput CRISPR screens have focused on the coding genome to date, an increasing number of CRISPR screens targeting noncoding genomic regions continue to emerge. Here, we review high-throughput CRISPR-based approaches to uncover and understand functional elements within the noncoding genome and discuss practical aspects of noncoding library design and screen analysis.

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“…Several computational and statistical methods have been designed specifically for the analysis of MPRAs, focusing on methods for differential expression [10,11,12,13,14,15,16]. Myint et al [10] classified MPRAs into three broad categories: characterization studies [17,18,19], which examine and classify the sequence features of regulatory elements; saturation mutagenesis studies [4,20,21,22], which look at the impact of all possible mutations to a functional element; and differential analysis studies [23,24], which seek to determine the differential impact of multiple variants.…”

Section: Introductionmentioning

confidence: 99%

MPRAudit Quantifies the Fraction of Variance Described by Unknown Features in Massively Parallel Reporter Assays

Siegel

Tonquèze

Biton

et al. 2020

Preprint

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Transformative advances in molecular technologies, such as massively parallel reporter assays (MPRAs) and CRISPR screens, can efficiently characterize the effects of genetic and genomic variation on cellular phenotypes. Analysis approaches to date have focused on identifying individual genomic regions or genetic variants that perturb a phenotype of interest. In this work, we develop a wholistic framework (MPRAudit) to determine the global contribution of sequence to phenotypic variation across subsets of the entire experiment, opening the door to myriad novel analyses. For example, MPRAudit can reliably estimate the upper limit of predictive performance, the fraction of variation attributed to specific biological categories, and the total contribution of experimental noise. We demonstrate through simulation and application to several types of real MPRA data sets how MPRAudit can lead to an improved understanding of experimental quality, molecular biology, and guide future research. Applying MPRAudit to real MPRA data, we observe that sequence variation is the primary driver of outcome variability, but that known biological categories explain only a fraction of this variance. We conclude that our understanding of how sequence variation impacts phenotype, even at the level of MPRAs, remains open to further scientific discovery.

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Comprehensive analysis of high-throughput screens with HiTSeekR

Cited by 17 publications

References 46 publications

Mechanistic Modeling and Multiscale Applications for Precision Medicine: Theory and Practice

Mechanistic Modeling and Multiscale Applications for Precision Medicine: Theory and Practice

High-Throughput Approaches to Pinpoint Function within the Noncoding Genome

MPRAudit Quantifies the Fraction of Variance Described by Unknown Features in Massively Parallel Reporter Assays

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