State of the Field in Multi-Omics Research: From Computational Needs to Data Mining and Sharing

Frontiers in Genetics, Journal Year: 2020, Volume and Issue: 11

Published: Dec. 10, 2020

Multi-omics, variously called integrated omics, pan-omics, and trans-omics, aims to combine two or more omics data sets aid in analysis, visualization interpretation determine the mechanism of a biological process. Multi-omics efforts have taken center stage biomedical research leading development new insights into events processes. However, mushrooming myriad tools, datasets, approaches tends inundate literature overwhelm researchers field. The this review are provide an overview current state field, inform on available reliable resources, discuss application statistics machine/deep learning multi-omics analyses, findable, accessible, interoperable, reusable (FAIR) research, point best practices benchmarking. Thus, we guidance interested users domain by addressing challenges underlying biology, giving toolset, common pitfalls, acknowledging methods’ limitations. We conclude with practical advice recommendations software engineering reproducibility share comprehensive awareness for end-to-end workflow.

Language: Английский

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Spatial components of molecular tissue biology

Nature Biotechnology, Journal Year: 2022, Volume and Issue: 40(3), P. 308 - 318

Published: Feb. 7, 2022

Language: Английский

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Network analysis methods for studying microbial communities: A mini review

Computational and Structural Biotechnology Journal, Journal Year: 2021, Volume and Issue: 19, P. 2687 - 2698

Published: Jan. 1, 2021

Microorganisms including bacteria, fungi, viruses, protists and archaea live as communities in complex contiguous environments. They engage numerous inter- intra- kingdom interactions which can be inferred from microbiome profiling data. In particular, network-based approaches have proven helpful deciphering microbial interaction patterns. Here we give an overview of state-of-the-art methods to infer intra-kingdom ranging simple correlation- conditional dependence-based methods. We highlight common biases encountered profiles discuss mitigation strategies employed by different tools their trade-off with increased computational complexity. Finally, current limitations that motivate further method development inter-kingdom robustly comprehensively characterize environments the future.

Language: Английский

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Multi-omics integration in biomedical research – A metabolomics-centric review

Analytica Chimica Acta, Journal Year: 2020, Volume and Issue: 1141, P. 144 - 162

Published: Oct. 22, 2020

Language: Английский

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Hotspot identifies informative gene modules across modalities of single-cell genomics

Cell Systems, Journal Year: 2021, Volume and Issue: 12(5), P. 446 - 456.e9

Published: May 1, 2021

Two fundamental aims that emerge when analyzing single-cell RNA-seq data are identifying which genes vary in an informative manner and determining how these organize into modules. Here, we propose a general approach to problems, called "Hotspot," operates directly on given metric of cell-cell similarity, allowing for its integration with any method (linear or non-linear) the primary axes transcriptional variation between cells. In addition, show using multimodal data, Hotspot can be used identify whose expression reflects alternative notions similarity cells, such as physical proximity tissue clonal relatedness cell lineage tree. this manner, demonstrate while is capable reflect nuanced variability T helper it also spatially dependent patterns gene cerebellum well developmentally heritable programs during embryogenesis. implemented open-source Python package available use at http://www.github.com/yoseflab/hotspot. A record paper's transparent peer review process included supplemental information.

Language: Английский

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Multi-omics integration in the age of million single-cell data

Nature Reviews Nephrology, Journal Year: 2021, Volume and Issue: 17(11), P. 710 - 724

Published: Aug. 20, 2021

Language: Английский

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Machine learning for multi-omics data integration in cancer

iScience, Journal Year: 2022, Volume and Issue: 25(2), P. 103798 - 103798

Published: Jan. 22, 2022

Multi-omics data analysis is an important aspect of cancer molecular biology studies and has led to ground-breaking discoveries. Many efforts have been made develop machine learning methods that automatically integrate omics data. Here, we review tools categorized as either general-purpose or task-specific, covering both supervised unsupervised for integrative multi-omics We benchmark the performance five approaches using from Cancer Cell Line Encyclopedia, reporting accuracy on type classification mean absolute error drug response prediction, evaluating runtime efficiency. This provides recommendations researchers regarding suitable method selection their specific applications. It should also promote development novel methodologies integration, which will be essential discovery, clinical trial design, personalized treatments.

Language: Английский

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Characterizing cis-regulatory elements using single-cell epigenomics

Nature Reviews Genetics, Journal Year: 2022, Volume and Issue: 24(1), P. 21 - 43

Published: July 15, 2022

Language: Английский

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Characterizing the molecular regulation of inhibitory immune checkpoints with multimodal single-cell screens

Nature Genetics, Journal Year: 2021, Volume and Issue: 53(3), P. 322 - 331

Published: March 1, 2021

Language: Английский

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Neutrophils and emergency granulopoiesis drive immune suppression and an extreme response endotype during sepsis

Nature Immunology, Journal Year: 2023, Volume and Issue: 24(5), P. 767 - 779

Published: April 24, 2023

Sepsis arises from diverse and incompletely understood dysregulated host response processes following infection that leads to life-threatening organ dysfunction. Here we showed neutrophils emergency granulopoiesis drove a maladaptive during sepsis. We generated whole-blood single-cell multiomic atlas (272,993 cells, n = 39 individuals) of the sepsis immune identified populations immunosuppressive mature immature neutrophils. In co-culture, CD66b+ inhibited proliferation activation CD4+ T cells. Single-cell mapping circulating hematopoietic stem progenitor cells (HSPCs) (29,366 27) indicated altered in patients with These features were enriched patient subset poor outcome specific signature displayed higher frequencies IL1R2+ neutrophils, epigenetic transcriptomic signatures HSPCs STAT3-mediated gene regulation across different infectious etiologies syndromes. Our findings offer potential therapeutic targets opportunities for stratified medicine severe infection.

Language: Английский

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