Integrative approaches to reconstruct regulatory networks from multi-omics data: A review of state-of-the-art methods

Wani, Nisar Ahmad; Raza, Khalid

doi:10.1016/j.compbiolchem.2019.107120

Cited by 44 publications

(31 citation statements)

References 148 publications

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“…Nevertheless, simple omics allow understanding the functioning of biological and pathological processes at a single level, as the different methodologies assess different parts of the complex physiopathology of disease development and progression. However, it is essential to understand the relationships between different molecular entities and their interactions, as well as their role in regulating gene expression (Wani and Raza, 2019). Despite the wide range of data that can be generated to characterize differences between healthy and diseased cells or tissues, the analysis of a single subset therefore provides an incomplete picture of the underlying biology.…”

Section: Simple Omics As a Tool For MD Diagnosismentioning

confidence: 99%

Multi-Omics Approaches to Improve Mitochondrial Disease Diagnosis: Challenges, Advances, and Perspectives

Labory

Fierville

Ait-El-Mkadem

et al. 2020

Front. Mol. Biosci.

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Mitochondrial diseases (MD) are rare disorders caused by deficiency of the mitochondrial respiratory chain, which provides energy in each cell. They are characterized by a high clinical and genetic heterogeneity and in most patients, the responsible gene is unknown. Diagnosis is based on the identification of the causative gene that allows genetic counseling, prenatal diagnosis, understanding of pathological mechanisms, and personalized therapeutic approaches. Despite the emergence of Next Generation Sequencing (NGS), to date, more than one out of two patients has no diagnosis in the absence of identification of the responsible gene. Technologies currently used for detecting causal variants (genetic alterations) is far from complete, leading many variants of unknown significance (VUS) and mainly based on the use of whole exome sequencing thus neglecting the identification of non-coding variants. The complexity of human genome and its regulation at multiple levels has led biologists to develop several assays to interrogate the different aspects of biological processes. While one-dimension single omics investigation offers a peek of this complex system, the combination of different omics data allows the discovery of coherent signatures. The community of computational biologists and bioinformaticians, in order to integrate data from different omics, has developed several approaches and tools. However, it is difficult to understand which suits the best to predict diverse phenotypic outcome. First attempts to use multi-omics approaches showed an improvement of the diagnostic power. However, we are far from a complete understanding of MD and their diagnosis. After reviewing multi-omics algorithms developed in the latest years, we are proposing here a novel data-driven classification and we will discuss how multi-omics will change and improve the diagnosis of MD. Due to the growing use of multi-omics approaches in MD, we foresee that this work will contribute to set up good practices to perform multi-omics data integration to improve the prediction of phenotypic outcomes and the diagnostic power of MD.

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Section: Simple Omics As a Tool For MD Diagnosismentioning

confidence: 99%

Multi-Omics Approaches to Improve Mitochondrial Disease Diagnosis: Challenges, Advances, and Perspectives

Labory

Fierville

Ait-El-Mkadem

et al. 2020

Front. Mol. Biosci.

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“…(ii) They do not predict the order of action of the key regulators of transcription, i.e. transcription factors (TFs) ( 2 , 14 ). (iii) While there are several pipelines to analyze each omic data type (RNA-seq or ChIP-seq) in isolation, joint analysis of multiple omic data types is required to uncover a GRN that models changes at different molecular levels and over time ( 16 , 17 ).…”

Section: Introductionmentioning

confidence: 99%

TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data

Conard

Goodman

et al. 2021

Nucleic Acids Research

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Uncovering how transcription factors regulate their targets at DNA, RNA and protein levels over time is critical to define gene regulatory networks (GRNs) and assign mechanisms in normal and diseased states. RNA-seq is a standard method measuring gene regulation using an established set of analysis stages. However, none of the currently available pipeline methods for interpreting ordered genomic data (in time or space) use time-series models to assign cause and effect relationships within GRNs, are adaptive to diverse experimental designs, or enable user interpretation through a web-based platform. Furthermore, methods integrating ordered RNA-seq data with protein–DNA binding data to distinguish direct from indirect interactions are urgently needed. We present TIMEOR (Trajectory Inference and Mechanism Exploration with Omics data in R), the first web-based and adaptive time-series multi-omics pipeline method which infers the relationship between gene regulatory events across time. TIMEOR addresses the critical need for methods to determine causal regulatory mechanism networks by leveraging time-series RNA-seq, motif analysis, protein–DNA binding data, and protein-protein interaction networks. TIMEOR’s user-catered approach helps non-coders generate new hypotheses and validate known mechanisms. We used TIMEOR to identify a novel link between insulin stimulation and the circadian rhythm cycle. TIMEOR is available at https://github.com/ashleymaeconard/TIMEOR.git and http://timeor.brown.edu.

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“…The need to predict the effects of a drug on the individual patient led to the development of different computational methods [17,18]. One example integrating genomic and transcriptomic profiles is HIT'nDRIVE.…”

Section: Introductionmentioning

confidence: 99%

Patient-Specific Network for Personalized Breast Cancer Therapy with Multi-Omics Data

Cava

Sabetian

Castiglioni

2021

Entropy

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The development of new computational approaches that are able to design the correct personalized drugs is the crucial therapeutic issue in cancer research. However, tumor heterogeneity is the main obstacle to developing patient-specific single drugs or combinations of drugs that already exist in clinics. In this study, we developed a computational approach that integrates copy number alteration, gene expression, and a protein interaction network of 73 basal breast cancer samples. 2509 prognostic genes harboring a copy number alteration were identified using survival analysis, and a protein–protein interaction network considering the direct interactions was created. Each patient was described by a specific combination of seven altered hub proteins that fully characterize the 73 basal breast cancer patients. We suggested the optimal combination therapy for each patient considering drug–protein interactions. Our approach is able to confirm well-known cancer related genes and suggest novel potential drug target genes. In conclusion, we presented a new computational approach in breast cancer to deal with the intra-tumor heterogeneity towards personalized cancer therapy.

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Integrative approaches to reconstruct regulatory networks from multi-omics data: A review of state-of-the-art methods

Cited by 44 publications

References 148 publications

Multi-Omics Approaches to Improve Mitochondrial Disease Diagnosis: Challenges, Advances, and Perspectives

Multi-Omics Approaches to Improve Mitochondrial Disease Diagnosis: Challenges, Advances, and Perspectives

TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data

Patient-Specific Network for Personalized Breast Cancer Therapy with Multi-Omics Data

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