Gene network reverse engineering: The Next Generation.

  title={Gene network reverse engineering: The Next Generation.},
  author={Federico Manuel Giorgi},
  journal={Biochimica et biophysica acta. Gene regulatory mechanisms},
  • F. Giorgi
  • Published 2020
  • Medicine, Biology
  • Biochimica et biophysica acta. Gene regulatory mechanisms
Single-Cell Gene Network Analysis and Transcriptional Landscape of MYCN-Amplified Neuroblastoma Cell Lines
A transcriptome-wide quantitative measurement of gene expression and transcriptional network activity in BE2C and Kelly cell lines at an unprecedented single-cell resolution is provided, and it is shown that MYCN is not constantly active or expressed within Kelly andBE2C cells, independently of cell cycle phase. Expand


Interoperable RNA-Seq analysis in the cloud.
This work identifies suitable gene count quantification methods to facilitate cost-effective, accurate, and cloud-based RNA-Seq analysis service and demonstrates how newly generated RNA- Seq data can be placed in the context of thousands of previously published datasets in near real time. Expand
Plant Cell Walls Tackling Climate Change: Biotechnological Strategies to Improve Crop Adaptations and Photosynthesis in Response to Global Warming
Specific cases in crops of interest carrying cell wall modifications that enhance tolerance to climate change-related stresses are discussed; from cereals such as rice, wheat, barley, or maize to dicots of interest such as brassica oilseed, cotton, soybean, tomato, or potato. Expand
Transcriptomic landscape, gene signatures and regulatory profile of aging in the human brain.
A regulatory analysis identified the transcription factors associated with the signature of 258 genes, common to cortex and hippocampus; revealing the role of MEF2(A,D), PDX1, FOSL(1,2) and RFX(5,1) as candidate regulators of the signature; a deep-learning neural network algorithm was used to build a biological age predictor based on the aging signature. Expand
A paradigm shift in medicine: A comprehensive review of network-based approaches.
  • F. Conte, G. Fiscon, +4 authors P. Paci
  • Computer Science, Medicine
  • Biochimica et biophysica acta. Gene regulatory mechanisms
  • 2019
A comprehensive overview of network types and algorithms used in the framework of network medicine is offered, to reveal new disease genes and/or disease pathways and identify possible targets for new drug development, as well as new uses for existing drugs. Expand
Data generation and network reconstruction strategies for single cell transcriptomic profiles of CRISPR-mediated gene perturbations.
This article discusses how to overcome several key challenges to generate and analyse data for the confident reconstruction of models of the underlying cellular network, and highlights the potential of Nested Effects Models for network reconstruction from scRNA-seq data. Expand
Deficiency of Mitochondrial Aspartate-Glutamate Carrier 1 Leads to Oligodendrocyte Precursor Cell Proliferation Defects Both In Vitro and In Vivo
Data clearly show that AGC 1 impairment alters myelination not only by acting on N-acetyl-aspartate production in neurons but also on OPC proliferation and suggest new potential therapeutic targets for the treatment of AGC1 deficiency. Expand
Gaussian and Mixed Graphical Models as (multi-)omics data analysis tools.
The theoretical foundations of Gaussian Graphical Models are provided, extensions such as MGMs or multi-class GGMs are presented, and how those methods can provide insight in biological mechanisms are illustrated. Expand
Gene networks in cancer are biased by aneuploidies and sample impurities
This work takes networks inferred from TCGA cohorts as an example to show that (1) transcription factor annotations are essential to obtaining reliable networks, and (2) even when taking these into account, the authors should expect between 20 and 80% of edges to be caused by copy number changes and cell mixtures rather than transcription factor regulation. Expand
Gene regulatory network inference resources: A practical overview.
It is believed that the integration of multiple methods described herein provides an effective means with which experimental and computational biologists alike may obtain the most complete pictures of transcriptional relationships. Expand
Identification of non-cancer cells from cancer transcriptomic data☆
This review describes the state-of-the-art approaches for the quantification of non-cancer cells from bulk and single-cell cancer transcriptomic data, with a focus on immune cells. Expand