RNA Networks in Prokaryotes I: CRISPRs and Riboswitches

Biggs, Patrick J.; Collins, Lesley J.

doi:10.1007/978-1-4614-0332-6_13

Cited by 2 publications

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“…Until very recently ncRNAs in prokaryotes (often called “small RNAs”) were not commonly thought of as being important in pathogenic studies. The characterization of the CRISPR system and small RNA pathways (e.g., Hfq-binding sRNAs) has made us more aware that an RNA-based backbone exists just as much in prokaryotes as in eukaryotes (for review see Biggs and Collins, 2011; Collins and Biggs, 2011). Eukaryotic pathogens (e.g., nematodes, yeast, and protists) have received a little more attention but lag behind our understanding of host (typically human and mice) ncRNAs (review by Batista and Marques, 2011).…”

Section: Introductionmentioning

confidence: 99%

Characterizing ncRNAs in human pathogenic protists using high-throughput sequencing technology

Collins¹

2011

fgene

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ncRNAs are key genes in many human diseases including cancer and viral infection, as well as providing critical functions in pathogenic organisms such as fungi, bacteria, viruses, and protists. Until now the identification and characterization of ncRNAs associated with disease has been slow or inaccurate requiring many years of testing to understand complicated RNA and protein gene relationships. High-throughput sequencing now offers the opportunity to characterize miRNAs, siRNAs, small nucleolar RNAs (snoRNAs), and long ncRNAs on a genomic scale, making it faster and easier to clarify how these ncRNAs contribute to the disease state. However, this technology is still relatively new, and ncRNA discovery is not an application of high priority for streamlined bioinformatics. Here we summarize background concepts and practical approaches for ncRNA analysis using high-throughput sequencing, and how it relates to understanding human disease. As a case study, we focus on the parasitic protists Giardia lamblia and Trichomonas vaginalis, where large evolutionary distance has meant difficulties in comparing ncRNAs with those from model eukaryotes. A combination of biological, computational, and sequencing approaches has enabled easier classification of ncRNA classes such as snoRNAs, but has also aided the identification of novel classes. It is hoped that a higher level of understanding of ncRNA expression and interaction may aid in the development of less harsh treatment for protist-based diseases.

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Section: Introductionmentioning

confidence: 99%

Characterizing ncRNAs in human pathogenic protists using high-throughput sequencing technology

Collins¹

2011

fgene

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Gene Regulatory Networks: A Primer in Biological Processes and Statistical Modelling

Angelin-Bonnet

Biggs

Vignes

2018

Methods in Molecular Biology

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Modelling gene regulatory networks not only requires a thorough understanding of the biological system depicted but also the ability to accurately represent this system from a mathematical perspective. Throughout this chapter, we aim to familiarise the reader with the biological processes and molecular factors at play in the process of gene expression regulation. We first describe the different interactions controlling each step of the expression process, from transcription to mRNA and protein decay. In the second section, we provide statistical tools to accurately represent this biological complexity in the form of mathematical models. Amongst other considerations, we discuss the topological properties of biological networks, the application of deterministic and stochastic frameworks and the quantitative modelling of regulation. We particularly focus on the use of such models for the simulation of expression data that can serve as a benchmark for the testing of network inference algorithms.

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RNA Networks in Prokaryotes I: CRISPRs and Riboswitches

Cited by 2 publications

References 62 publications

Characterizing ncRNAs in human pathogenic protists using high-throughput sequencing technology

Characterizing ncRNAs in human pathogenic protists using high-throughput sequencing technology

Gene Regulatory Networks: A Primer in Biological Processes and Statistical Modelling

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