Dataset Information

Mus musculus

ABSTRACT: RNA-seq analysis reveals profound changes in transcript profiles between siCon- and siH19-transfected myotubes

PROVIDER: PRJNA295374 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR2352570_1.fastq.gz	Fastqsanger.gz
	SRR2352570_2.fastq.gz	Fastqsanger.gz
	SRR2352571_1.fastq.gz	Fastqsanger.gz
	SRR2352571_2.fastq.gz	Fastqsanger.gz
	SRR2352572_1.fastq.gz	Fastqsanger.gz

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Project description:Hoppe2012 - Predicting changes in metabolic function using transcript profiles Measuring metabolite concentrations, reaction fluxes, and enzyme activities on large scale are tricky tasks in the study of cellular metabolism. Here, a method that predicts activity changes of metabolic functions based on relative transcript profiles, has been presented. It provides a ranked list of most regulated functions. The method has been applied to TGF-beta treatment of hepatocyte cultures. This stoichiometric model of the mouse hepatocyte is based on a corrected and extended version of HepatoNet1. This model is described in the article: ModeScore: A Method to Infer Changed Activity of Metabolic Function from Transcript Profiles Andreas Hoppe and Hermann-Georg Holzhütter German Conference on Bioinformatics 2012; Publ.13.09.2012 Abstract: Genome-wide transcript profiles are often the only available quantitative data for a particular perturbation of a cellular system and their interpretation with respect to the metabolism is a major challenge in systems biology, especially beyond on/off distinction of genes. We present a method that predicts activity changes of metabolic functions by scoring reference flux distributions based on relative transcript profiles, providing a ranked list of most regulated functions. Then, for each metabolic function, the involved genes are ranked upon how much they represent a specific regulation pattern. Compared with the naïve pathway-based approach, the reference modes can be chosen freely, and they represent full metabolic functions, thus, directly provide testable hypotheses for the metabolic study. In conclusion, the novel method provides promising functions for subsequent experimental elucidation together with outstanding associated genes, solely based on transcript profiles. This model is hosted on BioModels Database and identified by: MODEL1208060000 . To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. PMID: 20587024 . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to [CC0 Public Domain Dedication>http://creativecommons.org/publicdomain/zero/1.0/] for more information.

Dataset Information

Mus musculus

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