Dataset Information

Time-course analysis of cyanobacterium transcriptome: detecting oscillatory genes.

ABSTRACT: The microarray technique allows the simultaneous measurements of the expression levels of thousands of mRNAs. By mining these data one can identify the dynamics of the gene expression time series. The detection of genes that are periodically expressed is an important step that allows us to study the regulatory mechanisms associated with the circadian cycle. The problem of finding periodicity in biological time series poses many challenges. Such challenge occurs due to the fact that the observed time series usually exhibit non-idealities, such as noise, short length, outliers and unevenly sampled time points. Consequently, the method for finding periodicity should preferably be robust against such anomalies in the data. In this paper, we propose a general and robust procedure for identifying genes with a periodic signature at a given significance level. This identification method is based on autoregressive models and the information theory. By using simulated data we show that the suggested method is capable of identifying rhythmic profiles even in the presence of noise and when the number of data points is small. By recourse of our analysis, we uncover the circadian rhythmic patterns underlying the gene expression profiles from Cyanobacterium Synechocystis.

SUBMITTER: Layana C

PROVIDER: S-EPMC3196541 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Time-course analysis of cyanobacterium transcriptome: detecting oscillatory genes.

Layana Carla C Diambra Luis L

PloS one 20111018 10

The microarray technique allows the simultaneous measurements of the expression levels of thousands of mRNAs. By mining these data one can identify the dynamics of the gene expression time series. The detection of genes that are periodically expressed is an important step that allows us to study the regulatory mechanisms associated with the circadian cycle. The problem of finding periodicity in biological time series poses many challenges. Such challenge occurs due to the fact that the observed ...[more]

PMID: 22028849

Similar Datasets

Project description:BackgroundIdiopathic pulmonary fibrosis (IPF) is an irreversible disorder with a poor prognosis. The incomplete understanding of IPF pathogenesis and the lack of accurate animal models is limiting the development of effective treatments. Thus, the selection of clinically relevant animal models endowed with similarities with the human disease in terms of lung anatomy, cell biology, pathways involved and genetics is essential. The bleomycin (BLM) intratracheal murine model is the most commonly used preclinical assay to evaluate new potential therapies for IPF. Here, we present the findings derived from an integrated histomorphometric and transcriptomic analysis to investigate the development of lung fibrosis in a time-course study in a BLM rat model and to evaluate its translational value in relation to IPF.MethodsRats were intratracheally injected with a double dose of BLM (days 0-4) and sacrificed at days 7, 14, 21, 28 and 56. Histomorphometric analysis of lung fibrosis was performed on left lung sections. Transcriptome profiling by RNAseq was performed on the right lung lobes and results were compared with nine independent human gene-expression IPF studies.ResultsThe histomorphometric and transcriptomic analyses provided a detailed overview in terms of temporal gene-expression regulation during the establishment and repair of the fibrotic lesions. Moreover, the transcriptomic analysis identified three clusters of differentially coregulated genes whose expression was modulated in a time-dependent manner in response to BLM. One of these clusters, centred on extracellular matrix (ECM)-related process, was significantly correlated with histological parameters and gene sets derived from human IPF studies.ConclusionsThe model of lung fibrosis presented in this study lends itself as a valuable tool for preclinical efficacy evaluation of new potential drug candidates. The main finding was the identification of a group of persistently dysregulated genes, mostly related to ECM homoeostasis, which are shared with human IPF.

Dataset Information

Time-course analysis of cyanobacterium transcriptome: detecting oscillatory genes.

Publications

Time-course analysis of cyanobacterium transcriptome: detecting oscillatory genes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets