Time-course analysis of cyanobacterium transcriptome: detecting oscillatory genes

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...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Layana, Carla, Diambra, Luis Aníbal
Formato: Articulo
Lenguaje:Inglés
Publicado: 2011
Materias:
Ciencias Exactas
Biología
Genética
Ritmo Circadiano
Metabolismo Energético
Cianobacterias
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/29563
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0026291
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-29563
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Exactas
Biología
Genética
Ritmo Circadiano
Metabolismo Energético
Cianobacterias
spellingShingle Ciencias Exactas
Biología
Genética
Ritmo Circadiano
Metabolismo Energético
Cianobacterias
Layana, Carla
Diambra, Luis Aníbal
Time-course analysis of cyanobacterium transcriptome: detecting oscillatory genes
topic_facet Ciencias Exactas
Biología
Genética
Ritmo Circadiano
Metabolismo Energético
Cianobacterias
description 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.
format Articulo
Articulo
author Layana, Carla
Diambra, Luis Aníbal
author_facet Layana, Carla
Diambra, Luis Aníbal
author_sort Layana, Carla
title Time-course analysis of cyanobacterium transcriptome: detecting oscillatory genes
title_short Time-course analysis of cyanobacterium transcriptome: detecting oscillatory genes
title_full Time-course analysis of cyanobacterium transcriptome: detecting oscillatory genes
title_fullStr Time-course analysis of cyanobacterium transcriptome: detecting oscillatory genes
title_full_unstemmed Time-course analysis of cyanobacterium transcriptome: detecting oscillatory genes
title_sort time-course analysis of cyanobacterium transcriptome: detecting oscillatory genes
publishDate 2011
url http://sedici.unlp.edu.ar/handle/10915/29563
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0026291
work_keys_str_mv AT layanacarla timecourseanalysisofcyanobacteriumtranscriptomedetectingoscillatorygenes
AT diambraluisanibal timecourseanalysisofcyanobacteriumtranscriptomedetectingoscillatorygenes
bdutipo_str Repositorios
_version_ 1764820468280328192

Ejemplares similares