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...
Main Authors: | , |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2011-01-01
|
Series: | PLoS ONE |
Online Access: | http://europepmc.org/articles/PMC3196541?pdf=render |
id |
doaj-ea1fb373056b49868bf2517c57cef934 |
---|---|
record_format |
Article |
spelling |
doaj-ea1fb373056b49868bf2517c57cef9342020-11-25T02:28:43ZengPublic Library of Science (PLoS)PLoS ONE1932-62032011-01-01610e2629110.1371/journal.pone.0026291Time-course analysis of cyanobacterium transcriptome: detecting oscillatory genes.Carla LayanaLuis DiambraThe 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.http://europepmc.org/articles/PMC3196541?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Carla Layana Luis Diambra |
spellingShingle |
Carla Layana Luis Diambra Time-course analysis of cyanobacterium transcriptome: detecting oscillatory genes. PLoS ONE |
author_facet |
Carla Layana Luis Diambra |
author_sort |
Carla Layana |
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. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2011-01-01 |
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. |
url |
http://europepmc.org/articles/PMC3196541?pdf=render |
work_keys_str_mv |
AT carlalayana timecourseanalysisofcyanobacteriumtranscriptomedetectingoscillatorygenes AT luisdiambra timecourseanalysisofcyanobacteriumtranscriptomedetectingoscillatorygenes |
_version_ |
1724836963033284608 |