MARD: a new method to detect differential gene expression in treatment-control time courses

Improving Sesame (Sesamum indicum L.) Seed Yield through Selection under Infection of Fusarium oxysporum f. sp. sesami

Sesame (Sesamum indicum L.), the Queen of oilseeds, is infected with different pathogens, restricting its yield. Fusarium oxysporum f. sp. sesami is the most destructive disease of sesame worldwide, causing economic losses. This work aimed to develop new high-yielding strains, resistant and/or tolerant to Fusarium. Two cycles of pedigree selection were achieved under infection of Fusarium oxysporum f. sp. sesami. Two populations in the F₂ (600 plants each) were used. The selection criteria were five single traits and another three restricted by yield. The restricted selection was better in preserving variability than the single trait selection. The observed genetic gain in percentage from the mid-parent in the F₄-generation was significant for the eight selection criteria. Single trait selection proved to be an effective method for improving the selection criterion, but it caused deleterious effects on the other correlated traits in most cases. The seed yield increased by 30.67% and 20.31% from the better parent in the first and second populations, respectively. The infection% was significantly reduced by 24.04% in the first, and 9.3% in the second, population. The selection index improved seed yield, and its attributes can be recommended.

TTCA: an R package for the identification of differentially expressed genes in time course microarray data

BACKGROUND

The analysis of microarray time series promises a deeper insight into the dynamics of the cellular response following stimulation. A common observation in this type of data is that some genes respond with quick, transient dynamics, while other genes change their expression slowly over time. The existing methods for detecting significant expression dynamics often fail when the expression dynamics show a large heterogeneity. Moreover, these methods often cannot cope with irregular and sparse measurements.

RESULTS

The method proposed here is specifically designed for the analysis of perturbation responses. It combines different scores to capture fast and transient dynamics as well as slow expression changes, and performs well in the presence of low replicate numbers and irregular sampling times. The results are given in the form of tables including links to figures showing the expression dynamics of the respective transcript. These allow to quickly recognise the relevance of detection, to identify possible false positives and to discriminate early and late changes in gene expression. An extension of the method allows the analysis of the expression dynamics of functional groups of genes, providing a quick overview of the cellular response. The performance of this package was tested on microarray data derived from lung cancer cells stimulated with epidermal growth factor (EGF).

CONCLUSION

Here we describe a new, efficient method for the analysis of sparse and heterogeneous time course data with high detection sensitivity and transparency. It is implemented as R package TTCA (transcript time course analysis) and can be installed from the Comprehensive R Archive Network, CRAN. The source code is provided with the Additional file 1.

Analysis of time course Omics datasets

Over the past 20 years, Omics technologies emerged as the consensual denomination of holistic molecular profiling. These techniques enable parallel measurements of biological -omes, or "all constituents considered collectively", and utilize the latest advancements in transcriptomics, proteomics, metabolomics, imaging, and bioinformatics. The technological accomplishments in increasing the sensitivity and throughput of the analytical devices, the standardization of the protocols and the widespread availability of reagents made the capturing of static molecular portraits of biological systems a routine task. The next generation of time course molecular profiling already allows for extensive molecular snapshots to be taken along the trajectory of time evolution of the investigated biological systems. Such datasets provide the basis for application of the inverse scientific approach. It consists in the inference of scientific hypotheses and theories about the structure and dynamics of the investigated biological system without any a priori knowledge, solely relying on data analysis to unveil the underlying patterns. However, most temporal Omics data still contain a limited number of time points, taken over arbitrary time intervals, through measurements on biological processes shifted in time. The analysis of the resulting short and noisy time series data sets is a challenge. Traditional statistical methods for the study of static Omics datasets are of limited relevance and new methods are required. This chapter discusses such algorithms which enable the application of the inverse analysis approach to short Omics time series.

Network-based comparison of temporal gene expression patterns

MOTIVATION

In the pursuits of mechanistic understanding of cell differentiation, it is often necessary to compare multiple differentiation processes triggered by different external stimuli and internal perturbations. Available methods for comparing temporal gene expression patterns are limited to a gene-by-gene approach, which ignores co-expression information and thus is sensitive to measurement noise.

METHODS

We present a method for co-expression network based comparison of temporal expression patterns (NACEP). NACEP compares the temporal patterns of a gene between two experimental conditions, taking into consideration all of the possible co-expression modules that this gene may participate in. The NACEP program is available at http://biocomp.bioen.uiuc.edu/nacep.

RESULTS

We applied NACEP to analyze retinoid acid (RA)-induced differentiation of embryonic stem (ES) cells. The analysis suggests that RA may facilitate neural differentiation by inducing the shh and insulin receptor pathways. NACEP was also applied to compare the temporal responses of seven RNA inhibition (RNAi) experiments. As proof of concept, we demonstrate that the difference in the temporal responses to RNAi treatments can be used to derive interaction relationships of transcription factors (TFs), and therefore infer regulatory modules within a transcription network. In particular, the analysis suggested a novel regulatory relationship between two pluripotency regulators, Esrrb and Tbx3, which was supported by in vivo binding of Esrrb to the promoter of Tbx3.

AVAILABILITY

The NACEP program and the supplementary documents are available at http://biocomp.bioen.uiuc.edu/nacep.

CONTACT

szhong@illinois.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Environmental regulation of leaf colour in red 35S:PAP1 Arabidopsis thaliana

* High-temperature, low-light (HTLL) treatment of 35S:PAP1 Arabidopsis thaliana over-expressing the PAP1 (Production of Anthocyanin Pigment 1) gene results in reversible reduction of red colouration, suggesting the action of additional anthocyanin regulators. High-performance liquid chromatography (HPLC), liquid chromatography mass spectrometry (LCMS) and Affimetrix-based microarrays were used to measure changes in anthocyanin, flavonoids, and gene expression in response to HTLL. * HTLL treatment of control and 35S:PAP1 A. thaliana resulted in a reversible reduction in the concentrations of major anthocyanins despite ongoing over-expression of the PAP1 MYB transcription factor. Twenty-one anthocyanins including eight cis-coumaryl esters were identified by LCMS. The concentrations of nine anthocyanins were reduced and those of three were increased, consistent with a sequential process of anthocyanin degradation. Analysis of gene expression showed down-regulation of flavonol and anthocyanin biosynthesis and of transport-related genes within 24 h of HTLL treatment. No catabolic genes up-regulated by HTLL were found. * Reductions in the concentrations of anthocyanins and down-regulation of the genes of anthocyanin biosynthesis were achieved by environmental manipulation, despite ongoing over-expression of PAP1. Quantitative PCR showed reduced expression of three genes (TT8, TTG1 and EGL3) of the PAP1 transcriptional complex, and increased expression of the potential transcriptional repressors AtMYB3, AtMYB6 and AtMYBL2 coincided with HTLL-induced down-regulation of anthocyanin biosynthesis. * HTLL treatment offers a model system with which to explore anthocyanin catabolism and to discover novel genes involved in the environmental control of anthocyanins.

Principal components analysis based methodology to identify differentially expressed genes in time-course microarray data

Background

Time-course microarray experiments are being increasingly used to characterize dynamic biological processes. In these experiments, the goal is to identify genes differentially expressed in time-course data, measured between different biological conditions. These differentially expressed genes can reveal the changes in biological process due to the change in condition which is essential to understand differences in dynamics.

Results

In this paper, we propose a novel method for finding differentially expressed genes in time-course data and across biological conditions (say C₁ and C₂). We model the expression at C₁ using Principal Component Analysis and represent the expression profile of each gene as a linear combination of the dominant Principal Components (PCs). Then the expression data from C₂ is projected on the developed PCA model and scores are extracted. The difference between the scores is evaluated using a hypothesis test to quantify the significance of differential expression. We evaluate the proposed method to understand differences in two case studies (1) the heat shock response of wild-type and HSF1 knockout mice, and (2) cell-cycle between wild-type and Fkh1/Fkh2 knockout Yeast strains.

Conclusion

In both cases, the proposed method identified biologically significant genes.