Metabolomics aims at identification and quantitation of small molecules involved in metabolic reactions. LC-MS has enjoyed a growing popularity as the platform for metabolomic studies due to its high throughput, soft ionization, and good coverage of metabolites. The success of a LC-MS-based metabolomic study often depends on multiple experimental, analytical, and computational steps. This review presents a workflow of a typical LC-MS-based metabolomic analysis for identification and quantitation of metabolites indicative of biological/environmental perturbations. Challenges and current solutions in each step of the workflow are reviewed. The review intends to help investigators understand the challenges in metabolomic studies and to determine appropriate experimental, analytical, and computational methods to address these challenges.

Background: Plants respond to low temperature through an intricately coordinated transcriptional network. TheCBF/DREB-regulated network of genes has been shown to play a prominent role in freeze-tolerance ofArabidopsisthrough the process of cold acclimation (CA). Recent evidence also showed that theCBF/DREBregulon is not unique to CA but evolutionarily conserved between chilling-insensitive (temperate) and chilling-sensitive (warm-season) plants. In this study, the wide contrast in chilling sensitivity between indica and japonica rice was used as model to identify other regulatory clusters by integrative analysis of promoter architecture (ab initio) and gene expression profiles.Results: Transcriptome analysis in chilling tolerant japonica rice identified a subset of 121 'early response' genes that were upregulated during the initial 24 hours at 10°C. Among this group were four transcription factors includingROS-bZIP1and another larger sub-group with a common feature of having as1/ocs-like elements in their promoters. Cold-induction ofROS-bZIP1preceded the induction of as1/ocs-like element-containing genes and they were also induced by exogenous H₂O₂at ambient temperature. Coordinated expression patterns and similar promoter architectures among the 'early response' genes suggest that they belong to a potential regulon (ROS-bZIP – as1/ocsregulatory module) that responds to elevated levels of ROS during chilling stress. Cultivar-specific expression signatures of the candidate genes indicate a positive correlation between the activity of the putative regulon and genotypic variation in chilling tolerance.Conclusion: A hypothetical model of an ROS-mediated regulon (ROS-bZIP – as1/ocs) triggered by chilling stress was assembled in rice. Based on the current results, it appears that this regulon is independent of ABA andCBF/DREB, and that its activation has an important contribution in configuring the rapid responses of rice seedlings to chilling stress.

Motivation: Due to the large number of peaks in mass spectra of low-molecular-weight (LMW) enriched sera, a systematic method is needed to select a parsimonious set of peaks to facilitate biomarker identification. We present computational methods for matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) spectral data preprocessing and peak selection. In particular, we propose a novel method that combines ant colony optimization (ACO) with support vector machines (SVM) to select a small set of useful peaks. Results: The proposed hybrid ACO-SVM algorithm selected a panel of eight peaks out of 228 candidate peaks from MALDI-TOF spectra of LMW enriched sera. An SVM classifier built with these peaks achieved 94% sensitivity and 100% specificity in distinguishing hepatocellular carcinoma from cirrhosis in a blind validation set of 69 samples. Area under the receiver operating characteristic (ROC) curve was 0.996. The classification capability of these peaks is compared with those selected by the SVM-recursive feature elimination method. Availability: Supplementary material and MATLAB scripts to implement the methods described in this article are available at http://microarray.georgetown.edu/web/files/bioinf.htm Contact:hwr@georgetown.edu Supplementary information: Supplementary data are available at Bioinformatics online.