MetaCyc (MetaCyc.org) is a universal database of metabolic pathways and enzymes from all domains of life. The pathways in MetaCyc are curated from the primary scientific literature, and are experimentally determined small-molecule metabolic pathways. Each reaction in a MetaCyc pathway is annotated with one or more well-characterized enzymes. Because MetaCyc contains only experimentally elucidated knowledge, it provides a uniquely high-quality resource for metabolic pathways and enzymes. BioCyc (BioCyc.org) is a collection of more than 350 organism-specific Pathway/Genome Databases (PGDBs). Each BioCyc PGDB contains the predicted metabolic network of one organism, including metabolic pathways, enzymes, metabolites and reactions predicted by the Pathway Tools software using MetaCyc as a reference database. BioCyc PGDBs also contain predicted operons and predicted pathway hole fillerspredictions of which enzymes may catalyze pathway reactions that have not been assigned to an enzyme. The BioCyc website offers many tools for computational analysis of PGDBs, including comparative analysis and analysis of omics data in a pathway context. The BioCyc PGDBs generated by SRI are offered for adoption by any interested party for the ongoing integration of metabolic and genome-related information about an organism.

The great recent progress made in identifying the molecular parts lists of organisms revealed the paucity of our understanding of what most of the parts do. In this review, we introduce computational and statistical approaches and omics data used for inferring gene function in plants, with an emphasis on network-based inference. We also discuss caveats associated with network-based function predictions such as performance assessment, annotation propagation, the guilt-by-association concept, and the meaning of hubs. Finally, we note the current limitations and possible future directions such as the need for gold standard data from several species, unified access to data and tools, quantitative comparison of data and tool quality, and high-throughput experimental validation platforms for systematic gene function elucidation in plants.

Truncated transcription factor-like proteins called microProteins (miPs) can modulate transcription factor activities, thereby increasing transcriptional regulatory complexity. To understand their prevalence, evolution, and function, we predicted over 400 genes that encode putative miPs from Arabidopsis (Arabidopsis thaliana) using a bioinformatics pipeline and validated two novel miPs involved in flowering time and response to abiotic and biotic stress. We provide an evolutionary perspective for a class of miPs targeting homeodomain transcription factors in plants and metazoans. We identify domain loss as one mechanism of miP evolution and suggest the possible roles of miPs on the evolution of their target transcription factors. Overall, we reveal a prominent layer of transcriptional regulation by miPs, show pervasiveness of such proteins both within and across genomes, and provide a framework for studying their function and evolution.

All plants synthesize basic metabolites needed for survival (primary metabolism), but different taxa produce distinct metabolites that are specialized for specific environmental interactions (specialized metabolism). Because evolutionary pressures on primary and specialized metabolism differ, we investigated differences in the emergence and maintenance of these processes across 16 species encompassing major plant lineages from algae to angiosperms. We found that, relative to their primary metabolic counterparts, genes coding for specialized metabolic functions have proliferated to a much greater degree and by different mechanisms and display lineage-specific patterns of physical clustering within the genome and coexpression. These properties illustrate the differential evolution of specialized metabolism in plants, and collectively they provide unique signatures for the potential discovery of novel specialized metabolic processes.

Cellular membranes act as signaling platforms and control solute transport. Membrane receptors, transporters, and enzymes communicate with intracellular processes through protein-protein interactions. Using a split-ubiquitin yeast two-hybrid screen that covers a test-space of 6.4 x 10(6) pairs, we identified 12,102 membrane/signaling protein interactions from Arabidopsis. Besides confirmation of expected interactions such as heterotrimeric G protein subunit interactions and aquaporin oligomerization, >99% of the interactions were previously unknown. Interactions were confirmed at a rate of 32% in orthogonal in planta split-green flourescent protein interaction assays, which was statistically indistinguishable from the confirmation rate for known interactions collected from literature (38%). Regulatory associations in membrane protein trafficking, turnover, and phosphorylation include regulation of potassium channel activity through abscisic acid signaling, transporter activity by a WNK kinase, and a brassinolide receptor kinase by trafficking-related proteins. These examples underscore the utility of the membrane/signaling protein interaction network for gene discovery and hypothesis generation in plants and other organisms.

Plant biology is becoming a data-driven science. High-throughput technologies generate data quickly from molecular to ecosystem levels. Statistical and computational approaches enable describing and interpreting data quantitatively. We highlight the purpose, common problems, and general principles in data analysis. We use RNA sequencing (RNAseq) analysis to illustrate the rationale behind some of the choices made in statistical data analysis. Finally, we provide a list of free online resources that emphasize intuition behind quantitative data analysis.

Background: Gene Ontology (GO) has been used widely to study functional relationships between genes. The current semantic similarity measures rely only on GO annotations and GO structure. This limits the power of GO- based similarity because of the limited proportion of genes that are annotated to GO in most organisms. Results: We introduce a novel approach called NETSIM (network- based similarity measure) that incorporates information from gene co- function networks in addition to using the GO structure and annotations. Using metabolic reaction maps of yeast, Arabidopsis, and human, we demonstrate that NETSIM can improve the accuracy of GO term similarities. We also demonstrate that NETSIM works well even for genomes with sparser gene annotation data. We applied NETSIM on large Arabidopsis gene families such as cytochrome P450 monooxygenases to group the members functionally and show that this grouping could facilitate functional characterization of genes in these families. Conclusions: Using NETSIM as an example, we demonstrated that the performance of a semantic similarity measure could be significantly improved after incorporating genome- specific information. NETSIM incorporates both GO annotations and gene co- function network data as a priori knowledge in the model. Therefore, functional similarities of GO terms that are not explicitly encoded in GO but are relevant in a taxon- specific manner become measurable when GO annotations are limited. Supplementary information and software are available at http://www.msu.edu/similar to jinchen/NETSIM.

An emerging concept in transcriptional regulation is that a class of truncated transcription factors (TFs), called microProteins (miPs), engages in protein-protein interactions with TF complexes and provides feedback controls. A handful of miP examples have been described in the literature but the extent of their prevalence is unclear. Here we present an algorithm that predicts miPs and their target TFs from a sequenced genome. The algorithm is called miP prediction program (miP3), which is implemented in Python. The software will help shed light on the prevalence, biological roles, and evolution of miPs. Moreover, miP3 can be used to predict other types of miP-like proteins that may have evolved from other functional classes such as kinases and receptors. The program is freely available and can be applied to any sequenced genome.

Metabolomics enables quantitative evaluation of metabolic changes caused by genetic or environmental perturbations. However, little is known about how perturbing a single gene changes the metabolic system as a whole and which network and functional properties are involved in this response. To answer this question, we investigated the metabolite profiles from 136 mutants with single gene perturbations of functionally diverse Arabidopsis ( Arabidopsis thaliana) genes. Fewer than 10 metabolites were changed significantly relative to the wild type in most of the mutants, indicating that the metabolic network was robust to perturbations of single metabolic genes. These changed metabolites were closer to each other in a genome-scale metabolic network than expected by chance, supporting the notion that the genetic perturbations changed the network more locally than globally. Surprisingly, the changed metabolites were close to the perturbed reactions in only 30% of the mutants of the well-characterized genes. To determine the factors that contributed to the distance between the observed metabolic changes and the perturbation site in the network, we examined nine network and functional properties of the perturbed genes. Only the isozyme number affected the distance between the perturbed reactions and changed metabolites. This study revealed patterns of metabolic changes from large-scale gene perturbations and relationships between characteristics of the perturbed genes and metabolic changes.