Isotopically dimethyl labeling was applied in a quantitative post-translational modification (PTM) proteomic study of phosphoproteomic changes in the drought responses of two contrasting soybean cultivars. A total of 9457 phosphopeptides were identified subsequently, corresponding to 4571 phosphoprotein groups and 3889 leading phosphoproteins, which contained nine kinase families consisting of 279 kinases. These phosphoproteins contained a total of 8087 phosphosites, 6106 of which were newly identified and constituted 54% of the current soybean phosphosite repository. These phosphosites were converted into the highly conserved kinase docking sites by bioinformatics analysis, which predicted six kinase families that matched with those newly found nine kinase families. The overly post-translationally modified proteins (OPP) occupies 2.1% of these leading phosphoproteins. Most of these OPPs are photoreceptors, mRNA-, histone-, and phospholipidbinding proteins, as well as protein kinase/phosphatases. The subgroup population distribution of phosphoproteins over the number of phosphosites of phosphoproteins follows the exponential decay law, Y = 4.13e(-0.)(098X)_ 0.04. Out of 218 significantly regulated unique phosphopeptide groups, 188 phosphoproteins were regulated by the drought-tolerant cultivar under the water loss condition. These significantly regulated phosphoproteins (SRP) are mainly enriched in the biological functions of water transport and deprivation, methionine metabolic processes, photosynthesis/light reaction, and response to cadmium ion, osmotic stress, and ABA response. Seventeen and 15 SRPs are protein kinases/phosphatases and transcription factors, respectively. Bioinformatics analysis again revealed that three members of the calcium dependent protein kinase family (CAMK family), GmSRK2I, GmCIPK25, and GmAKIN beta 1 kinases, constitute a phosphor-relay-mediated signal transduction network, regulating ion channel activities and many nuclear events in this drought-tolerant cultivar, which presumably contributes to the development of the soybean drought tolerance under water deprivation process.

MetaCyc ( http:// metacyc. org) contains experimentally determined biochemical pathways to be used as a reference database for metabolism. In conjunction with the Pathway Tools software, MetaCyc can be used to computationally predict the metabolic pathway complement of an annotated genome. To increase the breadth of pathways and enzymes, more than 60 plant- specific pathways have been added or updated in MetaCyc recently. In contrast to MetaCyc, which contains metabolic data for a wide range of organisms, AraCyc is a species- specific database containing only enzymes and pathways found in the model plant Arabidopsis ( Arabidopsis thaliana). AraCyc ( http:// arabidopsis. org/ tools/ aracyc/) was the first computationally predicted plant metabolism database derived from MetaCyc. Since its initial computational build, AraCyc has been under continued curation to enhance data quality and to increase breadth of pathway coverage. Twenty- eight pathways have been manually curated from the literature recently. Pathway predictions in AraCyc have also been recently updated with the latest functional annotations of Arabidopsis genes that use controlled vocabulary and literature evidence. AraCyc currently features 1,418 unique genes mapped onto 204 pathways with 1,156 literature citations. The Omics Viewer, a user data visualization and analysis tool, allows a list of genes, enzymes, or metabolites with experimental values to be painted on a diagram of the full pathway map of AraCyc. Other recent enhancements to both MetaCyc and AraCyc include implementation of an evidence ontology, which has been used to provide information on data quality, expansion of the secondary metabolism node of the pathway ontology to accommodate curation of secondary metabolic pathways, and enhancement of the cellular component ontology for storing and displaying enzyme and pathway locations within subcellular compartments.

Metabolic labeling using stable isotopes is widely used for the relative quantification of proteins in proteomic studies. In plants, metabolic labeling using N-15 has great potential, but the associated complexity of data analysis has limited its usage. Here, we present the N-15 stable-isotope labeled protein quantification workflow utilizing open-access web-based software Protein Prospector. Further, we discuss several important features of N-15 labeling required to make reliable and precise protein quantification. These features include ratio adjustment based on labeling efficiency, median and interquartile range for protein ratios, isotope cluster pattern matching to flag incorrect monoisotopic peak assignment, and caching of quantification results for fast retrieval.

We present a novel approach to deriving stellar labels for stars observed in MUSE fields making use of data-driven machine learning methods. Taking advantage of the comparable spectral properties (resolution and wavelength coverage) of the LAMOST and MUSE instruments, we adopt the data-driven Payne (DD-Payne) model used on LAMOST observations and apply it to stars observed in MUSE fields. Remarkably, in spite of instrumental differences, according to the cross-validation of 27 LAMOST-MUSE common stars, we are able to determine stellar labels with precision better than 75K in T-eff, 0.15 dex in log g, and 0.1 dex in abundances of [Fe/H], [Mg/Fe], [Si/Fe], [Ti/Fe], [C/Fe], [Ni/Fe], and [Cr/Fe] for current MUSE observations over a parameter range of 3800 < T-eff < 7000 K, -1.5 < [Fe/H] < 0.5 dex. To date, MUSE has been used to target 13 000 fields across the southern sky since it was first commissioned 6 yr ago and it is unique in its ability to study dense star fields such as globular clusters or the Milky Way bulge. Our method will enable the automated determination of stellar parameters for all stars in these fields. Additionally, it opens the door for applications to data collected by other spectrographs having resolution similar to LAMOST. With the upcoming BlueMUSE and MAVIS, we will gain access to a whole new range of chemical abundances with higher precision, especially critical s-process elements, such as [Y/Fe] and [Ba/Fe], that provide key age diagnostics for stellar targets.

Here, we present PatMatch, an efficient, web-based pattern-matching program that enables searches for short nucleotide or peptide sequences such as cis-elements in nucleotide sequences or small domains and motifs in protein sequences. The program can be used to find matches to a user-specified sequence pattern that can be described using ambiguous sequence codes and a powerful and flexible pattern syntax based on regular expressions. A recent upgrade has improved performance and now supports both mismatches and wildcards in a single pattern. This enhancement has been achieved by replacing the previous searching algorithm, scan_for_ matches [D'Souza et al. ( 1997), Trends in Genetics, 13, 497-498], with nondeterministic-reverse grep ( NR- grep), a general pattern matching tool that allows for approximate string matching [Navarro ( 2001), Software Practice and Experience, 31, 1265-1312]. We have tailored NR- grep to be used for DNA and protein searches with PatMatch. The stand-alone version of the software can be adapted for use with any sequence dataset and is available for download at The Arabidopsis Information Resource (TAIR) at ftp://ftp.arabidopsis.org/home/ tair/ Software/ Patmatch/. The PatMatch server is available on the web at http://www.arabidopsis.org/cgi-bin/patmatch/ nph-patmatch.pl for searching Arabidopsis thaliana sequences.

Hundreds of leucine-rich repeat receptor kinases (LRR-RKs) have evolved to control diverse processes of growth, development and immunity in plants, but the mechanisms that link LRR-RKs to distinct cellular responses are not understood. Here we show that two LRR-RKs, the brassinosteroid hormone receptor BRASSINOSTEROID INSENSITIVE 1 (BRI1) and the flagellin receptor FLAGELLIN SENSING 2 (FLS2), regulate downstream glycogen synthase kinase 3 (GSK3) and mitogen-activated protein (MAP) kinases, respectively, through phosphocoding of the BRI1-SUPPRESSOR1 (BSU1) phosphatase. BSU1 was previously identified as a component that inactivates GSK3s in the BRI1 pathway. We surprisingly found that the loss of the BSU1 family phosphatases activates effector-triggered immunity and impairs flagellin-triggered MAP kinase activation and immunity. The flagellin-activated BOTRYTIS-INDUCED KINASE 1 (BIK1) phosphorylates BSU1 at serine 251. Mutation of serine 251 reduces BSU1's ability to mediate flagellin-induced MAP kinase activation and immunity, but not its abilities to suppress effector-triggered immunity and interact with GSK3, which is enhanced through the phosphorylation of BSU1 at serine 764 upon brassinosteroid signalling. These results demonstrate that BSU1 plays an essential role in immunity and transduces brassinosteroid-BRI1 and flagellin-FLS2 signals using different phosphorylation sites. Our study illustrates that phosphocoding in shared downstream components provides signalling specificities for diverse plant receptor kinases.

The Plant Ontology Consortium (POC) (www.plantontology.org) is a collaborative effort among several plant databases and experts in plant systematics, botany and genomics. A primary goal of the POC is to develop simple yet robust and extensible controlled vocabularies that accurately reflect the biology of plant structures and developmental stages. These provide a network of vocabularies linked by relationships (ontology) to facilitate queries that cut across datasets within a database or between multiple databases. The current version of the ontology integrates diverse vocabularies used to describe Arabidopsis, maize and rice (Oryza sp.) anatomy, morphology and growth stages. Using the ontology browser, over 3500 gene annotations from three species-specific databases, The Arabidopsis Information Resource (TAIR) for Arabidopsis, Gramene for rice and MaizeGDB for maize, can now be queried and retrieved. Copyright (c) 2006 John Wiley & Sons, Ltd.

MetaCyc is a database of metabolic pathways and enzymes located at http://MetaCyc.org/. Its goal is to serve as a metabolic encyclopedia, containing a collection of non-redundant pathways central to small molecule metabolism, which have been reported in the experimental literature. Most of the pathways in MetaCyc occur in microorganisms and plants, although animal pathways are also represented. MetaCyc contains metabolic pathways, enzymatic reactions, enzymes, chemical compounds, genes and review-level comments. Enzyme information includes substrate specificity, kinetic properties, activators, inhibitors, cofactor requirements and links to sequence and structure databases. Data are curated from the primary literature by curators with expertise in biochemistry and molecular biology. MetaCyc serves as a readily accessible comprehensive resource on microbial and plant pathways for genome analysis, basic research, education, metabolic engineering and systems biology. Querying, visualization and curation of the database is supported by SRI's Pathway Tools software. The PathoLogic component of Pathway Tools is used in conjunction with MetaCyc to predict the metabolic network of an organism from its annotated genome. SRI and the European Bioinformatics Institute employed this tool to create pathway/genome databases (PGDBs) for 165 organisms, available at the BioCyc. org website. These PGDBs also include predicted operons and pathway hole fillers.

Bioinformatics plays an essential role in today's plant science. As the amount of data grows exponentially, there is a parallel growth in the demand for tools and methods in data management, visualization, integration, analysis, modeling, and prediction. At the same time, many researchers in biology are unfamiliar with available bioinformatics methods, tools, and databases, which could lead to missed opportunities or misinterpretation of the information. In this review, we describe some of the key concepts, methods, software packages, and databases used in bioinformatics, with an emphasis on those relevant to plant science. We also cover some fundamental issues related to biological sequence analyses, transcriptome analyses, computational proteomics, computational metabolomics, bio-ontologies, and biological databases. Finally, we explore a few emerging research topics in bioinformatics.