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Abstract In this article we describe a new Bioconductor package ‘CALIB’ for normalization of two-color microarray data. This approach is based on the measurements of external controls and estimates an absolute target level for each gene and condition pair, as opposed to working with log-ratios as a relative measure of expression. Moreover, this method makes no assumptions regarding the distribution of gene expression divergence. Availability: http://bioconductor.org/packages/2.0/bioc Open Source Contact: Kathleen.marchal@biw.kuleuven.be

Abstract Background A quantitative trait locus (QTL) is a genomic region that correlates with a phenotype. Most of the experimental information about QTL mapping studies is described in tables of scientific publications. Traditional text mining techniques aim to extract information from unstructured text rather than from tables. We present QTLTableMiner++ (QTM), a table mining tool that extracts and semantically annotates QTL information buried in (heterogeneous) tables of plant science literature. QTM is a command line tool written in the Java programming language. This tool takes scientific articles from the Europe PMC repository as input, extracts QTL tables using keyword matching and ontology-based concept identification. The tables are further normalized using rules derived from table properties such as captions, column headers and table footers. Furthermore, table columns are classified into three categories namely column descriptors, properties and values based on column headers and data types of cell entries. Abbreviations found in the tables are expanded using the Schwartz and Hearst algorithm. Finally, the content of QTL tables is semantically enriched with domain-specific ontologies (e.g. Crop Ontology, Plant Ontology and Trait Ontology) using the Apache Solr search platform and the results are stored in a relational database and a text file. Results The performance of the QTM tool was assessed by precision and recall based on the information retrieved from two manually annotated corpora of open access articles, i.e. QTL mapping studies in tomato (Solanum lycopersicum) and in potato (S. tuberosum). In summary, QTM detected QTL statements in tomato with 74.53% precision and 92.56% recall and in potato with 82.82% precision and 98.94% recall. Conclusion QTM is a unique tool that aids in providing QTL information in machine-readable and semantically interoperable formats.

Gene expression signatures that are predictive of therapeutic response or prognosis are increasingly useful in clinical care; however, mechanistic (and intuitive) interpretation of expression arrays remains an unmet challenge. Additionally, there is surprisingly little gene overlap among distinct clinically validated expression signatures. These “causality challenges” hinder the adoption of signatures as compared to functionally well-characterized single gene biomarkers. To increase the utility of multi-gene signatures in survival studies, we developed a novel approach to generate “personal mechanism signatures” of molecular pathways and functions from gene expression arrays. FAIME, the Functional Analysis of Individual Microarray Expression, computes mechanism scores using rank-weighted gene expression of an individual sample. By comparing head and neck squamous cell carcinoma (HNSCC) samples with non-tumor control tissues, the precision and recall of deregulated FAIME-derived mechanisms of pathways and molecular functions are comparable to those produced by conventional cohort-wide methods (e.g. GSEA). The overlap of “Oncogenic FAIME Features of HNSCC” (statistically significant and differentially regulated FAIME-derived genesets representing GO functions or KEGG pathways derived from HNSCC tissue) among three distinct HNSCC datasets (pathways:46%, p<0.001) is more significant than the gene overlap (genes:4%). These Oncogenic FAIME Features of HNSCC can accurately discriminate tumors from control tissues in two additional HNSCC datasets (n = 35 and 91, F-accuracy = 100% and 97%, empirical p<0.001, area under the receiver operating characteristic curves = 99% and 92%), and stratify recurrence-free survival in patients from two independent studies (p = 0.0018 and p = 0.032, log-rank). Previous approaches depending on group assignment of individual samples before selecting features or learning a classifier are limited by design to discrete-class prediction. In contrast, FAIME calculates mechanism profiles for individual patients without requiring group assignment in validation sets. FAIME is more amenable for clinical deployment since it translates the gene-level measurements of each given sample into pathways and molecular function profiles that can be applied to analyze continuous phenotypes in clinical outcome studies (e.g. survival time, tumor volume). Author Summary Clinical utilization of multi-gene expression signatures that are predictive of therapeutic response has been steadily increasing, however, interpretation of such results remains challenging because multi-gene signatures, generated from analyzing different patient cohorts, tend to be equally predictive but contain minimal overlap. Whereas pathway-level analyses of expression arrays show promise for generating clinically meaningful mechanistic signatures, current approaches do not permit single-patient based analyses that are independent of cross-group calculations. To bridge the gap between deterministic biological mechanisms of single-gene biomarkers and the statistical predictive power of multi-gene signatures that are disconnected from mechanisms, we developed FAIME, a novel method that transforms microarray gene expression data into individualized patient profiles of molecular mechanisms. We have validated its capability for predicting clinical outcomes, including cancer patient samples derived from six different clinical trial cohorts of head and neck cancers. This method provides opportunities to harness an untapped resource for personal genomics: clinical evaluation and testing of individually interpretable mechanistic profiles derived from gene expression arrays.

AphidBase is a centralized bioinformatic resource that was developed to facilitate community annotation of the pea aphid genome by the International Aphid Genomics Consortium (IAGC). The AphidBase Information System designed to organize and distribute genomic data and annotations for a large international community was constructed using open source software tools from the Generic Model Organism Database (GMOD). The system includes Apollo and GBrowse utilities as well as a wiki, blast search capabilities and a full text search engine. AphidBase strongly supported community cooperation and coordination in the curation of gene models during community annotation of the pea aphid genome. AphidBase can be accessed at http://www.aphidbase.com. International audience

Amphibious plants, living in land-water ecotones, have to cope with challenging and continuously changing growth conditions in their habitats with respect to nutrient and light availability. They have thus evolved a variety of mechanisms to tolerate and adapt to these changes. Therefore, the study of these plants is a major area of ecophysiology and environmental ecological research. However, our understanding of their capacity for physiological adaptation and tolerance remains limited and requires systemic approaches for comprehensive analyses. To this end, in this study, we have conducted a mesocosm experiment to analyze the response of Butomus umbellatus, a common amphibious species in Denmark, to nutrient enrichment and shading. Our study follows a systematic integration of morphological (including plant height, leaf number, and biomass accumulation), ecophysiological (photosynthesis-irradiance responses, leaf pigment content, and C and N content in plant organs), and leaf metabolomic measurements using gas chromatography-mass spectrometry (39 mainly primary metabolites), based on bioinformatic methods. No studies of this type have been previously reported for this plant species. We observed that B. umbellatus responds to nutrient enrichment and light reduction through different mechanisms and were able to identify its nutrient enrichment acclimation threshold within the applied nutrient gradient. Up to that threshold, the morpho-physiological response to nutrient enrichment was profound, indicating fast-growing trends (higher growth rates and biomass accumulation), but only few parameters changed significantly from light to shade [specific leaf area (SLA); quantum yield (φ)]. Metabolomic analysis supported the morpho-physiological results regarding nutrient overloading, indicating also subtle changes due to shading not directly apparent in the other measurements. The combined profile analysis revealed leaf metabolite and morpho-physiological parameter associations. In this context, leaf lactate, currently of uncertain role in higher plants, emerged as a shading acclimation biomarker, along with SLA and φ. The study enhances both the ecophysiology methodological toolbox and our knowledge of the adaptive capacity of amphibious species. It demonstrates that the educated combination of physiological with metabolomic measurements using bioinformatic approaches is a promising approach for ecophysiology research, enabling the elucidation of discriminatory metabolic shifts to be used for early diagnosis and even prognosis of natural ecosystem responses to climate change.

We introduce Goslin, a polyglot grammar for common lipid shorthand nomenclatures based on the LIPID MAPS nomenclature and the shorthand nomenclature established by Liebisch and coauthors and used by LipidHome and SwissLipids. Goslin was designed to address the following pressing issues in the lipidomics field: (1) to simplify the implementation of lipid name handling for developers of mass spectrometry-based lipidomics tools, (2) to offer a tool that unifies and normalizes the main existing lipid name dialects enabling a lipidomics analysis in a high-throughput fashion, and (3) to provide a consistent mapping from lipid shorthand names to lipid building blocks and structural properties. We provide implementations of Goslin in four major programming languages, namely, C++, Java, Python 3, and R to kick-start adoption and integration. Further, we set up a web service for users to work with Goslin directly. All implementations are available free of charge under a permissive open source license.

Creation of a WebTool based on human gene coexpression analysis, that discovers functional partners to a gene of interest or proposes biological roles to genes of unknown function