Loïc Royer

Gene mention normalization and interaction extraction with context models and sentence motifs

Background:The goal of text mining is to make the information conveyed in scientific publications accessible to structured search and automatic analysis. Two important subtasks of text mining are entity mention normalization - to identify biomedical objects in text - and extraction of qualified relationships between those objects. We describe a method for identifying genes and relationships between proteins.Results:We present solutions to gene mention normalization and extraction of protein-protein interactions. For the first task, we identify genes by using background knowledge on each gene, namely annotations related to function, location, disease, and so on. Our approach currently achieves an f-measure of 86.4% on the BioCreative II gene normalization data. For the extraction of protein-protein interactions, we pursue an approach that builds on classical sequence analysis: motifs derived from multiple sequence alignments. The method achieves an f-measure of 24.4% (micro-average) in the BioCreative II interaction pair subtask.Conclusion:For gene mention normalization, our approach outperforms strategies that utilize only the matching of genes names against dictionaries, without invoking further knowledge on each gene. Motifs derived from alignments of sentences are successful at identifying protein interactions in text; the approach we present in this report is fully automated and performs similarly to systems that require human intervention at one or more stages.Availability:Our methods for gene, protein, and species identification, and extraction of protein-protein are available as part of the BioCreative Meta Services (BCMS), see http://bcms.bioinfo.cnio.es/.

Adaptive light-sheet microscopy for long-term, high-resolution imaging in living organisms

Optimal image quality in light-sheet microscopy requires a perfect overlap between the illuminating light sheet and the focal plane of the detection objective. However, mismatches between the light-sheet and detection planes are common owing to the spatiotemporally varying optical properties of living specimens. Here we present the AutoPilot framework, an automated method for spatiotemporally adaptive imaging that integrates (i) a multi-view light-sheet microscope capable of digitally translating and rotating light-sheet and detection planes in three dimensions and (ii) a computational method that continuously optimizes spatial resolution across the specimen volume in real time. We demonstrate long-term adaptive imaging of entire developing zebrafish (Danio rerio) and Drosophila melanogaster embryos and perform adaptive whole-brain functional imaging in larval zebrafish. Our method improves spatial resolution and signal strength two to five-fold, recovers cellular and sub-cellular structures in many regions that are not resolved by non-adaptive imaging, adapts to spatiotemporal dynamics of genetically encoded fluorescent markers and robustly optimizes imaging performance during large-scale morphogenetic changes in living organisms.

GoGene: gene annotation in the fast lane

High-throughput screens such as microarrays and RNAi screens produce huge amounts of data. They typically result in hundreds of genes, which are often further explored and clustered via enriched GeneOntology terms. The strength of such analyses is that they build on high-quality manual annotations provided with the GeneOntology. However, the weakness is that annotations are restricted to process, function and location and that they do not cover all known genes in model organisms. GoGene addresses this weakness by complementing high-quality manual annotation with high-throughput text mining extracting co-occurrences of genes and ontology terms from literature. GoGene contains over 4 000 000 associations between genes and gene-related terms for 10 model organisms extracted from more than 18 000 000 PubMed entries. It does not cover only process, function and location of genes, but also biomedical categories such as diseases, compounds, techniques and mutations. By bringing it all together, GoGene provides the most recent and most complete facts about genes and can rank them according to novelty and importance. GoGene accepts keywords, gene lists, gene sequences and protein sequences as input and supports search for genes in PubMed, EntrezGene and via BLAST. Since all associations of genes to terms are supported by evidence in the literature, the results are transparent and can be verified by the user. GoGene is available at http://gopubmed.org/gogene.

ClearVolume: open-source live 3D visualization for light-sheet microscopy

To the editor: Current state-of-the-art light sheet microscopes rely on sophisticated control software to perform the acquisition of gigabytes of image data per second over the course of hours or even days. Typically the microscopes acquire data in a first step, and only in a second step this data is processed and visualized offline. The delay between data acquisition and data assessment wastes time and storage space. Technology that makes it possible to view and assess the data during imaging would offer significant advantages. However, even the most advanced microscopes only display the latest image plane acquired or projection while the raw volumetric data is saved to disk [1–4].

Genome-wide expression profiling and functional network analysis upon neuroectodermal conversion of human mesenchymal stem cells suggest HIF-1 and miR-124a as important regulators

Tissue-specific stem cells, such as bone-marrow-derived human mesenchymal stem cells (hMSCs), are thought to be lineage restricted and therefore, could only be differentiated into cell types of the tissue of origin. Several recent studies however have suggested that these types of stem cells might be able to break barriers of germ layer commitment and differentiate in vitro into cells with neuroectodermal properties. We reported earlier about efficient conversion of adult hMSCs into a neural stem cell (NSC)-like population (hmNSCs, for human marrow-derived NSC-like cells) with all major properties of NSCs including functional neuronal differentiation capacity. Here we compared the transcriptomes from hMSCs and hmNSCs using a novel strategy by combining classic Affymetrix oligonucleotide microarray profiling with regulatory and protein interaction network analyses to shed light on regulatory protein networks involved in this neuroectodermal conversion process. We found differential regulation of extracellular matrix protein transcripts, up-regulation of distinct neuroectodermal and NSCs marker genes and local chromosomal transcriptional up-regulation at chromosome 4q13.3. In comparison to hMSCs and primary adult hippocampal NSCs, the transcriptome of hmNSCs displayed minor overlap with both other cell populations. Advanced bioinformatics of regulated genes upon neuroectodermal conversion identified transcription factor networks with HIF-1 and microRNA miR-124a as potential major regulators. Together, transgerminal neuroectodermal conversion of hMSCs into NSC-like cells is accompanied by extensive changes of their global gene expression profile, which might be controlled in part by transcription factor networks related to HIF-1 and miR-124a.

Improving text mining with controlled natural language: a case study for protein interactions

Linking the biomedical literature to other data resources is notoriously difficult and requires text mining. Text mining aims to automatically extract facts from literature. Since authors write in natural language, text mining is a great natural language processing challenge, which is far from being solved. We propose an alternative: If authors and editors summarize the main facts in a controlled natural language, text mining will become easier and more powerful. To demonstrate this approach, we use the language Attempto Controlled English (ACE). We define a simple model to capture the main aspects of protein interactions. To evaluate our approach, we collected a dataset of 459 paragraph headings about protein interaction from literature. 56% of these headings can be represented exactly in ACE and another 23% partially. These results indicate that our approach is feasible.

Prova: rule-based java scripting for distributed web applications

Prova is a language for rule-based Java scripting to support information integration and agent programming on the web. Prova integrates Java with derivation and reaction rules supporting message exchange with various comminication frameworks. Prova supports transparent access to databases, retrieval of URLs, access to web services, and querying of XML documents. We briefly illustrate Prova and show how to implement a distributed bioinformatics application, which includes access to an ontology stored in a database and to XML data for protein structures. Finally, we compare Prova to other event-condition-action rule systems.

Network Compression as a Quality Measure for Protein Interaction Networks

With the advent of large-scale protein interaction studies, there is much debate about data quality. Can different noise levels in the measurements be assessed by analyzing network structure? Because proteomic regulation is inherently co-operative, modular and redundant, it is inherently compressible when represented as a network. Here we propose that network compression can be used to compare false positive and false negative noise levels in protein interaction networks. We validate this hypothesis by first confirming the detrimental effect of false positives and false negatives. Second, we show that gold standard networks are more compressible. Third, we show that compressibility correlates with co-expression, co-localization, and shared function. Fourth, we also observe correlation with better protein tagging methods, physiological expression in contrast to over-expression of tagged proteins, and smart pooling approaches for yeast two-hybrid screens. Overall, this new measure is a proxy for both sensitivity and specificity and gives complementary information to standard measures such as average degree and clustering coefficients.

Whole blood genome-wide expression profiling and network analysis suggest MELAS master regulators

Abstract Background: The heteroplasmic mitochondrial DNA (mtDNA) mutation A3243G causes the mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes (MELAS) syndrome as one of the most frequent mitochondrial diseases. The process of reconfiguration of nuclear gene expression profile to accommodate cellular processes to the functional status of mitochondria might be a key to MELAS disease manifestation and could contribute to its diverse phenotypic presentation. Objective: To determine master regulatory protein networks and disease-modifying genes in MELAS syndrome. Methods: Analyses of whole blood transcriptomes from 10 MELAS patients using a novel strategy by combining classic Affymetrix oligonucleotide microarray profiling with regulatory and protein interaction network analyses. Results: Hierarchical cluster analysis elucidated that the relative abundance of mutant mtDNA molecules is decisive for the nuclear gene expression response. Further analyses confirmed not only transcription factors already known to be involved in mitochondrial diseases (such as TFAM), but also detected the hypoxia-inducible factor 1 complex, nuclear factor Y and cAMP responsive element-binding protein-related transcription factors as novel master regulators for reconfiguration of nuclear gene expression in response to the MELAS mutation. Correlation analyses of gene alterations and clinico-genetic data detected significant correlations between A3243G-induced nuclear gene expression changes and mutant mtDNA load as well as disease characteristics. These potential disease-modifying genes influencing the expression of the MELAS phenotype are mainly related to clusters primarily unrelated to cellular energy metabolism, but important for nucleic acid and protein metabolism, and signal transduction. Discussion: Our data thus provide a framework to search for new pathogenetic concepts and potential therapeutic approaches to treat the MELAS syndrome.

Convexity Shape Constraints for Image Segmentation

Segmenting an image into multiple components is a central task in computer vision. In many practical scenarios, prior knowledge about plausible components is available. Incorporating such prior knowledge into models and algorithms for image segmentation is highly desirable, yet can be non-trivial. In this work, we introduce a new approach that allows, for the first time, to constrain some or all components of a segmentation to have convex shapes. Specifically, we extend the Minimum Cost Multicut Problem by a class of constraints that enforce convexity. To solve instances of this NP-hard integer linear program to optimality, we separate the proposed constraints in the branch-and-cut loop of a state-of-the-art ILP solver. Results on photographs and micrographs demonstrate the effectiveness of the approach as well as its advantages over the state-of-the-art heuristic.