Assessing functional novelty of PSI structures via structure-function analysis of large and diverse superfamilies

The structural genomics initiatives have had as one of their aims to improve our understanding of protein function by providing representative structures for many structurally uncharacterised protein families. As suggested by the recent assessment of the Protein Structure Initiative (Structural Genomics Initiative, funded by the NIH), doubts have arisen as to whether Structural Genomics as initially planned were really beneficial to our understanding of biological issues, and in particular of protein function.
A few protein domain superfamilies have been shown to account for unexpectedly large numbers of proteins encoded in fully sequenced genomes. These large superfamilies are generally very diverse, spanning a wide range of functions, both in terms of molecular activities and biological processes. Some of these superfamilies, such as the Rossmann-fold P-loop nucleotide hydrolases or the TIM-barrel glycosidases, have been the subject of extensive structural studies which in turn have shed light on how evolution of the sequence and structure properties produce functional diversity amongst homologues. Recently, the Structure-Function Linkage Database (SFLD) has been setup with the aim of helping the study of structure-function correlations in such superfamilies. Since the evolutionary success of these large superfamilies suggests biological importance, several Structural Genomics Centers have focused on providing full structural coverage for representatives of all sequence families in these superfamilies.
In this work we evaluate structure/function diversity in a set of these large superfamilies and attempt to assess the quality and quantity of biological information gained from Structural Genomics.
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Comparing large covariance matrices under weak conditions on the dependence structure and its application to gene clustering

Comparing large covariance matrices has important applications in modern genomics, where scientists are often interested in understanding whether relationships (e.g., dependencies or co-regulations) among a large number of genes vary between different biological states. We propose a computationally fast procedure for testing the equality of two large covariance matrices when the dimensions of the covariance matrices are much larger than the sample sizes. A distinguishing feature of the new procedure is that it imposes no structural assumptions on the unknown covariance matrices. Hence the test is robust with respect to various complex dependence structures that frequently arise in genomics. We prove that the proposed procedure is asymptotically valid under weak moment conditions. As an interesting application, we derive a new gene clustering algorithm which shares the same nice property of avoiding restrictive structural assumptions for high-dimensional genomics data. Using an asthma gene expression dataset, we illustrate how the new test helps compare the covariance matrices of the genes across different gene sets/pathways between the disease group and the control group, and how the gene clustering algorithm provides new insights on the way gene clustering patterns differ between the two groups. The proposed methods have been implemented in an R-package HDtest and is available on CRAN.Comment: The original title dated back to May 2015 is "Bootstrap Tests on High Dimensional Covariance Matrices with Applications to Understanding Gene Clustering

(Re)-conciliation of genetics and genomics approaches for cotton fiber quality improvement

The integration of genomics and plant breeding is driven by the increasing availability of sequence resources and by technological developments. The simultaneous measurement of the expression of thousands of genes is possible, and comparisons between contrasting genotypes and/or biological states, as well as within segregating populations has become feasible. In genetical genomics, the merger of genetics and genomics, gene expression profiles are quantitatively assessed within a segregating population, and expression quantitative trait loci (eQTL) can be mapped like classical QTLs. Methods and examples of applications related to genetical genomics will be reviewed, with emphasis on hybridisation-based (microarray) and PCR-based (cDNA-AFLP) techniques. Despite the complexity of the molecular mechanisms underlying its development, the study of the cotton fiber has become a trait of primary interest. Several maps, including QTL maps, have been published, structural and metabolic genes related to fiber initiation or elongation have been identified, and several large EST projects have been developed. In this context, the applicability of a genetical genomics approach for the study of cotton fiber quality will be discussed. A new cooperative project with CIRAD, Bayer CropScience and CSIRO, and supported by the French National Agency for Research, ANR, was initiated in 2007. The project aims at the genetic and genomic dissection of fiber quality using an interspecific Gossypium hirsutum X G. barbadense RIL population. Classical QTL mapping of fiber properties will be undertaken using data from different locations, and eQTLs will be detected using both microarray and cDNA-AFLP population-wide profiling. (Résumé d'auteur

OMSim : a simulator for optical map data

Motivation: The Bionano Genomics platform allows for the optical detection of short sequence patterns in very long DNA molecules (up to 2.5 Mbp). Molecules with overlapping patterns can be assembled to generate a consensus optical map of the entire genome. In turn, these optical maps can be used to validate or improve de novo genome assembly projects or to detect large-scale structural variation in genomes. Simulated optical map data can assist in the development and benchmarking of tools that operate on those data, such as alignment and assembly software. Additionally, it can help to optimize the experimental setup for a genome of interest. Such a simulator is currently not available. Results: We have developed a simulator, OMSim, that produces synthetic optical map data that mimics real Bionano Genomics data. These simulated data have been tested for compatibility with the Bionano Genomics Irys software system and the Irys-scaffolding scripts. OMSim is capable of handling very large genomes (over 30 Gbp) with high throughput and low memory requirements

Discovery of a second SALMFamide gene in the sea urchin Strongylocentrotus purpuratus reveals that L-type and F-type SALMFamide neuropeptides coexist in an echinoderm species

NOTICE: this is the author’s version of a work that was accepted for publication in MARINE GENOMICS. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in MARINE GENOMICS, [VOL 3, ISSUE 2, (2010)] DOI: 10.1016/j.margen.2010.08.00

Structural term extraction for expansion of template-based genomic queries

This paper describes our experiments run to address the ad hoc task of the TREC 2005 Genomics track. The task topics were expressed with 5 different structures called Generic Topic Templates (GTTs). We hypothesized the presence of GTT-specific structural terms in the free-text fields of documents relevant to a topic instantiated from that same GTT. Our experiments aimed at extracting and selecting candidate structural terms for each GTT. Selected terms were used to expand initial queries and the quality of the term selection was measured by the impact of the expansion on initial search results. The evaluation used the task training topics and the associated relevance information. This paper describes the two term extraction methods used in the experiments and the resulting two runs sent to NIST for evaluation

The protein structure initiative structural genomics knowledgebase

The Protein Structure Initiative Structural Genomics Knowledgebase (PSI SGKB, http://kb.psi-structuralgenomics.org) has been created to turn the products of the PSI structural genomics effort into knowledge that can be used by the biological research community to understand living systems and disease. This resource provides central access to structures in the Protein Data Bank (PDB), along with functional annotations, associated homology models, worldwide protein target tracking information, available protocols and the potential to obtain DNA materials for many of the targets. It also offers the ability to search all of the structural and methodological publications and the innovative technologies that were catalyzed by the PSI's high-throughput research efforts. In collaboration with the Nature Publishing Group, the PSI SGKB provides a research library, editorials about new research advances, news and an events calendar to present a broader view of structural biology and structural genomics. By making these resources freely available, the PSI SGKB serves as a bridge to connect the structural biology and the greater biomedical communitie

Structure of purine nucleoside phosphorylase (DeoD) from Bacillus anthracis

Protein structures from the causative agent of anthrax (Bacillus anthracis) are being determined as part of a structural genomics programme. Amongst initial candidates for crystallographic analysis are enzymes involved in nucleotide biosynthesis, since these are recognized as potential targets in antibacterial therapy. Purine nucleoside phosphorylase is a key enzyme in the purine-salvage pathway. The crystal structure of purine nucleoside phosphorylase (DeoD) from B. anthracis has been solved by molecular replacement at 2.24 Å resolution and refined to an R factor of 18.4%. This is the first report of a DeoD structure from a Gram-positive bacterium

Exploitation of proteomics strategies in protein structure-function studies

Mass spectrometry plays a central role in structural proteomics, particularly in highly intensive structural genomics projects. This review paper reports some examples taken from recent work from the authors' laboratory and is aimed at showing that modem proteomics strategies are instrumental in the integration of structural genomic projects in fields such as: (i) protein-protein interactions, (ii) protein-DNA interactions, (iii) protein-ligand interactions, and (iv) protein-folding intermediates