MultiMSOAR 2.0: An Accurate Tool to Identify Ortholog Groups among Multiple Genomes

The identification of orthologous genes shared by multiple genomes plays an important role in evolutionary studies and gene functional analyses. Based on a recently developed accurate tool, called MSOAR 2.0, for ortholog assignment between a pair of closely related genomes based on genome rearrangement, we present a new system MultiMSOAR 2.0, to identify ortholog groups among multiple genomes in this paper. In the system, we construct gene families for all the genomes using sequence similarity search and clustering, run MSOAR 2.0 for all pairs of genomes to obtain the pairwise orthology relationship, and partition each gene family into a set of disjoint sets of orthologous genes (called super ortholog groups or SOGs) such that each SOG contains at most one gene from each genome. For each such SOG, we label the leaves of the species tree using 1 or 0 to indicate if the SOG contains a gene from the corresponding species or not. The resulting tree is called a tree of ortholog groups (or TOGs). We then label the internal nodes of each TOG based on the parsimony principle and some biological constraints. Ortholog groups are finally identified from each fully labeled TOG. In comparison with a popular tool MultiParanoid on simulated data, MultiMSOAR 2.0 shows significantly higher prediction accuracy. It also outperforms MultiParanoid, the Roundup multi-ortholog repository and the Ensembl ortholog database in real data experiments using gene symbols as a validation tool. In addition to ortholog group identification, MultiMSOAR 2.0 also provides information about gene births, duplications and losses in evolution, which may be of independent biological interest. Our experiments on simulated data demonstrate that MultiMSOAR 2.0 is able to infer these evolutionary events much more accurately than a well-known software tool Notung. The software MultiMSOAR 2.0 is available to the public for free

PathEx: a novel multi factors based datasets selector web tool

<p>Abstract</p> <p>Background</p> <p>Microarray experiments have become very popular in life science research. However, if such experiments are only considered independently, the possibilities for analysis and interpretation of many life science phenomena are reduced. The accumulation of publicly available data provides biomedical researchers with a valuable opportunity to either discover new phenomena or improve the interpretation and validation of other phenomena that partially understood or well known. This can only be achieved by intelligently exploiting this rich mine of information.</p> <p>Description</p> <p>Considering that technologies like microarrays remain prohibitively expensive for researchers with limited means to order their own experimental chips, it would be beneficial to re-use previously published microarray data. For certain researchers interested in finding gene groups (requiring many replicates), there is a great need for tools to help them to select appropriate datasets for analysis. These tools may be effective, if and only if, they are able to re-use previously deposited experiments or to create new experiments not initially envisioned by the depositors. However, the generation of new experiments requires that all published microarray data be completely annotated, which is not currently the case. Thus, we propose the PathEx approach.</p> <p>Conclusion</p> <p>This paper presents PathEx, a human-focused web solution built around a two-component system: one database component, enriched with relevant biological information (expression array, omics data, literature) from different sources, and another component comprising sophisticated web interfaces that allow users to perform complex dataset building queries on the contents integrated into the PathEx database.</p

Gene Coexpression Network Analysis as a Source of Functional Annotation for Rice Genes

With the existence of large publicly available plant gene expression data sets, many groups have undertaken data analyses to construct gene coexpression networks and functionally annotate genes. Often, a large compendium of unrelated or condition-independent expression data is used to construct gene networks. Condition-dependent expression experiments consisting of well-defined conditions/treatments have also been used to create coexpression networks to help examine particular biological processes. Gene networks derived from either condition-dependent or condition-independent data can be difficult to interpret if a large number of genes and connections are present. However, algorithms exist to identify modules of highly connected and biologically relevant genes within coexpression networks. In this study, we have used publicly available rice (Oryza sativa) gene expression data to create gene coexpression networks using both condition-dependent and condition-independent data and have identified gene modules within these networks using the Weighted Gene Coexpression Network Analysis method. We compared the number of genes assigned to modules and the biological interpretability of gene coexpression modules to assess the utility of condition-dependent and condition-independent gene coexpression networks. For the purpose of providing functional annotation to rice genes, we found that gene modules identified by coexpression analysis of condition-dependent gene expression experiments to be more useful than gene modules identified by analysis of a condition-independent data set. We have incorporated our results into the MSU Rice Genome Annotation Project database as additional expression-based annotation for 13,537 genes, 2,980 of which lack a functional annotation description. These results provide two new types of functional annotation for our database. Genes in modules are now associated with groups of genes that constitute a collective functional annotation of those modules. Additionally, the expression patterns of genes across the treatments/conditions of an expression experiment comprise a second form of useful annotation

Normal radial migration and lamination are maintained in dyslexia-susceptibility candidate gene homolog Kiaa0319 knockout mice

AbstractDevelopmental dyslexia is a common disorder with a strong genetic component, but the underlying molecular mechanisms are still unknown. Several candidate dyslexia-susceptibility genes, including KIAA0319, DYX1C1, and DCDC2, have been identified in humans. RNA interference experiments targeting these genes in rat embryos have shown impairments in neuronal migration, suggesting that defects in radial cortical migration could be involved in the disease mechanism of dyslexia. Here we present the first characterisation of a Kiaa0319 knockout mouse line. Animals lacking KIAA0319 protein do not show anatomical abnormalities in any of the layered structures of the brain. Neurogenesis and radial migration of cortical projection neurons are not altered, and the intrinsic electrophysiological properties of Kiaa0319-deficient neurons do not differ from those of wild-type neurons. Kiaa0319 overexpression in cortex delays radial migration, but does not affect final neuronal position. However, knockout animals show subtle differences suggesting possible alterations in anxiety-related behaviour and in sensorimotor gating. Our results do not reveal a migration disorder in the mouse model, adding to the body of evidence available for Dcdc2 and Dyx1c1 that, unlike in the rat in utero knockdown models, the dyslexia-susceptibility candidate mouse homolog genes do not play an evident role in neuronal migration. However, KIAA0319 protein expression seems to be restricted to the brain, not only in early developmental stages but also in adult mice, indicative of a role of this protein in brain function. The constitutive and conditional knockout lines reported here will be useful tools for further functional analyses of Kiaa0319

DYX1C1 is required for axonemal dynein assembly and ciliary motility

DYX1C1 has been associated with dyslexia and neuronal migration in the developing neocortex. Unexpectedly, we found that deleting exons 2–4 of Dyx1c1 in mice caused a phenotype resembling primary ciliary dyskinesia (PCD), a disorder characterized by chronic airway disease, laterality defects and male infertility. This phenotype was confirmed independently in mice with a Dyx1c1 c.T2A start-codon mutation recovered from an N-ethyl-N-nitrosourea (ENU) mutagenesis screen. Morpholinos targeting dyx1c1 in zebrafish also caused laterality and ciliary motility defects. In humans, we identified recessive loss-of-function DYX1C1 mutations in 12 individuals with PCD. Ultrastructural and immunofluorescence analyses of DYX1C1-mutant motile cilia in mice and humans showed disruptions of outer and inner dynein arms (ODAs and IDAs, respectively). DYX1C1 localizes to the cytoplasm of respiratory epithelial cells, its interactome is enriched for molecular chaperones, and it interacts with the cytoplasmic ODA and IDA assembly factor DNAAF2 (KTU). Thus, we propose that DYX1C1 is a newly identified dynein axonemal assembly factor (DNAAF4)

Common variants in left/right asymmetry genes and pathways are associated with relative hand skill

This work was supported by the University of St Andrews, the UK Medical Research Council (grant number G0800523/86473 to SP), the Max Plank Society, and the the EU (Neurodys, 018696). Genotyping at the Wellcome Trust Centre for Human Genetics was supported by the Wellcome Trust (090532/Z/ 09/Z) and a Medical Research Council Hub Grant (G0900747 91070). Core support for ALSPAC was provided by the UK Medical Research Council and the Wellcome Trust (092731) and the University of Bristol. SP is a Royal Society University Research Fellow. CW is also funded by the UK Medical Research Funding and the EU (GENCODYS, 241995). APMor was supported by the Wellcome Trust (grant numbers WT075491, WT090532, and WT098017). WMB is the recipient of a Nuffield Department of Medicine Prize Studentship. JPK is funded by a Wellcome Trust PhD studentship (WT083431MA).Humans display structural and functional asymmetries in brain organization, strikingly with respect to language and handedness. The molecular basis of these asymmetries is unknown. We report a genome-wide association study meta-analysis for a quantitative measure of relative hand skill in individuals with dyslexia [reading disability (RD)] (n = 728). The most strongly associated variant, rs7182874 (P = 8.68×10−9), is located in PCSK6, further supporting an association we previously reported. We also confirmed the specificity of this association in individuals with RD; the same locus was not associated with relative hand skill in a general population cohort (n = 2,666). As PCSK6 is known to regulate NODAL in the development of left/right (LR) asymmetry in mice, we developed a novel approach to GWAS pathway analysis, using gene-set enrichment to test for an over-representation of highly associated variants within the orthologs of genes whose disruption in mice yields LR asymmetry phenotypes. Four out of 15 LR asymmetry phenotypes showed an over-representation (FDR≤5%). We replicated three of these phenotypes; situs inversus, heterotaxia, and double outlet right ventricle, in the general population cohort (FDR≤5%). Our findings lead us to propose that handedness is a polygenic trait controlled in part by the molecular mechanisms that establish LR body asymmetry early in development.Publisher PDFPeer reviewe