PhenoFam-gene set enrichment analysis through protein structural information

<p>Abstract</p> <p>Background</p> <p>With the current technological advances in high-throughput biology, the necessity to develop tools that help to analyse the massive amount of data being generated is evident. A powerful method of inspecting large-scale data sets is gene set enrichment analysis (GSEA) and investigation of protein structural features can guide determining the function of individual genes. However, a convenient tool that combines these two features to aid in high-throughput data analysis has not been developed yet. In order to fill this niche, we developed the user-friendly, web-based application, PhenoFam.</p> <p>Results</p> <p>PhenoFam performs gene set enrichment analysis by employing structural and functional information on families of protein domains as annotation terms. Our tool is designed to analyse complete sets of results from quantitative high-throughput studies (gene expression microarrays, functional RNAi screens, <it>etc</it>.) without prior pre-filtering or hits-selection steps. PhenoFam utilizes Ensembl databases to link a list of user-provided identifiers with protein features from the InterPro database, and assesses whether results associated with individual domains differ significantly from the overall population. To demonstrate the utility of PhenoFam we analysed a genome-wide RNA interference screen and discovered a novel function of plexins containing the cytoplasmic RasGAP domain. Furthermore, a PhenoFam analysis of breast cancer gene expression profiles revealed a link between breast carcinoma and altered expression of PX domain containing proteins.</p> <p>Conclusions</p> <p>PhenoFam provides a user-friendly, easily accessible web interface to perform GSEA based on high-throughput data sets and structural-functional protein information, and therefore aids in functional annotation of genes.</p

Prediction of Human Disease Genes by Human-Mouse Conserved Coexpression Analysis

One of the most limiting aspects of biological research in the post-genomic era is the capability to integrate massive datasets on gene structure and function for producing useful biological knowledge. In this report we have applied an integrative approach to address the problem of identifying likely candidate genes within loci associated with human genetic diseases. Despite the recent progress in sequencing technologies, approaching this problem from an experimental perspective still represents a very demanding task, because the critical region may typically contain hundreds of positional candidates. We found that by concentrating only on genes sharing similar expression profiles in both human and mouse, massive microarray datasets can be used to reliably identify disease-relevant relationships among genes. Moreover, we found that integrating the coexpression criterion with systematic phenome analysis allows efficient identification of disease genes in large genomic regions. Using this approach on 850 OMIM loci characterized by unknown molecular basis, we propose high-probability candidates for 81 genetic diseases

Genome-Wide Interrogation of Mammalian Stem Cell Fate Determinants by Nested Chromosome Deletions

Understanding the function of important DNA elements in mammalian stem cell genomes would be enhanced by the availability of deletion collections in which segmental haploidies are precisely characterized. Using a modified Cre-loxP–based system, we now report the creation and characterization of a collection of ∼1,300 independent embryonic stem cell (ESC) clones enriched for nested chromosomal deletions. Mapping experiments indicate that this collection spans over 25% of the mouse genome with good representative coverage of protein-coding genes, regulatory RNAs, and other non-coding sequences. This collection of clones was screened for in vitro defects in differentiation of ESC into embryoid bodies (EB). Several putative novel haploinsufficient regions, critical for EB development, were identified. Functional characterization of one of these regions, through BAC complementation, identified the ribosomal gene Rps14 as a novel haploinsufficient determinant of embryoid body formation. This new library of chromosomal deletions in ESC (DelES: http://bioinfo.iric.ca/deles) will serve as a unique resource for elucidation of novel protein-coding and non-coding regulators of ESC activity

Insulin-like growth factor axis in pregnancies affected by fetal growth disorders

Background: Insulin-like growth factors 1 and 2 (IGF1 and IGF2) and their binding proteins (IGFBPs) are expressed in the placenta and known to regulate fetal growth. DNA methylation is an epigenetic mechanism which involves addition of methyl group to a cytosine base in the DNA forming a methylated cytosine-phosphate-guanine (CpG) dinucleotide which is known to silence gene expression. This silences gene expression, potentially altering the expression of IGFs and their binding proteins. This study investigates the relationship between DNA methylation of components of the IGF axis in the placenta and disorders in fetal growth. Placental samples were obtained from cord insertions immediately after delivery from appropriate, small (defined as birthweight the 90th percentile for the gestation [LGA]) neonates. Placental DNA methylation, mRNA expression and protein levels of components of the IGF axis were determined by pyrosequencing, rtPCR and Western blotting. Results: In the placenta from small for gestational age (SGA) neonates (n = 16), mRNA and protein levels of IGF1 were lower and of IGFBPs (1, 2, 3, 4 and 7) were higher (p < 0.05) compared to appropriately grown neonates (n = 37). In contrast, in the placenta from large for gestational age (LGA) neonates (n = 20), mRNA and protein levels of IGF1 was not different and those of IGFBPs (1, 2, 3 and 4) were lower (p < 0.05) compared to appropriately grown neonates. Compared to appropriately grown neonates, CpG methylation of the promoter regions of IGF1 was higher in SGA neonates. The CpG methylation of the promoter regions of IGFBP1, IGFBP2, IGFBP3, IGFBP4 and IGFBP7 was lower in the placenta from SGA neonates as compared to appropriately grown neonates, but was unchanged in the placenta from LGA neonates. Conclusions: Our results suggest that changes in CpG methylation contribute to the changes in gene expression of components of the IGF axis in fetal growth disorders. Differential methylation of the IGF1 gene and its binding proteins is likely to play a role in the pathogenesis of SGA neonates

Gender and sexuality I: Genderqueer geographies?

This review considers gender diversity across a range of spaces and places. I note that while the notion of gender has been troubled, there exists opportunities to trouble it further. I highlight the scholarship that has sought to deconstruct genders, and the binary framing of man/woman and male/female roles and relationships. The queering of sexuality has meant that geographers are now tracing the ways in which lesbian, gay, bisexual, transgender, intersex, queer (LGBTIQ) bodies experience and live their gender beyond normative binaries. Research concerned with relational gendered subjectivities within LGBTIQ communities is discussed, and I flag the trend that this research may conflate gendered experiences while privileging sexual subjectivities. Finally, I turn to the recent interest by geographers who - drawing on queer and trans* theories - argue for new and innovative understandings of gender diversity

A user's guide to the Encyclopedia of DNA elements (ENCODE)

The mission of the Encyclopedia of DNA Elements (ENCODE) Project is to enable the scientific and medical communities to interpret the human genome sequence and apply it to understand human biology and improve health. The ENCODE Consortium is integrating multiple technologies and approaches in a collective effort to discover and define the functional elements encoded in the human genome, including genes, transcripts, and transcriptional regulatory regions, together with their attendant chromatin states and DNA methylation patterns. In the process, standards to ensure high-quality data have been implemented, and novel algorithms have been developed to facilitate analysis. Data and derived results are made available through a freely accessible database. Here we provide an overview of the project and the resources it is generating and illustrate the application of ENCODE data to interpret the human genome