Clustering of gene ontology terms in genomes.

Although protein coding genes occupy only a small fraction of genomes in higher species, they are not randomly distributed within or between chromosomes. Clustering of genes with related function(s) and/or characteristics has been evident at several different levels. To study how common the clustering of functionally related genes is and what kind of functions the end products of these genes are involved, we collected gene ontology (GO) terms for complete genomes and developed a method to detect previously undefined gene clustering. Exhaustive analysis was performed for seven widely studied species ranging from human to Escherichia coli. To overcome problems related to varying gene lengths and densities, a novel method was developed and a fixed number of genes were analyzed irrespective of the genome span covered. Statistically very significant GO term clustering was apparent in all the investigated genomes. The analysis window, which ranged from 5 to 50 consecutive genes, revealed extensive GO term clusters for genes with widely varying functions. Here, the most interesting and significant results are discussed and the complete dataset for each analyzed species is available at the GOme database at http://bioinf.uta.fi/GOme. The results indicated that clusters of genes with related functions are very common, not only in bacteria, in which operons are frequent, but also in all the studied species irrespective of how complex they are. There are some differences between species but in all of them GO term clusters are common and of widely differing sizes. The presented method can be applied to analyze any genome or part of a genome for which descriptive features are available, and thus is not restricted to ontology terms. This method can also be applied to investigate gene and protein expression patterns. The results pave a way for further studies of mechanisms that shape genome structure and evolutionary forces related to them

A Comparison of Approaches to Estimate the Inbreeding Coefficient and Pairwise Relatedness Using Genomic and Pedigree Data in a Sheep Population

Genome-wide SNP data provide a powerful tool to estimate pairwise relatedness among individuals and individual inbreeding coefficient. The aim of this study was to compare methods for estimating the two parameters in a Finnsheep population based on genome-wide SNPs and genealogies, separately. This study included ninety-nine Finnsheep in Finland that differed in coat colours (white, black, brown, grey, and black/white spotted) and were from a large pedigree comprising 319 119 animals. All the individuals were genotyped with the Illumina Ovine SNP50K BeadChip by the International Sheep Genomics Consortium. We identified three genetic subpopulations that corresponded approximately with the coat colours (grey, white, and black and brown) of the sheep. We detected a significant subdivision among the colour types (FST = 5.4%, P<0.05). We applied robust algorithms for the genomic estimation of individual inbreeding (FSNP) and pairwise relatedness (ΦSNP) as implemented in the programs KING and PLINK, respectively. Estimates of the two parameters from pedigrees (FPED and ΦPED) were computed using the RelaX2 program. Values of the two parameters estimated from genomic and genealogical data were mostly consistent, in particular for the highly inbred animals (e.g. inbreeding coefficient F>0.0625) and pairs of closely related animals (e.g. the full- or half-sibs). Nevertheless, we also detected differences in the two parameters between the approaches, particularly with respect to the grey Finnsheep. This could be due to the smaller sample size and relative incompleteness of the pedigree for them

ETS1 Mediates MEK1/2-Dependent Overexpression of Cancerous Inhibitor of Protein Phosphatase 2A (CIP2A) in Human Cancer Cells

EGFR-MEK-ERK signaling pathway has an established role in promoting malignant growth and disease progression in human cancers. Therefore identification of transcriptional targets mediating the oncogenic effects of the EGFR-MEK-ERK pathway would be highly relevant. Cancerous inhibitor of protein phosphatase 2A (CIP2A) is a recently characterized human oncoprotein. CIP2A promotes malignant cell growth and is over expressed at high frequency (40–80%) in most of the human cancer types. However, the mechanisms inducing its expression in cancer still remain largely unexplored. Here we present systematic analysis of contribution of potential gene regulatory mechanisms for high CIP2A expression in cancer. Our data shows that evolutionary conserved CpG islands at the proximal CIP2A promoter are not methylated both in normal and cancer cells. Furthermore, sequencing of the active CIP2A promoter region from altogether seven normal and malignant cell types did not reveal any sequence alterations that would increase CIP2A expression specifically in cancer cells. However, treatment of cancer cells with various signaling pathway inhibitors revealed that CIP2A mRNA expression was sensitive to inhibition of EGFR activity as well as inhibition or activation of MEK-ERK pathway. Moreover, MEK1/2-specific siRNAs decreased CIP2A protein expression. Series of CIP2A promoter-luciferase constructs were created to identify proximal −27 to −107 promoter region responsible for MEK-dependent stimulation of CIP2A expression. Additional mutagenesis and chromatin immunoprecipitation experiments revealed ETS1 as the transcription factor mediating stimulation of CIP2A expression through EGFR-MEK pathway. Thus, ETS1 is probably mediating high CIP2A expression in human cancers with increased EGFR-MEK1/2-ERK pathway activity. These results also suggest that in addition to its established role in invasion and angiogenesis, ETS1 may support malignant cellular growth via regulation of CIP2A expression and protein phosphatase 2A inhibition

Clustering of Gene Ontologies in Genomes

Tutkimuksen tausta ja tavoitteet: Proteiineja koodaavat geenit eivät ole satunnaisesti jakautuneina kromosomeihin, vaikkakin ne edustavat vain pientä osaa genomista. Geenien klusteroitumisesta on saatu viitteitä usealla eri menetelmällä. Toiminnallisuutensa perusteella klusteroituvien geenien tutkimista varten geeniontologiat kerättiin kokonaisille genomeille sekä kehitettiin klusteroitumista mittaava menetelmä. Geeniontologioiden klusteroitumista vertailtiin yhdeksällä eri organismilla. Tutkimusmenetelmät: Genomin sisäisen järjestyksen tutkimiseksi geenit järjestettiin positioidensa perusteella kromosomeihin ja data analysoitiin käyttäen liukuvaa ikkunaa otosalueena. Liukuva ikkuna koostuu 10:stä eri lukuraamista, joissa on 5 – 50 geeniä. Tulokset järjestetään p-arvojen mukaan ja kaikista rikastuneimmat geeniryppäät esitetään visuaalisesti ’ontologianauhoina’ organismeittain. Tutkimustulokset: Geenien vaihtelevan pituuden ja tiheyden tuomien ongelmien vuoksi analyysiin otettiin aina kiinteä määrä geenejä riippumatta otoksen emäsmäärällisestä pituudesta. Tilastollisesti merkittävää klusteroitumista oli havaittavissa jokaisessa tutkituista genomeista ja lisäksi liukuva ikkuna-menetelmä paljasti useita suuria ontologiaryppäitä. Datan suuresta määrästä johtuen tässä tutkielmassa esitellään vain mielenkiintoisimpia löytöjä. Kaikki tulokset ovat saatavilla GOme-tietokannasta: http://bioinf.uta.fi/GOme. Johtopäätökset Ontologiarikastumat ovat organismikohtaisesti hyvin erilaisia, mutta kaikilla eliöillä on havaittavissa taipumus geenien ja ontologioiden järjestykseen genomissa. Käytettyä menetelmää voidaan soveltaa mihin tahansa saatavilla olevaan genomiin tai osaan siitä sekä myös muihin tiedon luokittelijoihin kuin geeniontologioihin. Asiasanat: geeniontologia, klusterointi, genom

Clustering of Gene Ontologies in Genomes

Tutkimuksen tausta ja tavoitteet: Proteiineja koodaavat geenit eivät ole satunnaisesti jakautuneina kromosomeihin, vaikkakin ne edustavat vain pientä osaa genomista. Geenien klusteroitumisesta on saatu viitteitä usealla eri menetelmällä. Toiminnallisuutensa perusteella klusteroituvien geenien tutkimista varten geeniontologiat kerättiin kokonaisille genomeille sekä kehitettiin klusteroitumista mittaava menetelmä. Geeniontologioiden klusteroitumista vertailtiin yhdeksällä eri organismilla. Tutkimusmenetelmät: Genomin sisäisen järjestyksen tutkimiseksi geenit järjestettiin positioidensa perusteella kromosomeihin ja data analysoitiin käyttäen liukuvaa ikkunaa otosalueena. Liukuva ikkuna koostuu 10:stä eri lukuraamista, joissa on 5 – 50 geeniä. Tulokset järjestetään p-arvojen mukaan ja kaikista rikastuneimmat geeniryppäät esitetään visuaalisesti ’ontologianauhoina’ organismeittain. Tutkimustulokset: Geenien vaihtelevan pituuden ja tiheyden tuomien ongelmien vuoksi analyysiin otettiin aina kiinteä määrä geenejä riippumatta otoksen emäsmäärällisestä pituudesta. Tilastollisesti merkittävää klusteroitumista oli havaittavissa jokaisessa tutkituista genomeista ja lisäksi liukuva ikkuna-menetelmä paljasti useita suuria ontologiaryppäitä. Datan suuresta määrästä johtuen tässä tutkielmassa esitellään vain mielenkiintoisimpia löytöjä. Kaikki tulokset ovat saatavilla GOme-tietokannasta: http://bioinf.uta.fi/GOme. Johtopäätökset Ontologiarikastumat ovat organismikohtaisesti hyvin erilaisia, mutta kaikilla eliöillä on havaittavissa taipumus geenien ja ontologioiden järjestykseen genomissa. Käytettyä menetelmää voidaan soveltaa mihin tahansa saatavilla olevaan genomiin tai osaan siitä sekä myös muihin tiedon luokittelijoihin kuin geeniontologioihin. Asiasanat: geeniontologia, klusterointi, genom

Data from: A genome-wide scan study identifies a single nucleotide substitution in ASIP associated with white versus non-white coat-colour variation in sheep (Ovis aries)

In sheep, coat colour (and pattern) is one of the important traits of great biological, economic and social importance. However, the genetics of sheep coat colour has not yet been fully clarified. We conducted a genome-wide association study of sheep coat colours by genotyping 47 303 single-nucleotide polymorphisms (SNPs) in the Finnsheep population in Finland. We identified 35 SNPs associated with all the coat colours studied, which cover genomic regions encompassing three known pigmentation genes (TYRP1, ASIP and MITF) in sheep. Eighteen of these associations were confirmed in further tests between white versus non-white individuals, but none of the 35 associations were significant in the analysis of only non-white colours. Across the tests, the s66432.1 in ASIP showed significant association (P=4.2 × 10−11 for all the colours; P=2.3 × 10−11 for white versus non-white colours) with the variation in coat colours and strong linkage disequilibrium with other significant variants surrounding the ASIP gene. The signals detected around the ASIP gene were explained by differences in white versus non-white alleles. Further, a genome scan for selection for white coat pigmentation identified a strong and striking selection signal spanning ASIP. Our study identified the main candidate gene for the coat colour variation between white and non-white as ASIP, an autosomal gene that has been directly implicated in the pathway regulating melanogenesis. Together with ASIP, the two other newly identified genes (TYRP1 and MITF) in the Finnsheep, bordering associated SNPs, represent a new resource for enriching sheep coat-colour genetics and breeding

Finnsheep coat colour phenotypes

Finnsheep coat colour phenotype

Hunting candidates for the net blotch resistance gene Rpt5 in barley

Abstract P309vo

Finnsheep 50K SNP genotypes

Finnsheep 50K SNP genotype

Gene expression in barley : Pyrenophora teres interaction

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