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Search Results: 1 - 10 of 228947 matches for " Markus Ringnér "
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Folding Free Energies of 5′-UTRs Impact Post-Transcriptional Regulation on a Genomic Scale in Yeast
Markus Ringnér ,Morten Krogh
PLOS Computational Biology , 2005, DOI: 10.1371/journal.pcbi.0010072
Abstract: Using high-throughput technologies, abundances and other features of genes and proteins have been measured on a genome-wide scale in Saccharomyces cerevisiae. In contrast, secondary structure in 5′–untranslated regions (UTRs) of mRNA has only been investigated for a limited number of genes. Here, the aim is to study genome-wide regulatory effects of mRNA 5′-UTR folding free energies. We performed computations of secondary structures in 5′-UTRs and their folding free energies for all verified genes in S. cerevisiae. We found significant correlations between folding free energies of 5′-UTRs and various transcript features measured in genome-wide studies of yeast. In particular, mRNAs with weakly folded 5′-UTRs have higher translation rates, higher abundances of the corresponding proteins, longer half-lives, and higher numbers of transcripts, and are upregulated after heat shock. Furthermore, 5′-UTRs have significantly higher folding free energies than other genomic regions and randomized sequences. We also found a positive correlation between transcript half-life and ribosome occupancy that is more pronounced for short-lived transcripts, which supports a picture of competition between translation and degradation. Among the genes with strongly folded 5′-UTRs, there is a huge overrepresentation of uncharacterized open reading frames. Based on our analysis, we conclude that (i) there is a widespread bias for 5′-UTRs to be weakly folded, (ii) folding free energies of 5′-UTRs are correlated with mRNA translation and turnover on a genomic scale, and (iii) transcripts with strongly folded 5′-UTRs are often rare and hard to find experimentally.
Revealing signaling pathway deregulation by using gene expression signatures and regulatory motif analysis
Yingchun Liu, Markus Ringnér
Genome Biology , 2007, DOI: 10.1186/gb-2007-8-5-r77
Abstract: Genetic aberrations and variations in cellular processes are usually reflected in the expression levels of many genes. Hence, such alterations can potentially be characterized by their gene expression profiles. Gene expression profiling, in particular DNA microarray analysis, has been widely used in attempts to reveal the underlying mechanisms of many diseases, different developmental stages, cellular responses to different conditions, and many other biological phenomena (for example, [1-3]). Gene expression signatures consisting of tens to hundreds of genes have been associated with many important aspects of the systems studied. To help realize the full potential of gene expression studies, a variety of methods, such as GenMAPP [4], GoMiner [5], DAVID [6] and its desktop version EASE [7], Catmap [8], ArrayXPath [9], and Gene Set Enrichment Analysis (GSEA) [10], have been developed to relate gene expression profiles or signatures to a broad range of biological categories. Although some of these methods include signaling pathways in their categories, their focus has not been on regulatory mechanisms that control the observed gene expression changes.Signal transduction is at the core of many regulatory systems. Cellular functions such as growth, proliferation, differentiation, and apoptosis are regulated by signaling pathways. Appropriate regulation of such pathways is essential for the normal functioning of cells. Cells affected by disease often have one or several signaling pathways abnormally activated or inactivated. For example, cancer is a disease of deregulated cell proliferation and death [11]. To uncover mechanisms underlying cellular phenotypes, therefore, it is crucial to systematically analyze gene expression signatures in the context of signaling pathways. In signal transduction, ligands, usually from outside the cell, interact with receptors on the surface of the cell membrane or with nuclear receptors. These interactions trigger a cascade of biochemical
Multiclass discovery in array data
Yingchun Liu, Markus Ringnér
BMC Bioinformatics , 2004, DOI: 10.1186/1471-2105-5-70
Abstract: We describe the implementation of an unsupervised classification method for class discovery in microarray data. The method allows for discovery of more than two classes. We applied our method on two published microarray data sets: small round blue cell tumors and breast tumors. The method predicts relevant classes in the data sets with high success rates.We conclude that the proposed method is accurate and efficient in finding biologically relevant classes in microarray data. Additionally, the method is useful for quality control of microarray experiments. We have made the method available as a computer program.A common application in microarray data analysis is to identify genes that, based on their expression levels, discriminate between known classes of experiments. This identification is often achieved by using various statistical measures to, gene-by-gene, correlate the expression levels with the classes of interest. In this way a discriminatory weight is calculated for each gene. For example, Golub et al. used a signal-to-noise statistic to find genes with expression patterns that discriminate between samples obtained from patients with acute myeloid leukemia and patients with acute lymphoblastic leukemia [1]. Other examples include using a standard t-test to discriminate between breast tumors from carriers of BRCA1 mutations and carriers of BRCA2 mutations [2]. For an overview of applications see [3]. In most studies, the number of genes is much larger than the number of experiments. For such a large number of genes, it is crucial to estimate how many genes would correlate with the classes of interest by chance. Often, a P value corresponding to the probability of obtaining a given weight by chance is calculated for each weight. One can then investigate if there is an over-abundance of discriminatory genes for classes of interest as compared to randomly selected classes. Indeed, such an over-abundance has been found for many microarray-based classification ap
The Landscape of Candidate Driver Genes Differs between Male and Female Breast Cancer
Ida Johansson, Markus Ringnér, Ingrid Hedenfalk
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0078299
Abstract: The rapidly growing collection of diverse genome-scale data from multiple tumor types sheds light on various aspects of the underlying tumor biology. With the objective to identify genes of importance for breast tumorigenesis in men and to enable comparisons with genes important for breast cancer development in women, we applied the computational framework COpy Number and EXpression In Cancer (CONEXIC) to detect candidate driver genes among all altered passenger genes. Unique to this approach is that each driver gene is associated with several gene modules that are believed to be altered by the driver. Thirty candidate drivers were found in the male breast cancers and 67 in the female breast cancers. We identified many known drivers of breast cancer and other types of cancer, in the female dataset (e.g. GATA3, CCNE1, GRB7, CDK4). In contrast, only three known cancer genes were found among male breast cancers; MAP2K4, LHP, and ZNF217. Many of the candidate drivers identified are known to be involved in processes associated with tumorigenesis, including proliferation, invasion and differentiation. One of the modules identified in male breast cancer was regulated by THY1, a gene involved in invasion and related to epithelial-mesenchymal transition. Furthermore, men with THY1 positive breast cancers had significantly inferior survival. THY1 may thus be a promising novel prognostic marker for male breast cancer. Another module identified among male breast cancers, regulated by SPAG5, was closely associated with proliferation. Our data indicate that male and female breast cancers display highly different landscapes of candidate driver genes, as only a few genes were found in common between the two. Consequently, the pathobiology of male breast cancer may differ from that of female breast cancer and can be associated with differences in prognosis; men diagnosed with breast cancer may consequently require different management and treatment strategies than women.
Genome-wide transcription factor binding site/promoter databases for the analysis of gene sets and co-occurrence of transcription factor binding motifs
Srinivas Veerla, Markus Ringnér, Mattias H?glund
BMC Genomics , 2010, DOI: 10.1186/1471-2164-11-145
Abstract: We develop a strategy that efficiently produces TFBS/promoter databases based on user-defined criteria. The resulting databases constitute all genes in the Santa Cruz database and the positions for all TFBS provided by the user as position weight matrices. These databases are then used for two purposes, to identify significant TFBS in the promoters in sets of genes and to identify clusters of co-occurring TFBS. We use two criteria for significance, significantly enriched TFBS in terms of total number of binding sites for the promoters, and significantly present TFBS in terms of the fraction of promoters with binding sites. Significant TFBS are identified by a re-sampling procedure in which the query gene set is compared with typically 105 gene lists of similar size randomly drawn from the TFBS/promoter database. We apply this strategy to a large number of published ChIP-Chip data sets and show that the proposed approach faithfully reproduces ChIP-Chip results. The strategy also identifies relevant TFBS when analyzing gene signatures obtained from the MSigDB database. In addition, we show that several TFBS are highly correlated and that co-occurring TFBS define functionally related sets of genes.The presented approach of promoter analysis faithfully reproduces the results from several ChIP-Chip and MigDB derived gene sets and hence may prove to be an important method in the analysis of gene signatures obtained through ChIP-Chip or global gene expression experiments. We show that TFBS are organized in clusters of co-occurring TFBS that together define highly coherent sets of genes.The use of global gene expression profiling is a well established approach to characterize biological states or responses. One of the major goals of these investigations is to identify sets of genes with similar expression patterns that may shed new light on the underlying biological process leading to the observed states. A logical and systematic next step is to reduce the identified gene s
GOBO: Gene Expression-Based Outcome for Breast Cancer Online
Markus Ringnér,Erik Fredlund,Jari H?kkinen,?ke Borg,Johan Staaf
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0017911
Abstract: Microarray-based gene expression analysis holds promise of improving prognostication and treatment decisions for breast cancer patients. However, the heterogeneity of breast cancer emphasizes the need for validation of prognostic gene signatures in larger sample sets stratified into relevant subgroups. Here, we describe a multifunctional user-friendly online tool, GOBO (http://co.bmc.lu.se/gobo), allowing a range of different analyses to be performed in an 1881-sample breast tumor data set, and a 51-sample breast cancer cell line set, both generated on Affymetrix U133A microarrays. GOBO supports a wide range of applications including: 1) rapid assessment of gene expression levels in subgroups of breast tumors and cell lines, 2) identification of co-expressed genes for creation of potential metagenes, 3) association with outcome for gene expression levels of single genes, sets of genes, or gene signatures in multiple subgroups of the 1881-sample breast cancer data set. The design and implementation of GOBO facilitate easy incorporation of additional query functions and applications, as well as additional data sets irrespective of tumor type and array platform.
Landscape of somatic allelic imbalances and copy number alterations in HER2-amplified breast cancer
Johan Staaf, G?ran J?nsson, Markus Ringnér, Bo Baldetorp, ?ke Borg
Breast Cancer Research , 2011, DOI: 10.1186/bcr3075
Abstract: High-density whole genome array-based comparative genomic hybridization (aCGH) and single nucleotide polymorphism (SNP) array data from 260 HER2-amplified breast tumors or cell lines, and 346 HER2-negative breast cancers with molecular subtype information were assembled from different repositories. Copy number alteration (CNA), loss-of-heterozygosity (LOH), copy number neutral allelic imbalance (CNN-AI), subclonal CNA and patterns of tumor DNA ploidy were analyzed using bioinformatical methods such as genomic identification of significant targets in cancer (GISTIC) and genome alteration print (GAP). The patterns of tumor ploidy were confirmed in 338 unrelated breast cancers analyzed by DNA flow cytometry with concurrent BAC aCGH and gene expression data.A core set of 36 genomic regions commonly affected by copy number gain or loss was identified by integrating results with a previous study, together comprising > 400 HER2-amplified tumors. While CNN-AI frequency appeared evenly distributed over chromosomes in HER2-amplified tumors, not targeting specific regions and often < 20% in frequency, the occurrence of LOH was strongly associated with regions of copy number loss. HER2-amplified and HER2-negative tumors stratified by molecular subtypes displayed different patterns of LOH and CNN-AI, with basal-like tumors showing highest frequencies followed by HER2-amplified and luminal B cases. Tumor aneuploidy was strongly associated with increasing levels of LOH, CNN-AI, CNAs and occurrence of subclonal copy number events, irrespective of subtype. Finally, SNP data from individual tumors indicated that genomic amplification in general appears as monoallelic, that is, it preferentially targets one parental chromosome in HER2-amplified tumors.We have delineated the genomic landscape of CNAs, amplifications, LOH, and CNN-AI in HER2-amplified breast cancer, but also demonstrated a strong association between different types of genomic aberrations and tumor aneuploidy irrespectiv
The gene expression landscape of breast cancer is shaped by tumor protein p53 status and epithelial-mesenchymal transition
Erik Fredlund, Johan Staaf, Juha K Rantala, Olli Kallioniemi, ?ke Borg, Markus Ringnér
Breast Cancer Research , 2012, DOI: 10.1186/bcr3236
Abstract: Modules of highly connected genes were extracted from a gene co-expression network that was constructed based on Pearson correlation, and module activities were then calculated using a pathway activity score. Functional annotations of modules were experimentally validated with an siRNA cell spot microarray system using the KPL-4 breast cancer cell line, and by using gene expression data from functional studies. Modules were derived using gene expression data representing 1,608 breast cancer samples and validated in data sets representing 971 independent breast cancer samples as well as 1,231 samples from other cancer forms.The initial co-expression network analysis resulted in the characterization of eight tightly regulated gene modules. Cell cycle genes were divided into two transcriptional programs, and experimental validation using an siRNA screen showed different functional roles for these programs during proliferation. The division of the two programs was found to act as a marker for tumor protein p53 (TP53) gene status in luminal breast cancer, with the two programs being separated only in luminal tumors with functional p53 (encoded by TP53). Moreover, a module containing fibroblast and stroma-related genes was highly expressed in fibroblasts, but was also up-regulated by overexpression of epithelial-mesenchymal transition factors such as transforming growth factor beta 1 (TGF-beta1) and Snail in immortalized human mammary epithelial cells. Strikingly, the stroma transcriptional program related to less malignant tumors for luminal disease and aggressive lymph node positive disease among basal-like tumors.We have derived a robust gene expression landscape of breast cancer that reflects known subtypes as well as heterogeneity within these subtypes. By applying the modules to TP53-mutated samples we shed light on the biological consequences of non-functional p53 in otherwise low-proliferating luminal breast cancer. Furthermore, as in the case of the stroma module
Normalization of array-CGH data: influence of copy number imbalances
Johan Staaf, G?ran J?nsson, Markus Ringnér, Johan Vallon-Christersson
BMC Genomics , 2007, DOI: 10.1186/1471-2164-8-382
Abstract: Here we demonstrate that copy number imbalances correlate with intensity in array-CGH data thereby causing problems for conventional normalization methods. We propose a strategy to circumvent these problems by taking copy number imbalances into account during normalization, and we test the proposed strategy using several data sets from the analysis of cancer genomes. In addition, we show how the strategy can be applied to conveniently define adaptive sample-specific boundaries between balanced copy number, losses, and gains to facilitate management of variation in tissue heterogeneity when calling copy number changes.We highlight the importance of considering copy number imbalances during normalization of array-CGH data, and show how failure to do so can deleteriously affect data and hamper interpretation.Microarray-based techniques for genome-wide investigation of copy number aberrations (CNAs) have recently gained much attention. Initially employing arrays developed for gene expression analysis [1], or low-density arrays produced from large-insert genomic clones such as bacterial artificial chromosomes (BACs) [2], the application has evolved rapidly. Currently, specialized high-density arrays with oligonucleotide probes or probes derived from BAC clones are predominately used. Two-channel array-based comparative genomic hybridization (aCGH) is a direct successor to conventional metaphase CGH [3]. In both cases, DNA from two samples are differentially labeled with fluorescent dyes and co-hybridized to immobilized genomic capture probes. By use of aCGH, DNA derived from tumor tissue can be compared with reference DNA, e.g., normal whole blood DNA, and genomic imbalances can effectively be investigated. The main advantage of aCGH over conventional CGH is the increased resolution achieved by microarrays with a large number of individual probes, routinely up to hundreds of thousands, covering the entire genome [4]. The power of aCGH has been demonstrated in tumor studi
Normalization of Illumina Infinium whole-genome SNP data improves copy number estimates and allelic intensity ratios
Johan Staaf, Johan Vallon-Christersson, David Lindgren, Gunnar Juliusson, Richard Rosenquist, Mattias H?glund, ?ke Borg, Markus Ringnér
BMC Bioinformatics , 2008, DOI: 10.1186/1471-2105-9-409
Abstract: We demonstrate an asymmetry in the detection of the two alleles for each SNP, which deleteriously influences both allelic proportions and copy number estimates. The asymmetry is caused by a remaining bias between the two dyes used in the Infinium II assay after using the normalization method in Illumina's proprietary software (BeadStudio). We propose a quantile normalization strategy for correction of this dye bias. We tested the normalization strategy using 535 individual hybridizations from 10 data sets from the analysis of cancer genomes and normal blood samples generated on Illumina Infinium II 300 k version 1 and 2, 370 k and 550 k BeadChips. We show that the proposed normalization strategy successfully removes asymmetry in estimates of both allelic proportions and copy numbers. Additionally, the normalization strategy reduces the technical variation for copy number estimates while retaining the response to copy number alterations.The proposed normalization strategy represents a valuable tool that improves the quality of data obtained from Illumina Infinium arrays, in particular when used for LOH and copy number variation studies.Genomic copy number alterations (CNA) and allelic imbalances are common events in the development of cancer and certain genetic disorders [1,2]. The introduction of whole genome genotyping (WGG) arrays based on single nucleotide polymorphism (SNP) genotyping [3,4] allows for combined DNA copy number (SNP-CGH) and loss-of-heterozygosity (LOH) analysis at high resolution [5]. Currently, two major SNP array platforms are in use, Affymetrix GeneChip arrays [6] and Illumina BeadChips [7]. The Infinium assay for Illumina BeadChips is based on allele-specific hybridization coupled with primer extension of genomic DNA using primers directly surrounding the SNP on randomly ordered bead arrays [4]. The Infinium assay has been further developed into allele-specific single base extension using two color labeling with the Cy3 and Cy5 fluorescent dy
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