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MACPET: Model-based Analysis for ChIA-PET [article]

Ioannis Vardaxis, Finn Drablos, Morten Beck Rye, Bo Henry Lindqvist
2018 bioRxiv   pre-print
We present Model-based Analysis for ChIA-PET (MACPET) which analyzes paired-end read sequences provided by ChIA-PET for finding binding sites of a protein of interest. MACPET uses information from both tags of each PET and searches for binding sites in a two-dimensional space, while taking into account different noise levels in different genomic regions. MACPET shows favorable results compared to MACS in terms of motif occurrence, spatial resolution and false discovery rate. Significant binding
more » ... sites discovered by MACPET are involved in a higher number of significant 3D interactions than those discovered by MACS. MACPET is freely available on Bioconductor.
doi:10.1101/272559 fatcat:2mktbe7imzczbb4bratf65urpq

FunHoP analysis reveals upregulation of mitochondrial genes in prostate cancer [article]

Kjersti Rise, May-Britt Tessem, Finn Drabløs, Morten Rye
2022 bioRxiv   pre-print
Mitochondrial activity in cancer cells has been central to cancer research since Otto Warburg first published his thesis on the topic in 1956. Although Warburg proposed that oxidative phosphorylation in the tricarboxylic acid (TCA) cycle was perturbed in cancer, later research has shown that oxidative phosphorylation is activated in most cancers, including prostate cancer (PCa). However, more detailed knowledge on mitochondrial metabolism and metabolic pathways in cancers is still lacking. In
more » ... is study we expand our previously developed method for analyzing functional homologous proteins (FunHoP), which can provide a more detailed view of metabolic pathways. FunHoP uses results from differential expression analysis of RNA-Seq data to improve pathway analysis. By adding information on subcellular localization based on experimental data and computational predictions we can use FunHoP to differentiate between mitochondrial and non-mitochondrial processes in cancerous and normal prostate cell lines. Our results show that mitochondrial pathways are upregulated in PCa and that splitting metabolic pathways into mitochondrial and non-mitochondrial counterparts using FunHoP adds to the interpretation of the metabolic properties of PCa cells.
doi:10.1101/2022.03.01.482475 fatcat:bj7i45tpo5fddi7r2hvid6h6em

Breeding for disease resistance of Penaeid shrimps

James Cock, Thomas Gitterle, Marcela Salazar, Morten Rye
2009 Aquaculture  
Diseases are a major constraint on the intensive production of shrimps. Conditions in production ponds favour disease development, and epidemics of several previously unreported diseases have occurred and caused severe losses. When elimination, eradication or cultural control is difficult, selective breeding for host resistance to the pathogen may be an attractive option for disease control. However, host resistance is not a panacea and should only be considered when (a) the disease causes
more » ... e damage (b) there are no other existing simple cost effective control measures and (c) there is demonstrable genetic variation in resistance and this is not coupled with an excessive level of negative associations with other desirable characteristics. Shrimp have only recently been domesticated and breeding for resistance only began in the mid 1990s; there is limited experience with shrimp breeding in particular and crustaceans in general. Consequently, the principles and concepts behind breeding programmes are based largely on experiences with other species in both the plant and animal kingdoms. Commercial growers now seed ponds with shrimp populations selected for resistance to Taura Syndrome Virus with excellent results, whilst up to now development of White Spot Syndrome Virus resistant populations has been an elusive goal. The original TSV resistant populations were developed using simple mass selection techniques (Colombia). In later generations family based selection has been applied on populations, which initially had survival rates of about 30%, with care taken to reduce inbreeding and loss of genetic variation. This suggests that when the original populations have a reasonable level of resistance, and straightforward, effective selection protocols exist, it is relatively simple to breed for resistance. With catastrophic diseases, such as WSSV, which cause mortalities of 98% or more the frequency of resistance is low and it is suggested that for theoretical reasons single gene, rather than polygenic, resistance is likely to develop. The low frequency of resistance genes in breeding populations may cause genetic bottlenecks which will greatly reduce the genetic variation in the populations. In order to maintain the genetic variation the genes from the small numbers of survivors should be introgressed into populations with broader genetic variability. Furthermore, in order to minimize the probability of breakdown of resistance pyramiding of resistant genes on different loci would be advantageous. Genetic variation in resistance may be encountered either in the initial base populations or may spontaneously arise due to mutations or new recombinants. With extremely prolific species such as shrimps, millions of animals can readily be screened for survival and hence resistant mutants or recombinants may be identified. Once genetic variation has been detected the most appropriate breeding methodology will depend on the nature of both the resistance and the disease or diseases that are of interest to the producers.
doi:10.1016/j.aquaculture.2008.09.011 fatcat:fcl6q5eylzf4ljf5x2at454ltq

Increased profits in aquaculture through optimised dissemination schemes

Vibeke Skagemo, Anna K. Sonesson, Theo H.E. Meuwissen, Morten Rye
2010 Aquaculture  
Rye, pers. comm. 2008), however, we are not aware of any published work quantifying the gains resulting from this strategy.  ... 
doi:10.1016/j.aquaculture.2010.01.004 fatcat:q7mk7idpgfcw3dvbj56ljwodwu

Cell-type specificity of ChIP-predicted transcription factor binding sites

Tony Håndstad, Morten Rye, Rok Močnik, Finn Drabløs, Pål Sætrom
2012 BMC Genomics  
Context-dependent transcription factor (TF) binding is one reason for differences in gene expression patterns between different cellular states. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) identifies genome-wide TF binding sites for one particular context-the cells used in the experiment. But can such ChIP-seq data predict TF binding in other cellular contexts and is it possible to distinguish context-dependent from ubiquitous TF binding? Results: We compared
more » ... ChIP-seq data on TF binding for multiple TFs in two different cell types and found that on average only a third of ChIP-seq peak regions are common to both cell types. Expectedly, common peaks occur more frequently in certain genomic contexts, such as CpG-rich promoters, whereas chromatin differences characterize cell-type specific TF binding. We also find, however, that genotype differences between the cell types can explain differences in binding. Moreover, ChIP-seq signal intensity and peak clustering are the strongest predictors of common peaks. Compared with strong peaks located in regions containing peaks for multiple transcription factors, weak and isolated peaks are less common between the cell types and are less associated with data that indicate regulatory activity. Conclusions: Together, the results suggest that experimental noise is prevalent among weak peaks, whereas strong and clustered peaks represent high-confidence binding events that often occur in other cellular contexts. Nevertheless, 30-40% of the strongest and most clustered peaks show context-dependent regulation. We show that by combining signal intensity with additional data-ranging from context independent information such as binding site conservation and position weight matrix scores to context dependent chromatin structure-we can predict whether a ChIP-seq peak is likely to be present in other cellular contexts.
doi:10.1186/1471-2164-13-372 pmid:22863112 pmcid:PMC3574057 fatcat:aqe2a3iy5fcdhbq45zhzrkfuo4

The Triform algorithm: improved sensitivity and specificity in ChIP-Seq peak finding

Karl Kornacker, Morten Rye, Tony Håndstad, Finn Drabløs
2012 BMC Bioinformatics  
Meta was the method used by Rye et al. during the original benchmark, and is a combination of outputs from the programs MACS and SISSRs [7] .  ...  The performance of Triform was compared with that of seven other programs for ChIP-Seq peak identification: QuEST, MACS, the Meta approach by Rye et al., PICS, FindPeaks, PeakRanger, and TPic.  ... 
doi:10.1186/1471-2105-13-176 pmid:22827163 pmcid:PMC3480842 fatcat:5oz3v3tjdzabffsjr6bj6hiumu

MOESM1 of DNA hypermethylation associated with upregulated gene expression in prostate cancer demonstrates the diversity of epigenetic regulation

Ieva Rauluseviciute, Finn Drabløs, Morten Rye
2020 Figshare  
Additional file 1: Table S1. Numbers of genes in three datasets Absher, Kirby and TCGA (with overlap) that can be assigned to the four groups of regulation patterns, based on DNA methylation of the probes and expression of the associated gene: UPUP (gain of methylation — upregulated expression), UPDOWN (gain of methylation — downregulated expression), DOWNUP (loss of methylation — upregulated expression) and DOWNDOWN (loss of methylation — downregulated expression). Table S2. Number of genes,
more » ... sociated with multiple probes, in Absher, Kirby and TCGA datasets for all four established groups of gene regulation patterns: UPUP, UPDOWN, DOWNUP and DOWNDOWN. Genes that are associated with both hypermethylated and hypomethylated probes are defined as inconsistent and indicated in the table. Table S3. Probes in TCGA combined methylation/gene expression dataset for UPUP-only and UPDOWN-only groups that can be assigned to different groups, according to their probe DNA methylation and associated gene expression correlation. In addition, number of genes, associated with the probes in each correlation group, are displayed. Table S4. Number of probes in the TCGA methylation dataset for UPUP-only and UPDOWN-only groups that are located in different distances from the TSSs of the associated genes. Table S5. Number of probes in the TCGA DNA methylation dataset for UPUP-only and UPDOWN-only regulation pattern groups that are located in CGIs, their shores, shelves or all locations. Number of genes that the probes can be associated with is also displayed. Table S6. Top 10 most significantly hypermethylated genes in UPUP-only and UPDOWN-only regulation pattern groups. Figure S1. Hypermethylated probes associated with genes following UPUP-only and UPDOWN-only are located 50 to 2000 bp upstream or downstream from the TSS of the genes. Figure S2. UCSC Genome Browser window for the gene GSC (Goosecoid) together with methylation fold changes for each visualized position in the same order.
doi:10.6084/m9.figshare.11557215.v1 fatcat:pefx5aeyxbbopiqrd2cexhpefu

MOESM1 of DNA methylation data by sequencing: experimental approaches and recommendations for tools and pipelines for data analysis

Ieva Rauluseviciute, Finn DrabløS, Morten Rye
2019 Figshare  
Additional file 1. The file consists of short instructions on how to run recommended tools and pipelines, including some examples.
doi:10.6084/m9.figshare.11364686 fatcat:mlov2k6hgvavlpxpxr6mdgkzfa

SFRP4 gene expression is increased in aggressive prostate cancer

Elise Sandsmark, Maria K. Andersen, Anna M. Bofin, Helena Bertilsson, Finn Drabløs, Tone F. Bathen, Morten B. Rye, May-Britt Tessem
2017 Scientific Reports  
Increased knowledge of the molecular differences between indolent and aggressive prostate cancer is needed for improved risk stratification and treatment selection. Secreted frizzled-related protein 4 (SFRP4) is a modulator of the cancer-associated Wnt pathway, and previously suggested as a potential marker for prostate cancer aggressiveness. In this study, we investigated and validated the association between SFRP4 gene expression and aggressiveness in nine independent cohorts (n = 2157). By
more » ... fferential expression and combined meta-analysis of all cohorts, we detected significantly higher SFRP4 expression in cancer compared with normal samples, and in high (3-5) compared with low (1-2) Grade Group samples. SFRP4 expression was a significant predictor of biochemical recurrence in six of seven cohorts and in the overall analysis, and was a significant predictor of metastatic event in one cohort. In our study cohort, where metabolic information was available, SFRP4 expression correlated significantly with the concentrations of citrate and spermine, two previously suggested biomarkers for aggressive prostate cancer. SFRP4 immunohistochemistry in an independent cohort (n = 33) was not associated with aggressiveness. To conclude, high SFRP4 gene expression is associated with high Grade Group and recurrent prostate cancer after surgery. Future studies investigating the mechanistic and clinical usefulness of SFRP4 in prostate cancer are warranted. Prostate cancer is the second most common cancer and the fifth leading cause of cancer related death in men worldwide 1 . The lack of accurate markers to separate aggressive from non-aggressive prostate cancer at an early time point is causing considerable overtreatment of indolent cancers 2 . Discovery of new biomarkers of aggressiveness, as well as improved understanding of differences between indolent and aggressive prostate cancer, are therefore highly needed. The family of secreted frizzled-related proteins (SFRP1-5) are extracellular inhibitors of Wnt signalling, a pathway identified for its role in carcinogenesis 3 . The SFRPs are in general regarded as tumour suppressors, however, oncogenic properties have also been suggested due to biphasic modulation of Wnt signalling 4,5 and interactions with other signalling pathways 4 . SFRP4 is the largest and the most structurally different of the family members 6 . In several types of cancer, SFRP4 follows a tumour suppressor pattern with epigenetic silencing and reduced gene expression, as reviewed by Pohl et al. 7 . However, for prostate cancer, increased gene expression of SFRP4 has been observed 8,9 , and shown to be a predictor of recurrent disease 10 . Additionally, SFRP4 has been included in different gene expression signatures linked to prostate cancer aggressiveness and recurrence 10,11 , including our previously published signature for non-canonical Wnt pathway and epithelial-to-mesenchymal transition (NCWP-EMT) markers 12 . Protein levels of SFRP4 measured by immunohistochemistry is discordant in prostate cancer; Horvath et al. 13, 14 reported increased expression of membranous SFRP4 staining to be associated with good prognosis, while Mortensen et al. 10 reported cytoplasmic expression to be linked to worse prognosis. Overall SFRP4 appears to be a potential biomarker candidate for prostate cancer aggressiveness, and there is a need to validate and clarify the role of SFRP4 in prostate cancer. Reprogramming of metabolism is one of the hallmarks of cancer development 15 . For prostate cancer, the metabolites citrate and spermine have shown promise as biomarkers and are found in lower concentrations in aggressive compared to indolent cancers 16,17 . Our NCWP-EMT gene expression signature was associated with reduced concentrations of these metabolites 12 , but the correlation between SFRP4 gene and protein expression levels, and citrate and spermine has not previously been investigated in prostate cancer. Our previously published method for integration of gene expression levels with metabolic data and histopathology of the exact same samples, gives an excellent opportunity to examine this 18 . The overall aim of this study was to investigate and validate SFRP4 gene expression in prostate cancer, and its relation to cancer aggressiveness. The results were validated in eight independent, publically available gene expression prostate cancer cohorts with patient follow-up data. Furthermore, SFRP4 protein expression was assessed using immunohistochemistry in a separate cohort. Our approach of including several independent patient cohorts gave increased statistical power, and improved the accuracy and generalisation of the results.
doi:10.1038/s41598-017-14622-3 pmid:29079735 pmcid:PMC5660209 fatcat:lhu3mwulsffylheg44htcdmbtq

Petroleum geoscience in Norden – exploration, production and organization

Anthony M. Spencer, Per Ivar Briskeby, Lone Dyrmose Christensen, Rune Foyn, Marie Kjølleberg, Erling Kvadsheim, Ian Knight, Morten Rye-Larsen, John Williams
2008 Episodes  
doi:10.18814/epiiugs/2008/v31i1/016 fatcat:kmd2bi4zanbzpmjwce72j7f3ru

Carriers in mesenchymal stem cell osteoblast mineralization—State-of-the-art

Morten Dahl, Niklas Rye Jørgensen, Mette Hørberg, Else Marie Pinholt
2014 Journal of Cranio-Maxillofacial Surgery  
Purpose: Tissue engineering is a new way to regenerate bone tissue, where osteogenic capable cells combine with an appropriate scaffolding material. Our aim was in a Medline Search to evaluate osteoblast mineralization in vitro and in vivo including gene expressing combining mesenchymal stem cells (MSCs) and five different carriers, titanium, collagen, calcium carbonate, calcium phosphate and polylactic acid-polyglycolic acid copolymer for purpose of a metador a descriptive analysis. Materials
more » ... nd methods: The search included the following MeSH words in different combinationsdmesenchymal stem cells, alkaline phosphatase, bone regeneration, tissue engineering, drug carriers, tissue scaffolds, titanium, collagen, calcium carbonate, calcium phosphates and polylactic acid-polyglycolic acid copolymer. Results: Two out of 80 articles included numerical values and as control, carriers and cells, on mineralization and gene expression. b-tricalcium phosphate (b-TCP) revealed elevated alkaline phosphatase activity, and calcium-deficient hydroxyapatite a greater gene expression of osteocalcin when seeded with induced MSCs. Conclusion: No data are published on titanium used as a carrier in MSC osteoblast mineralization. A metaas well as a descriptive analysis includes numerical values of test materials and of control reactions from carrier and cells, respectively. Only two articles fulfilled these requirements. Ó
doi:10.1016/j.jcms.2013.01.047 pmid:23497988 fatcat:stitve4u3naodo7ycvh3tbjnbe

Cholesterol Synthesis Pathway Genes in Prostate Cancer are consistently downregulated when tissue confounding is minimized [article]

Morten B Rye, Helena Bertilsson, Maria K Andersen, Kjersti Rise, Tone F Bathen, Finn Drablos, May-Britt Tessem
2017 bioRxiv   pre-print
The relationship between cholesterol and prostate cancer has been extensively studied for decades, where high levels of cellular cholesterol are generally associated with cancer progression and less favorable outcomes. However, the role of in vivo cellular cholesterol synthesis in this process is unclear, and data on the transcriptional activity of cholesterol synthesis pathway genes in tissue from prostate cancer patients are inconsistent. A common problem with cancer tissue data from patient
more » ... ohorts is the presence of heterogeneous tissue which confounds molecular analysis of the samples. In this study we present a method to minimize systematic confounding from stroma tissue in seven patient cohorts consisting of 1713 prostate cancer and 230 normal tissue samples. When confounding was minimized, differential gene expression analysis over all cohorts showed robust and consistent downregulation of nearly all genes in the cholesterol synthesis pathway. Additional analysis also identified cholesterol synthesis as the most significantly altered metabolic pathway in prostate cancer. This surprising observation is important for our understanding of how prostate cancer cells regulate cholesterol levels in vivo. Moreover, we show that tissue heterogeneity explains the lack of consistency in previous expression analysis of cholesterol synthesis genes in prostate cancer.
doi:10.1101/220400 fatcat:d2sqykaqnjfh5fdw4d4i2pzvgu

DNA methylation data by sequencing: experimental approaches and recommendations for tools and pipelines for data analysis

Ieva Rauluseviciute, Finn Drabløs, Morten Beck Rye
2019 Clinical Epigenetics  
Sequencing technologies have changed not only our approaches to classical genetics, but also the field of epigenetics. Specific methods allow scientists to identify novel genome-wide epigenetic patterns of DNA methylation down to single-nucleotide resolution. DNA methylation is the most researched epigenetic mark involved in various processes in the human cell, including gene regulation and development of diseases, such as cancer. Increasing numbers of DNA methylation sequencing datasets from
more » ... man genome are produced using various platforms-from methylated DNA precipitation to the whole genome bisulfite sequencing. Many of those datasets are fully accessible for repeated analyses. Sequencing experiments have become routine in laboratories around the world, while analysis of outcoming data is still a challenge among the majority of scientists, since in many cases it requires advanced computational skills. Even though various tools are being created and published, guidelines for their selection are often not clear, especially to non-bioinformaticians with limited experience in computational analyses. Separate tools are often used for individual steps in the analysis, and these can be challenging to manage and integrate. However, in some instances, tools are combined into pipelines that are capable to complete all the essential steps to achieve the result. In the case of DNA methylation sequencing analysis, the goal of such pipeline is to map sequencing reads, calculate methylation levels, and distinguish differentially methylated positions and/or regions. The objective of this review is to describe basic principles and steps in the analysis of DNA methylation sequencing data that in particular have been used for mammalian genomes, and more importantly to present and discuss the most pronounced computational pipelines that can be used to analyze such data. We aim to provide a good starting point for scientists with limited experience in computational analyses of DNA methylation and hydroxymethylation data, and recommend a few tools that are powerful, but still easy enough to use for their own data analysis.
doi:10.1186/s13148-019-0795-x pmid:31831061 pmcid:PMC6909609 fatcat:khl5mza45nfqhnlx7ozu7qguua

Gastrointestinal Events with Clopidogrel: A Nationwide Population-Based Cohort Study

Erik Lerkevang Grove, Morten Würtz, Peter Schwarz, Niklas Rye Jørgensen, Peter Vestergaard
2012 Journal of general internal medicine  
BACKGROUND: Clopidogrel prevents cardiovascular events, but has been linked with adverse gastrointestinal (GI) complications, particularly bleeding events. OBJECTIVE: We aimed to investigate the risk of adverse GI events in patients treated with clopidogrel. DESIGN: A nationwide population-based cohort study based on linkage of three administrative registries in Denmark. PARTICIPANTS: All individuals who redeemed at least one prescription of clopidogrel from 1996 to 2008 were included as
more » ... subjects (n = 77,503). For each exposed subject, three matched controls were randomly selected from the background population (n = 232,510). ANALYSES: Follow-up began on January 1, 1996, and was censored on December 31, 2007, or if patients emigrated or died. The study endpoint was the occurrence of any gastritis, GI ulcer or bleeding. Analyses were adjusted for comorbidity and medication. RESULTS: Regardless of dose, adjusted odds ratios associating clopidogrel use with the study endpoint were statistically significant and followed a doseresponse pattern. The crude absolute risk of GI events were: never users: 2.2 %; <0.1 defined daily dose (DDD) of clopidogrel per day: 7.1 %; 0.1-0.39 DDD: 6.0 %; 0.4-0.79 DDD: 5.7 %; ≥0.80 DDD: 4.4 %. Adjusted odds ratios were: <0.1 DDD: 1.34, 95 % CI: 1.26-1.42; 0.1-0.39 DDD: 1.58, 95 % CI: 1.48-1.68; 0.4-0.79 DDD: 1.91, 95 % CI: 1.77-2.06; ≥0.80 DDD: 1.77, 95 % CI: 1.66-1.89, all p-values<0.01. Depending on the dose, numbers needed to harm ranged from 58 to 33 patients receiving 12 months of clopidogrel treatment. CONCLUSIONS: The well-known cardioprotective effect of clopidogrel must be carefully weighed against an increased risk of GI events.
doi:10.1007/s11606-012-2208-0 pmid:22948933 pmcid:PMC3614150 fatcat:emjo7h6ykjacflqlb56u5bhvie

A manually curated ChIP-seq benchmark demonstrates room for improvement in current peak-finder programs

Morten Beck Rye, Pål Sætrom, Finn Drabløs
2010 Nucleic Acids Research  
Chromatin immunoprecipitation (ChIP) followed by high throughput sequencing (ChIP-seq) is rapidly becoming the method of choice for discovering cell-specific transcription factor binding locations genome wide. By aligning sequenced tags to the genome, binding locations appear as peaks in the tag profile. Several programs have been designed to identify such peaks, but program evaluation has been difficult due to the lack of benchmark data sets. We have created benchmark data sets for three
more » ... ription factors by manually evaluating a selection of potential binding regions that cover typical variation in peak size and appearance. Performance of five programs on this benchmark showed, first, that external control or background data was essential to limit the number of false positive peaks from the programs. However, >80% of these peaks could be manually filtered out by visual inspection alone, without using additional background data, showing that peak shape information is not fully exploited in the evaluated programs. Second, none of the programs returned peakregions that corresponded to the actual resolution in ChIP-seq data. Our results showed that ChIP-seq peaks should be narrowed down to 100-400 bp, which is sufficient to identify unique peaks and binding sites. Based on these results, we propose a meta-approach that gives improved peak definitions.
doi:10.1093/nar/gkq1187 pmid:21113027 pmcid:PMC3045577 fatcat:uac4dpuu6neophypemq4uqjqti
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