| 1. |
- Gao, Jiangning, et al.
(författare)
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ACES : a machine learning toolbox for clustering analysis and visualization
- 2018
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Ingår i: BMC Genomics. - : BMC. - 1471-2164. ; 19
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Tidskriftsartikel (refereegranskat)abstract
- Background: Studies that aim at explaining phenotypes or disease susceptibility by genetic or epigenetic variants often rely on clustering methods to stratify individuals or samples. While statistical associations may point at increased risk for certain parts of the population, the ultimate goal is to make precise predictions for each individual. This necessitates tools that allow for the rapid inspection of each data point, in particular to find explanations for outliers.Results: ACES is an integrative cluster- and phenotype-browser, which implements standard clustering methods, as well as multiple visualization methods in which all sample information can be displayed quickly. In addition, ACES can automatically mine a list of phenotypes for cluster enrichment, whereby the number of clusters and their boundaries are estimated by a novel method. For visual data browsing, ACES provides a 2D or 3D PCA or Heat Map view. ACES is implemented in Java, with a focus on a user-friendly, interactive, graphical interface.Conclusions: ACES has been proven an invaluable tool for analyzing large, pre-filtered DNA methylation data sets and RNA-Sequencing data, due to its ease to link molecular markers to complex phenotypes. The source code is available from https://github.com/GrabherrGroup/ACES.
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| 2. |
- Höppner, Marc P., et al.
(författare)
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An Improved Canine Genome and a Comprehensive Catalogue of Coding Genes and Non-Coding Transcripts
- 2014
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 9:3, s. e91172-
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Tidskriftsartikel (refereegranskat)abstract
- The domestic dog, Canis familiaris, is a well-established model system for mapping trait and disease loci. While the original draft sequence was of good quality, gaps were abundant particularly in promoter regions of the genome, negatively impacting the annotation and study of candidate genes. Here, we present an improved genome build, canFam3.1, which includes 85 MB of novel sequence and now covers 99.8% of the euchromatic portion of the genome. We also present multiple RNA-Sequencing data sets from 10 different canine tissues to catalog similar to 175,000 expressed loci. While about 90% of the coding genes previously annotated by EnsEMBL have measurable expression in at least one sample, the number of transcript isoforms detected by our data expands the EnsEMBL annotations by a factor of four. Syntenic comparison with the human genome revealed an additional similar to 3,000 loci that are characterized as protein coding in human and were also expressed in the dog, suggesting that those were previously not annotated in the EnsEMBL canine gene set. In addition to,20,700 high-confidence protein coding loci, we found,4,600 antisense transcripts overlapping exons of protein coding genes, similar to 7,200 intergenic multi-exon transcripts without coding potential, likely candidates for long intergenic non-coding RNAs (lincRNAs) and,11,000 transcripts were reported by two different library construction methods but did not fit any of the above categories. Of the lincRNAs, about 6,000 have no annotated orthologs in human or mouse. Functional analysis of two novel transcripts with shRNA in a mouse kidney cell line altered cell morphology and motility. All in all, we provide a much-improved annotation of the canine genome and suggest regulatory functions for several of the novel non-coding transcripts.
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| 3. |
- Moghadam, Behrooz Torabi, et al.
(författare)
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Analyzing DNA methylation patterns in subjects diagnosed with schizophrenia using machine learning methods
- 2019
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Ingår i: Journal of Psychiatric Research. - : PERGAMON-ELSEVIER SCIENCE LTD. - 0022-3956 .- 1879-1379. ; 114, s. 41-47
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Tidskriftsartikel (refereegranskat)abstract
- Schizophrenia is a common mental disorder with high heritability. It is genetically complex and to date more than a hundred risk loci have been identified. Association of environmental factors and schizophrenia has also been reported, while epigenetic analyses have yielded ambiguous and sometimes conflicting results. Here, we analyzed fresh frozen post-mortem brain tissue from a cohort of 73 subjects diagnosed with schizophrenia and 52 control samples, using the Illumina Infinium HumanMethylation450 Bead Chip, to investigate genome-wide DNA methylation patterns in the two groups. Analysis of differential methylation was performed with the Bioconductor Minfi package and modern machine-learning and visualization techniques, which were shown previously to be successful in detecting and highlighting differentially methylated patterns in case-control studies. In this dataset, however, these methods did not uncover any significant signals discerning the patient group and healthy controls, suggesting that if there are methylation changes associated with schizophrenia, they are heterogeneous and complex with small effect.
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| 4. |
- Torabi Moghadam, Behrooz, et al.
(författare)
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An unsupervised approach subgroups cancer types by distinct local DNA methylation patterns
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Cancer is one of the most common causes of death in humans. It can arise from many different cell types, and even cancers originating from the same tissue can constitute a heterogeneous group of diseases. While cytogenetics, the analysis of mutations and karyotypic alterations, has greatly improved the accuracy of diagnosis, it is likely that there are more categories in which cancers can be divided than is known today. Moreover, new biomarkers confirming existing classification schemes are desirable. Here, we interrogated the DNA methylation (DNAm) landscape as a novel indicator for discerning cancer subtypes.We developed and applied an unsupervised method, methylSaguaro, which is based on the combination of a Hidden Markov Model and a Neural Net. We first compared the concept of hypothesizing patterns and grouping to statistical methods that require a priori hypotheses to perform enrichment tests. We then analyzed samples from four cancer groups, Gliomas, Chronic Lymphocytic Leukemia (CLL), Renal Cell Carcinomas (RCC), and Acute Myeloid Leukemia (AML). On gliomas and CLL, we confirmed known cancer groupings in DNAm that perfectly correspond to known mutations. On Renal Cell Carcinomas, our method disagrees with the histological classification on 4% of the samples, and finds a novel cluster, suggesting that there might be a novel subtype that was hitherto unknown. On AML, methylSaguaro spreads the samples out on a continuous spectrum, enriching one end with patients assessed as having “poor” risk based on cytogenetics, but indicating that DNAm patterns would suggest a different risk assessment. Since methylSaguaro reports both the patterns and the specific sites behind the signals, we analyzed regions and genes indicative of subtypes across the cancers, revealing 41 genes affected by alterations in more than one cancer. In summary, we expect that DNAm, coupled with a hypothesis-free analysis method, will add to the set of clinical instruments to diagnose, assess, and treat cancer.
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| 5. |
- Torabi Moghadam, Behrooz, et al.
(författare)
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Analyzing DNA methylation patterns in Schizophrenic patients using machine learning methods
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Schizophrenia is common mental disorder with known genetic component involved. Since the association of environmental factors and schizophrenia has been reported, we analyzed a cohort of 75 schizophrenic and 50 control samples to investigate DNA methylation patterns, as one of the key players of epigenetic gene regulation.Here we applied machine-learning and visualization methods, which were shown previously to be successful in detecting and highlighting differentially methylated patterns between cases and controls. On this data set, however, these methods did not uncover any signal discerning schizophrenia patients and healthy controls, suggesting that if a link exists, it is heterogeneous and complex.
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| 6. |
- Torabi Moghadam, Behrooz, et al.
(författare)
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Combinatorial identification of DNA methylation patterns over age in the human brain
- 2016
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Ingår i: BMC Bioinformatics. - : Springer Science and Business Media LLC. - 1471-2105. ; 17
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Tidskriftsartikel (refereegranskat)abstract
- Background: DNA methylation plays a key role in developmental processes, which is reflected in changing methylation patterns at specific CpG sites over the lifetime of an individual. The underlying mechanisms are complex and possibly affect multiple genes or entire pathways. Results: We applied a multivariate approach to identify combinations of CpG sites that undergo modifications when transitioning between developmental stages. Monte Carlo feature selection produced a list of ranked and statistically significant CpG sites, while rule-based models allowed for identifying particular methylation changes in these sites. Our rule-based classifier reports combinations of CpG sites, together with changes in their methylation status in the form of easy-to-read IF-THEN rules, which allows for identification of the genes associated with the underlying sites. Conclusion: We utilized machine learning and statistical methods to discretize decision class (age) values to get a general pattern of methylation changes over the lifespan. The CpG sites present in the significant rules were annotated to genes involved in brain formation, general development, as well as genes linked to cancer and Alzheimer's disease.
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| 7. |
- Torabi Moghadam, Behrooz
(författare)
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Computational discovery of DNA methylation patterns as biomarkers of ageing, cancer, and mental disorders : Algorithms and Tools
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Epigenetics refers to the mitotically heritable modifications in gene expression without a change in the genetic code. A combination of molecular, chemical and environmental factors constituting the epigenome is involved, together with the genome, in setting up the unique functionality of each cell type.DNA methylation is the most studied epigenetic mark in mammals, where a methyl group is added to the cytosine in a cytosine-phosphate-guanine dinucleotides or a CpG site. It has been shown to have a major role in various biological phenomena such as chromosome X inactivation, regulation of gene expression, cell differentiation, genomic imprinting. Furthermore, aberrant patterns of DNA methylation have been observed in various diseases including cancer.In this thesis, we have utilized machine learning methods and developed new methods and tools to analyze DNA methylation patterns as a biomarker of ageing, cancer subtyping and mental disorders.In Paper I, we introduced a pipeline of Monte Carlo Feature Selection and rule-base modeling using ROSETTA in order to identify combinations of CpG sites that classify samples in different age intervals based on the DNA methylation levels. The combination of genes that showed up to be acting together, motivated us to develop an interactive pathway browser, named PiiL, to check the methylation status of multiple genes in a pathway. The tool enhances detecting differential patterns of DNA methylation and/or gene expression by quickly assessing large data sets.In Paper III, we developed a novel unsupervised clustering method, methylSaguaro, for analyzing various types of cancers, to detect cancer subtypes based on their DNA methylation patterns. Using this method we confirmed the previously reported findings that challenge the histological grouping of the patients, and proposed new subtypes based on DNA methylation patterns. In Paper IV, we investigated the DNA methylation patterns in a cohort of schizophrenic and healthy samples, using all the methods that were introduced and developed in the first three papers.
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| 8. |
- Torabi Moghadam, Behrooz, 1982-, et al.
(författare)
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PiiL : visualization of DNA methylation and gene expression data in gene pathways
- 2017
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Ingår i: BMC Genomics. - : Springer Science and Business Media LLC. - 1471-2164. ; 18
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Tidskriftsartikel (refereegranskat)abstract
- Background: DNA methylation is a major mechanism involved in the epigenetic state of a cell. It has been observed that the methylation status of certain CpG sites close to or within a gene can directly affect its expression, either by silencing or, in some cases, up-regulating transcription. However, a vertebrate genome contains millions of CpG sites, all of which are potential targets for methylation, and the specific effects of most sites have not been characterized to date. To study the complex interplay between methylation status, cellular programs, and the resulting phenotypes, we present PiiL, an interactive gene expression pathway browser, facilitating analyses through an integrated view of methylation and expression on multiple levels.Results: PiiL allows for specific hypothesis testing by quickly assessing pathways or gene networks, where the data is projected onto pathways that can be downloaded directly from the online KEGG database. PiiL provides a comprehensive set of analysis features that allow for quick and specific pattern searches. Individual CpG sites and their impact on host gene expression, as well as the impact on other genes present in the regulatory network, can be examined. To exemplify the power of this approach, we analyzed two types of brain tumors, Glioblastoma multiform and lower grade gliomas.Conclusion: At a glance, we could confirm earlier findings that the predominant methylation and expression patterns separate perfectly by mutations in the IDH genes, rather than by histology. We could also infer the IDH mutation status for samples for which the genotype was not known. By applying different filtering methods, we show that a subset of CpG sites exhibits consistent methylation patterns, and that the status of sites affect the expression of key regulator genes, as well as other genes located downstream in the same pathways.PiiL is implemented in Java with focus on a user-friendly graphical interface. The source code is available under the GPL license from https://github.com/behroozt/PiiL.git.
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