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- Diamanti, Klev, et al.
(author)
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Maps of context-dependent putative regulatory regions and genomic signal interactions
- 2016
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In: Nucleic Acids Research. - : Oxford University Press (OUP). - 0305-1048 .- 1362-4962. ; 44:19, s. 9110-9120
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Journal article (peer-reviewed)abstract
- Gene transcription is regulated mainly by transcription factors (TFs). ENCODE and Roadmap Epigenomics provide global binding profiles of TFs, which can be used to identify regulatory regions. To this end we implemented a method to systematically construct cell-type and species-specific maps of regulatory regions and TF-TF interactions. We illustrated the approach by developing maps for five human cell-lines and two other species. We detected similar to 144k putative regulatory regions among the human cell-lines, with the majority of them being similar to 300 bp. We found similar to 20k putative regulatory elements in the ENCODE heterochromatic domains suggesting a large regulatory potential in the regions presumed transcriptionally silent. Among the most significant TF interactions identified in the heterochromatic regions were CTCF and the cohesin complex, which is in agreement with previous reports. Finally, we investigated the enrichment of the obtained putative regulatory regions in the 3D chromatin domains. More than 90% of the regions were discovered in the 3D contacting domains. We found a significant enrichment of GWAS SNPs in the putative regulatory regions. These significant enrichments provide evidence that the regulatory regions play a crucial role in the genomic structural stability. Additionally, we generated maps of putative regulatory regions for prostate and colorectal cancer human cell-lines.
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| 2. |
- Kruczyk, Marcin, et al.
(author)
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Integration of genome-wide of Stat3 binding and epigenetic modification mapping with transcriptome reveals novel Stat3 target genes in glioma cells
- 2014
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In: Biochimica et Biophysica Acta. - : Elsevier BV. - 0006-3002 .- 1878-2434. ; 1839:11, s. 1341-1350
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Journal article (peer-reviewed)abstract
- BACKGROUND: Signal transducer and activator of transcription 3 (STAT3) is constitutively activated in many human tumors, including gliomas, and regulates the expression of genes implicated in proliferation, survival, apoptosis, angiogenesis and immune regulation. Only a small fraction of those genes has been proven to be direct STAT3 targets. In gliomas, STAT3 can play tumor suppressive or oncogenic roles depending on the tumor genetic background with target genes being largely unknown.RESULTS: We used chromatin immunoprecipitation, promoter microarrays and deep sequencing to assess the genome-wide occupancy of phospho (p)-Stat3 and epigenetic modifications of H3K4me3 and H3ac in C6 glioma cells. This combined assessment identified a list of 1200 genes whose promoters have both Stat3 binding sites and epigenetic marks characteristic for actively transcribed genes. The Stat3 and histone markings data were also intersected with a set of microarray data from C6 glioma cells after inhibition of Jak2/Stat3 signaling. Subsequently, we found 284 genes characterized by p-Stat3 occupancy, activating histone marks and transcriptional changes. Novel genes were screened for their potential involvement in oncogenesis, and the most interesting hits were verified by ChIP-PCR and STAT3 knockdown in human glioma cells.CONCLUSIONS: Non-random association between silent genes, histone marks and p-Stat3 binding near transcription start sites was observed, consistent with its repressive role in transcriptional regulation of target genes in glioma cells with specific genetic background.
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| 3. |
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| 4. |
- Kruczyk, Marcin, et al.
(author)
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Monte Carlo feature selection and rule-based models to predict Alzheimer's disease in mild cognitive impairment.
- 2012
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In: Journal of neural transmission (Vienna, Austria : 1996). - : Springer Science and Business Media LLC. - 1435-1463 .- 0300-9564. ; 119:7, s. 821-31
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Journal article (peer-reviewed)abstract
- The objective of the present study was to evaluate a Monte Carlo feature selection (MCFS) and rough set Rosetta pipeline for generating rule-based models as a tool for comprehensive risk estimates for future Alzheimer's disease (AD) in individual patients with mild cognitive impairment (MCI). Risk estimates were generated on the basis of age, gender, Mini-Mental State Examination scores, apolipoprotein E (APOE) genotype and the cerebrospinal fluid (CSF) biomarkers total tau (T-tau), phospho-tau(181) (P-tau) and the 42 amino acid form of amyloid β (Aβ42) in two sets of longitudinally followed MCI patients (n = 217 in total). The predictive model was created in Rosetta, evaluated with the standard tenfold cross-validation approach and tested on an external set. Features were ranked and selected by the MCFS algorithm. Using the combined pipeline of MCFS and Rosetta, it was possible to predict AD among patients with MCI with an area under the receiver operating characteristics curve of 0.92. Risk estimates were produced for the individual patients and showed good correlation with actual diagnosis in cross validation, and on an external dataset from a new study. Analysis of the importance of attributes showed that the biochemical CSF markers contributed the most to the predictions, and that added value was gained by combining several biochemical markers. Despite a correlation with the biochemical markers, the genetic marker APOE ε4 did not contribute to the predictive power of the model.
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| 7. |
- Kruczyk, Marcin, et al.
(author)
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Peak Finder Metaserver - a novel application for finding peaks in ChIP-seq data
- 2013
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In: BMC Bioinformatics. - : Springer Science and Business Media LLC. - 1471-2105. ; 14, s. 280-
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Journal article (peer-reviewed)abstract
- Background: Finding peaks in ChIP-seq is an important process in biological inference. In some cases, such as positioning nucleosomes with specific histone modifications or finding transcription factor binding specificities, the precision of the detected peak plays a significant role. There are several applications for finding peaks (called peak finders) based on different algorithms (e.g. MACS, Erange and HPeak). Benchmark studies have shown that the existing peak finders identify different peaks for the same dataset and it is not known which one is the most accurate. We present the first meta-server called Peak Finder MetaServer (PFMS) that collects results from several peak finders and produces consensus peaks. Our application accepts three standard ChIP-seq data formats: BED, BAM, and SAM. Results: Sensitivity and specificity of seven widely used peak finders were examined. For the experiments we used three previously studied Transcription Factors (TF) ChIP-seq datasets and identified three of the selected peak finders that returned results with high specificity and very good sensitivity compared to the remaining four. We also ran PFMS using the three selected peak finders on the same TF datasets and achieved higher specificity and sensitivity than the peak finders individually. Conclusions: We show that combining outputs from up to seven peak finders yields better results than individual peak finders. In addition, three of the seven peak finders outperform the remaining four, and running PFMS with these three returns even more accurate results. Another added value of PFMS is a separate report of the peaks returned by each of the included peak finders.
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| 8. |
- Kruczyk, Marcin, et al.
(author)
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Random Reducts : A Monte Carlo Rough Set-based Method for Feature Selection in Large Datasets
- 2013
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In: Fundamenta Informaticae. - 0169-2968 .- 1875-8681. ; 127:1-4, s. 273-288
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Journal article (peer-reviewed)abstract
- An important step prior to constructing a classifier for a very large data set is feature selection. With many problems it is possible to find a subset of attributes that have the same discriminative power as the full data set. There are many feature selection methods but in none of them are Rough Set models tied up with statistical argumentation. Moreover, known methods of feature selection usually discard shadowed features, i.e. those carrying the same or partially the same information as the selected features. In this study we present Random Reducts (RR) - a feature selection method which precedes classification per se. The method is based on the Monte Carlo Feature Selection (MCFS) layout and uses Rough Set Theory in the feature selection process. On synthetic data, we demonstrate that the method is able to select otherwise shadowed features of which the user should be made aware, and to find interactions in the data set.
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| 9. |
- Kruczyk, Marcin
(author)
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Rule-Based Approaches for Large Biological Datasets Analysis : A Suite of Tools and Methods
- 2013
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Doctoral thesis (other academic/artistic)abstract
- This thesis is about new and improved computational methods to analyze complex biological data produced by advanced biotechnologies. Such data is not only very large but it also is characterized by very high numbers of features. Addressing these needs, we developed a set of methods and tools that are suitable to analyze large sets of data, including next generation sequencing data, and built transparent models that may be interpreted by researchers not necessarily expert in computing. We focused on brain related diseases.The first aim of the thesis was to employ the meta-server approach to finding peaks in ChIP-seq data. Taking existing peak finders we created an algorithm that produces consensus results better than any single peak finder.The second aim was to use supervised machine learning to identify features that are significant in predictive diagnosis of Alzheimer disease in patients with mild cognitive impairment. This experience led to a development of a better feature selection method for rough sets, a machine learning method. The third aim was to deepen the understanding of the role that STAT3 transcription factor plays in gliomas. Interestingly, we found that STAT3 in addition to being an activator is also a repressor in certain glioma rat and human models. This was achieved by analyzing STAT3 binding sites in combination with epigenetic marks. STAT3 regulation was determined using expression data of untreated cells and cells after JAK2/STAT3 inhibition.The four papers constituting the thesis are preceded by an exposition of the biological, biotechnological and computational background that provides foundations for the papers.The overall results of this thesis are witness of the mutually beneficial relationship played by Bioinformatics in modern Life Sciences and Computer Science.
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| 10. |
- Umer, Husen M., et al.
(author)
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A Significant Regulatory Mutation Burden at a High-Affinity Position of the CTCF Motif in Gastrointestinal Cancers
- 2016
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In: Human Mutation. - : Hindawi Limited. - 1059-7794 .- 1098-1004. ; 37:9, s. 904-913
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Journal article (peer-reviewed)abstract
- Somatic mutations drive cancer and there are established ways to study those in coding sequences. It has been shown that some regulatory mutations are over-represented in cancer. We develop a new strategy to find putative regulatory mutations based on experimentally established motifs for transcription factors (TFs). In total, we find 1,552 candidate regulatory mutations predicted to significantly reduce binding affinity of many TFs in hepatocellular carcinoma and affecting binding of CTCF also in esophagus, gastric, and pancreatic cancers. Near mutated motifs, there is a significant enrichment of (1) genes mutated in cancer, (2) tumor-suppressor genes, (3) genes in KEGG cancer pathways, and (4) sets of genes previously associated to cancer. Experimental and functional validations support the findings. The strategy can be applied to identify regulatory mutations in any cell type with established TF motifs and will aid identifications of genes contributing to cancer.
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