References of "Nykter, Matti"
     in
Bookmark and Share    
Full Text
Peer Reviewed
See detailHemap: An nteractive online resource for characterizing molecular phenotypes across hematologic malignancies
Pölönen, Petri; Mehtonen, Juha; Lin, Jake et al

in Cancer Research (2019)

Large collections of genome-wide data can facilitate the characterization of disease states and subtypes, permitting pan-cancer analysis of molecular phenotypes and evaluation of disease contexts for new ... [more ▼]

Large collections of genome-wide data can facilitate the characterization of disease states and subtypes, permitting pan-cancer analysis of molecular phenotypes and evaluation of disease contexts for new therapeutic approaches. We analyzed 9,544 transcriptomes from over 30 hematologic malignancies, normal blood cell types and cell lines, and show that the disease types can be stratified in a data-driven manner. We utilized the obtained molecular clustering for discovery of cluster-specific pathway activity, new biomarkers and in silico drug target prioritization through integration with drug target databases. Using known vulnerabilities and available drug screens in benchmarking, we highlight the importance of integrating the molecular phenotype context and drug target expression for in silico prediction of drug responsiveness. Our analysis implicates BCL2 expression level as important indicator of venetoclax responsiveness and provides a rationale for its targeting in specific leukemia subtypes and multiple myeloma, links several polycomb group proteins that could be targeted by small molecules (SFMBT1, CBX7 and EZH1) with CLL, and supports CDK6 as disease-specific target in AML. Through integration with proteomics data, we characterized target protein expression for pre-B leukemia immunotherapy candidates, including DPEP1. These molecular data can be explored using our freely available interactive resource, Hemap, for expediting therapeutic innovations in hematologic malignancies. [less ▲]

Detailed reference viewed: 81 (7 UL)
Full Text
Peer Reviewed
See detailPOMO - Plotting Omics analysis results for Multiple Organisms
Lin, Jake UL; Kreisberg, Richard; Kallio, Aleksi et al

in BMC Genomics (2013), 14(918),

Background Systems biology experiments studying different topics and organisms produce thousands of data values across different types of genomic data. Further, data mining analyses are yielding ranked ... [more ▼]

Background Systems biology experiments studying different topics and organisms produce thousands of data values across different types of genomic data. Further, data mining analyses are yielding ranked and heterogeneous results and association networks distributed over the entire genome. The visualization of these results is often difficult and standalone web tools allowing for custom inputs and dynamic filtering are limited. Results We have developed POMO (http://pomo.cs.tut.fi), an interactive web-based application to visually explore omics data analysis results and associations in circular, network and grid views. The circular graph represents the chromosome lengths as perimeter segments, as a reference outer ring, such as cytoband for human. The inner arcs between nodes represent the uploaded network. Further, multiple annotation rings, for example depiction of gene copy number changes, can be uploaded as text files and represented as bar, histogram or heatmap rings. POMO has built-in references for human, mouse, nematode, fly,yeast, zebrafish, rice, tomato, Arabidopsis, and Escherichia coli. In addition, POMO provides custom options that allow integrated plotting of unsupported strains or closely related species associations, such as human and mouse orthologs or two yeast wild types, studied together within a single analysis. The web application also supports interactive label and weight filtering. Every iterative filtered result in POMO can be exported as image file and text file for sharing or direct future input. Conclusions The POMO web application is a unique tool for omics data analysis, which can be used to visualize and filter the genome-wide networks in the context of chromosomal locations as well as multiple network layouts. With the several illustration and filtering options the tool supports the analysis and visualization of any heterogeneous omics data analysis association results for many organisms. POMO is freely available and does not require any installation or registration. [less ▲]

Detailed reference viewed: 123 (5 UL)
Full Text
Peer Reviewed
See detailQuantitative analysis of colony morphology in yeast
Ruusuvuori, Pekka; Lin, Jake UL; Shmulevich, Ilya et al

in BioTechniques (2013)

Microorganisms often form multicellular structures,such as biofilms and structured colonies, which can influence the organism’s virulence, drug resistance, and adherence to medical devices. Phenotypic ... [more ▼]

Microorganisms often form multicellular structures,such as biofilms and structured colonies, which can influence the organism’s virulence, drug resistance, and adherence to medical devices. Phenotypic classification of these structures has traditionally relied on qualitative scoring systems that limit detailed phenotypic comparisons between strains. Automated imaging and quantitative analysis have the potential to improve the speed and accuracy of experiments designed to study the genetic and molecular networks underlying different morphological traits. We have developeda platform that uses automated image analysis and pattern recognition to quantify phenotypic signatures of yeast colonies. The strategy enables quantitative analysis of individual colonies, measured at a single time point or over a series of time-lapse images, as well as the classification of distinct colony shapes based on image-derived features. Phenotypic changes in colonymorphology can be expressed achanges in feature space trajectories over time, thereby enabling the visualization and quantitative analysis of morphological development. To facilitate data exploration, results are plotted dynamically through an interactive web application that integrates the raw and processed images across all time points, allowing exploration of the image-based features and principal components associated with morphological development. The web application YIMAA is available at http://yimaa.cs.tut.fi. [less ▲]

Detailed reference viewed: 93 (3 UL)
Full Text
Peer Reviewed
See detailGene-pair expression signatures reveal lineage control
Heinäniemi, Merja UL; Nykter, Matti; Kramer, Roger et al

in Nature Methods (2013)

The distinct cell types of multicellular organisms arise due to constraints imposed by gene regulatory networks on the collective change of gene expression across the genome, creating self-stabilizing ... [more ▼]

The distinct cell types of multicellular organisms arise due to constraints imposed by gene regulatory networks on the collective change of gene expression across the genome, creating self-stabilizing expression states, or attractors. We compiled a resource of curated human expression data comprising 166 cell types and 2,602 transcription regulating genes and developed a data driven method built around the concept of expression reversal defined at the level of gene pairs, such as those participating in toggle switch circuits. This approach allows us to organize the cell types into their ontogenetic lineage-relationships and to reflect regulatory relationships among genes that explain their ability to function as determinants of cell fate. We show that this method identifies genes belonging to regulatory circuits that control neuronal fate, pluripotency and blood cell differentiation, thus offering a novel large-scale perspective on lineage specification. [less ▲]

Detailed reference viewed: 177 (28 UL)