Other data

Statistical bioinformatics methods can be applied to any biological data, not just sequencing. In addition to individual data sources, integration of multiple data types allows access to knowledge that would be impossible to gather from any one type of data alone. Further, our data management solutions enable secure storage, flexible databases as well as facilitate easy interpretation and data sharing using interactive web-based visualization methods.

We are a team of bioinformatics engineers – our training with statistical techniques enables analyzing all quantifiable biological data!

Tuomas Tikkanen
Tuomas TikkanenData ScientistGenevia Technologies Oy
  • Proteomics
    • Quantitation of proteins using parallel mass spectrometers is becoming more and more popular method of studying gene expression within cells and excretion of proteins to extracellular space. We can run statistical analysis on proteomics data to find differentially expressed or excreted proteins, for example.

  • Microarrays
    • Large number of biological samples have been characterized using gene expression, gene copy number, and SNP arrays, and new samples are still being studied. We have an extensive experience in analyzing all microarray types, and we are happy to help you with re-analysis of your old data, or analyzing new arrays. Generally from microarray studies, we deliver comparable results to all applicable analyses listed for genomics, transcriptomics, and epigenomics.

  • Chemometrics
    • Extraction of information from chemical or physiological systems results in biochemical signals. We apply signal processing and multivariate statistical methods to transform these signals into meaningful biological quantities. Multiple signals can be integrated and the wealth of data may be used as an input to correlative methods to understand their clinical relevance, or to machine learning methods to build predictive systems.

  • Medical images
    • High-throughput imaging assays, for example, accumulate large quantities of data that are laborious to analyze by hand. We can apply image processing algorithms to identify relevant features from image data and convert them to easily analyzable quantities for further statistical analysis.

  • Text mining
    • Informatics challenges in biology are not always limited to quantitative molecular data, and understanding textual data can be a relevant problem as well. For example, integrating, classifying, storing and visualizing sample meta data from extensive collections, such as biobanks, is a prerequisite for their use. We can apply informatics approaches to organize such data into coherent databases and visualize the contents. After proper organization is made, we can run association and machine learning algorithms to make unexpected discoveries from your archives.

  • Biomarker detection
    • Indicative features, genetic or other types, can be found by statistically comparing samples of interest with a control group. We can use genomic, transcriptomic and epigenomic data with metadata to find a biomarker or a combination of biomarkers that can be used to classify future samples into relevant categories, such as patients likely responding to a treatment versus non-responders.

  • Genomic signatures
    • Panels of classifying genetic features that differentiate between known groups or unexpected subgroups (such as cancer subgroups) can be found using statistical methods. We can analyze these features further, using pathway analysis, for example, to understand the implications of molecular differences.

  • Regulatory network inference
    • To model the gene regulatory network pertinent to a pathological or normal condition, we may infer the regulatory interactions from a time-series RNA-sequencing experiment. Transcription factor binding site prediction or ChIP experiments can be used to support the modeling task. Furthermore, the model can be constructed to enable simulations shedding light on the network functionality.

  • Modeling
    • Insight into some biological systems can be acquired by applying mathematical modeling schemes, such as differential equation models for cellular signaling pathways. We will suggest you a proper modeling strategy if we think it will help you to gain relevant information in your research question.

  • Clinical nomogram
    • Accumulated patient data enables creating nomograms which can be used to assess disease prognosis or risks for clinical operations. For example, a nomogram could be used to estimate the risk of nodal metastasis of a tumor. The prediction is based on clinical and/or biological parameters, such as patient age, data on symptoms and treatment, genetic variants, gene expression levels, or other biomarkers. Depending on the number of parameters used, the nomogram can be implemented as a simple mathematical formula, MS Excel™ spreadsheet or an interactive online tool to promote easy and widespread usage.

Interested? Schedule a free Skype meeting with our expert!

Placeholder for short description

See our other analyses: