Advanced bioinformatics analysis, computation and consulting

Apart from the specific and targeted data analysis services described in the previous sections, HybridStat offers custom-tailored advanced bioinformatics analysis services, based on specific needs of the customer. HybridStat also offers consulting services regarding bioinformatics data analytics and other issues of statistical issues related to biological and life sciences.

Advanced bioinformatics analysis of -omics data

• Clustering of gene/protein/metabolite abundance for the identification of common regulation and presence patterns with a variety of clustering algorithms and provision of help on identifying the best performing one for the client’s data. Use of HybridStat’s heuristics algorithms to identify important variables such as the optimal number of clusters for an expression dataset.
• Classification modeling, supervised and unsupervised learning for the detection of potential signatures characterizing several biological conditions (e.g. healthy vs diseased tissue). Efficient use of popular unsupervised (e.g. k-means clustering) and superior supervised machine learning methodologies (e.g. Random Forests and Support Vector Machines) coupled with feature selection based on information content towards the identification of molecular (gene/protein/miRNA/metabolite) joint signatures able to distinguish between healthy and disease status or between pathologies which are hard to distinguish by macroscopic methods. Sensitivity/specificity and classification accuracy reports for the screening of potential drug targets.
• Analysis of X-Seq data (other than RNA-, ChIP- and DNA/Exome-, for example FAIRE-Seq) requiring more specialized data handling and statistical modeling.
• Computational association of putative binding sites derived from ChIP-Seq experiments with gene expression (absolute RNA abundance or deregulated genes). Use of advanced algorithms for the derivation of association scores of TF profiles with gene expression.
• Scanning for DNA motifs in promoters of genes belonging to similar expression groups, for the identification of common regulatory elements.
• De novo motif discovery in ChIP-Seq data for the motif enrichment in binding sites and the identification of possible co-factors, using a combination of widely verified motif discovery tools. Motif clustering to identify regulatory element consensuses.
• Advanced data visualizations and custom analytics upon discussions about the goals of the client.
• Network visualization of gene and metabolic networks based on public repositories and known protein-protein interactions.
• Inference of chemical formula for metabolites and/or small molecules that could not be matched against any known database in metabolomics experiments.
• Screening of public databases for gene/protein/miRNA/metabolite disease associations
• Custom programming/scripting when existing tools are not sufficient to reach the analysis goals or when the client requires advanced data handling and visualization.

Consulting

As the questions in life sciences, either from focused researchers who study basic mechanisms of biological systems or disease mechanisms in human/animal models or from biotechnology and pharmaceutical companies with focused R&D departments for the discovery of new drug targets and the effective drug design can be endless. HybridStat is dedicated to discussing in details the needs of the client project and offer guidance and custom analysis services and/or software development based on its extensive bioinformatics and bioistatistics expertise. Even if a project’s goals are vague, HybridStat will discuss with the client in an effort to demystify and rationalize the project and design a strategy that has to be followed in terms of statistical designing to make the most out of the anticipated data.