Leveraging large-scale omics data for pre-clinical studies of disorders  

We make your data speak [16S rRNA sequencing, bacterial, next-gen sequencing (NGS), single cell RNA sequencing (scRNAs)] using our proven methodology used for one of our pharmaceutical industry clients and in pre-clinical research.

  • Pre-processing : performing data cleanup, outlier identification, principal component analysis [PCA] 

  • Differential expression : leveraging toolboxes such as DESeq2, EdgeR (NGS), SEURAT for scRNAs

  • Gene clustering : using WGCNA, autocorrelation between identified clusters

  • Gene ontology mapping : encompassing biological processes, Kegg & Reactome pathways, TRRUST, metascape, string-db

  • Recommendations : throughout the project, we provide you with insights and recommendations


Understanding molecular mechanisms to help designing a putative vaccine strategy for COVID-19

We have experience in protein structural analysis, particularly in the light of SARS CoV2 pandemic (COVID-19). We can help you predict the next vaccine target or the next promising drug candidate using :

  • Protein structure modeling

  • Protein-protein interactions and interaction partners

  • Structure-function relationship

  • Drug interaction predictions

  • Mutational studies

  • Molecular dynamics simulations



Pipeline design for imaging time-series analysis

Our team has a demonstrated experience in large scale imaging data (2-photon microcopy and micro-endoscope). Our pipeline includes treatment of imaging data along the following axes:

  • Noise reduction using principal component analysis (PCA) based pixel reconstruction.

  • Motion correction of imaging data

  • Defining regions of interest (ROIs)

  • Time series analysis involving hierarchical agglomerative Clustering on the data.

  • Future possibilities: Inclusion of Machine Learning and Natural Language processing (NLP) algorithms for predictions using time series data.

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