CLARITY GENOMICS

HOST-MICROBIOME INTERACTIONS ANALYSIS FOR MICROBIOME-BASED THERAPEUTICS DEVELOPMENT

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What We Do

LONGITUDINAL MICROBIOME AND METABOLOME DATA ANALYSIS AND VARIATION IN HOST CLINICAL RESPONSE

MECHANISMS OF HOST-MICROBIOME INTERACTIONS IN MODULATION OF DRUG METABOLISM AND RESPONSE

MICROBIOME AND METABOLOME MACHINE LEARNING MODELS ON DISEASE BIOMARKER DISCOVERY COHORTS

Longitudinal microbiome-metabolome data analysis in clinical research 

How We Do It

Identification of molecules or species predictive of response

Statistical analysis to identify microbes, metabolites or proteins predictive of metadata responses (such as disease phenotype, sample time collection, protein measurements in blood). Microbe data can be in form of 16S, shotgun metagenomics, RNA-Seq or microarray.

2D/3D molecular and microbial mapping

2D/3D molecular or microbiome cartography to view spatial distribution of molecules or microbes on the sample surface.

Untargeted metabolomics for novel molecule biomarker leads

Raw tandem MS/MS spectra from untargeted liquid or gas chromatography (LC/GC) mass spectrometry are converted into analyte feature tables and molecular annotations are explored beyond simple library matching using molecular networking and in silico screening.

Machine learning on control and disease cohorts

Predictive analytics models using machine learning and covariate analysis on public and private cohorts for specific disease types.

Microbiome-metabolome or proteome correlation analysis

Paired samples are analyzed to identify significant correlations between microbes, metabolites or proteins, yielding insight into microbial-derived metabolites and their metabolic pathways. Microbe data can be in form of 16S, shotgun metagenomics, RNA-Seq or microarray.

Small molecule profiling of microbial cultures

Small molecule profiling of microbial cultures via mass spectrometry for metabolite provenance identification in molecular networking.

 

Who We Are

Bringing together worldwide experts in mass spectrometry and next-generation sequencing, on the cloud, to elucidate how human microbiota communicate with their host and provide valuable insights for clinical research.

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Evguenia Kopylova, PhD

Managing Director

Creating advanced analytical algorithms to accelerate the development of microbiome-based therapeutics.

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Alexey Melnik

Metabolomics Lead

Identifying potential therapeutics within complex microbial communities using modern mass spectrometry methods and advanced computational approaches.

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Josh Daulton

Marketing & Analytics Lead

Accelerating the adoption of statistical programming to automate marketing processes and optimize the performance of sales enablement initiatives.

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Rayan Chikhi, PhD

Genome Assembly Lead

Developing big data computation algorithms and optimizing tools for analysis of DNA sequencing data.

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Zhenjiang Zech Xu, PhD

NGS Analysis Adviser

Utilizing machine learning techniques for predictive modeling and biomarker identification for metatranscriptome and metagenome sequencing data.

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Ajay Kshatriya

Business Adviser

Pioneering genomics in new markets as founder/CEO. Formerly a VC at XSeed Capital and senior manager at Genentech.

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Alexander Aksenov, PhD

Metabolomics Adviser

Researching bioanalytical chemistry with a focus on biomarker discovery and chemical aspects of biological processes.

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Nicola Segata, PhD

NGS Analysis Adviser

Employing experimental meta'omic tools and novel computational approaches to study the diversity of the microbiome across conditions and populations.

Clarity Genomics is honored to collaborate with world-leading experts in microbiome analysis.

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Aksenov Lab
University of Connecticut

Featured Clients

Our clients include pharmaceutical (top 5), consumer products (top 30), biotech companies and universities developing microbiome-based therapeutics.