OMICSCRAFT LLC — Department of Health and Human Services SBIR Phase I: 400

Description

PROJECT SUMMARY In a typical untargeted metabolomic analysis by liquid chromatography mass spectrometryLC MSaboutof the detected ions represent unknown analytesWhile identification of the unknowns without putative IDs remains a significant challengewe have the opportunity to identify more metabolites by improving the ability to prioritize multiple putative IDs assigned to the known unknownsThis will be tremendously helpful in selecting promising metabolites for the subsequent experimental verification of the IDsIn this SBIR proposalwe seek to develop a probabilistic framework that assigns a priority score to each putative metabolite ID by combining information from multiple resources including compound databasespathwaysbiochemical networksand spectral librariesIn additionthe probabilistic model will use results from various computational tools to improve the annotation accuracyFor examplea tool that clusters the detected ions based on their measured mass and retention timeRTvalues will be used to recognize isotopes and adductsThis step will enable the user to determine the monoisotopic mass of the ions prior to searching for putative IDs against mass based databasesthereby reducing potential annotation errors caused by mass change due to isotopes and adductsPutative IDs derived from multiple databases will be merged based on the IUPAC International Chemical IdentifierInChIkeysThe proposed probabilistic model will exploit the inter dependent relationships between metabolites in biological organisms based on knowledge derived from pathways and biochemical networks to assign priority score to each putative metabolite IDsIf MS MS data are availablethe score for a putative ID will take into account how well the measured MS MS matches against those in spectral libraries or fragment patterns predicted by in silico spectral interpretationWe will assemble the algorithms and scripts developed in this project into a browser friendly cloud based toolMetaboCraftWe will use Java scripts to implement MetaboCraft s graphical user interfaceGUIwhich will allow users to import m zRTand MS MS data and to export prioritized putative IDs in their desired formatFurthermorethe GUI will provide users with interactive visualization of putative IDsextracted ion chromatogramsisotopic patternsand MS MS dataThe performance of MetaboCraft in metabolite annotation and its computational efficiency will be compared against other existing tools based on LC MS MS data from metabolomic studies that consist of ground truth informationSuccessful implementation and validation of MetaboCraft will enable users to accurately identify putative metabolite IDs and assign priority scores by taking advantage of publicly available databasespathwaysand biochemical networksspectral librariesas well as various tools designed for isotope adduct recognitiondecomposition of isotopic patternsand in silico spectral interpretationBy assigning priority scores to putative metabolite IDsMetaboCraft will contribute to addressing the major bottleneck in metabolomicsmetabolite identificationthereby enhancing the contribution of metabolomics in studies such as biomarker discovery and systems biology research PROJECT NARRATIVE This project aims to develop a new cloud based toolMetaboCraftfor accurate annotation of metabolites detected by liquid chromatography coupled with mass spectrometryLC MSThis will be accomplished by using a probabilistic framework that combines information from existing resourcescompound databasespathwaysbiochemical networksand spectral librariesand tools designed for isotope adduct recognitionisotopic pattern analysisand spectral interpretationSuccessful implementation of MetaboCraft will enhance the use of metabolomics in biomarker discovery and systems biology research by addressing the major bottleneck in metabolomicsmetabolite identification