Bioinformatics Application and Development
Trupti Joshi Lab
Research Interests
The Joshi lab has expertise in the areas of bioinformatics and its application to biomedical sciences, plants sciences, animal sciences, and health informatics fields. The lab has three key research pillars:
- Multiomics Informatics Data Analytics and Framework Development
- Multiomics Informatics Method Development
- Multiomics Data Integration Tool Development
PRINCIPAL INVESTIGATOR
Trupti Joshi
Associate Professor of Biomedical Informatics, Biostatistics and Medical Epidemiology
Associate Professor of Division of Plant Sciences and Technology
Core Faculty in the MU Data Science and Informatics Institute
phone(573) 884-5963
email joshitr@missouri.edu
Trupti Joshi is an Associate Professor in the Department of Biomedical Informatics, Biostatistics and Medical Epidemiology (BBME) in the School of Medicine (SOM), with a joint appointment with Department of Plant Sciences and Technology (DPST) at University of Missouri-Columbia (MU). She also has Core Faculty appointments with MU Data Science and Informatics Institute (MUIDSI), Interdisciplinary Plant Group (IPG), Christopher S. Bond Life Science Center (LSC), and Department of Electrical Engineering and Computer Science (EECS). Joshi served as Director of Translation Bioinformatics with School of Medicine from 2015-2020 and currently serves as a Faculty Lead for Translational Bioinformatics for the NextGen BioMedical Informatics (NextGen BMI). She has over 20+ years of experience working in academia, collaborations with multi-disciplinary researchers and several Industry partners.
Her expertise is in the areas of bioinformatics and its application to biomedical sciences, plants sciences, animal sciences, and health informatics fields. She has published more than 168 scientific papers and co-developed several bioinformatics software systems, methods and tools.
ABOUT THE LAB
The lab currently focuses on:
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- Building knowledge base frameworks such as SoyKB and KBCommons for genomics and multiomics data integration in agricultural and biomedical domains.
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- Development of data analytics pipelines using HPC and cloud based resources (PGen, SnakyVC).
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- Multiomics data integration methods and tools development (Allele Catalog, GenVarX, SNPViz).
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- Deep learning (G2PDeep, IRnet) and machine learning methods development for phenotype predictions and biomarker identification.
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- Computational algorithm development for in silico hypothesis generation (IMPRes).
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- Application of translational bioinformatics techniques towards advances in precision medicine, precision agriculture, and genomic epidemiology (Covid Portal).
Examples of our research:
Web-based Frameworks and Portal Development
Knowledge Base Commons (KBCommons): https://kbcommons.org
Soybean Knowledge Base (SoyKB): https://soykb.org
Covid Genomic Epidemiology: https://dataportals.missouri.edu/SARSCoV2 and https://kbcommons.org/system/browse/msphl/index/SARSCoV2
Web-based Tools Development Within KBCommons and SoyKB
Allele Catalog: Tool for new allele discovery using large scale genomics variations SNPs/Indels and phenotypic data.
GenVarX: Tool for uncovering regulatory and structural changes using large scale genomics variations SNPs/Indels and phenotypic data.
SNPViz: Tool for causative genes discovery to further assist GWAS using large-scale genomics variations data.
MADis: Tool for mutative allele discovery composed of multiple mutative allele position combinations.
Computational Methods Development
G2PDeep (Deep learning method for phenotype prediction)
IMPRes (Integrative Multiomics Pathway Resolution)
IRnet (Immunotherapy Response Prediction)
CrossMP (Single cell cross-modality prediction between scRNAseq and scATACseq)
LAB MEMBERS
Yen On Chan
Ph.D. Candidate, Informatics
place113 Bond Life Sciences Center
Research Topics
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Animal science
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Applied computing
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Artificial intelligence
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Autoimmune disease mechanisms
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Biomedical informatics
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Cancer immunotherapy
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Chronic inflammatory diseases and immune responses
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Crop improvement and agricultural sustainability
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Data management
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Deep learning and AI for biological data
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Drug delivery and therapeutic development
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Fatty acid biosynthesis
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Genomic tools and systems biology
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Global warming effects on crops
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Host-pathogen interactions
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Immunotherapies and immune system regulation
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Machine learning
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Metabolic pathway engineering
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Metabolic pathways and mechanisms
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Multi-omics and bioinformatics tools
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Multi-stress plant responses
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Neurodegeneration and neuromuscular diseases
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Nitrogen fixation in soybeans
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Plant immunity and stress responses
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Plant-microbe interactions
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Reactive Oxygen Species (ROS) signaling
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Single-cell genomics
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Software engineering
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Spatial transcriptomics
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Translational bioinformatics
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Vaccine development
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Virus pathogenesis and transmission
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Animal science
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Applied computing
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Artificial intelligence
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Autoimmune disease mechanisms
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Biomedical informatics
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Cancer immunotherapy
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Chronic inflammatory diseases and immune responses
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Crop improvement and agricultural sustainability
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Data management
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Deep learning and AI for biological data
-
Drug delivery and therapeutic development
-
Fatty acid biosynthesis
-
Genomic tools and systems biology
-
Global warming effects on crops
-
Host-pathogen interactions
-
Immunotherapies and immune system regulation
-
Machine learning
-
Metabolic pathway engineering
-
Metabolic pathways and mechanisms
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Multi-omics and bioinformatics tools
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Multi-stress plant responses
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Neurodegeneration and neuromuscular diseases
-
Nitrogen fixation in soybeans
-
Plant immunity and stress responses
-
Plant-microbe interactions
-
Reactive Oxygen Species (ROS) signaling
-
Single-cell genomics
-
Software engineering
-
Spatial transcriptomics
-
Translational bioinformatics
-
Vaccine development
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Virus pathogenesis and transmission
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Nov. 30, 2018
#IAmScience Trupti Joshi
By Erica Overfelt | Bond LSC Juggling research, teaching, collaborative meetings, grant writing, and training postdocs and students is no problem for Trupti Joshi. That array of responsibilities is just part of the job for a Bond LSC researcher focused on bioinformatics, an area that connects so many types of science by collecting and analyzing genetic data. “Bioinformatics is a very interdisciplinary science that marries the wet and the dry labs,” Joshi said. “It applies software tools and computational techniques from computer science, engineering, mathematics and statistics towards efficient ways to analyze, integrate and mine large-scale genomics and…
Aug. 18, 2017
Drowning in Data
New web-based framework helps scientists analyze and integrate data By Emily Kummerfeld | Bond LSC Large-scale data analysis on computers is not exactly what comes to mind when thinking about biological research. But these days, the potential benefit of work done in the lab or the field depends on them. That’s because often research doesn’t focus on a single biological process, but must be viewed within the context of other processes. Known as multi-omics, this particular field of study seeks to draw a clearer picture of dynamic biological interactions from gigantic amounts of data. But, how exactly can scientists suitably…