SEARCH

Senior Microbiome Research Scientist Resume Example

Resume Score: 80%

Love this resume?Build Your Own Now
SENIOR MICROBIOME RESEARCH SCIENTIST
Technical Skills

    Computational Biology

    1. Data Processing Tools for next generation sequencing data and large multi-omic datasets

  • 16S data:QIIME1/ QIIME2 / MOTHUR / UPARSE / PICRUSt / PICRUSt2
  • Metagenomic shotgun sequencing data: MetaPhlAn / MetaPhlAn2 / MetaPhlAn3 / HUMAnN / HUMAnN2 / HUMAnN3
  • Metatranscriptomic sequencing data:HUMAnN2 / HUMAnN3
  • RNA-Sequencing data: FastqPuri / Illumina® DRAGEN RNA Pipeline / https://usegalaxy.org/ https://transcriptomics.cloud
  • Metabolomics: MeltDB / IDEOM / MetaboAnalyst-R /NMR R
  • 16S ribosomal databases: SILVA/GreenGenes/RDP/EzBioCloud
  • Metagenomic sequencing reference database: BLAST / SEED
  • Knowledgeable about challenges in microbiome data analysis

    • 2.Data Analysis tools for next-generation sequencing data and large multi-omic datasets

  • R software / Rstudio / MaAsLin2 / WGCNA R package
  • Bioconductor / DESeq2 / edgeR
  • MicrobiomeAnalyst-R / MetaboAnalyst-R / IPA (QIAGEN)
  • XLSTAT / XLSTAT-R / XLSTAT-3D / PAST / Gephi / Cytoscape / MENAP / SIMCA-P / Secondgenome Piphillin and Sequence Editor / STRING: Protein-Protein Interaction Networks
    Functional Enrichment Analysis
  • Https://huttenhower.sph.harvard.edu/galaxy (GraPhlAn / LEfSe / NGS QC and manipulation / Phenotype Association / MaAsLin
  • Multiple omic data integration: MEGAN4/ mixOmics R / XLSTAT / Matrix eQTL R package / OmicsIntegrator
  • Git / GitHub (managing version control in data science projects)
  • Https://www.metabolomicsworkbench.org/tools/externaltools.php
  • Https://bioinformaticshome.com/tools/proteomics/proteomics.html

    • 3. Analytical methods for host-microbiome integration

  • Dimensionality reduction (e.g., PCA / PCOA / FA / MFA / EFA / CFA / SEM / MDS / LDA)
  • Correlation-based approach (e.g., Spearman / Pearson / CCA / CIA / PA/ SparCC)
  • Regression-based approach (e.g., GLMM / PLS / LASSO / Kernal association test)
  • Network-based approach (e.g., Trans-kingdom net-work / WGCNA / GLM)
  • Transformation based integration / Concatenation based integration / Model based integration using DIABLO / MINT pipelines in R
  • Supervised Learning Methods: Hypothesis testing and variation analysis (MANOVA / PERMANOVA / ANOSIM) / Regression and correlation analysis / Classification (RF / SVM)
  • Unsupervised Learning Methods: Dimensionality reduction / Cluster analysis

    • 3. Tools for managing large amounts of data

  • Version-control systems: GitHub / Harvard Dataverse / Zenodo / Dat. Recording entire data workflow with video-capture tool asciinema and, meta-data with README / JSON
  • Automation tools (Apache Spark and Apache Hbase) to validate data-quality assurance steps. Self-contained computing environment-Docker container / online platform Code Ocean / Binder / Gigantum / Nextjournal
  • Tools to create documents that combine software code, text and figures: Jupyter Notebook / Terra / Seven Bridges Genomics. Stack Overflow / The Carpentries

    • Molecular Biology

  • QPCR, ddPCR, RT-PCR,
  • Sequencing (NGS, 16S, WGS, MicroSeq 500, WGBS, Shotgun and Nanopore),
  • DNA / RNA / Protein extraction, Nanodrop, Qubit, cDNA preparation, Genotyping, library preparation for sequencing,
  • Terminal restriction fragment length polymorphism (TRFLP), Cloning using PCR and restriction enzymes
  • Strengths and weaknesses of individual omics technologies
Professional Summary
  • 6 years of demonstrated expertise in analysis of DNA and RNA sequencing data of the gut microbiota / Proven expertise in integrative analysis of multi-omic data using several standard statistical software, including R. 11 years of expertise in molecular biology techniques and gut microbiome research.
  • 6 years of demonstrated experience with bioinformatics computational (e.g., QIIME) and data analysis pipelines (e.g., MicrobiomeAnalyst). Strong expertise in microbiome / metabolome data analysis.
  • Comfortable in Unix environment (e.g., QIIME Virtual Machine with Linux OS) / Experience with R programming language.
  • Proven experience with next-generation sequencing (e.g., 16S rRNA sequencing) / Demonstrated ability to work well with other members of the group and outside collaborators (e.g., APC Microbiome Institute, Ireland).
  • Highly developed communication and interpersonal skills / Experienced in managing large amounts of data (e.g., Harvard Dataverse / figshare / Docker container / Jupyter Notebook / Apache Spark / Stack Overflow) and running bioinformatics pipelines. Self-starter and highly motivated.
Work History
Senior Microbiome Research Scientist, 03/2020 to 09/2020
UPMC Children's Hospital Of Pittsburgh - Pittsburgh , PA
  • Served as Subject Matter Expert for 5 different microbiome project (urinary and respiratory tracts microbiome / Gut microbiome), resulting in development of diverse molecular and computational tools to analyze next generation sequencing data
  • Investigated mechanisms of host-microbiome interactions in variety of common pediatric infectious conditions (urinary tract/ear/sinus/throat infections)
  • Conduced bioinformatics analysis on Illumina MiSeq 16S gene sequencing FASTQ data (demultiplexing, quality filtering and open-reference OTU picking by searching reads against the Greengenes database) by applying quantitative insights into microbial ecology (QIIME) pipeline to get operational taxonomic unit (OTU) table with BIOM format
  • Predicted metagenome functional content from 16S marker gene using phylogenetic reconstruction of unobserved states (PICRUSt) software, aligned predicted genes and function to KEGG database and determined significant putative KEGG orthologs and pathways using Linear discriminant analysis Effect Size (LEfSe) analysis
  • Predicted metagenomic functional content from 16S marker gene using Piphillin secondgenome pipeline/OTU table/FASTA sequences
  • Used variety of R packages (ranacapa R package phyloseq / metacoder / hclust / randomForest / DESeq2) and LEfSe analysis to perform microbiome analysis (rarefaction curves/phylogenetic tree/heat tree/ alpha and beta diversity analysis/hierarchical clustering/random forest analysis/differential expression/biomarker discovery)
  • Performed integrative transcriptome-metagenome analysis using mixOmics R package(N-integration with DIABLO) and explained molecular mechanism and the impact of microbial variation on gene expression
  • Worked with bioinformatician and learned how to integrate epigenomics data with host transcriptome data using Matrix eQTL and SVA R packages
  • Worked with bioinformatician and learned how to perform mediation analysis with epigenomics and transcriptome data using Mediation R package to examine degree to which association between methylation and disease susceptibility is mediated by gene expression
  • Developed prediction models and investigated whether baseline measurements of methylation or gene expression predict infection status (i.e., susceptibility) using three classic machine learning models (gradient boosting machine / Lasso and elastic-net regularized generalized linear models / random forest and XLSTAT-R
  • Performed enterotype analysis on 16S genus abundance table using cluster and clusterSim R packages and enterotype tutorial (https://enterotype.embl.de/enterotypes.html#intro)
  • Performed microbe-microbe interaction (co-occurrence and anti-occurrence) analysis using SparCC algorithm and SpiecEasi R package (https://www.rdocumentation.org/packages/SpiecEasi
  • Trained 2 research technicians to perform DNA/RNA isolation/purification/quantification/library preparation
  • Collaborated with biostatisticians to perform large-scale sequencing (16S/WGS/shotgun/WGBS/DNA methylation) data analysis
  • Communicated with genomic core specialists to design (sequencing depth/coverage/how many biological or technical replicate) and identify right Illumina technique and library preparation methods for RNA-Sequencing/Dual RNA-Sequencing
Non-Clinical Research Staff, 11/2017 to 02/2020
Massachusetts General Hospital - Boston, MA
  • Investigated mechanisms of host-microbiome interactions in chronic inflammatory diseases using 3 different transgenic mouse models (FAT-1/FAT-2/FAT-1+2) and multi-omics technologies (metagenomics/metabolomics)
  • Investigated effects of green tea liquid consumption on human gut and oral microbiota, maternal omega-3 fatty acids on offspring gut microbiota and sex-differences on gut microbiota using 16S sequencing and predicted metagenomics
  • Collaborated with APC Microbiome Institute, Ireland to perform 16S sequencing/metabolomics/bioinformatics analysis
  • Analyzed stool 16S sequencing data and stool/serum metabolomics data and performed integrated metagenomic-metabolomic data analysis using several high-thoroughput microbiome data analyzing tools (QIIME, XLSTAT, XLSTAT-R, XLSTAT-3D, PAST, SIMCA-P, Galaxy web application, MicrobiomeAnalyst with R, MetaboAnalyst and Gephi/Cytoscape graph visualization and manipulation software
  • Conduced bioinformatics analysis on Illumina MiSeq 16S gene sequencing FASTQ data (demultiplexing, quality filtering and open-reference OTU picking by searching reads against the Greengenes database) by applying QIIME pipeline to get OTU table (BIOM format)
  • Performed microbiome analysis such as visual exploration (stacked bar/area plot/rarefaction curve/phylogenetic tree/heat tree), community profiling (alpha and beta diversity analysis/core-microbiome analysis), clustering analysis (heatmap/dendogram/correlation analysis/pattern search), differential abundance analysis (univariate/DESeq2/edgeR) and biomarker analysis (LEfSe/random forest/PLS-DA)
  • Installed 64-bit QIIME Virtual Box inside an Ubuntu Linux virtual machine, predicted metagenome functional content from demuliplexed fasta file using GG reference collection/closed reference OTU picking method/PICRUSt software, aligned predicted genes/function to KEGG database and determined significant putative KEGG orthologs/pathways using Linear discriminant analysis Effect Size (LEfSe) analysis
  • Disentangled host-microbe interactions driven by fatty acid status or estrogen status by performing dimensionality reduction (principal component analysis) and net-work-based (trans-kingdom network)/correlation-based (canonical correspondence analysis/co-inertia analysis)/regression-based (partial least squares) analytical approaches
  • Performed Inter-omic analysis to reveal microbe–metabolite interactions between tissue n-6/n-3 PUFA-associated microbial community type and metabotype using co-inertia analysis, partial least squares-discriminant analysis and net-work-based analytical approaches
  • Performed partial least squares discriminant analysis (PLS-DA)/ orthogonal projections to latent structures discriminant analysis (OPLS-DA) using SIMCA-P multivariate data analysis tool to investigate effects of different fatty acid status on fecal and serum metabolite profiles
  • Performed PCOA/PLS-CCA to investigate influence of demographic characteristics on overall composition of human gut/oral microbiota
  • Performed hierarchical Ward‐linkage clustering based on the Pearson correlation coefficients using XLSTAT software to identify presence of oral bacterial net work in gut microbiota profile
  • Analyzed liver PROTEOMICS data/identified differentially abundant proteins/performed protein-protein interaction and
    Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis with STRING biological database/Co-expression network analysis with Weighted Gene Co-expression Network Analysis (WGCNA) R package/PCA and procrustes analysis/randomForest biomarker analysis/net-work-based association analysis between proteins and metabolic parameters using Cytoscape app version of association network inference tool CoNet
  • Collaborated with Chongqing Medical Nutrition Research Center, China to plan and implement computational strategies for bioinformatics and data analysis of genomic data
Research Fellow, 10/2012 to 10/2017
Massachusetts General Hospital - Boston, MA
  • Investigated role of host-gut microbiome interaction for opposing effects of omega-6 and omega-3 fatty acids on chronic inflammatory disease/antibiotic-associated obesity/alcohol-induced liver injury/anti-cancer drug-induced gut toxicities using qPCR/16S sequencing/PICRUSt/FAT-1 transgenic mouse model/high-throughput multivariate data analyzing tools
  • Investigated role of host-gut microbiome interactions for combined effects of omega-3 fat and isoflavones on obesity and metabolic disease using 16S sequencing/PICRUSt/fecal metabolomics/fecal short-chain fatty acid analysis/ integrated multi-omic data analysis
  • Created FuncTree map based on KEGG brite functional hierarchy to directly visualize and calculate statistical significance of functionality of predicted metagenomics data and broadly overview what kind of biological functions are present in dataset
  • Performed multivariate receiver operator characteristic (ROC) curve-based automated important feature identification and performance evaluation using MetaboAnalystR/random forest classification/PLS-DA feature ranking method
  • Processed FASTQ data using QIIME work flow/performed gut microbiome and PICRUSt analysis/PCA and PLS-DA analysis on fecal/serum metabolomics data using XLSTAT-R/PAST software
  • Performed microbial co-occurrence analysis using the CoNet plugin and visualized resulting network in Cytoscape
  • Developed/optimized/validated qPCR-based assay for absolute quantification of gut microbiota
  • Collaborated with Department of Epidemiology, School of Public Health, Fourth Military Medical University, China to perform bioinformatics and data analysis on gut and oral microbiome sequencing data
Postdoctoral Research Fellow, 11/2009 to 09/2012
Massachusetts General Hospital - Boston, MA
  • Learned commonly used molecular biology, microbiology and techniques and gained in-depth understanding of theories/principles underlying these methodologies
  • Participated in 16S rRNA gene pyrosequencing to determine gut microbiome composition of antibiotics treated mice/processing and analysis of pyrosequencing data using Research and Testing Laboratory's in-house pipeline (http://www.researchandtesting.com/docs/Data_Analysis_Methodology.pdf)/vegan and ladbsv and DESeq2 R packages
  • Involved in development of terminal restriction fragment length polymorphism, phylogenetic analyses and quantitative real-time PCR of subphylum-specific bacterial 16S rRNA to determine the compositional profiles of gut microbiota in IAP knock-out mice
  • Collaborated with Research and Testing Laboratory, Texas to perform 16S rRNA gene pyrosequencing and data analysis
Education
Bachelor of Medicine And Bachelor of Surgery: : Biology And Medicine, 01/2008The Tamil Nadu Dr. M.G.R. Medical University - TN, India
Publications
  • Kaliannan K et al. Multi-omic analysis in transgenic mice implicates omega-6/omega-3 fatty acid imbalance as a risk factor for chronic disease. Commun Biol (2019).
  • Yuan X et al. Green Tea Liquid Consumption Alters the Human Intestinal and Oral Microbiome. Mol Nutr Food Res (2018) / Corresponding author.
  • Kaliannan K et al. Estrogen-mediated gut microbiome alterations influence sexual dimorphism in metabolic syndrome in mice. Microbiome (2018).
  • Kaliannan K et al. A host-microbiome interaction mediates the opposing effects of omega-6 and omega-3 fatty acids on metabolic endotoxemia. Sci Rep (2015).
  • Kaliannan K et al. Omega-3 fatty acids prevent early-life antibiotic exposure-induced gut microbiota dysbiosis and later-life obesity. Int J Obes (Lond) (2016).
  • Robertson RC et al. Maternal omega-3 fatty acids regulate offspring obesity through persistent modulation of gut microbiota. Microbiome (2018) / 2nd author
  • Kang C et al. Gut Microbiota Mediates the Protective Effects of Dietary Capsaicin against Chronic Low-Grade Inflammation and Associated Obesity Induced by High-Fat Diet. mBio (2017) / 2nd author
  • Kaliannan K et al. Decreased Tissue Omega-6/Omega-3 Fatty Acid Ratio Prevents Chemotherapy-Induced Gastrointestinal Toxicity Associated with Alterations of Gut Microbiome. mSystems (2020) / status: accepted
  • Note: Complete list of presentations will be due upon request
Presentations
  • 2017/Oral/ Cambridge Healthtech Institute's 3rd Annual Targeting the Microbiome (industry's preeminent event on novel drug targets and technologies)/Boston, Massachusetts
  • 2018/Oral/Cambridge Healthtech Institute's 4th Annual Targeting the Microbiome/Boston, Massachusetts
  • 2020/Oral/Rhode Island Microbiome Symposium/Kingston, Rhode Island
  • 2016/Poster/Gut Health, Microbiota & Probiotics Throughout the Lifespan: Metabolic & Brain Function/Cambridge, Massachusetts
  • Note: Complete list of presentations will be due upon request
Certifications

Data Science: Foundations using R Specialization

  • Course 1: The Data Scientist's Toolbox
  • Course 2: R Programming
  • Course 3: Getting and Cleaning Data in R
  • Course 4: Exploratory Data Analysis in R
  • Course 5: Reproducible Research
  • Instructors (Jeff Leek, PhD, Associate Professor, Biostatistics, Johns Hopkins University; Brian Caffo, PhD, Professor, Biostatistics, Johns Hopkins University; Roger D. Peng, PhD, Associate Professor, Biostatistics, Johns Hopkins University)
  • Total course duration: 20weeks
  • Skill gained: Data Science/Machine Learning/GitHub/R Programming/Exploratory Data Analysis/Rstudio/Data Analysis/Debugging/Data Manipulation/Regular Expression (REGEX)/Data Cleansing/Cluster Analysis
  • Successfully completed hands-on project (installing tools, programming in R, cleaning data, performing analyses, and peer review assignments) at ending of each course in this specialization and earned certificates

Data Science: Statistics and Machine Learning Specialization

  • Course 1: Statistical Inference
  • Course 2: Regression Models
  • Course 3: Practical Machine Learning
  • Course 4: Developing Data Products
  • Course 5: Data Science Capstone
  • Instructors (Jeff Leek, PhD, Associate Professor, Biostatistics, Johns Hopkins University; Brian Caffo, PhD, Professor, Biostatistics, Johns Hopkins University; Roger D. Peng, PhD, Associate Professor, Biostatistics, Johns Hopkins University)
  • Total course duration: 20weeks
  • Skill gained: Machine Learning/GitHub/R Programming/Regression Analysis/Data Visualization (DataViz)/Statistics/Statistical Inference/Statistical Hypothesis/Testing/Model Selection/Generalized Linear Model/Linear Regression/Random Forest
  • Successfully completed hands-on project (peer-graded assignment in each course, including final Capstone Project and building data product using real-world data) at ending of each course in this specialization and earned certificates

Mastering Software Development in R Specialization

  • Course 1: The R Programming Environment
  • Course 2: Advanced R Programming
  • Course 3: Building R Packages
  • Course 4: Building Data Visualization Tools
  • Instructors: Roger D. Peng, PhD, Associate Professor, Biostatistics, Johns Hopkins University; Brooke Anderson,
    Assistant Professor, Environmental & Radiological Health Sciences, Johns Hopkins University
  • Skills gained: R Programming/Data Visualization (DataViz)/Ggplot2/tidyverse/Object-Oriented Programming (OOP)/Data Manipulation/Regular Expression (REGEX)/Rstudio/Logic Programming/Functional Programming/Programming Tool/GitHub
  • Successfully completed hands-on project (manipulating complex datasets, writing powerful functions, creating new R package, and develop new visualization tools for building custom data graphics) at ending of each course in this specialization and earned certificates
Build Your Own Now

DISCLAIMER

Resumes, and other information uploaded or provided by the user, are considered User Content governed by our Terms & Conditions. As such, it is not owned by us, and it is the user who retains ownership over such content.

Resume Overview

Companies Worked For:

  • UPMC Children's Hospital Of Pittsburgh
  • Massachusetts General Hospital

School Attended

  • The Tamil Nadu Dr. M.G.R. Medical University

Job Titles Held:

  • Senior Microbiome Research Scientist
  • Non-Clinical Research Staff
  • Research Fellow
  • Postdoctoral Research Fellow

Degrees

  • Bachelor of Medicine And Bachelor of Surgery : : Biology And Medicine , 01/2008

Similar Resumes

View All
Senior-Microbiome-Research-Scientist-resume-sample

Senior Microbiome Research Scientist

Pediatric Clinical and Translational Research Center, UPMC Children’s Hospital of Pittsburgh

Natick, Massachusetts