Margaret Sunitha
Associated Researcher @Broad Institute of MIT and Harvard
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WORK HISTORY
Associated Researcher @Broad Institute of MIT and Harvard
Boston, MA, US
EDUCATION
National centre for biological sciences
Doctor of Philosophy (Ph.D.), Computational Biology, Bioinformatics
Auxillium Womens College, Katpadi, Vellore
Bachelor's Degree, Biochemistry, Biophysics and Molecular Biology, University Rank Holder, Distinction
Little flower convent, Ranipet, Vellore
High School, Maths, Physics, Chemistry, Biology, First class, Distinction
Stella Maris College
Master's Degree, Biomathematics, Bioinformatics, and Computational Biology, College Topper, Distinction
ABOUT MARGARET SUNITHA
Over 5+ years of experience in computational biology research. Focused on various projects in the domains of bioinformatics, predictive modelling, data analytics, computational structural studies of macro-molecular protein complex and protein biochemistry. Associated in different projects related to: Predictive modelling using clinical and gene expression data Immunology/Oncology data analysis Target Ideation Patient stratification based on gene expression data Metagenomics Next generation sequencing/ Big-data analysis Cardiomyopathy disease model Protein structural analysis (coiled-coils, cardiac muscle proteins)/Programming I am passionate about translational research that benefits the drug discovery domain. Practical Exposure Predictive Modelling methods- • Regression (linear, logistic – limma, glm, gmlnet, lasso). • SVM, Random Forest, ANN. • Classification and Clustering methodologies. Bioinformatics methods- • BLAST, FASTA, RASMOL, Swiss-PDB viewer, PYMOL. • MALDI-TOF, ClustalW, MODELLER, Docking tools, MD. • Various other Bioinformatics tools. NGS/Metagenomics analysis methods- • FASTX, FASTQC, SoapDenovo, Velvet assembler. • Megahit, CLARK, PPSP, Megan, MaxBin. Databases- • TCGA, CBioportal, NCBI, SwissProt, GenBank, DDBJ, EMBL, SGD, PIR, Pfam, PDB, Prosite, Pubmed, OMIM, TIFR Computer Skills- • Os – Windows XP, Linux • Language – R, Perl, Python, HTML Protein Biochemistry methods- • Protein purification-high/low salt, affinity chromatography. • Hands on experience-AKTA, TECAN. • SDS-PAGE, UV & visible Spectroscopy.
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