ANANTHI T

6/77, M.RayarPalayam, S.C.Pudur (Post), Karumathampatti (via), Coimbatore – 641659

ACADEMIC QUALIFICATION:

• PG Certificate course in Data Analytics and Management in Bioinformatics – PSG College of Technology, Coimbatore. (2021 – 2022)
• B.Tech (Bioinformatics) – TamilNadu Agricultural University, Coimbatore (2014 – 2018, CGPA – 8.0)
• HSC – K.G Girls Higher Secondary School, Coimbatore (Percentage – 96%)
• SSLC – Government Higher Secondary School, Coimbatore (Percentage – 90%)

WORK EXPERIENCE:

• Bioinformatics Analyst, Latlon Technologies, March 2023 – present
• Data Analyst, ACROPHASE – IIT Madras, November 2021 – March 2023
• Junior Bioinformatic Programmer, Genome Life Sciences Pvt. Ltd, September 2018 – September 2019

CERTIFICATIONS:

• LSSDC Certified Bioinformatics Analyst

PROJECT WORKS:

• Gene Panel Data Bank
• Insilico screening of phytochemicals targeting Rheumatoid arthritis and Osteoarthritis
• Comparative genomics of ASD60 and ADT43 for identifying genetic variants using genome sequencing approach
• Data Curation and Database Development using COSMIC database

PROFESSIONAL EXPERIENCE:

• Worked on the product Development – Splice Atlas
• Built analysis models using GRCh38.p13 assembly data
• Working experience in Python, HTML, CSS, Github
• Good exposure to MySQL related activities. Database Development, Bash scripting, and Web scraping

AREAS OF INTEREST:

• Data Analytics
• GWAS, NGS Analysis
• Database and Tool Development

COMPUTER SKILLS:

• Programming Languages: Python, MySQL, R, Bash scripting, PostgreSQL
• Packages: Microsoft Office
• Operating Systems: Windows 7/8/10, Linux – Ubuntu

BIOINFORMATICS SKILLS:

• Tools and Databases: NCBI, BLAST, PDB, Pubchem, UCSF Chimera, Swiss ADME, ROBETTA, Autodock Vina, Galaxy, TCGA, UKBIOBANK
• Languages Known: English, Tamil

JOB DUTIES HANDLED:

1. Project Name – Target Indication Summarizer for GWAS analysis and Disease risk prediction
   • Developing executable applets within UK Biobank for phenotype/patient data extraction, WGS, WES, genotype array data extraction, and imputed array data extraction
   • Annotating variants and conducting GWAS analyses
   • Experience in developing docker applications and deploying through shinyproxy
   • Implementing PRS prediction models and machine learning models for genetic risk assessment
2. Project Name – Advanced human performance Monitoring
   • Developed algorithms for data analytics and performance evaluation
   • Worked on stress recovery balance algorithm and training effect scores
   • Experience in statistical and physiological data analytics
3. Project Name – Splice Atlas
   • Created Atlas of splicing information
   • Collected mutation information and developed tools for splicing score prediction
   • Analyzed information using Python and bioinformatics tools

SKILLS:

Data Analytics, GWAS, NGS Analysis, Database and Tool Development, Python, MySQL, R, Bash scripting, PostgreSQL

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Cover Letter:

During my tenure as a Junior Bioinformatics Programmer at Genome Life Sciences Pvt Ltd, I played a pivotal role in developing the GenePanel DataBank for different cancer types. Additionally, I contributed to the development of the Splice Atlas Product and created a web-based tool for Splicing score prediction based on the Shapiro-senapathy algorithm. This tool proved invaluable to researchers within the company, enabling them to make informed decisions about splicing in their research projects.

Currently, as a Bioinformatics Analyst at Latlon Technologies, I am responsible for developing executable applets and workflows (pipelines) within the UK Biobank – DNANexus for GWAS analysis. This entails extracting phenotype and patient data, working diverse genomic variant data types, such as WGS, WES, genotype array data, and imputed array data. Utilizing shell scripts and Python programming to extract, preprocess, and analyze the genetic data. Conducting GWAS analysis to identify genetic variants associated with various phenotypes of interest. Enhancing the GWAS results through fine mapping and Mendelian Randomization techniques. Implementing polygenic risk score prediction models to assess the genetic risk of complex diseases. Created machine learning models to predict type2diabetes in patients from UK Biobank dataset. I also annotate variants using tools like SnpEff, ANNOVAR, and VEP as necessary, integrating these steps into comprehensive pipelines.