CHANDRIKA S
Bioinformatics • Computational Biology • GenomicsM.Sc. Bioinformatics & Biotechnology — Expected 2027

Chandrika S

Turning biological data into
computational insight.

M.Sc. Bioinformatics & Biotechnology student working across RNA-seq analysis, NGS workflows, computational pipelines and scientific software.

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  • Bioinformatics
  • Computational Biology
  • Scientific Software
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/ 01 — About

Reading biology through code, pipelines
and data.

I'm an M.Sc. Bioinformatics & Biotechnology student at Chanakya University, with a B.Sc. in Biotechnology behind me. My work sits where biotechnology knowledge meets computational analysis and software development.

Across my projects I have worked on RNA-seq analysis, NGS workflows and differential expression analysis, molecular docking for drug discovery, and DNA barcoding with phylogenetic analysis — and built the scientific software around them, including AI/ML components.

I am currently seeking internships in bioinformatics, computational biology, genomics, NGS data analysis, AI/ML for life sciences or biotechnology research.

  • RNA-seq analysis
  • NGS workflows
  • Differential expression analysis
  • Computational pipelines
  • Molecular docking & drug discovery
  • DNA barcoding & phylogenetics
  • Scientific software development
  • AI / ML
Get in touch

/ 02 — Selected work

Bioinformatics · Full-stack · AI
01RNA-seq

RNA-seq Analysis Platform

Glucocorticoid Response Analysis

An end-to-end RNA-seq workflow built on GSE52778 — from raw reads through quality control, alignment and counting to donor-aware differential expression.

FastQCfastpSTARfeatureCountsDESeq2
Mini ProjectGSE52778
FastQCfastpSTARfeatureCountsDESeq2Nextflow
View Case Study

RNA-seq workflow

  1. Raw dataGSE52778
  2. FastQCQuality control
  3. fastpPreprocessing
  4. STARSpliced alignment
  5. featureCountsGene-level counting
  6. DESeq2Differential expression
  7. ResultsReporting
02Dock

Drug Discovery Against Mtb InhA

Comparative molecular docking on the InhA target

A structural bioinformatics investigation of the InhA target in Mycobacterium tuberculosis, examining the S94A mutation and its effect on isoniazid interactions, with three inhibitors compared by docking affinity, interaction distances and drug-likeness.

PyMOLAutoDockPDBPubChemSwissADME
Mini ProjectMtb InhA
PDBPubChemPyMOLAutoDockSwissADMEMolecular docking
View Case Study

Docking workflow

  1. InhA targetM. tuberculosis
  2. StructuresPDB · PubChem
  3. Active sitePreparation
  4. Molecular dockingAutoDock
  5. Interaction analysisPyMOL
  6. Drug-likenessSwissADME
  7. PrioritisationNITD-916
04Barcode

DNA Barcoding & Phylogenetics

COI gene analysis of prawn & shrimp species

Species identification of prawns and shrimp by COI DNA barcoding — DNA extraction, PCR amplification of the mitochondrial COI gene, gel electrophoresis and Sanger sequencing, followed by BLAST-based identification, MSA and phylogenetic analysis.

NCBI BLASTClustal OmegaMEGABOLD SystemsSanger Sequencing
Mini ProjectWet lab → Analysis
DNA extractionCOI PCRAgarose gel electrophoresisSanger sequencingNCBI BLASTClustal Omega
View Case Study

Barcoding pipeline

  1. DNA extractionWet lab
  2. COI PCRMitochondrial COI
  3. Gel & SangerSequencing
  4. Species IDNCBI BLAST
  5. AlignmentClustal Omega
  6. PhylogeneticsMEGA
  7. Barcodes & treeBOLD Systems

/ 03 — Skills

Where the wet lab meets
the command line.

Eight working areas, from sequence analysis and pipeline tooling through to the software and models built around them.

RNA-seqNGSSequence analysisDifferential expression analysisGenome annotationBiopythonBLASTNCBIUniProtGalaxy

/ 04 — Education

Chanakya University · BIHER
Expected 2027

M.Sc. Bioinformatics & Biotechnology

Chanakya University

CGPA8.77/10
2025

B.Sc. Biotechnology

Bharath Institute of Higher Education and Research

CGPA8.69/10

Academic coursework

Programming in Python

Python programming, NumPy, Pandas, Matplotlib, Jupyter Notebook, data handling, Biopython, sequence analysis, motif discovery, image processing, and computational pipeline development.

Biostatistics & R Programming

R, RStudio, biological data analysis, descriptive statistics, probability, hypothesis testing, parametric and non-parametric tests, correlation, regression, ggplot2, and Bioconductor.

Advanced Bioinformatics

Genomics, transcriptomics, proteomics, metabolomics, structural bioinformatics, systems biology, NGS data analysis, genome annotation, variant analysis, multi-omics, and computational workflows.

AI / ML & Applications in Biology

Supervised and unsupervised learning, regression, classification, clustering, model evaluation, neural networks, CNNs, RNNs, transformers, generative AI, and AI applications in genomics and drug discovery.

/ 05 — Contact

LET'S BUILD SOMETHING
AT THE INTERSECTION OF
BIOLOGY & COMPUTATION.

Say hello
Emailchandrtikadps18@gmail.comLinkedInwww.linkedin.com/in/chandrika-sGitHubgithub.com/chandrika-s
Open to bioinformatics & computational biology internshipsBioinformatics · Computational Biology · Scientific Software