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Bioinformatics Internship Topics

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Bioinformatics Internships with Accommodation

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Showing 61–72 of 105 internship topics
AlphaFold2 Model Optimization and Validation
Interns will work on implementing and optimizing AlphaFold2 pipelines for novel protein sequences, including model training on custom datasets and validation against experimentally determined structures. This involves benchmarking prediction accuracy, analyzing confidence metrics, and troubleshooting cases where predictions diverge from experimental data.
Protein Folding and Structure PredictionView internship →
Peak Calling and Quality Control
Interns will learn to process raw ChIP-Seq data and implement peak calling algorithms using tools like MACS2 and HOMER. They will develop expertise in quality assessment metrics, including read distribution analysis, peak enrichment validation, and FRiP score calculation to ensure data reliability.
ChIP-Seq Data Analysis ResearchView internship →
Molecular Dynamics Simulations for Protein Stability Analysis
Interns will conduct molecular dynamics simulations using tools like GROMACS or NAMD to study protein folding pathways, conformational dynamics, and stability under various conditions. They will analyze trajectory data, calculate free energy landscapes, and investigate how mutations affect protein structure and function.
Protein Folding and Structure PredictionView internship →
Transcription Factor Binding Site Analysis
Interns will analyze ChIP-Seq peaks to identify and characterize transcription factor binding sites and their genomic locations. This includes motif discovery, comparative binding analysis across different cell types, and integration with gene regulatory networks.
ChIP-Seq Data Analysis ResearchView internship →
Chromatin State Mapping and Epigenetic Integration
Interns will combine ChIP-Seq data from multiple histone modifications to create comprehensive chromatin state maps and predict gene regulatory regions. They will integrate these findings with RNA-Seq and ATAC-Seq data to understand epigenetic regulation of gene expression.
ChIP-Seq Data Analysis ResearchView internship →
Structure-Function Relationship Studies via Computational Mutagenesis
Interns will perform in silico mutagenesis studies to predict how specific amino acid substitutions affect protein folding, stability, and ligand binding. This includes running FoldX calculations, analyzing energy landscapes, and generating hypotheses for experimental validation of computationally designed variants.
Protein Folding and Structure PredictionView internship →
Cryo-EM Data Analysis and Structure Refinement
Interns will participate in processing cryo-electron microscopy datasets, performing 3D reconstruction, and refining protein structures at near-atomic resolution. They will work with software such as RELION or CryoSPARC to improve map quality and validate structural models against biochemical data.
Protein Folding and Structure PredictionView internship →
Differential Binding Analysis Across Conditions
Interns will perform comparative ChIP-Seq analysis to identify differentially bound regions between experimental conditions, disease states, or developmental stages. This involves statistical testing, visualization of binding changes, and functional interpretation of condition-specific binding patterns.
ChIP-Seq Data Analysis ResearchView internship →
Bioinformatics Pipeline Development and Automation
Interns will design and implement reproducible ChIP-Seq analysis pipelines using Python, R, and shell scripting, incorporating best practices for data processing and quality control. They will develop tools for automated workflow execution and create comprehensive documentation for pipeline deployment.
ChIP-Seq Data Analysis ResearchView internship →
Machine Learning Applications in Secondary Structure Prediction
Interns will develop and train machine learning models (neural networks, random forests) to predict alpha-helices, beta-sheets, and coil regions from amino acid sequences. This includes feature engineering from sequence data, model validation, and comparing performance with established tools like PSIPRED or JPRED.
Protein Folding and Structure PredictionView internship →
Long-Read Sequencing Data Analysis for SV Detection
Interns will work with long-read sequencing technologies (PacBio, Oxford Nanopore) to identify and characterize structural variants. They will learn to process raw sequencing data, apply SV detection algorithms, and validate findings using bioinformatics pipelines.
Structural Variant Detection ResearchView internship →
Machine Learning Models for SV Classification
Interns will develop and train machine learning models to classify and predict structural variants from genomic data. This includes feature engineering, model optimization, and evaluation of classification accuracy using benchmark datasets.
Structural Variant Detection ResearchView internship →
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