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Bioinformatics Internship Topics

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Bioinformatics Internships with Accommodation

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Showing 49–60 of 105 internship topics
Epigenetic Biomarker Discovery for Disease
Interns will apply machine learning and statistical approaches to identify epigenetic signatures associated with disease phenotypes, cancer progression, or drug response. They will validate candidate biomarkers through feature selection, cross-validation, and functional interpretation of identified epigenetic markers.
AI Epigenome Bioinformatics ResearchView internship →
Non-Coding RNA Regulation in Epigenetic Pathways
Interns will investigate the role of microRNAs and long non-coding RNAs in epigenetic regulation by integrating RNA-seq data with chromatin modification datasets. They will construct regulatory networks to identify how ncRNAs influence histone modifications, DNA methylation, and chromatin remodeling complex activity.
AI Epigenome Bioinformatics ResearchView internship →
Transcript Abundance Estimation and Quantification
Interns will learn to process RNA-Seq raw reads and apply quantification tools like Kallisto, Salmon, or RSEM to estimate transcript and gene-level expression abundances. They will work with various reference genomes and annotation files to optimize quantification accuracy across different experimental designs.
RNA-Seq Expression Quantification ResearchView internship →
Quality Control and Data Preprocessing for RNA-Seq
Interns will develop expertise in quality assessment of sequencing data using FastQC, adapter trimming, and alignment validation techniques. They will implement preprocessing pipelines to identify and handle low-quality reads, contamination, and technical artifacts before expression quantification.
RNA-Seq Expression Quantification ResearchView internship →
Differential Expression Analysis and Statistical Modeling
Interns will conduct differential expression analysis using R packages such as DESeq2, edgeR, and Limma to identify significantly expressed genes between biological conditions. They will perform statistical testing, multiple hypothesis correction, and validate results through visualization and functional interpretation.
RNA-Seq Expression Quantification ResearchView internship →
Alternative Splicing Detection and Isoform Analysis
Interns will explore transcript-level expression patterns to detect alternative splicing events and isoform-specific abundance changes across samples. They will utilize tools like StringTie and Cufflinks to reconstruct transcriptomes and analyze splicing variations in disease or developmental contexts.
RNA-Seq Expression Quantification ResearchView internship →
Gene Expression Normalization and Batch Effect Correction
Interns will investigate various normalization methods (TPM, FPKM, quantile normalization) and implement batch correction techniques like ComBat and SVA to account for technical variability. They will evaluate the impact of normalization strategies on downstream analysis and biological interpretation of expression data.
RNA-Seq Expression Quantification ResearchView internship →
Machine Learning Model Development for PPI Prediction
Interns will develop and optimize machine learning algorithms such as graph neural networks and deep learning models to predict protein-protein interactions from sequence and structural data. They will work with datasets like STRING, BioGRID, and DIP to train, validate, and benchmark models for accuracy and generalization.
Protein-Protein Interaction Prediction ResearchView internship →
Structural Docking and Molecular Simulation Analysis
Interns will conduct molecular docking simulations using tools like AutoDock, HADDOCK, and Rosetta to model binding interfaces between protein pairs. They will analyze binding affinities, conformational changes, and interaction hotspots to validate computationally predicted interactions.
Protein-Protein Interaction Prediction ResearchView internship →
Biological Network Analysis and PPI Network Reconstruction
Interns will reconstruct and analyze protein interaction networks using bioinformatics platforms like Cytoscape and STRING, identifying functional modules, network motifs, and pathway interactions. They will apply network topology analysis and clustering algorithms to extract biological insights from predicted interactions.
Protein-Protein Interaction Prediction ResearchView internship →
Sequence-Based Feature Engineering for Interaction Prediction
Interns will extract and engineer relevant features from protein sequences including physicochemical properties, evolutionary information, and domain annotations to improve PPI prediction models. They will explore sequence alignment methods, position-specific scoring matrices, and embedding techniques like ProtBERT.
Protein-Protein Interaction Prediction ResearchView internship →
Cross-Species PPI Transfer Learning and Homology-Based Prediction
Interns will develop transfer learning approaches to predict interactions in organisms with limited experimental data by leveraging known interactions from model organisms. They will investigate ortholog mapping, evolutionary conservation patterns, and domain-based prediction methods for cross-species interaction inference.
Protein-Protein Interaction Prediction ResearchView internship →
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