Computational Biology Research Projects
Computational biology applies algorithms and models to biological data — sequences, structures, networks and phenotypes. Final-year projects that implement analysis pipelines, docking workflows or ML classifiers on public data produce clear, reproducible results.
Below are 90+ topics across sequence/genomics, structure, docking, networks, ML and systems biology, with tools (Biopython, BLAST, scikit-learn, PyTorch, R/Bioconductor) and databases (NCBI, UniProt, PDB, GEO).
Biopython
BLAST / NCBI
PyTorch
scikit-learn
R / Bioconductor
PDB / UniProt
| # | Computational Biology Project Topic | Tools Used |
|---|---|---|
| 🧬 Sequence Analysis · Genomics · NGS Concepts | ||
| 01 | SeqLocal and Global Sequence Alignment (Smith–Waterman / Needleman–Wunsch) | Biopython, custom DP |
| 02 | SeqBLAST Pipeline: Query, Parse and Annotate Hits | BLAST+, Biopython |
| 03 | SeqMultiple Sequence Alignment and Conservation Analysis | Clustal/MUSCLE concepts, Biopython |
| 04 | SeqORF Finding and Simple Gene Prediction Rules | Biopython, GC content |
| 05 | Seqk-mer Frequency Analysis for Sequence Classification | Python, scikit-learn |
| 06 | SeqVariant Calling Concepts from Aligned Reads | SAMtools concepts, VCF |
| 07 | SeqRNA-seq Differential Expression Pipeline Sketch | DESeq2 / edgeR concepts |
| 08 | SeqGenome Browser Track Visualisation from BED/GFF | Python plotting, pyGenomeTracks |
| 09 | SeqMotif Discovery: Simple Pattern and PWM Methods | Biopython motifs |
| 10 | SeqCodon Usage Bias Analysis Across Organisms | Biopython, NCBI sequences |
| 11 | SeqQuality Control of FASTQ Files (FastQC-style Metrics) | Python QC scripts |
| 12 | SeqMetagenomic Taxonomic Classification Concepts | k-mer / marker methods |
| 13 | SeqPrimer Design Helper with Specificity Checks | Biopython, BLAST |
| 14 | SeqComparative Genomics: Synteny / Ortholog Mapping Sketch | BLAST reciprocal, plots |
| 15 | SeqCOVID/Viral Sequence Mutation Tracking Pipeline | Public FASTA, alignment |
| 🧱 Protein Structure · Prediction · Analysis | ||
| 16 | StrPDB Structure Parsing and Secondary Structure Stats | Biopython PDB, DSSP concepts |
| 17 | StrRamachandran Plot Generation and Outlier Detection | Biopython, matplotlib |
| 18 | StrHomology Modelling Workflow Concepts | Modeller concepts, templates |
| 19 | StrAlphaFold / ESMFold Output Analysis and Confidence Maps | Predicted structures, pLDDT |
| 20 | StrDomain Annotation and Family Classification | Pfam / InterPro concepts |
| 21 | StrSurface Accessibility and Pocket Detection Concepts | Structure analysis tools |
| 22 | StrStructure Comparison: RMSD and Superposition | Biopython Superimposer |
| 23 | StrDisorder Prediction and Intrinsically Disordered Regions | Sequence-based predictors |
| 24 | StrMembrane Protein Topology Prediction Concepts | TMHMM-style methods |
| 25 | StrProtein–Protein Interface Residue Analysis | Structure contacts, PDB |
| 26 | StrMutation Effect on Stability — Simple Energy Models | ΔΔG concepts, literature |
| 27 | Str3D Visualisation Script Pipeline for Reports | PyMOL / NGLview concepts |
| 💊 Molecular Docking · Virtual Screening · Drug Discovery | ||
| 28 | DockLigand Preparation and Receptor Grid Setup | AutoDock/Vina concepts |
| 29 | DockMolecular Docking of a Known Drug–Target Pair | Vina, pose analysis |
| 30 | DockVirtual Screening of a Small Compound Library | Batch docking, ranking |
| 31 | DockScoring Function Comparison and Pose Ranking | Multiple score types |
| 32 | DockADMET Property Filtering Before Docking | RDKit, Lipinski rules |
| 33 | DockPharmacophore Modelling Concepts | Feature-based models |
| 34 | DockQ SAR / Descriptor-Based Activity Prediction | RDKit, scikit-learn |
| 35 | DockProtein–Ligand Interaction Fingerprints | Contact maps, analysis |
| 36 | DockRe-Docking Validation and RMSD Benchmark | Known complexes, metrics |
| 37 | DockNatural Product Virtual Screening Case Study | Public compound sets |
| 38 | DockMulti-Target Docking for Polypharmacology Concepts | Multiple receptors |
| 39 | DockMD Simulation Trajectory Analysis Concepts (Post-Docking) | GROMACS concepts, RMSD |
| 40 | DockBinding Free Energy Estimation Overview | MM-PBSA concepts |
| 🕸️ Biological Networks · Systems Biology | ||
| 41 | NetProtein–Protein Interaction Network Construction | STRING concepts, NetworkX |
| 42 | NetGene Co-Expression Network Analysis | Correlation, WGCNA concepts |
| 43 | NetPathway Enrichment Analysis (GO / KEGG style) | Enrichment tools, R |
| 44 | NetHub Gene Identification and Centrality Metrics | NetworkX, Cytoscape |
| 45 | NetDisease–Gene Association Network Visualisation | Public databases, plots |
| 46 | NetBoolean Network Modelling of a Simple Pathway | Boolean logic, simulation |
| 47 | NetFlux Balance Analysis Concepts for Metabolic Networks | COBRApy concepts |
| 48 | NetCommunity Detection in Biological Graphs | Louvain / modularity |
| 49 | NetDrug–Target–Disease Network Tripartite Analysis | Multi-layer networks |
| 50 | NetNetwork Robustness Under Node Removal Simulations | Attack tolerance metrics |
| 51 | NetCytoscape Automation for Report-Ready Figures | Cytoscape, styles |
| 52 | NetTime-Series Expression Network Dynamics Sketch | Dynamic correlations |
| 🤖 Machine Learning in Biology | ||
| 53 | MLSequence Classification with k-mer Features + SVM/RF | scikit-learn, Biopython |
| 54 | MLProtein Function Prediction from Sequence Embeddings | ESM / ProtBERT concepts |
| 55 | MLAntimicrobial Peptide Classification Pipeline | Public AMP datasets, ML |
| 56 | MLDrug–Target Interaction Prediction with Fingerprints | RDKit, scikit-learn |
| 57 | MLCNN for DNA Motif / Sequence Classification | PyTorch 1D CNN |
| 58 | MLGene Expression Classification (Tumour vs Normal) | GEO data, scikit-learn |
| 59 | MLSingle-Cell Clustering and Marker Gene Analysis Sketch | Scanpy concepts |
| 60 | MLImbalanced Learning for Rare Disease Gene Sets | SMOTE, class weights |
| 61 | MLFeature Importance for Biological Interpretability | SHAP / permutation |
| 62 | MLTransfer Learning from Protein LMs to Downstream Tasks | HuggingFace / ESM |
| 63 | MLCross-Validation Strategies for Omics Data Leakage Avoidance | Group CV, patient-level |
| 64 | MLBenchmark: Classical Features vs Deep Embeddings | Same labels, dual models |
| 65 | MLActive Learning for Label-Efficient Sequence Annotation | Query strategies |
| 66 | MLSurvival Analysis Concepts from Expression Data | Cox models, R/Python |
| 🌳 Phylogenetics · Evolution · Systems Modelling | ||
| 67 | SysPhylogenetic Tree Construction (Distance / Parsimony Concepts) | Biopython Phylo, distance |
| 68 | SysBootstrap Support and Tree Visualisation | ETE / Bio.Phylo plots |
| 69 | SysMolecular Clock and Divergence Time Concepts | Simple rate models |
| 70 | SysPositive Selection Detection Concepts (dN/dS) | Codon models overview |
| 71 | SysPopulation Genetics: Allele Frequency & Hardy–Weinberg | Python simulations |
| 72 | SysEpidemiological SIR Model Fitting to Case Data | SciPy ODE, public data |
| 73 | SysGene Regulatory Network Inference from Expression | Correlation / mutual info |
| 74 | SysAgent-Based Model of a Simple Biological Process | Python ABM framework |
| 75 | SysStochastic vs Deterministic Models Comparison | Gillespie vs ODE |
| 📊 Pipelines · Databases · Research Practices | ||
| 76 | PipeEnd-to-End Reproducible Analysis Notebook Package | Jupyter, conda env |
| 77 | PipeNCBI / UniProt / PDB API Access Scripts | Biopython Entrez, REST |
| 78 | PipeGEO Dataset Download and Metadata Harmonisation | GEOquery concepts, Python |
| 79 | PipeContainerised Bioinformatics Tool Demo (Docker Sketch) | Dockerfile, simple tool |
| 80 | PipeWorkflow Manager Concepts (Snakemake / Nextflow Sketch) | Pipeline DSL overview |
| 81 | EvalBenchmark Design for Sequence Classifiers | Hold-out, metrics suite |
| 82 | EvalReporting Standards for Computational Biology Projects | Methods checklist |
| 83 | ResearchFAIR Data Principles Applied to a Student Dataset | Metadata, repositories |
| 84 | ResearchBias and Batch Effects in Omics Analyses | Correction methods overview |
| 85 | ResearchOpen Science: Sharing Code, Data and Environments | GitHub, Zenodo concepts |
| 86 | ResearchLiterature Mining: Simple NLP on PubMed Abstracts | Biopython Medline, NLP |
| 87 | ResearchEducational CompBio Lab Module Design | Curriculum + datasets |
| 88 | ResearchCost and Compute Profiling of Student Pipelines | Runtime / memory logs |
| 89 | ResearchComparison of Local vs Cloud Bioinformatics Workflows | Architecture report |
| 90 | ResearchEthics of Genomic Data Sharing and Consent | Policy + case discussion |
| 91 | ResearchIntegrative Multi-Omics Analysis Sketch | Expression + mutation fusion |
| 92 | ResearchStudent Starter Kit: Sequence to Insight Report | End-to-end tutorial package |
Topics use Biopython, BLAST, scikit-learn, PyTorch, R/Bioconductor and public databases (NCBI, UniProt, PDB, GEO). Contact us for reference material, analysis code, evaluation setup, university-format report, PPT and viva Q&A for any topic above.
Computational Biology Research Topics
Why Choose Us for Computational Biology Projects?Bangalore-based guidance for BE, BTech and MTech students working on sequence analysis, docking, networks and ML in biology.
Sequence & Genomics
Alignment, BLAST pipelines, variant concepts and expression analysis with Biopython and public data.
Docking & Drugs
Virtual screening, ADMET filters, QSAR and pose analysis with AutoDock/Vina-style workflows.
Networks
PPI and co-expression networks, enrichment, hub genes and pathway visualisation.
ML in Biology
Sequence classifiers, protein LMs, drug–target prediction and interpretable omics models.
Frequently Asked Questions — Computational Biology
Top topics include sequence alignment and BLAST pipelines, molecular docking and virtual screening, protein structure analysis, phylogenetic trees, gene networks, and machine learning for sequence or expression classification.
Biopython, BLAST/NCBI tools, scikit-learn, PyTorch, R/Bioconductor, AutoDock/Vina concepts, NetworkX/Cytoscape, and databases NCBI, UniProt, PDB, GEO.
Yes. Packages include reference material, analysis code or pipelines, evaluation metrics, dataset notes, university-format report, PPT and viva Q&A.
The terms overlap. Bioinformatics often emphasises algorithms and tools for biological data; computational biology emphasises modelling of biological systems. Student projects frequently combine both.
Computational Biology Project Lab — Bangalore
Sequence analysis, docking, networks and ML support for BE, BTech and MTech computational biology projects.
Sequence Alignment
& BLAST Pipelines
Genomics& BLAST Pipelines
Protein Structure
& PDB Analysis
Structure& PDB Analysis
Molecular Docking
& Screening
Drug Discovery& Screening
Biological Networks
& Pathways
Systems& Pathways
ML on Sequences
& Expression
Machine Learning& Expression
Phylogenetic
Trees
EvolutionTrees
Reproducible
Pipelines
WorkflowsPipelines
Viva & Report
Preparation
DocumentationPreparation