biosuitestkod 3.0.5

BioSuite Ultra - Comprehensive open-source bioinformatics platform with 48 analysis modules

BioSuite Ultra

The most comprehensive open-source bioinformatics platform.

BioSuite Ultra is a full-stack bioinformatics platform with 48 analysis modules, 36+ visualization types, a cyberpunk GUI, and a 99+ option CLI — all in pure Python. No external binaries required.

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Features

48 Analysis Modules

Domain	Modules	Coverage
Sequence Analysis	FASTA/FASTQ I/O, GC%, translation, reverse complement, ORF finder, primer design, restriction enzymes, codon usage	80%
Alignment	Needleman-Wunsch, Smith-Waterman, BLAST (k-mer), MSA (progressive + Clustal)	70%
Phylogenetics	p-distance, UPGMA, NJ, ML (RAxML), Bayesian (MrBayes)	85%
Transcriptomics	CPM/TPM normalization, differential expression, GO/KEGG enrichment	60%
NGS/Genomics	BAM/VCF parsing, read alignment (BWA/Bowtie2), variant calling, SV/CNV detection	65%
Single-Cell	Scanpy-based scRNA-seq pipeline	80%
Proteins	PDB analysis, ESMFold structure prediction, molecular docking	50%
Epigenomics	Bisulfite methylation, DMR detection	40%
Metagenomics	K-mer classifier, 16S rRNA pipeline, alpha/beta diversity	65%
Metabolomics	Peak detection, ANOVA, feature alignment	50%
Population Genetics	HWE, FST, Tajima's D, LD, PCA	70%
CRISPR	Guide RNA design, PAM finding, off-target scoring	70%
Metabolism	Flux balance analysis (FBA), knockout simulation	55%
Machine Learning	Random Forest, SVM, SHAP, cross-validation	50%
Workflow	Pipeline builder, batch processor, HTML report generator	80%
GO/Pathways	GO browser, pathway visualization (KEGG-style maps)	60%
GWAS	Chi-squared test, Manhattan/QQ plots, lead SNP detection	70%
Epitope Prediction	T-cell (MHC binding), B-cell (surface propensity), linear epitopes	70%

36+ Visualization Types

Volcano, PCA, Manhattan, MA, Venn, Barplot, Boxplot, Heatmap, Scatter, Time Series, QQ-plot, Clustered Heatmap, Circos, Alignment Viewer, Violin, Raincloud, Ridge, Dot Plot, GSEA, Motif Logo, Sankey, UMAP, Network (PPI/Regulatory/Metabolic), UpSet, Genome Browser, Interactive (Plotly), Sequence Logo, Conservation, Synteny Dotplot, and more.

Dual-Mode Architecture

Every module follows a consistent pattern:

def analyze(input, ...):
    # Try external tool first (fast)
    if _has_external_tool():
        return _run_external(input, ...)
    # Fall back to pure Python (always works)
    return _run_builtin(input, ...)

Cyberpunk GUI

• 29 analysis tabs with scrollable sidebar
• 3 themes: Dark-Green-Cyber, Dark-Purple-Cyber, Light-Blue-Cyber
• Keyboard shortcuts (Ctrl+S, Ctrl+Q, F1, F5, Escape)
• Progress bars for long operations
• Plot history (last 10 plots)
• API key configuration panel
• 15 built-in help guides

CLI with 99+ Options

Professional CLI menu with organized sections for every analysis type.

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Installation

Via PyPI (recommended)

pip install biosuitestkod

Windows Users — If pip install fails on pysam

pysam needs C build tools. Two options:

Option A: Visual Studio Build Tools

1. Download: https://visualstudio.microsoft.com/visual-cpp-build-tools/
2. Run installer → select "Desktop development with C++" → Install
3. Open "x64 Native Tools Command Prompt for VS" (search in Start Menu)
4. Run: pip install pysam

Option B: Use Conda (easier)

1. Install Anaconda: https://anaconda.com/download
2. Run: conda install -c bioconda pysam

From source

git clone https://github.com/sahandtouri/BioSuite-Better.git
cd BioSuite-Better
pip install -r requirements.txt

Quick Start

CLI Mode

python run.py

GUI Mode

python run.py --gui

Programmatic API

from bioplatter.core.sequence import gc_content, reverse_complement, translate
from bioplatter.core.alignment import needleman_wunsch, smith_waterman
from bioplatter.core.phylogeny import distance_matrix, upgma_tree
from bioplatter.core.workflow.pipeline import Pipeline
from bioplatter.plotting.upset_plots import plot_upset

# Quick analysis
gc = gc_content("ATCGATCG")  # 50.0
rc = reverse_complement("ATCG")  # "CGAT"
protein = translate("ATGAAATTTTAA")  # "MKF"

# Pipeline
p = Pipeline("my_analysis")
p.add_step("gc", gc_content, args=("ATCGATCG",))
p.add_step("revcomp", reverse_complement, args=("ATCGATCG",))
p.run()
print(p.results)

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Project Structure

BioSuite-Better/
├── run.py                    # Entry point
├── bioplatter/
│   ├── core/                 # 48 analysis modules
│   │   ├── sequence.py       # Sequence I/O & analysis
│   │   ├── alignment.py      # Pairwise alignment (vectorized)
│   │   ├── blast.py          # Sequence search
│   │   ├── msa.py            # Multiple sequence alignment
│   │   ├── phylogeny.py      # Distance-based trees
│   │   ├── ml_phylogeny.py   # ML trees (NJ + RAxML)
│   │   ├── bayesian_phylogeny.py  # Bayesian trees
│   │   ├── expression.py     # Differential expression
│   │   ├── enrichment.py     # GO/KEGG enrichment
│   │   ├── single_cell.py    # scRNA-seq (scanpy)
│   │   ├── ngs.py            # BAM/VCF utilities
│   │   ├── read_aligner.py   # Read mapping
│   │   ├── variant_calling.py # Variant detection + SV/CNV
│   │   ├── peak_calling.py   # ChIP-seq peaks
│   │   ├── assembly.py       # Genome assembly
│   │   ├── metagenomics.py   # Taxonomic classification + 16S
│   │   ├── trimming.py       # Read QC
│   │   ├── quantification.py # RNA-seq quantification
│   │   ├── structure.py      # PDB analysis
│   │   ├── structure_prediction.py  # Protein structure
│   │   ├── docking.py        # Molecular docking
│   │   ├── crispr.py         # Guide RNA design
│   │   ├── metabolism.py     # Flux balance analysis
│   │   ├── popgen.py         # Population genetics
│   │   ├── epigenomics.py    # Methylation analysis
│   │   ├── metabolomics.py   # Mass spec analysis
│   │   ├── md_simulation.py  # Molecular dynamics
│   │   ├── bio_ml.py         # Machine learning
│   │   ├── orf_finder.py     # ORF, restriction enzymes, primers
│   │   ├── codon_usage.py    # Codon tables, k-mer, complexity
│   │   ├── survival.py       # Kaplan-Meier, Cox PH
│   │   ├── file_formats.py   # BED/GFF/Newick/Stockholm/BigWig
│   │   ├── databases.py      # NCBI/UniProt/PDB/KEGG/Ensembl
│   │   ├── go_browser.py     # Gene Ontology browser
│   │   ├── pathway_viz.py    # Pathway visualization
│   │   ├── gwas.py           # GWAS analysis
│   │   ├── epitope.py        # Epitope prediction
│   │   └── workflow/         # Pipeline, batch, report
│   ├── plotting/             # 36+ visualization types
│   ├── gui/                  # Cyberpunk GUI (29 tabs)
│   ├── cli/                  # CLI with 99+ options
│   └── tests/                # 271 automated tests
├── examples/                 # Jupyter notebooks
└── requirements.txt

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Testing

# Run all 271 tests
python -m pytest tests/ -v

# Run specific test file
python -m pytest tests/test_phase4.py -v

# Run with short traceback
python -m pytest tests/ -v --tb=short

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Requirements

All pip-installable:

numpy, pandas, matplotlib, seaborn, scipy, scikit-learn
biopython, customtkinter, tqdm, goatools, gseapy
scanpy, anndata, pysam, scikit-bio, biotite, networkx
plotly, ete3, cobra, shap, statsmodels

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Platform

• OS: Windows, macOS, Linux
• Python: 3.10+
• GPU: Not required (CPU-only)
• RAM: 4GB minimum, 8GB recommended
• External tools: Optional (BLAST+, Clustal Omega, etc. provide speed boosts)

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Author

Sahand Touri Molecular Cell Biology Student, Urmia IAU, Iran

Built as a comprehensive bioinformatics portfolio project demonstrating:

• Full-stack software engineering (48 modules, 21,000+ lines, 271 tests)
• Domain expertise across 20+ bioinformatics areas
• Dual-mode architecture (pure Python + optional external tools)
• Professional GUI and CLI design
• Automated testing and quality assurance

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License

MIT License - Free for academic and commercial use.