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Algorithms"],"sub_categories":["Sequence Alignment \u003ca id=\"sequence-alignment\"\u003e\u003c/a\u003e"],"readme":"\u003ch1 align=\"center\"\u003eAwesome Bioinformatics Algorithms\u003c/h1\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://awesome.re\"\u003e\u003cimg src=\"https://awesome.re/badge.svg\" alt=\"Awesome\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://github.com/LessUp/awesome-bioinfo-algorithms/actions/workflows/ci.yml\"\u003e\u003cimg src=\"https://github.com/LessUp/awesome-bioinfo-algorithms/actions/workflows/ci.yml/badge.svg\" alt=\"CI\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://lessup.github.io/awesome-bioinfo-algorithms/\"\u003e\u003cimg src=\"https://img.shields.io/badge/Docs-GitHub%20Pages-blue?logo=github\" alt=\"Documentation\"\u003e\u003c/a\u003e\n  \u003ca href=\"http://creativecommons.org/publicdomain/zero/1.0/\"\u003e\u003cimg src=\"https://img.shields.io/badge/License-CC0%201.0-lightgrey.svg\" alt=\"License\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://github.com/LessUp/awesome-bioinfo-algorithms/blob/main/CITATION.cff\"\u003e\u003cimg src=\"https://img.shields.io/badge/Cite%20Me-APA-blue\" alt=\"Citation\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Algorithms-195-blue.svg\" alt=\"Algorithms\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Categories-16-green.svg\" alt=\"Categories\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Tags-392-orange.svg\" alt=\"Tags\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Python-3.9%2B-blue?logo=python\" alt=\"Python\"\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003cb\u003e🧬 A curated collection of bioinformatics algorithms with complexity analysis\u003c/b\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"README.zh-CN.md\"\u003e简体中文\u003c/a\u003e • \n  \u003ca href=\"https://lessup.github.io/awesome-bioinfo-algorithms/\"\u003e📖 Documentation Site\u003c/a\u003e • \n  \u003ca href=\"CONTRIBUTING.md\"\u003e🤝 Contributing\u003c/a\u003e • \n  \u003ca href=\"#-citation\"\u003e📚 Citation\u003c/a\u003e\n\u003c/p\u003e\n\n---\n\n## ✨ Highlights\n\n\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd width=\"50%\"\u003e\n\n**🎯 For Researchers**\n- 195+ curated algorithms\n- Time/space complexity analysis\n- Paper and implementation links\n- Multi-language support\n\n\u003c/td\u003e\n\u003ctd width=\"50%\"\u003e\n\n**💻 For Developers**\n- CLI toolkit for data management\n- Automated validation \u0026 generation\n- Structured YAML data format\n- Extensive test coverage\n\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/table\u003e\n\n---\n\n## 🚀 Quick Start\n\n```bash\n# Clone repository\ngit clone https://github.com/LessUp/awesome-bioinfo-algorithms.git\ncd awesome-bioinfo-algorithms\n\n# Install dependencies (includes MkDocs support)\npip install -e \".[dev,docs]\"\n\n# Validate data\npython -m awesome_bioinfo validate\n\n# Show statistics\npython -m awesome_bioinfo stats\n```\n\n---\n\n## 📊 Statistics\n\n| Metric | Value |\n|:-------|------:|\n| Total Algorithms | **195** |\n| Categories | **16** |\n| Unique Tags | **392** |\n\n---\n\n## 📑 Table of Contents\n\n\u003cdetails\u003e\n\u003csummary\u003eClick to expand\u003c/summary\u003e\n\n- [Sequence Alignment](#sequence-alignment)\n- [Sequence Assembly](#sequence-assembly)\n- [Variant Calling](#variant-calling)\n- [Gene Expression Analysis](#gene-expression-analysis)\n- [Protein Structure Prediction](#protein-structure-prediction)\n- [Phylogenetics](#phylogenetics)\n- [Functional Annotation](#functional-annotation)\n- [Data Compression](#data-compression)\n- [Single-Cell Genomics](#single-cell-genomics)\n- [Metagenomics](#metagenomics)\n- [Epigenomics](#epigenomics)\n- [Gene Prediction](#gene-prediction)\n- [Population Genetics](#population-genetics)\n- [Spatial Omics](#spatial-omics)\n- [Graph Genomics](#graph-genomics)\n- [Protein Language Model](#protein-language-model)\n\n\u003c/details\u003e\n\n---\n\n## Category Overview\n\n| Category | Algorithms | Description |\n|----------|------------|-------------|\n| Sequence Alignment | 19 | Algorithms for comparing and aligning biological sequences |\n| Sequence Assembly | 14 | Algorithms for reconstructing complete sequences from short reads |\n| Variant Calling | 14 | Algorithms for detecting genomic variations |\n| Gene Expression Analysis | 12 | Algorithms for analyzing gene expression levels |\n| Protein Structure Prediction | 14 | Algorithms for predicting protein 3D structures |\n| Phylogenetics | 12 | Algorithms for building and analyzing evolutionary trees |\n| Functional Annotation | 12 | Algorithms for predicting gene and protein functions |\n| Data Compression | 10 | Algorithms for compressing bioinformatics data |\n| Single-Cell Genomics | 15 | Algorithms for single-cell genomics and transcriptomics |\n| Metagenomics | 14 | Algorithms for microbial community genomics |\n| Epigenomics | 6 | Algorithms for analyzing epigenetic modifications |\n| Gene Prediction | 12 | Algorithms for gene structure prediction and annotation |\n| Population Genetics | 12 | Algorithms for population genetic structure and evolution |\n| Spatial Omics | 10 | Algorithms for spatially-resolved omics data |\n| Graph Genomics | 9 | Algorithms based on graph representations of genomes |\n| Protein Language Model | 10 | Protein analysis using large-scale pre-trained models |\n\n\n---\n\n## Featured Algorithms\n\n### Sequence Alignment \u003ca id=\"sequence-alignment\"\u003e\u003c/a\u003e\n\u003ca href=\"#table-of-contents\"\u003e↑ Back to Top\u003c/a\u003e\n\n**Pairwise Alignment**\n\n| Algorithm | Year | Time | Space | Tags |\n|-----------|------|------|-------|------|\n| ⭐ BLAST | 1990 | O(mn) | O(mn) | `heuristic` `database-search` `classic` |\n| ⭐ Smith-Waterman | 1981 | O(mn) | O(mn) | `dynamic-programming` `local-alignment` `classic` |\n| ⭐ Needleman-Wunsch | 1970 | O(mn) | O(mn) | `dynamic-programming` `global-alignment` `classic` |\n| 🆕 WFA2-lib | 2023 | O(ns) | O(s) | `wavefront` `adaptive` `ultra-fast` |\n| Minimap2 | 2018 | O(n) | O(n) | `minimizer` `long-read` `versatile` |\n\n*[View all 14 algorithms in this category →](https://lessup.github.io/awesome-bioinfo-algorithms/)*\n\n**Multiple Sequence Alignment**\n\n| Algorithm | Year | Time | Space | Tags |\n|-----------|------|------|-------|------|\n| Clustal Omega | 2011 | O(n * L^2) | O(n * L) | `multiple-alignment` `guide-tree` `progressive` |\n| Kalign | 2005 | O(n^2 * L) | O(n * L) | `multiple-alignment` `fast` `wu-manber` |\n| MUSCLE | 2004 | O(n^2 * L) | O(n * L) | `multiple-alignment` `iterative` `refinement` |\n| MAFFT | 2002 | O(n * L * log L) | O(n * L) | `fft` `multiple-alignment` `scalable` |\n| POA | 2002 | O(n^2 * L^2) | O(n * L^2) | `multiple-alignment` `partial-order` `graph-based` |\n\n\n### Sequence Assembly \u003ca id=\"sequence-assembly\"\u003e\u003c/a\u003e\n\u003ca href=\"#table-of-contents\"\u003e↑ Back to Top\u003c/a\u003e\n\n**De Novo Assembly**\n\n| Algorithm | Year | Time | Space | Tags |\n|-----------|------|------|-------|------|\n| 🆕 Verkko | 2023 | O(n log n) | O(n) | `t2t` `hybrid` `hifi` |\n| Hifiasm | 2021 | O(n log n) | O(n) | `hifi` `haplotype-aware` `long-read` |\n| Shasta | 2020 | O(n) | O(n) | `long-read` `fast` `run-length` |\n| Flye | 2019 | O(n log n) | O(n) | `repeat-graph` `long-read` `ont` |\n| Wtdbg2 | 2019 | O(n) | O(n) | `long-read` `fuzzy-bruijn` `fast` |\n\n*[View all 12 algorithms in this category →](https://lessup.github.io/awesome-bioinfo-algorithms/)*\n\n**Reference-Guided Assembly**\n\n| Algorithm | Year | Time | Space | Tags |\n|-----------|------|------|-------|------|\n| RagTag | 2022 | O(n log n) | O(n) | `reference-based` `scaffolding` `assembly-polishing` |\n| Reference-Guided Assembly | 2011 | O(n log n) | O(n) | `reference-based` `scaffolding` `resequencing` |\n\n\n### Variant Calling \u003ca id=\"variant-calling\"\u003e\u003c/a\u003e\n\u003ca href=\"#table-of-contents\"\u003e↑ Back to Top\u003c/a\u003e\n\n**SNV Detection**\n\n| Algorithm | Year | Time | Space | Tags |\n|-----------|------|------|-------|------|\n| 🆕 DeepSomatic | 2024 | O(n * r) | O(r) | `deep-learning` `somatic` `cancer` |\n| Clair3 | 2022 | O(n * r) | O(r) | `long-read` `nanopore` `pacbio` |\n| Octopus | 2021 | O(n * h) | O(h) | `bayesian` `haplotype` `germline-somatic` |\n| DeepVariant | 2018 | O(n * r) | O(r) | `deep-learning` `cnn` `snp` |\n| Strelka2 | 2018 | O(n * r) | O(r) | `somatic` `germline` `fast` |\n\n*[View all 8 algorithms in this category →](https://lessup.github.io/awesome-bioinfo-algorithms/)*\n\n**Structural Variants**\n\n| Algorithm | Year | Time | Space | Tags |\n|-----------|------|------|-------|------|\n| 🆕 Sniffles2 | 2023 | O(n * c) | O(n) | `structural-variant` `long-read` `breakpoint` |\n| cuteSV | 2020 | O(n * c) | O(n) | `structural-variant` `long-read` `clustering` |\n| GRIDSS | 2017 | O(n * c) | O(n) | `structural-variant` `breakend` `assembly-based` |\n| Manta | 2016 | O(n * c) | O(c) | `structural-variant` `graph-assembly` `clinical` |\n| SvABA | 2016 | O(n * c) | O(c) | `structural-variant` `somatic` `assembly-based` |\n\n*[View all 6 algorithms in this category →](https://lessup.github.io/awesome-bioinfo-algorithms/)*\n\n\n### Gene Expression Analysis \u003ca id=\"gene-expression-analysis\"\u003e\u003c/a\u003e\n\u003ca href=\"#table-of-contents\"\u003e↑ Back to Top\u003c/a\u003e\n\n**Expression Quantification**\n\n| Algorithm | Year | Time | Space | Tags |\n|-----------|------|------|-------|------|\n| Salmon | 2017 | O(n) | O(t) | `selective-alignment` `quantification` `rna-seq` |\n| Kallisto | 2016 | O(n) | O(t) | `pseudoalignment` `quantification` `rna-seq` |\n| tximport | 2016 | O(n * t) | O(g) | `import` `summarization` `offset-correction` |\n| StringTie | 2015 | O(n) | O(g) | `transcript-assembly` `quantification` `rna-seq` |\n| STAR | 2013 | O(n) | O(g) | `rna-seq` `splice-aware` `alignment` |\n\n*[View all 6 algorithms in this category →](https://lessup.github.io/awesome-bioinfo-algorithms/)*\n\n**Differential Expression**\n\n| Algorithm | Year | Time | Space | Tags |\n|-----------|------|------|-------|------|\n| Sleuth | 2017 | O(n * g) | O(g) | `differential-expression` `bootstrap` `rna-seq` |\n| NOISeq | 2015 | O(n * g) | O(g) | `differential-expression` `non-parametric` `noiseq` |\n| DESeq2 | 2014 | O(n * g) | O(g) | `rna-seq` `differential-expression` `negative-binomial` |\n| limma-voom | 2014 | O(n * g) | O(g) | `differential-expression` `precision-weight` `linear-model` |\n| Ballgown | 2014 | O(n * g) | O(g) | `differential-expression` `fpkm` `transcript-level` |\n\n*[View all 6 algorithms in this category →](https://lessup.github.io/awesome-bioinfo-algorithms/)*\n\n\n### Protein Structure Prediction \u003ca id=\"protein-structure-prediction\"\u003e\u003c/a\u003e\n\u003ca href=\"#table-of-contents\"\u003e↑ Back to Top\u003c/a\u003e\n\n**Ab Initio Prediction**\n\n| Algorithm | Year | Time | Space | Tags |\n|-----------|------|------|-------|------|\n| 🆕 AlphaFold3 | 2024 | O(n^2) | O(n^2) | `deep-learning` `structure-prediction` `multi-modal` |\n| 🆕 Chai-1 | 2024 | O(n^2) | O(n^2) | `structure-prediction` `multi-modal` `drug-discovery` |\n| 🆕 Boltz-1 | 2024 | O(n^2) | O(n^2) | `structure-prediction` `open-source` `diffusion` |\n| 🆕 ESMFold | 2023 | O(n^2) | O(n^2) | `language-model` `single-sequence` `fast` |\n| OmegaFold | 2022 | O(n^2) | O(n^2) | `language-model` `single-sequence` `structure-prediction` |\n\n*[View all 9 algorithms in this category →](https://lessup.github.io/awesome-bioinfo-algorithms/)*\n\n**Template-Based Modeling**\n\n| Algorithm | Year | Time | Space | Tags |\n|-----------|------|------|-------|------|\n| 🆕 Foldseek | 2023 | O(n) | O(n) | `structure-search` `fast` `3Di` |\n| I-TASSER | 2008 | O(n^3) | O(n^2) | `threading` `template-based` `fragment-assembly` |\n| TM-align | 2005 | O(n^3) | O(n^2) | `structure-alignment` `rmsd` `classic` |\n| Rosetta | 2003 | O(n^3) | O(n^2) | `energy-function` `monte-carlo` `protein-design` |\n| MODELLER | 1993 | O(n^2) | O(n^2) | `homology-modeling` `template-based` `comparative-modeling` |\n\n\n### Phylogenetics \u003ca id=\"phylogenetics\"\u003e\u003c/a\u003e\n\u003ca href=\"#table-of-contents\"\u003e↑ Back to Top\u003c/a\u003e\n\n**Distance Methods**\n\n| Algorithm | Year | Time | Space | Tags |\n|-----------|------|------|-------|------|\n| ⭐ Neighbor-Joining | 1987 | O(n^3) | O(n^2) | `distance-based` `tree-building` `classic` |\n| FastTree | 2010 | O(n * s * log n) | O(n * s) | `tree-building` `approximate-likelihood` `scalable` |\n\n**Character-Based Methods**\n\n| Algorithm | Year | Time | Space | Tags |\n|-----------|------|------|-------|------|\n| IQ-TREE 2 | 2020 | O(n^2 * s) | O(n * s) | `maximum-likelihood` `model-finder` `partition` |\n| RAxML-NG | 2019 | O(n^2 * s * r) | O(n * s) | `maximum-likelihood` `scalable` `ultrafast-bootstrap` |\n| ASTRAL | 2018 | O(n * m) | O(n * m) | `species-tree` `summary-method` `coalescent` |\n| RevBayes | 2016 | O(n^2 * s * r) | O(n * s) | `bayesian` `probabilistic-programming` `flexible` |\n| IQ-TREE | 2015 | O(n^2 * s) | O(n * s) | `maximum-likelihood` `model-selection` `ultrafast-bootstrap` |\n\n*[View all 10 algorithms in this category →](https://lessup.github.io/awesome-bioinfo-algorithms/)*\n\n\n### Functional Annotation \u003ca id=\"functional-annotation\"\u003e\u003c/a\u003e\n\u003ca href=\"#table-of-contents\"\u003e↑ Back to Top\u003c/a\u003e\n\n**Homology-Based**\n\n| Algorithm | Year | Time | Space | Tags |\n|-----------|------|------|-------|------|\n| ⭐ BLAST-based Annotation | 1990 | O(mn) | O(m) | `sequence-similarity` `database-search` `classic` |\n| Bakta | 2021 | O(n) | O(n) | `prokaryotic` `annotation` `standardized` |\n| KofamKOALA | 2020 | O(n * m) | O(m) | `kegg` `orthology` `annotation` |\n| OrthoFinder | 2019 | O(n^2) | O(n^2) | `orthology` `comparative-genomics` `gene-family` |\n| eggNOG-mapper | 2017 | O(n * m) | O(m) | `orthology` `go-annotation` `kegg` |\n\n*[View all 6 algorithms in this category →](https://lessup.github.io/awesome-bioinfo-algorithms/)*\n\n**Domain-Based**\n\n| Algorithm | Year | Time | Space | Tags |\n|-----------|------|------|-------|------|\n| SignalP | 2019 | O(n) | O(n) | `signal-peptide` `deep-learning` `secretion` |\n| InterProScan | 2014 | O(m * d) | O(m) | `multi-database` `domain-detection` `go-annotation` |\n| InterPro | 2014 | O(m * d) | O(m) | `database` `domain` `protein-family` |\n| HMMER | 2011 | O(mn) | O(m) | `hmm` `domain-detection` `remote-homology` |\n| PfamScan | 2011 | O(mn) | O(m) | `domain-detection` `pfam` `protein-family` |\n\n*[View all 6 algorithms in this category →](https://lessup.github.io/awesome-bioinfo-algorithms/)*\n\n\n### Data Compression \u003ca id=\"data-compression\"\u003e\u003c/a\u003e\n\u003ca href=\"#table-of-contents\"\u003e↑ Back to Top\u003c/a\u003e\n\n**Specialized Compression**\n\n| Algorithm | Year | Time | Space | Tags |\n|-----------|------|------|-------|------|\n| Genozip | 2021 | O(n) | O(1) | `multi-format` `high-ratio` `random-access` |\n| SPRING Compress | 2020 | O(n log n) | O(n) | `fastq` `reordering` `high-ratio` |\n| SPRING | 2019 | O(n) | O(n) | `fastq` `specialized-compression` `high-ratio` |\n| MANGO | 2018 | O(n) | O(n) | `reference-free` `genome-compression` `context-modeling` |\n| Orione | 2015 | O(n) | O(1) | `reference-assisted` `fastq` `sam` |\n\n*[View all 8 algorithms in this category →](https://lessup.github.io/awesome-bioinfo-algorithms/)*\n\n**General Compression**\n\n| Algorithm | Year | Time | Space | Tags |\n|-----------|------|------|-------|------|\n| BGZF and Tabix | 2011 | O(n) | O(1) | `block-compression` `indexing` `random-access` |\n| GZIP for FASTQ | 1992 | O(n) | O(1) | `lossless` `general-purpose` `standard` |\n\n\n### Single-Cell Genomics \u003ca id=\"single-cell-genomics\"\u003e\u003c/a\u003e\n\u003ca href=\"#table-of-contents\"\u003e↑ Back to Top\u003c/a\u003e\n\n**Cell Clustering \u0026 Annotation**\n\n| Algorithm | Year | Time | Space | Tags |\n|-----------|------|------|-------|------|\n| 🆕 scVI-tools | 2023 | O(c * g * e) | O(c * g) | `variational-autoencoder` `deep-learning` `batch-correction` |\n| scArches | 2022 | O(c * g * e) | O(c * g) | `reference-mapping` `transfer-learning` `surgery` |\n| CellTypist | 2022 | O(c * g) | O(c * g) | `cell-type` `annotation` `logistic-regression` |\n| scANVI | 2021 | O(c * g * e) | O(c * g) | `semi-supervised` `annotation` `deep-learning` |\n| SCENIC | 2020 | O(c * g^2) | O(c * g) | `regulatory-network` `transcription-factor` `grn` |\n\n*[View all 10 algorithms in this category →](https://lessup.github.io/awesome-bioinfo-algorithms/)*\n\n**Preprocessing**\n\n| Algorithm | Year | Time | Space | Tags |\n|-----------|------|------|-------|------|\n| alevin-fry | 2022 | O(n * k) | O(g) | `quantification` `memory-efficient` `simpleaf` |\n| STARsolo | 2021 | O(n * g) | O(c * g) | `preprocessing` `alignment` `umi` |\n| kallisto | bustools | 2021 | O(n * k) | O(g) | `preprocessing` `pseudoalignment` `fast` |\n| Alevin | 2019 | O(n * g) | O(c * g) | `preprocessing` `umi` `lightweight-mapping` |\n| Cell Ranger | 2017 | O(n * g) | O(c * g) | `10x-genomics` `preprocessing` `umi` |\n\n\n### Metagenomics \u003ca id=\"metagenomics\"\u003e\u003c/a\u003e\n\u003ca href=\"#table-of-contents\"\u003e↑ Back to Top\u003c/a\u003e\n\n**Taxonomic Profiling**\n\n| Algorithm | Year | Time | Space | Tags |\n|-----------|------|------|-------|------|\n| 🆕 MetaPhlAn 4 | 2023 | O(n * m) | O(m) | `marker-gene` `profiling` `enhanced` |\n| Kraken2 | 2019 | O(n * k) | O(d) | `k-mer` `classification` `fast` |\n| QIIME 2 | 2019 | O(n * d) | O(n) | `pipeline` `microbiome` `diversity` |\n| MetaBAT 2 | 2019 | O(n * c) | O(n) | `binning` `metagenome` `adaptive` |\n| mOTUs | 2017 | O(n * m) | O(m) | `marker-gene` `profiling` `universal` |\n\n*[View all 9 algorithms in this category →](https://lessup.github.io/awesome-bioinfo-algorithms/)*\n\n**Functional Profiling**\n\n| Algorithm | Year | Time | Space | Tags |\n|-----------|------|------|-------|------|\n| metaPOST | 2021 | O(n * c) | O(n) | `post-processing` `refinement` `assembly` |\n| MetaBAT 2 | 2019 | O(n * c) | O(n) | `binning` `mags` `coverage` |\n| HUMAnN 3 | 2018 | O(n * d) | O(d) | `functional-profiling` `pathway` `gene-families` |\n| MaxBin 2 | 2016 | O(n * c) | O(n) | `binning` `expectation-maximization` `mags` |\n| HUMAnN | 2014 | O(n * d) | O(d) | `pathway-analysis` `gene-family` `functional` |\n\n\n### Epigenomics \u003ca id=\"epigenomics\"\u003e\u003c/a\u003e\n\u003ca href=\"#table-of-contents\"\u003e↑ Back to Top\u003c/a\u003e\n\n**ChIP-seq Analysis**\n\n| Algorithm | Year | Time | Space | Tags |\n|-----------|------|------|-------|------|\n| HMMRATAC | 2019 | O(n) | O(n) | `atac-seq` `hmm` `peak-calling` |\n| ChromHMM | 2012 | O(n * s^2) | O(n * s) | `hmm` `chromatin-state` `histone` |\n| MACS2 | 2008 | O(n) | O(n) | `peak-calling` `chip-seq` `histone` |\n\n**Methylation Analysis**\n\n| Algorithm | Year | Time | Space | Tags |\n|-----------|------|------|-------|------|\n| DSS | 2014 | O(n * s) | O(n) | `methylation` `beta-binomial` `dmr` |\n| methylKit | 2012 | O(n * s) | O(n) | `methylation` `differential-analysis` `rrbs` |\n| Bismark | 2011 | O(n * g) | O(g) | `bisulfite-seq` `methylation` `cpg` |\n\n\n### Gene Prediction \u003ca id=\"gene-prediction\"\u003e\u003c/a\u003e\n\u003ca href=\"#table-of-contents\"\u003e↑ Back to Top\u003c/a\u003e\n\n**Eukaryotic Gene Prediction**\n\n| Algorithm | Year | Time | Space | Tags |\n|-----------|------|------|-------|------|\n| BRAKER | 2016 | O(n * g) | O(n) | `pipeline` `evidence-based` `automated` |\n| MAKER | 2008 | O(n * g) | O(n) | `annotation-pipeline` `evidence-based` `eukaryotic` |\n| AUGUSTUS | 2005 | O(n * s^2) | O(n * s) | `gene-prediction` `eukaryotic` `hmm` |\n| SNAP | 2004 | O(n * s) | O(n) | `semi-hmm` `ab-initio` `eukaryotic` |\n| AUGUSTUS | 2003 | O(n * s^2) | O(n * s) | `ghmm` `ab-initio` `exon-intron` |\n\n**Prokaryotic Gene Prediction**\n\n| Algorithm | Year | Time | Space | Tags |\n|-----------|------|------|-------|------|\n| Prodigal | 2010 | O(n) | O(n) | `prokaryotic` `self-training` `metagenome` |\n| Prodigal | 2010 | O(n) | O(n) | `gene-prediction` `prokaryotic` `fast` |\n| RNAmmer | 2007 | O(n * s) | O(n) | `rrna` `gene-prediction` `hmm` |\n| GeneMark-ES | 2005 | O(n) | O(n) | `gene-prediction` `hmm` `prokaryotic` |\n| GLIMMER | 1998 | O(n) | O(n) | `interpolated-markov-model` `prokaryotic` `gene-finding` |\n\n*[View all 7 algorithms in this category →](https://lessup.github.io/awesome-bioinfo-algorithms/)*\n\n\n### Population Genetics \u003ca id=\"population-genetics\"\u003e\u003c/a\u003e\n\u003ca href=\"#table-of-contents\"\u003e↑ Back to Top\u003c/a\u003e\n\n**Selection Signature Detection**\n\n| Algorithm | Year | Time | Space | Tags |\n|-----------|------|------|-------|------|\n| ⭐ Tajima's D | 1989 | O(n * L) | O(L) | `neutrality-test` `selection` `classic` |\n| PCAdapt | 2016 | O(n * m * k) | O(n * m) | `selection` `pca` `outlier-detection` |\n| Selscan | 2014 | O(n * m) | O(n * m) | `selection` `haplotype` `ihs` |\n| HapFLK | 2013 | O(n * m * K) | O(m * K) | `selection` `haplotype` `population-differentiation` |\n| BayeScan | 2008 | O(m * k * n) | O(m * k) | `selection` `bayesian` `fst` |\n\n**Genome-Wide Association Study**\n\n| Algorithm | Year | Time | Space | Tags |\n|-----------|------|------|-------|------|\n| REGENIE | 2021 | O(n * m) | O(n * m) | `gwas` `scalable` `two-stage` |\n| SAIGE | 2018 | O(n * m) | O(n * m) | `gwas` `mixed-model` `rare-variant` |\n| BOLT-LMM | 2015 | O(n * m) | O(n * m) | `lmm` `gwas` `scalable` |\n| PLINK | 2007 | O(n * m) | O(n * m) | `gwas` `association` `qc` |\n\n**PCA \u0026 Population Structure**\n\n| Algorithm | Year | Time | Space | Tags |\n|-----------|------|------|-------|------|\n| ADMIXTURE | 2009 | O(n * m * k) | O(n * m) | `ancestry` `maximum-likelihood` `population-structure` |\n| PCA for Population Structure | 2006 | O(n * m * k) | O(n * m) | `pca` `population-structure` `ancestry` |\n| STRUCTURE | 2000 | O(n * m * k * g) | O(n * k) | `bayesian` `mcmc` `population-structure` |\n\n\n### Spatial Omics \u003ca id=\"spatial-omics\"\u003e\u003c/a\u003e\n\u003ca href=\"#table-of-contents\"\u003e↑ Back to Top\u003c/a\u003e\n\n**Spatial Transcriptomics**\n\n| Algorithm | Year | Time | Space | Tags |\n|-----------|------|------|-------|------|\n| Seurat Spatial | 2021 | O(c * g) | O(c * g) | `spatial` `clustering` `integration` |\n| Giotto Suite | 2021 | O(c * g) | O(c * g) | `spatial` `multi-platform` `comprehensive` |\n| Squidpy | 2021 | O(c * g) | O(c * g) | `spatial` `graph-analysis` `cell-interaction` |\n| SPARK-X | 2021 | O(g * n) | O(n) | `spatial` `fast` `non-parametric` |\n| stLearn | 2021 | O(c * g) | O(c * g) | `spatial` `image-integration` `trajectory` |\n\n*[View all 7 algorithms in this category →](https://lessup.github.io/awesome-bioinfo-algorithms/)*\n\n**Spatial Proteomics**\n\n| Algorithm | Year | Time | Space | Tags |\n|-----------|------|------|-------|------|\n| CellChat | 2021 | O(c^2 * g) | O(c^2) | `cell-communication` `ligand-receptor` `signaling` |\n| Cellpose | 2020 | O(p) | O(p) | `segmentation` `deep-learning` `cell-detection` |\n| StarDist | 2018 | O(p) | O(p) | `segmentation` `deep-learning` `cell-nuclei` |\n\n\n### Graph Genomics \u003ca id=\"graph-genomics\"\u003e\u003c/a\u003e\n\u003ca href=\"#table-of-contents\"\u003e↑ Back to Top\u003c/a\u003e\n\n**Variation Graph**\n\n| Algorithm | Year | Time | Space | Tags |\n|-----------|------|------|-------|------|\n| 🆕 PanVC | 2023 | O(n * m) | O(n) | `pangenome` `variant-calling` `genotyping` |\n| HiFiBD | 2022 | O(n * d) | O(n) | `variation-graph` `hifi` `genotyping` |\n| GraphAligner | 2019 | O(n * d) | O(n) | `graph-alignment` `long-read` `variation-graph` |\n| VG (Variation Graph) | 2017 | O(n log n) | O(n) | `variation-graph` `alignment` `variant-calling` |\n| GCSA2 | 2017 | O(n) | O(n) | `indexing` `k-mer` `compressed-suffix-array` |\n\n**Pangenome**\n\n| Algorithm | Year | Time | Space | Tags |\n|-----------|------|------|-------|------|\n| Minigraph | 2020 | O(n log n) | O(n) | `pangenome` `graph-alignment` `minimizer` |\n| odgi | 2020 | O(n) | O(n) | `pangenome` `graph-operations` `visualization` |\n| seqwish | 2020 | O(n^2) | O(n^2) | `pangenome` `graph-construction` `alignment-to-graph` |\n| Cactus | 2011 | O(n^2 * k) | O(n * k) | `pangenome` `alignment` `progressive` |\n\n\n### Protein Language Model \u003ca id=\"protein-language-model\"\u003e\u003c/a\u003e\n\u003ca href=\"#table-of-contents\"\u003e↑ Back to Top\u003c/a\u003e\n\n**Protein Function Prediction**\n\n| Algorithm | Year | Time | Space | Tags |\n|-----------|------|------|-------|------|\n| 🆕 ESMFold | 2023 | O(n^2) | O(n^2) | `structure-prediction` `single-sequence` `fast` |\n| 🆕 RFdiffusion | 2023 | O(n^2 * T) | O(n^2) | `diffusion-model` `protein-design` `structure-generation` |\n| ProteinMPNN | 2022 | O(n^2 * d) | O(n^2) | `protein-design` `inverse-folding` `graph-neural-network` |\n| ESM-1v | 2021 | O(n^2 * d) | O(n^2) | `variant-effect` `zero-shot` `pathogenicity` |\n| ProGen | 2020 | O(n^2 * d) | O(n^2) | `generative` `protein-design` `conditional-generation` |\n\n*[View all 6 algorithms in this category →](https://lessup.github.io/awesome-bioinfo-algorithms/)*\n\n**Protein Language Model Pretraining**\n\n| Algorithm | Year | Time | Space | Tags |\n|-----------|------|------|-------|------|\n| 🆕 Ankh | 2023 | O(n^2 * d) | O(n^2) | `language-model` `lightweight` `efficient` |\n| ESM-2 | 2022 | O(n^2 * d) | O(n^2) | `language-model` `transformer` `representation-learning` |\n| ProtTrans | 2021 | O(n^2 * d) | O(n^2) | `language-model` `transfer-learning` `representation-learning` |\n| ProtBERT | 2020 | O(n^2 * d) | O(n^2) | `language-model` `bert` `sequence-embedding` |\n\n\n\n---\n\n## 🛠️ CLI Commands\n\n```bash\n# Search for algorithms\npython -m awesome_bioinfo search \"alignment\"\n\n# Get algorithm details\npython -m awesome_bioinfo info smith-waterman\n\n# Compare two algorithms\npython -m awesome_bioinfo compare smith-waterman needleman-wunsch\n\n# Export data to JSON\npython -m awesome_bioinfo export --format json \u003e algorithms.json\n\n# Generate MkDocs site\npython -m awesome_bioinfo mkdocs\n\n# Generate README\npython -m awesome_bioinfo generate\n```\n\n---\n\n## 📚 Resources\n\n### Learning Platforms\n- [Rosalind](http://rosalind.info/) — Bioinformatics algorithm learning\n- [NCBI](https://www.ncbi.nlm.nih.gov/) — National Center for Biotechnology\n- [EBI](https://www.ebi.ac.uk/) — European Bioinformatics Institute\n\n### Tools \u0026 Communities\n- [Bioconductor](https://www.bioconductor.org/) — R bioinformatics toolkit\n- [Galaxy](https://usegalaxy.org/) — Open analysis platform\n- [BioStars](https://www.biostars.org/) — Bioinformatics Q\u0026A\n- [scverse](https://scverse.org/) — Single-cell Python ecosystem\n\n---\n\n## 🤝 Contributing\n\nWe welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.\n\n### Contribution Types\n\n- 🆕 **Add new algorithms**\n- 📝 **Improve descriptions**\n- 🔗 **Add references**\n- 🐛 **Report and fix bugs**\n- 📚 **Improve documentation**\n\n---\n\n## 📚 Citation\n\nIf you use this project in your research, please cite it as:\n\n```bibtex\n@software{awesome_bioinfo_algorithms,\n  title = {Awesome Bioinformatics Algorithms},\n  author = {{LessUp Community}},\n  year = {2025},\n  url = {https://github.com/LessUp/awesome-bioinfo-algorithms}\n}\n```\n\nOr see [CITATION.cff](CITATION.cff) for more citation formats.\n\n---\n\n## 📄 License\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://creativecommons.org/publicdomain/zero/1.0/\"\u003e\n    \u003cimg src=\"https://licensebuttons.net/p/zero/1.0/88x31.png\" alt=\"CC0\"\u003e\n  \u003c/a\u003e\n\u003c/p\u003e\n\nThis project is licensed under [CC0 1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/) (Public Domain).\n\nYou are free to:\n- ✅ Copy, modify, distribute\n- ✅ Use for commercial purposes\n- ✅ No attribution required\n\n---\n\n\u003cp align=\"center\"\u003e\n  \u003cb\u003eMade with ❤️ by the community\u003c/b\u003e\u003cbr\u003e\n  © 2025-2026 \u003ca href=\"https://github.com/LessUp\"\u003eLessUp\u003c/a\u003e Community\n\u003c/p\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/lessup.github.io%2Fawesome-bioinfo-algorithms%2F","html_url":"https://awesome.ecosyste.ms/projects/lessup.github.io%2Fawesome-bioinfo-algorithms%2F","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/lessup.github.io%2Fawesome-bioinfo-algorithms%2F/lists"}