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This function aligns sequences, constructs a phylogenetic tree, and visualizes evolutionary relationships.\n\n**Python deps:** Bio, matplotlib, numpy, pandas, torch\n\n**Source:** `biomni/tool/genetics.py:1445`","operationId":"run_analyze_protein_phylogeny_api_v1_tools_analyze_protein_phylogeny_post","security":[{"APIKeyHeader":[]}],"parameters":[{"name":"mode","in":"query","required":false,"schema":{"enum":["sync","async"],"type":"string","description":"sync waits for the result; async returns a job to poll","default":"sync","title":"Mode"},"description":"sync waits for the result; async returns a job to poll"}],"requestBody":{"required":true,"content":{"application/json":{"schema":{"$ref":"#/components/schemas/AnalyzeProteinPhylogenyRequest"}}}},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/tools/annotate_celltype_scRNA":{"post":{"tags":["tools: genomics"],"summary":"annotate_celltype_scRNA","description":"Annotate cell types based on gene markers and transferred labels using LLM. After leiden clustering, annotate clusters using differentially expressed genes and optionally incorporate transferred labels from reference datasets.\n\n**Flags:** needs_llm, uses_data_lake\n\n**Python deps:** biomni, esm, gget, gseapy, langchain_core, numpy, pandas, pybiomart, requests, scanpy, torch, tqdm\n\n**Source:** `biomni/tool/genomics.py:463`","operationId":"run_annotate_celltype_scRNA_api_v1_tools_annotate_celltype_scRNA_post","security":[{"APIKeyHeader":[]}],"parameters":[{"name":"mode","in":"query","required":false,"schema":{"enum":["sync","async"],"type":"string","description":"sync waits for the result; async returns a job to poll","default":"sync","title":"Mode"},"description":"sync waits for the result; async returns a job to poll"}],"requestBody":{"required":true,"content":{"application/json":{"schema":{"$ref":"#/components/schemas/AnnotateCelltypeScrnaRequest"}}}},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/tools/annotate_celltype_with_panhumanpy":{"post":{"tags":["tools: genomics"],"summary":"annotate_celltype_with_panhumanpy","description":"Perform cell type annotation of single-cell RNA-seq data using Panhuman Azimuth Neural Network. This function implements the Panhuman Azimuth workflow for cell type annotation using the panhumanpy package, providing hierarchical cell type labels for tissues across the human body. \n\n**Flags:** needs_network\n\n**Python deps:** biomni, esm, gget, gseapy, numpy, pandas, pybiomart, requests, scanpy, torch, tqdm\n\n**Source:** `biomni/tool/genomics.py:600`","operationId":"run_annotate_celltype_with_panhumanpy_api_v1_tools_annotate_celltype_with_panhumanpy_post","security":[{"APIKeyHeader":[]}],"parameters":[{"name":"mode","in":"query","required":false,"schema":{"enum":["sync","async"],"type":"string","description":"sync waits for the result; async returns a job to poll","default":"sync","title":"Mode"},"description":"sync waits for the result; async returns a job to poll"}],"requestBody":{"required":true,"content":{"application/json":{"schema":{"$ref":"#/components/schemas/AnnotateCelltypeWithPanhumanpyRequest"}}}},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/tools/create_scvi_embeddings_scRNA":{"post":{"tags":["tools: genomics"],"summary":"create_scvi_embeddings_scRNA","description":"Create scVI and scANVI embeddings for single-cell RNA-seq data, saving the results to an AnnData object.\n\n**Python deps:** biomni, esm, gget, gseapy, numpy, pandas, pybiomart, requests, scanpy, scvi, torch, tqdm\n\n**Source:** `biomni/tool/genomics.py:801`","operationId":"run_create_scvi_embeddings_scRNA_api_v1_tools_create_scvi_embeddings_scRNA_post","security":[{"APIKeyHeader":[]}],"parameters":[{"name":"mode","in":"query","required":false,"schema":{"enum":["sync","async"],"type":"string","description":"sync waits for the result; 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async returns a job to poll","default":"sync","title":"Mode"},"description":"sync waits for the result; async returns a job to poll"}],"requestBody":{"required":true,"content":{"application/json":{"schema":{"$ref":"#/components/schemas/GeneSetEnrichmentAnalysisRequest"}}}},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/tools/analyze_chromatin_interactions":{"post":{"tags":["tools: genomics"],"summary":"analyze_chromatin_interactions","description":"Analyze chromatin interactions from Hi-C data to identify enhancer-promoter interactions and TADs.\n\n**Python deps:** biomni, cooler, esm, gget, gseapy, numpy, pandas, pybiomart, requests, scanpy, scipy, torch, tqdm\n\n**Source:** `biomni/tool/genomics.py:1091`","operationId":"run_analyze_chromatin_interactions_api_v1_tools_analyze_chromatin_interactions_post","security":[{"APIKeyHeader":[]}],"parameters":[{"name":"mode","in":"query","required":false,"schema":{"enum":["sync","async"],"type":"string","description":"sync waits for the result; async returns a job to poll","default":"sync","title":"Mode"},"description":"sync waits for the result; async returns a job to poll"}],"requestBody":{"required":true,"content":{"application/json":{"schema":{"$ref":"#/components/schemas/AnalyzeChromatinInteractionsRequest"}}}},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/tools/analyze_comparative_genomics_and_haplotypes":{"post":{"tags":["tools: genomics"],"summary":"analyze_comparative_genomics_and_haplotypes","description":"Perform comparative genomics and haplotype analysis on multiple genome samples. Aligns genome samples to a reference, identifies variants, analyzes shared and unique genomic regions, and determines haplotype structure.\n\n**Python deps:** Bio, biomni, esm, gget, gseapy, numpy, pandas, pybiomart, requests, scanpy, torch, tqdm\n\n**Source:** `biomni/tool/genomics.py:1424`","operationId":"run_analyze_comparative_genomics_and_haplotypes_api_v1_tools_analyze_comparative_genomics_and_haplotypes_post","security":[{"APIKeyHeader":[]}],"parameters":[{"name":"mode","in":"query","required":false,"schema":{"enum":["sync","async"],"type":"string","description":"sync waits for the result; async returns a job to poll","default":"sync","title":"Mode"},"description":"sync waits for the result; async returns a job to poll"}],"requestBody":{"required":true,"content":{"application/json":{"schema":{"$ref":"#/components/schemas/AnalyzeComparativeGenomicsAndHaplotypesRequest"}}}},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/tools/perform_chipseq_peak_calling_with_macs2":{"post":{"tags":["tools: genomics"],"summary":"perform_chipseq_peak_calling_with_macs2","description":"Perform ChIP-seq peak calling using MACS2 to identify genomic regions with significant binding.\n\n**Python deps:** biomni, esm, gget, gseapy, numpy, pandas, pybiomart, requests, scanpy, torch, tqdm\n\n**Source:** `biomni/tool/genomics.py:1617`","operationId":"run_perform_chipseq_peak_calling_with_macs2_api_v1_tools_perform_chipseq_peak_calling_with_macs2_post","security":[{"APIKeyHeader":[]}],"parameters":[{"name":"mode","in":"query","required":false,"schema":{"enum":["sync","async"],"type":"string","description":"sync waits for the result; async returns a job to poll","default":"sync","title":"Mode"},"description":"sync waits for the result; async returns a job to poll"}],"requestBody":{"required":true,"content":{"application/json":{"schema":{"$ref":"#/components/schemas/PerformChipseqPeakCallingWithMacs2Request"}}}},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/tools/find_enriched_motifs_with_homer":{"post":{"tags":["tools: genomics"],"summary":"find_enriched_motifs_with_homer","description":"Find DNA sequence motifs enriched in genomic regions using the HOMER motif discovery software.\n\n**Python deps:** biomni, esm, gget, gseapy, numpy, pandas, pybiomart, requests, scanpy, torch, tqdm\n\n**Source:** `biomni/tool/genomics.py:1794`","operationId":"run_find_enriched_motifs_with_homer_api_v1_tools_find_enriched_motifs_with_homer_post","security":[{"APIKeyHeader":[]}],"parameters":[{"name":"mode","in":"query","required":false,"schema":{"enum":["sync","async"],"type":"string","description":"sync waits for the result; async returns a job to poll","default":"sync","title":"Mode"},"description":"sync waits for the result; async returns a job to poll"}],"requestBody":{"required":true,"content":{"application/json":{"schema":{"$ref":"#/components/schemas/FindEnrichedMotifsWithHomerRequest"}}}},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/tools/analyze_genomic_region_overlap":{"post":{"tags":["tools: genomics"],"summary":"analyze_genomic_region_overlap","description":"Analyze overlaps between two or more sets of genomic regions.\n\n**Python deps:** biomni, esm, gget, gseapy, numpy, pandas, pybedtools, pybiomart, requests, scanpy, torch, tqdm\n\n**Source:** `biomni/tool/genomics.py:1965`","operationId":"run_analyze_genomic_region_overlap_api_v1_tools_analyze_genomic_region_overlap_post","security":[{"APIKeyHeader":[]}],"parameters":[{"name":"mode","in":"query","required":false,"schema":{"enum":["sync","async"],"type":"string","description":"sync waits for the result; async returns a job to poll","default":"sync","title":"Mode"},"description":"sync waits for the result; async returns a job to poll"}],"requestBody":{"required":true,"content":{"application/json":{"schema":{"$ref":"#/components/schemas/AnalyzeGenomicRegionOverlapRequest"}}}},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/tools/unsupervised_celltype_transfer_between_scRNA_datasets":{"post":{"tags":["tools: genomics"],"summary":"unsupervised_celltype_transfer_between_scRNA_datasets","description":"Transfer cell type labels from an annotated reference scRNA-seq dataset to an unannotated query dataset using popV. Loads both AnnData .h5ad files, prepares count layers for scVI, processes the query against the reference, and runs selected annotation methods (default: SCANVI_POPV). Saves predictions to 'output_folder/popv_output/predictions.csv'. This function allows you to use different annotaiton methods i.e. CELLTYPIST, KNN_BBKNN, KNN_HARMONY, KNN_SCANORAMA, KNN_SCVI, ONCLASS, Random_Forest, SCANVI_POPV, Support_Vector, XGboost. Based on you transfer task you can select the multiple best annotation methods. Beware each annotation method adds computational requirements for running the tool. By default it uses SCANVI_POPV method.\n\n**Python deps:** biomni, esm, gget, gseapy, numpy, pandas, popv, pybiomart, requests, scanpy, torch, tqdm\n\n**Source:** `biomni/tool/genomics.py:18`","operationId":"run_unsupervised_celltype_transfer_between_scRNA_datasets_api_v1_tools_unsupervised_celltype_transfer_between_scRNA_datasets_post","security":[{"APIKeyHeader":[]}],"parameters":[{"name":"mode","in":"query","required":false,"schema":{"enum":["sync","async"],"type":"string","description":"sync waits for the result; async returns a job to poll","default":"sync","title":"Mode"},"description":"sync waits for the result; async returns a job to poll"}],"requestBody":{"required":true,"content":{"application/json":{"schema":{"$ref":"#/components/schemas/UnsupervisedCelltypeTransferBetweenScrnaDatasetsRequest"}}}},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/tools/generate_embeddings_with_state":{"post":{"tags":["tools: genomics"],"summary":"generate_embeddings_with_state","description":"Generate State embeddings for single-cell RNA-seq data using the SE-600M model. This function downloads the SE-600M model from Hugging Face, installs required dependencies (git-lfs, uv, arc-state), and generates embeddings for the input AnnData object. The SE-600M model is a state-of-the-art embedding model for single-cell data that can capture complex biological patterns and cell states. Features include real-time streaming output, automatic retry with reduced batch size on failure, GPU detection and warnings, and input validation.\n\n**Flags:** needs_network\n\n**Python deps:** biomni, esm, gget, gseapy, numpy, pandas, pybiomart, requests, scanpy, torch, tqdm\n\n**Source:** `biomni/tool/genomics.py:2167`","operationId":"run_generate_embeddings_with_state_api_v1_tools_generate_embeddings_with_state_post","security":[{"APIKeyHeader":[]}],"parameters":[{"name":"mode","in":"query","required":false,"schema":{"enum":["sync","async"],"type":"string","description":"sync waits for the result; async returns a job to poll","default":"sync","title":"Mode"},"description":"sync waits for the result; async returns a job to poll"}],"requestBody":{"required":true,"content":{"application/json":{"schema":{"$ref":"#/components/schemas/GenerateEmbeddingsWithStateRequest"}}}},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/tools/interspecies_gene_conversion":{"post":{"tags":["tools: genomics"],"summary":"interspecies_gene_conversion","description":"Convert ENSEMBL gene IDs between different species using BioMart homology mapping. This function converts a list of ENSEMBL gene IDs from one species to their homologous counterparts in another species using the Ensembl BioMart database. 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