Catalog¶

Tasks¶

Name	Description	Parameters
`apply_bqsr`	Apply Base Quality Score Recalibration to a BAM file.	`alignment` (`Alignment`), `ref` (`Reference`), `bqsr_report` (`BQSRReport`)
`apply_vqsr`	Apply VQSR recalibration to a VCF using GATK ApplyVQSR.	`vcf` (`Variants`), `ref` (`Reference`), `vqsr_model` (`VQSRModel`), `truth_sensitivity_filter_level` (`float \| NoneType`)
`base_recalibrator`	Generate a Base Quality Score Recalibration report.	`alignment` (`Alignment`), `ref` (`Reference`), `known_sites` (`list[KnownSites]`)
`bwa_index`	Create BWA index files for a reference genome using bwa index.	`ref` (`Reference`)
`bwa_mem`	Align FASTQ reads to reference genome using BWA-MEM.	`ref` (`Reference`), `r1` (`R1`), `r2` (`R2 \| NoneType`), `read_group` (`dict[str, str] \| NoneType`)
`bwa_mem2_index`	Create BWA-MEM2 index files for a reference genome.	`ref` (`Reference`)
`bwa_mem2_mem`	Align FASTQ reads to reference genome using BWA-MEM2.	`ref` (`Reference`), `r1` (`R1`), `r2` (`R2 \| NoneType`), `read_group` (`dict[str, str] \| NoneType`)
`cluster`	Assign cells to clusters using the Leiden algorithm.	`adata` (`AnnData`), `resolution` (`float`), `key_added` (`str`)
`combine_gvcfs`	Combine multiple per-sample GVCFs into a single multi-sample GVCF.	`gvcfs` (`list[Variants]`), `ref` (`Reference`), `cohort_id` (`str`)
`create_sequence_dictionary`	Create a sequence dictionary (.dict file) using GATK CreateSequenceDictionary.	`ref` (`Reference`)
`find_markers`	Identify marker genes for each cluster using differential expression.	`adata` (`AnnData`), `groupby` (`str`), `method` (`str`)
`haplotype_caller`	Call germline variants in GVCF mode using GATK HaplotypeCaller.	`alignment` (`Alignment`), `ref` (`Reference`)
`index_feature_file`	Index a VCF file using GATK IndexFeatureFile.	`known_sites` (`KnownSites`)
`joint_call_gvcfs`	Consolidate GVCFs into GenomicsDB and joint-genotype in a single task.	`gvcfs` (`list[Variants]`), `ref` (`Reference`), `intervals` (`list[str] \| NoneType`), `cohort_id` (`str`)
`mark_duplicates`	Mark duplicate reads in a BAM file.	`alignment` (`Alignment`)
`merge_bam_alignment`	Merge alignment data from aligned BAM with data in unmapped BAM.	`aligned_bam` (`Alignment`), `unmapped_bam` (`Alignment`), `ref` (`Reference`)
`normalize`	Normalize counts and apply log1p transformation.	`adata` (`AnnData`)
`qc_filter`	Filter low-quality cells and genes from raw scRNA-seq data.	`adata` (`AnnData`), `min_genes` (`int`), `min_cells` (`int`), `max_pct_mt` (`float`), `batch_key` (`str`)
`reduce_dimensions`	Compute PCA, k-nearest neighbor graph, and UMAP embedding.	`adata` (`AnnData`), `n_pcs` (`int`), `n_neighbors` (`int`)
`samtools_faidx`	Create a FASTA index (.fai file) using samtools faidx.	`ref` (`Reference`)
`select_features`	Select highly variable genes for dimensionality reduction.	`adata` (`AnnData`), `n_top_genes` (`int`), `batch_key` (`str`)
`sort_sam`	Sort a SAM/BAM file.	`alignment` (`Alignment`), `sort_order` (`str`)
`variant_recalibrator`	Build a VQSR recalibration model using GATK VariantRecalibrator.	`vcf` (`Variants`), `ref` (`Reference`), `resources` (`list[KnownSites]`), `mode` (`str`)

Workflows¶

Name	Description	Parameters
`germline_short_variant_discovery`	End-to-end germline short variant discovery from raw reads.	`build` (`str`), `sample_ids` (`list[str]`), `cohort_id` (`str`)
`prepare_reference`	Prepare reference genome for alignment and variant calling.	`build` (`str`)
`preprocess_sample`	Pre-process a single sample's reads for variant calling.	`build` (`str`), `sample_id` (`str`)
`scrna_clustering_pipeline`	End-to-end scRNA-seq clustering pipeline.	`sample_id` (`str`), `n_top_genes` (`int`), `resolution` (`float`), `max_pct_mt` (`float`)

Asset Types¶

Asset Key	Class	Module	Fields
`aligner_index`	`AlignerIndex`	`assets/reference.py`	`aligner`, `build`, `companion_of`
`alignment`	`Alignment`	`assets/alignment.py`	`bqsr_applied`, `duplicates_marked`, `r1_cid`, `reference_cid`, `sample_id`, `sorted`, `tool`
`alignment_index`	`AlignmentIndex`	`assets/alignment.py`	`companion_of`, `sample_id`
`anndata`	`AnnData`	`assets/scrna.py`	`n_obs`, `n_vars`, `organism`, `sample_id`, `source_cid`, `stage`
`bqsr_report`	`BQSRReport`	`assets/alignment.py`	`alignment_cid`, `sample_id`, `tool`
`duplicate_metrics`	`DuplicateMetrics`	`assets/alignment.py`	`alignment_cid`, `sample_id`, `tool`
`known_sites`	`KnownSites`	`assets/variants.py`	`build`, `known`, `prior`, `resource_name`, `training`, `truth`, `vqsr_mode`
`known_sites_index`	`KnownSitesIndex`	`assets/variants.py`	`companion_of`
`notebook`	`Notebook`	`assets/notebook.py`	`description`, `slug`, `title`
`r1`	`R1`	`assets/reads.py`	`mate_cid`, `sample_id`, `sequencing_platform`
`r2`	`R2`	`assets/reads.py`	`mate_cid`, `sample_id`, `sequencing_platform`
`reference`	`Reference`	`assets/reference.py`	`build`
`reference_index`	`ReferenceIndex`	`assets/reference.py`	`build`, `companion_of`, `tool`
`sequence_dict`	`SequenceDict`	`assets/reference.py`	`build`, `companion_of`, `tool`
`variants`	`Variants`	`assets/variants.py`	`build`, `caller`, `sample_count`, `sample_id`, `source_samples`, `variant_type`, `vqsr_mode`
`variants_index`	`VariantsIndex`	`assets/variants.py`	`companion_of`, `sample_id`
`vqsr_model`	`VQSRModel`	`assets/variants.py`	`build`, `mode`, `sample_id`, `variants_cid`
`vqsr_model_index`	`VQSRModelIndex`	`assets/variants.py`	`companion_of`, `sample_id`
`vqsr_tranches`	`VQSRTranches`	`assets/variants.py`	`companion_of`, `sample_id`

Bundles¶

Name	Description	Files
`scrna_demo`	Human bone marrow mononuclear cells from two healthy donors (10x Multiome gene expression) for demo workflows	2
`variant_calling_demo`	TP53 region test fixtures (reference and paired reads) for GRCh38	3