BGWGS Workflow The BGWGS (bigwigs) Workflow starts with a single CRAM or BAM file and provides variant calling, filtration, annotation, coverage, pharmacogenetic, risk and reporting capabilities. Input Parameters | Type | Name | Req'd | Description | Default Value | | :--- | :--- | :---: | :--- | :--- | | String | gcp_project_id | No | The GCP project to fetch secrets from | "mgb-lmm-gcp-infrast-1651079146" | | String | workspace_name | Yes | The name of the current workspace (for secret retrieval) | | | String | orchutils_docker_image | No | The name of the orchestration utils Docker image for FAST and file movement tasks | "gcr.io/mgb-lmm-gcp-infrast-1651079146/mgbpmbiofx/orchutils:latest" | | String | bcftools_docker_image | No | The name of the bcftools Docker image for VCF annotation | "gcr.io/mgb-lmm-gcp-infrast-1651079146/mgbpmbiofx/bcftools:1.17" | | String | subject_id | Yes | The subject id associated with the data | | | String | sample_id | Yes | The sample id associated with the data | | | String | sample_data_location | Yes | The cloud storage URL where the sample source data is located | | | Boolean | fetch_cram | No | Whether or not to fetch the CRAM (primarily for testing) | true | | Array[String] | fetch_cram_filter_keys | No | The list of strings that must appear in the CRAM file path | [subject_id, sample_id] | | Array[FileMatcher]? | fetch_cram_file_matchers | No | The list of file matchers to use for CRAM file fetching | | | Boolean | fetch_bam | No | Whether or not to fetch the BAM (primarily for testing) | true | | Array[String] | fetch_bam_filter_keys | No | The list of strings that must appear in the BAM file path | [subject_id, sample_id] | | Array[FileMatcher]? | fetch_bam_file_matchers | No | The list of file matching rules for BAM fetching | | | Array[String] | fetch_vcf_filter_keys | No | The list of strings that must appear in the VCF file path | [subject_id, sample_id] | | Array[FileMatcher]? | fetch_vcf_file_matchers | No | The list of file matching rules for VCF fetching | | | Boolean | fetch_files_verbose | No | If true, generate verbose output from file fetch tasks | false | | Boolean | do_variant_calling | No | Whether or not to generate a VCF by calling variants from BAM or CRAM; if false, the VCF is fetched from the sample_data_location | true | | Boolean | do_coverage | No | Whether or not to run depth of coverage analysis | true | | Boolean | do_pgx | No | Whether or not to generate pharmacogenomics report | true | | Boolean | do_risk_alleles | No | Whether or not to generate risk alleles report | true | | Boolean | do_gnomad | No | Whether or not to annotate/load gnomAD data | true | | String | reference_build | No | The genome reference build name | "GRCh38" | | File | ref_dict | Yes | The genome reference dict file | | | File | ref_fasta | Yes | The genome reference fasta file | | | File | ref_fasta_index | Yes | The genome reference fasta index file | | | File | dbsnp_vcf | No | dbSNP VCF file that matches the genome reference; required for PGx and Risk Alleles | | | File | dbsnp_vcf_index | No | dbSNP VCF file index | | | File | scattered_calling_intervals_list | No | File containing list of scattered calling interval files for Haplotype Caller | | | File | cov_roi_bed | No | The BED that defines the region of interest for coverage analysis; see DepthOfCoverage.md for details | "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed" | | Array[RoiAndRefGeneFilePair] | cov_roi_genes | No | List of ROI and ref gene file pairs; see DepthOfCoverage.md for details | See Below | | File | cov_gene_names | No | Tab-delimited file of gene information; see DepthOfCoverage.md for details | "gs://lmm-reference-data/roi/HGNC_genenames_05272022.txt" | | String | cov_docker_image | No | The name of the coverage Docker image to run the coverage summary task | "gcr.io/mgb-lmm-gcp-infrast-1651079146/mgbpmbiofx/coverage:20230630" | | String | gatk3_docker_image | No | The name of the GATK3 Docker image for coverage analysis | "broadinstitute/gatk3:3.7-0" | | String | pgx_test_code | No | Test code that defines which pharmacogenomics report to generate | "lmPGX-pnlD_L" | | String | pgx_docker_image | No | The name of the Docker image to generate the pharmacogenomics report | "gcr.io/mgb-lmm-gcp-infrast-1651079146/mgbpmbiofx/pgx:20241008" | | File | pgx_workflow_fileset | No | Tar file containing the pharmacogenomics reference data to generate the report | "gs://lmm-reference-data/pgx/lmPGX-pnlD_L_20241004.tar" | | File | pgx_roi_bed | No | BED file that defines the genomic regions to include in the pharmacogenomics analysis | "gs://lmm-reference-data/pgx/lmPGX-pnlD_L_genotyping.bed" | | String | risk_alleles_test_code | No | Test code that defines which risk alleles report to generate | "lmRISK-pnlB_L" | | String | risk_alleles_docker_image | No | The name of the Docker image to generate the risk alleles report | "gcr.io/mgb-lmm-gcp-infrast-1651079146/mgbpmbiofx/risk:20230724" | | File | risk_alleles_workflow_fileset | No | Tar file containing the risk alleles reference data to generate the report | "gs://lmm-reference-data/risk/lmRISK-pnlB_L_20230105.tar" | | File | risk_alleles_roi_bed | No | BED file that defines the genomic regions to include in the risk alleles analysis | "gs://lmm-reference-data/risk/lmRISK-pnlB_L_genotyping-chr_20230628.bed" | | File | target_roi_bed | No | The BED that defines the target region of interest for annotation and filtration | "gs://lmm-reference-data/roi/targetROI_hg38_2023_08_24_withCHR.bed" | | File | alamut_db | No | The database file for Alamut batch | "gs://lmm-reference-data/annotation/alamut/alamut_db-1.5-2022.01.12.db" | | File | alamut_fields_tsv | No | The file that defines how the Alamut output is transformed back to a VCF | | | String | alamut_db_name | No | The database name for the Alamut batch ini file | "alamut_db" | | String | alamut_server | No | The server name for the Alamut batch ini file | "a-ht-na.interactive-biosoftware.com" | | String | alamut_port | No | The server port for the Alamut batch ini file | "80" | | String | alamut_user_secret_name | No | The GCP secret name that contains the user stanza for the Alamut batch ini file | "alamut-batch-ini-user" | | Int | alamut_queue_limit | No | The maximum number of concurrent Alamut batch processes permitted | 4 | | String | alamut_queue_folder | No | The shared storage location for Alamut concurrency management | "gs://biofx-task-queue/alamut" | | Int | alamut_queue_wait_limit_hrs | No | The maximum number of hours to wait for a queue slot before failing | 16 | | String | alamut_docker_image | No | The name of the Alamut Docker image using to run Alamut Batch task | "gcr.io/mgb-lmm-gcp-infrast-1651079146/mgbpmbiofx/alamut:20230630" | | Boolean | alamut_save_working_files | No | Whether or not to retain intermediate Alamut Batch task files | false | | String | alamut_anno_src_id | No | When removing already annotated variants prior to Alamut annotation, the annotation source id to query for | "228" | | String | alamut_anno_min_age | No | When removing already annotated variants prior to Alamut annotation, the annotation minimum timestamp to query for (ISO8601 duration) | "P6M" | | String | qceval_project_type | No | The type of rules to apply for the QC evaluation task, one of "BGE_DRAGEN_TP_BINNING", "WGS", "WGS_DRAGEN", "WES" or "NONE" | "BGE_DRAGEN_TP_BINNING" | | String | qceval_docker_image | No | The name of the Docker image to run the QC evaluation task | "gcr.io/mgb-lmm-gcp-infrast-1651079146/mgbpmbiofx/qceval:20250923" | | File | thresholds | No | Thresholds for flagging possible false positives | "gs://lmm-reference-data/annotation/pmeval/thresholds_20250912.tsv" | | File | difficult_to_map_regions | No | List of difficult to map genomic regions used for flagging possible false positives | "gs://lmm-reference-data/annotation/pmeval/difficult_to_map_regions_20250912.tgz" | | File | gnomad_coverage_file | No | The gnomad coverage data file | "gs://lmm-reference-data/annotation/gnomad/genomes.r3.0.1.coverage_targetROI-filtered.dedup.txt.gz" | | File | gnomad_coverage_file_idx | No | The gnomad coverage data index file | "gs://lmm-reference-data/annotation/gnomad/genomes.r3.0.1.coverage_targetROI-filtered.dedup.txt.gz.tbi" | | Array[String] | gnomad_headers | No | List of VCF headers to add when annotating VCF with gnomad coverage data. For example, ##INFO=<ID=DP_gnomadG,Number=1,Type=Float,Description="Read depth of GnomAD Genome"> | [ "##INFO=<ID=DP_gnomadG,Number=1,Type=Float,Description="Read depth of GnomAD Genome">" ] | | String | gnomad_column_list | No | The column list to pass to bcftools annotate for gnomad coverage annotation | "CHROM,POS,INFO/DP_gnomadG" | | Boolean | has_haploid_sites | No | If true, modify the VCF file headers prior to FAST load to work around lack of support Number=G fields and haploid sites | false | | String | sample_data_load_config_name | No | The FAST load configuration name for the sample data VCF, use "Sample_vcf_BGE" for qceval_project_type=BGE_DRAGEN_TP_BINNING, "Sample_VCF_PPM_Eval" otherwise | "Sample_vcf_BGE" | | String | gnomad_data_load_config_name | No | The FAST load configuration name for the gnomad coverage VCF | "Coverage" | | String | alamut_data_load_config_name | No | The FAST load configuration name for the Alamut annotated VCF | "Alamut" | | Array[String] | fast_annotated_sample_data_regions | No | The list of regions to include in the FAST annotated sample data; each element is a "name:applyMask" pair | | | Array[String] | fast_annotated_sample_data_scripts | No | The list of custom scripts to run on the FAST annotated sample data after creation | | | String | fast_annotated_sample_data_saved_filter_name | No | The saved filter to apply to the FAST annotated sample data | | | Int | fast_data_load_wait_interval_secs | No | The number of seconds in between checks when waiting for FAST data loads to complete | 300 | | Int | fast_data_load_wait_max_intervals | No | The maximum number of checks to perform when waiting for FAST data loads to complete | 144 | | Int | fast_adi_wait_interval_secs | No | The number of seconds in between checks when waiting for FAST annotation data initialization to complete | 600 | | Int | fast_adi_wait_max_intervals | No | The maximum number of checks to perform when waiting for FAST annotation data initialization to complete | 144 | | String | igvreport_docker_image | No | The name of the Docker image to run the IGV report task | "us-central1-docker.pkg.dev/mgb-lmm-gcp-infrast-1651079146/mgbpmbiofx/igvreport:20230511" | | String | fast_parser_image | No | The name of the Docker image to run the FAST output parser task | "us-central1-docker.pkg.dev/mgb-lmm-gcp-infrast-1651079146/mgbpmbiofx/fastoutputparser:20250923" | | File | portable_db_file | No | A SQLite database that contains additional annotations that are merged into the Parser output | "gs://lmm-reference-data/annotation/gil_lmm/gene_info.db" | | String | fast_parser_sample_type | No | The sample type flag for the FAST output parser: S for single-sample Exome or M for multi-sample Exome or B for batch/Biobank or N for NVA-Lite | "S" | | Array[File] | igv_track_files | List of track files for inclusion in the IGV report | "gs://lmm-reference-data/annotation/ucsc/hg38/refGene_20231019.txt.gz" | | Array[File] | igv_track_index_files | List of track index files | "gs://lmm-reference-data/annotation/ucsc/hg38/refGene_20231019.txt.gz.tbi" | cov_roi_genes parameter default: [ { "roi_bed": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/roi0.bed", "ref_gene": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene0.txt", "ref_gene_idx": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene0.txt.idx" }, { "roi_bed": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/roi1.bed", "ref_gene": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene1.txt", "ref_gene_idx": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene1.txt.idx" }, { "roi_bed": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/roi10.bed", "ref_gene": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene10.txt", "ref_gene_idx": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene10.txt.idx" }, { "roi_bed": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/roi11.bed", "ref_gene": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene11.txt", "ref_gene_idx": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene11.txt.idx" }, { "roi_bed": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/roi12.bed", "ref_gene": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene12.txt", "ref_gene_idx": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene12.txt.idx" }, { "roi_bed": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/roi13.bed", "ref_gene": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene13.txt", "ref_gene_idx": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene13.txt.idx" }, { "roi_bed": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/roi14.bed", "ref_gene": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene14.txt", "ref_gene_idx": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene14.txt.idx" }, { "roi_bed": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/roi15.bed", "ref_gene": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene15.txt", "ref_gene_idx": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene15.txt.idx" }, { "roi_bed": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/roi16.bed", "ref_gene": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene16.txt", "ref_gene_idx": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene16.txt.idx" }, { "roi_bed": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/roi17.bed", "ref_gene": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene17.txt", "ref_gene_idx": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene17.txt.idx" }, { "roi_bed": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/roi18.bed", "ref_gene": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene18.txt", "ref_gene_idx": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene18.txt.idx" }, { "roi_bed": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/roi19.bed", "ref_gene": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene19.txt", "ref_gene_idx": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene19.txt.idx" }, { "roi_bed": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/roi2.bed", "ref_gene": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene2.txt", "ref_gene_idx": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene2.txt.idx" }, { "roi_bed": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/roi20.bed", "ref_gene": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene20.txt", "ref_gene_idx": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene20.txt.idx" }, { "roi_bed": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/roi21.bed", "ref_gene": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene21.txt", "ref_gene_idx": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene21.txt.idx" }, { "roi_bed": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/roi22.bed", "ref_gene": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene22.txt", "ref_gene_idx": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene22.txt.idx" }, { "roi_bed": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/roi24.bed", "ref_gene": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene24.txt", "ref_gene_idx": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene24.txt.idx" }, { "roi_bed": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/roi25.bed", "ref_gene": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene25.txt", "ref_gene_idx": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene25.txt.idx" }, { "roi_bed": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/roi26.bed", "ref_gene": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene26.txt", "ref_gene_idx": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene26.txt.idx" }, { "roi_bed": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/roi27.bed", "ref_gene": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene27.txt", "ref_gene_idx": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene27.txt.idx" }, { "roi_bed": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/roi29.bed", "ref_gene": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene29.txt", "ref_gene_idx": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene29.txt.idx" }, { "roi_bed": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/roi3.bed", "ref_gene": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene3.txt", "ref_gene_idx": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene3.txt.idx" }, { "roi_bed": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/roi30.bed", "ref_gene": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene30.txt", "ref_gene_idx": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene30.txt.idx" }, { "roi_bed": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/roi31.bed", "ref_gene": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene31.txt", "ref_gene_idx": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene31.txt.idx" }, { "roi_bed": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/roi32.bed", "ref_gene": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene32.txt", "ref_gene_idx": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene32.txt.idx" }, { "roi_bed": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/roi34.bed", "ref_gene": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene34.txt", "ref_gene_idx": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene34.txt.idx" }, { "roi_bed": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/roi36.bed", "ref_gene": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene36.txt", "ref_gene_idx": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene36.txt.idx" }, { "roi_bed": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/roi4.bed", "ref_gene": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene4.txt", "ref_gene_idx": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene4.txt.idx" }, { "roi_bed": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/roi5.bed", "ref_gene": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene5.txt", "ref_gene_idx": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene5.txt.idx" }, { "roi_bed": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/roi6.bed", "ref_gene": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene6.txt", "ref_gene_idx": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene6.txt.idx" }, { "roi_bed": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/roi7.bed", "ref_gene": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene7.txt", "ref_gene_idx": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene7.txt.idx" }, { "roi_bed": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/roi8.bed", "ref_gene": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene8.txt", "ref_gene_idx": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene8.txt.idx" }, { "roi_bed": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/roi9.bed", "ref_gene": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene9.txt", "ref_gene_idx": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene9.txt.idx" }, { "roi_bed": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/roi97.bed", "ref_gene": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene97.txt", "ref_gene_idx": "gs://lmm-reference-data/roi/clinicalROI_b38_padding2_withCHR_withM_2022-03-11.bed_splitoverlap/grp/refgene97.txt.idx" } ] Output Parameters | Type | Name | When | Description | | :--- | :--- | :--- | :--- | | File | cov_wgs_sample_summary | If coverage analysis is enabled | Coverage metrics summary for all bases | | File | cov_wgs_sample_statistics | If coverage analysis is enabled | Coverage metrics statistics for all bases | | File | cov_roi_sample_interval_summary | If coverage analysis is enabled | Coverage metrics interval summary for region of interest | | File | cov_roi_sample_interval_statistics | If coverage analysis is enabled Coverage metrics interval statistics for region of interest | | File | cov_roi_sample_statistics | If coverage analysis is enabled | Coverage metrics statistics for region of interest | | File | cov_roi_sample_summary | If coverage analysis is enabled | Coverage metrics summary for region of interest | | File | cov_roi_sample_cumulative_coverage_counts | If coverage analysis is enabled | Coverage cumulative counts for the region of interest | | File | cov_roi_sample_cumulative_coverage_proportions | If coverage analysis is enabled | Coverage cumulative proportions for the region of interest | | File | cov_mt_summary | If coverage analysis is enabled | Mitochondrial gene coverage summary | | File | cov_gene_summary | If coverage analysis is enabled | Gene coverage summary | | File | cov_gene_summary_unknown | If coverage analysis is enabled | Unknown entries from the gene coverage summary | | File | cov_gene_summary_entrez | If coverage analysis is enabled | Gene coverage summary enriched with Entrez IDs | | File | vcf | If variant calling is run | Variants called from BAM/CRAM | | File | pgx_summary_report | If PGx is enabled | Summary pharmacogenomics report | | File | pgx_details_report | If PGx is enabled | Detailed pharmacogenomics report | | File | pgx_genotype_xlsx | If PGx is enabled | Full list of pharmacogenomics genotypes in XLSX format | | File | pgx_genotype_txt | If PGx is enabled | Full list of pharmacogenomics genotypes in TSV format | | File | risk_alleles_report | If risk alleles is enabled | Risk alleles report | | File | risk_alleles_genotype_xlsx | If risk alleles is enabled | Full list of risk allele genotypes in XLSX format | | File | risk_alleles_genotype_txt | If risk alleles is enabled | Full list of risk allele genotypes in TSV format | | File | target_vcf_gz | Always | VCF file filtered to the target region of interest | | File | alamut_vcf_gz | Always | Target VCF file with Alamut annotations | | File | qceval_vcf_gz | Always | Target VCF file annotated with QC Evaluation | | File | gnomad_vcf_gz | If gnomad coverage is enabled | Target VCF annotated with gnomad coverage data | | File | fast_export_file | Always | Tab-delimited export of annotated sample data from FAST | | File | fast_summary_file | Always | Summary of FAST processing parameters | | File | igv_report | Always | HTML-based IGV report file | | File | fast_parsed_output | Always | Parsed FAST export | | File | nva_report | Always | NVA report Excel document |