Q (sonnet): Are there studies that were not processed using polyA enrichment in order to explore lncRNA-related questions? ▶ list_guides {} ◀ result {"result":[{"uri":"cbioportal://mutation-frequency-guide","description":"Comprehensive guide for calculating gene mutation frequencies with gene-specific profiling denominators"},{"uri":"cbioportal://clinical-data-guide","description":"Guide for querying clinical data including patient vs sample level considerations"},{"uri":"cbioportal://sample-filtering-guide","description":"Guide for filtering samples and studies in cBioPortal queries"},{"uri":"cbioportal://common-pitfalls","description":"Guide to avoid common mistakes when querying cBioPortal data. If you already know which numbered pitfall applies, fetch just that section via read_guide(\"cbioportal://common-pitfalls#\") (e.g. #16) instead of the full guide"},{"uri":"cbioportal://treatment-guide","description":"Guide for querying treatment/clinical event data including drug agents, timelines, and linking to genomic data"},{"uri":"cbioportal://faq-guide","description":"General cBioPortal FAQ: history, how to cite, data types, reference genome, abbreviations, GISTIC thresholds, API access"},{"uri":"cbioportal://statistical-tests-guide","description":"Statistical test selection guide — decision matrix for choosing Fisher's exact, Wilcoxon, chi-squared, t-test, ANOVA, etc. based on data type and group count"},{"uri":"cbioportal://gene-expression-guide","description":"Gene expression / copy-number / methylation analysis. Covers genetic_alteration_derived, profile_type discovery, and the gene_pair_coexpression view for Spearman correlation between two genes"},{"uri":"cbioportal://external-resources-guide","description":"Guide for finding external linked resources such as imaging, pathology, Minerva, HTAN, or other resource_* table links before declaring data unavailable"},{"uri":"cbioportal://gene-resolution-guide","description":"Guide for resolving ambiguous gene symbols, aliases, gene families, and shorthand such as CD3 before querying expression or alteration data"},{"uri":"cbioportal://study-resolution-guide","description":"Guide for resolving requested studies, avoiding silent substitute cohorts, and redirecting to known external cBioPortal instances when data is not in this deployment"},{"uri":"cbioportal://germline-guide","description":"Guide for querying germline variant data — storage columns, study discovery, query patterns, and somatic vs germline considerations"},{"uri":"cbioportal://study-guide/{study_id}","description":"Dynamic study-specific guide - use get_study_guide(study_id) tool to generate"}]} ▶ clickhouse_list_tables {} ◀ result {"tables":[{"name":"allele_specific_copy_number"},{"name":"alteration_driver_annotation"},{"name":"cancer_study"},{"name":"cancer_study_query_preferences"},{"name":"cancer_study_tags"},{"name":"cbioportal_sequence_state"},{"name":"clinical_attribute_counts"},{"name":"clinical_attribute_meta"},{"name":"clinical_data_derived"},{"name":"clinical_event"},{"name":"clinical_event_data"},{"name":"clinical_event_data_derived"},{"name":"clinical_event_derived"},{"name":"clinical_patient"},{"name":"clinical_sample"},{"name":"cna_event"},{"name":"cna_panel_gene_coverage"},{"name":"cna_wes_coverage"},{"name":"co_altered_genes_in_study"},{"name":"copy_number_seg"},{"name":"copy_number_seg_file"},{"name":"gene"},{"name":"gene_alias"},{"name":"gene_alteration_frequency_by_cancer_type"},{"name":"gene_cna_distribution_in_study"},{"name":"gene_mutation_frequency_by_cancer_type"},{"name":"gene_mutation_frequency_in_studies"},{"name":"gene_mutation_frequency_in_study"},{"name":"gene_mutation_variants_in_study"},{"name":"gene_pair_coexpression"},{"name":"gene_panel"},{"name":"gene_panel_list"},{"name":"gene_panel_to_gene_derived"},{"name":"generic_assay_data_derived"},{"name":"generic_assay_meta_derived"},{"name":"generic_assay_profile_entity_derived"},{"name":"generic_entity_properties"},{"name":"geneset"},{"name":"geneset_gene"},{"name":"geneset_hierarchy_leaf"},{"name":"geneset_hierarchy_node"},{"name":"genetic_alteration"},{"name":"genetic_alteration_derived"},{"name":"genetic_entity"},{"name":"genetic_profile"},{"name":"genetic_profile_link"},{"name":"genetic_profile_samples"},{"name":"genomic_event_derived"},{"name":"gistic"},{"name":"gistic_to_gene"},{"name":"info"},{"name":"mut_sig"},{"name":"mutation"},{"name":"mutation_count_by_keyword"},{"name":"mutation_derived"},{"name":"mutation_event"},{"name":"mutation_panel_gene_coverage"},{"name":"mutation_wes_coverage"},{"name":"patient"},{"name":"reference_genome"},{"name":"reference_genome_gene"},{"name":"resource_definition"},{"name":"resource_patient"},{"name":"resource_sample"},{"name":"resource_study"},{"name":"sample"},{"name":"sample_cna_event"},{"name":"sample_derived"},{"name":"sample_list"},{"name":"sample_list_list"},{"name":"sample_profile"},{"name":"sample_to_gene_panel_derived"},{"name":"structural_variant"},{"name":"sv_panel_gene_coverage"},{"name":"sv_wes_coverage"},{"name":"top_cna_genes_in_study"},{"name":"top_mutated_genes_in_cohort"},{"name":"top_mutated_genes_in_study"},{"name":"top_sv_genes_in_study"},{"name":"treatment_counts_in_study"},{"name":"treatment_regimens_in_study"},{"name":"type_of_cancer"}]} ▶ clickhouse_list_table_columns { "table": "genetic_profile" } ▶ clickhouse_list_table_columns { "table": "cancer_study" } ◀ result {"columns":[{"name":"genetic_profile_id","type":"Int64"},{"name":"stable_id","type":"String"},{"name":"cancer_study_id","type":"Int64"},{"name":"genetic_alteration_type","type":"String"},{"name":"generic_assay_type","type":"Nullable(String)"},{"name":"datatype","type":"String"},{"name":"name","type":"String"},{"name":"description","type":"Nullable(String)"},{"name":"show_profile_in_analysis_tab","type":"Int32"},{"name":"pivot_threshold","type":"Nullable(Float64)"},{"name":"sort_order","type":"Nullable(String)"},{"name":"patient_level","type":"Nullable(Int32)"}]} ◀ result {"columns":[{"name":"cancer_study_id","type":"Int64","comment":"Internal numeric ID. Prefer cancer_study_identifier for queries as it is more readable and stable."},{"name":"cancer_study_identifier","type":"Nullable(String)","comment":"Stable string identifier for the study (e.g., \"msk_chord_2024\", \"brca_tcga\"). Use this for filtering, not cancer_study_id."},{"name":"type_of_cancer_id","type":"String"},{"name":"name","type":"String","comment":"Full descriptive name of the study (e.g., \"MSK-CHORD (MSK, Nature 2024)\")."},{"name":"description","type":"String"},{"name":"public","type":"Int32"},{"name":"pmid","type":"Nullable(String)"},{"name":"citation","type":"Nullable(String)"},{"name":"groups","type":"Nullable(String)"},{"name":"status","type":"Nullable(Int64)"},{"name":"import_date","type":"Nullable(DateTime64(6))"},{"name":"reference_genome_id","type":"Nullable(Int64)"},{"name":"sample_count","type":"UInt32","comment":"Samples in the study (members of _all), as shown in the portal study list. Precomputed daily at LLM-prep time."},{"name":"mutation_sample_count","type":"UInt32","comment":"Samples profiled for mutations (_sequenced) — portal \"Data type\" filter: \"Mutations\". 0 = no mutation data."},{"name":"cna_sample_count","type":"UInt32","comment":"Samples profiled for copy-number alterations (_cna) — \"CNA\". 0 = no CNA data."},{"name":"structural_variant_sample_count","type":"UInt32","comment":"Distinct samples with at least one structural variant (fusions etc.). 0 = none."},{"name":"rna_seq_sample_count","type":"UInt32","comment":"Samples with RNA-Seq expression (_rna_seq_v2_mrna) — \"RNA-Seq\"."},{"name":"mrna_microarray_sample_count","type":"UInt32","comment":"Samples with microarray mRNA expression (_mrna) — \"RNA (microarray)\"."},{"name":"mirna_sample_count","type":"UInt32","comment":"Samples with microRNA expression (_microrna) — \"miRNA\"."},{"name":"rppa_sample_count","type":"UInt32","comment":"Samples with RPPA protein levels (_rppa) — \"RPPA\"."},{"name":"mass_spectrometry_sample_count","type":"UInt32","comment":"Samples with mass-spectrometry protein quantification (_protein_quantification) — \"Protein Mass-Spectrometry\"."},{"name":"treatment_patient_count","type":"UInt32","comment":"PATIENTS (not samples) with treatment clinical events — \"Treatment\". 0 = no treatment data."},{"name":"resource_sample_counts","type":"Map(String, UInt32)","comment":"Samples with each linked resource, keyed by display name: imaging and pathology such as 'Slide Microscopy', 'Computed Tomography', 'Magnetic Resonance', 'H&E Slide', 'MxIF Image'. Query with mapKeys(resource_sample_counts) or resource_sample_counts['Slide Microscopy'] > 0."},{"name":"mrna_expression_sample_count","type":"UInt32","comment":"Samples in any mRNA expression profile (RNA-Seq, microarray, TPM, z-scores; from sample_profile). Use this for \"does the study have expression data\" — rna_seq_sample_count and mrna_m … (136 more chars) ▶ clickhouse_run_select_query { "query": " SELECT gp.stable_id, gp.name, gp.description, cs.cancer_study_identifier FROM genetic_profile gp JOIN cancer_study cs ON gp.cancer_study_id = cs.cancer_study_id WHERE gp.genetic_alteration_type IN ('MRNA_EXPRESSION','RNA_EXPRESSION') AND ( lower(gp.description) LIKE '%polya%' OR lower(gp.description) LIKE '%ribo%' OR lower(gp.description) LIKE '%total rna%' OR lower(gp.description) LIKE '%lncrna%' OR lower(gp.description) LIKE '%long non%' OR lower(gp.name) LIKE '%lncrna%' ) LIMIT 100 " } ◀ result {"rows":[{"stable_id":"prad_su2c_2019_mrna_seq_fpkm_polya","name":"mRNA expression (FPKM polyA)","description":"mRNA expression from polyA (RNA Seq FPKM)","cancer_study_identifier":"prad_su2c_2019"},{"stable_id":"prad_su2c_2019_mrna_seq_fpkm_polya_Zscores","name":"mRNA expression z-scores relative to diploid samples (FPKM polyA)","description":"mRNA expression from polyA Z-scores (RNA Seq FPKM) compared to the expression distribution of each gene tumors that are diploid for this gene.","cancer_study_identifier":"prad_su2c_2019"},{"stable_id":"prad_su2c_2019_mrna_seq_fpkm_polya_all_sample_Zscores","name":"mRNA expression z-scores relative to all samples (log FPKM polyA)","description":"Log-transformed mRNA z-scores compared to expression distribution of all samples (RNA Seq FPKM polyA).","cancer_study_identifier":"prad_su2c_2019"}]} ▶ clickhouse_run_select_query { "query": " SELECT cancer_study_identifier, name, description FROM cancer_study WHERE lower(description) LIKE '%polya%' OR lower(description) LIKE '%ribo-deplet%' OR lower(description) LIKE '%ribodeplet%' OR lower(description) LIKE '%total rna%' OR lower(description) LIKE '%lncrna%' OR lower(description) LIKE '%long non-coding%' OR lower(description) LIKE '%long noncoding%' LIMIT 100 " } ◀ result {"rows":[]} ▶ clickhouse_run_select_query { "query": " SELECT DISTINCT gp.stable_id, gp.name, gp.description, cs.cancer_study_identifier FROM genetic_profile gp JOIN cancer_study cs ON gp.cancer_study_id = cs.cancer_study_id WHERE gp.genetic_alteration_type IN ('MRNA_EXPRESSION','RNA_EXPRESSION') ORDER BY cs.cancer_study_identifier LIMIT 300 " } ◀ result {"rows":[{"stable_id":"acc_tcga_rna_seq_v2_mrna","name":"mRNA expression (RNA Seq V2 RSEM)","description":"mRNA gene expression (RNA Seq V2 RSEM)","cancer_study_identifier":"acc_tcga"},{"stable_id":"acc_tcga_rna_seq_v2_mrna_median_Zscores","name":"mRNA expression z-scores relative to diploid samples (RNA Seq V2 RSEM)","description":"mRNA expression z-scores (RNA Seq V2 RSEM) compared to the expression distribution of each gene tumors that are diploid for this gene.","cancer_study_identifier":"acc_tcga"},{"stable_id":"acc_tcga_rna_seq_v2_mrna_median_all_sample_Zscores","name":"mRNA expression z-scores relative to all samples (log RNA Seq V2 RSEM)","description":"Log-transformed mRNA expression z-scores compared to the expression distribution of all samples (RNA Seq V2 RSEM).","cancer_study_identifier":"acc_tcga"},{"stable_id":"acc_tcga_gdc_mrna_seq_tpm","name":"mRNA Expression (TPM)","description":"mRNA expression from capture (RNA Seq TPM)","cancer_study_identifier":"acc_tcga_gdc"},{"stable_id":"acc_tcga_gdc_mrna_seq_tpm_Zscores","name":"mRNA Expression TPM z-scores","description":"mRNA expression from capture (RNA Seq TPM) zscores","cancer_study_identifier":"acc_tcga_gdc"},{"stable_id":"acc_tcga_gdc_mrna_seq_fpkm","name":"mRNA Expression (FPKM)","description":"mRNA expression from capture (RNA Seq FPKM)","cancer_study_identifier":"acc_tcga_gdc"},{"stable_id":"acc_tcga_gdc_mrna_seq_fpkm_Zscores","name":"mRNA Expression FPKM z-scores","description":"mRNA expression from capture (RNA Seq FPKM) zscores","cancer_study_identifier":"acc_tcga_gdc"},{"stable_id":"acc_tcga_gdc_rna_seq_mrna","name":"mRNA Expression","description":"Expression levels","cancer_study_identifier":"acc_tcga_gdc"},{"stable_id":"acc_tcga_gdc_mrna_seq_read_counts_Zscores","name":"mRNA Expression z-scores","description":"mRNA expression from capture read count zscores","cancer_study_identifier":"acc_tcga_gdc"},{"stable_id":"acc_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","name":"mRNA expression z-scores relative to all samples (log RNA Seq V2 RSEM)","description":"Log-transformed mRNA expression z-scores compared to the expression distribution of all samples (RNA Seq V2 RSEM).","cancer_study_identifier":"acc_tcga_pan_can_atlas_2018"},{"stable_id":"acc_tcga_pan_can_atlas_2018_rna_seq_v2_mrna","name":"mRNA Expression, RSEM (Batch normalized from Illumina HiSeq_RNASeqV2)","description":"mRNA Expression, RSEM (Batch normalized from Illumina HiSeq_RNASeqV2)","cancer_study_identifier":"acc_tcga_pan_can_atlas_2018"},{"stable_id":"acc_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","name":"mRNA expression z-scores relative to diploid samples (RNA Seq V2 RSEM)","description":"mRNA expression z-scores (RNA Seq V2 RSEM) compared to the expression distribution of each gene tumors that are diploid for this gene.","cancer_study_identifier":"acc_tcga_pan_can_atlas_2018"},{"stable_id":"alal_target_gdc_mrna_seq_read_counts_Zscores","name":"mRNA Expression z-scores","de … (20299 more chars) ═ answer (success) This confirms the key finding: cBioPortal's `genetic_profile` metadata almost never explicitly documents the RNA-seq library preparation protocol (polyA-selection vs. ribo-depletion/total RNA). The vast majority of profile descriptions just say "RNA Seq RSEM/FPKM/TPM" or "expression from capture" (referring to exome/target capture RNA-seq at GDC, not library prep chemistry) without specifying polyA vs. ribo-depletion. Only **one study** (`prad_su2c_2019`) explicitly labels its profile as polyA-selected in the metadata — and no study is explicitly labeled with a non-polyA/ribo-depletion method in this database's metadata. ## Answer I searched the `genetic_profile` and `cancer_study` description fields across the entire database for explicit mentions of RNA-seq library preparation method (polyA, ribo-depletion, total RNA, lncRNA). **Key finding: cBioPortal generally does not curate or expose RNA-seq library-prep methodology (polyA-selected vs. ribo-depleted/total RNA) as structured metadata.** Only one study explicitly labels its expression profile by protocol: | Study | Profile | Protocol (as labeled) | |---|---|---| | `prad_su2c_2019` | mRNA expression (FPKM polyA) | **PolyA-selected** | No study in this deployment is explicitly labeled as ribo-depleted, total-RNA, or otherwise non-polyA in its metadata — meaning I **cannot confirm from the database's descriptive fields** which specific studies avoided polyA enrichment. This absence of metadata is itself informative but not proof of absence: - Most RNA-seq profiles (TCGA, GDC re-processed cohorts, MSK panels, etc.) simply state the quantification unit (RSEM, FPKM, TPM, "capture") without specifying poly(A) selection vs. total-RNA/ribo-depletion at the wet-lab level. - Since standard poly(A)-selected RNA-seq depletes most lncRNAs (many are non-polyadenylated) as well as other non-coding RNA classes, this is a legitimate methodological concern for lncRNA studies — but cBioPortal's schema isn't set up to answer it directly. The original study publications (linked via `pmid`/`citation` in `cancer_study`) would need to be consulted to confirm library prep protocol. If you'd like, I can pull the `pmid`/`citation` fields for specific cancer types or cohorts of interest (e.g., studies known to use total-RNA-seq, such as certain CPTAC or pediatric TARGET cohorts) so you can verify their methods sections directly — let me know which cancer type(s) you're focused on.