nextflow-development — Skillopedia

nf-core Pipeline Deployment Run nf-core bioinformatics pipelines on local or public sequencing data. Target users: Bench scientists and researchers without specialized bioinformatics training who need to run large-scale omics analyses—differential expression, variant calling, or chromatin accessibility analysis. Workflow Checklist --- Step 0: Acquire Data (GEO/SRA Only) Skip this step if user has local FASTQ files. For public datasets, fetch from GEO/SRA first. See references/geo-sra-acquisition.md for the full workflow. Quick start: DECISION POINT: After fetching study info, confirm with use…

,\n r'^T\\d*[-_]',\n]\n\nNORMAL_KEYWORDS = [\n r'\\bnormal\\b',\n r'\\bgermline\\b',\n r'\\bblood\\b',\n r'\\bpbmc\\b',\n r'\\bcontrol\\b',\n r'\\bhealthy\\b',\n r'\\bmatched\\b',\n r'[-_]N[-_]',\n r'[-_]N\\d*

Important: agents should read /llm.txt, /llms.txt, or /.well-known/skills.json to discover the public Skillopedia API.

,\n r'^N\\d*[-_]',\n]\n\n# Lane pattern\nLANE_PATTERN = r'[_.]L(\\d{3})[_.]'\n\n# Patient/sample extraction patterns\nPATIENT_PATTERNS = [\n r'^(P\\d+)[-_]', # P001_sample\n r'^(patient\\d+)[-_]', # patient1_sample\n r'^(TCGA-\\w+-\\w+)', # TCGA format\n r'^([A-Z]{2,3}\\d{3,})[-_]', # AB123_sample\n]\n\n# Replicate patterns\nREPLICATE_PATTERNS = [\n r'[_.]rep(\\d+)', # _rep1, .rep2\n r'[_.]replicate(\\d+)', # _replicate1\n r'[_.]R(\\d+)[_.]', # _R1_ (but not R1/R2 for reads!)\n r'[-_](\\d+)

Important: agents should read /llm.txt, /llms.txt, or /.well-known/skills.json to discover the public Skillopedia API.

, # sample_1 (last resort)\n]\n\n\ndef extract_sample_info(filepath: str) -> Dict[str, str]:\n \"\"\"\n Extract sample metadata from filepath.\n\n Args:\n filepath: Path to sequencing file\n\n Returns:\n Dict with: sample, patient, lane (if detectable)\n \"\"\"\n filename = os.path.basename(filepath)\n\n # Remove extensions\n stem = filename\n for ext in ['.fastq.gz', '.fq.gz', '.fastq', '.fq', '.bam', '.cram', '.bai', '.crai']:\n if stem.lower().endswith(ext):\n stem = stem[:-len(ext)]\n break\n\n info = {}\n\n # Extract lane\n lane_match = re.search(LANE_PATTERN, stem)\n info['lane'] = f\"L{lane_match.group(1)}\" if lane_match else \"L001\"\n\n # Remove lane from stem\n clean_stem = re.sub(LANE_PATTERN, '_', stem)\n\n # Remove R1/R2 indicators and everything after\n for pattern, _ in R1_PATTERNS + R2_PATTERNS:\n clean_stem = re.sub(pattern + r'.*', '', clean_stem, flags=re.IGNORECASE)\n\n # Clean up trailing/multiple underscores and dots\n clean_stem = re.sub(r'[_.-]+

Important: agents should read /llm.txt, /llms.txt, or /.well-known/skills.json to discover the public Skillopedia API.

, '', clean_stem)\n clean_stem = re.sub(r'[_.-]{2,}', '_', clean_stem)\n\n # Try to extract patient ID\n for pattern in PATIENT_PATTERNS:\n match = re.match(pattern, clean_stem, re.IGNORECASE)\n if match:\n info['patient'] = match.group(1)\n break\n\n # Sample is the cleaned stem\n info['sample'] = clean_stem if clean_stem else filename.split('.')[0]\n\n # Default patient to sample if not extracted\n if 'patient' not in info:\n info['patient'] = info['sample']\n\n return info\n\n\ndef infer_tumor_normal_status(sample_name: str) -> Optional[int]:\n \"\"\"\n Infer tumor (1) or normal (0) status from sample name.\n\n Args:\n sample_name: Sample identifier\n\n Returns:\n 1 for tumor, 0 for normal, None if cannot determine\n \"\"\"\n name_lower = sample_name.lower()\n\n # Check tumor indicators\n for pattern in TUMOR_KEYWORDS:\n if re.search(pattern, name_lower, re.IGNORECASE):\n return 1\n\n # Check normal indicators\n for pattern in NORMAL_KEYWORDS:\n if re.search(pattern, name_lower, re.IGNORECASE):\n return 0\n\n return None\n\n\ndef extract_replicate_number(sample_name: str) -> Optional[int]:\n \"\"\"\n Extract replicate number from sample name.\n\n Args:\n sample_name: Sample identifier\n\n Returns:\n Replicate number if found, None otherwise\n \"\"\"\n for pattern in REPLICATE_PATTERNS:\n match = re.search(pattern, sample_name, re.IGNORECASE)\n if match:\n try:\n return int(match.group(1))\n except ValueError:\n continue\n return None\n\n\ndef _get_pattern_score(filename: str, patterns: List[Tuple[str, int]]) -> int:\n \"\"\"Get highest matching pattern score.\"\"\"\n max_score = 0\n for pattern, score in patterns:\n if re.search(pattern, filename, re.IGNORECASE):\n max_score = max(max_score, score)\n return max_score\n\n\ndef _get_sample_key(filepath: str) -> str:\n \"\"\"Generate a key for grouping related files.\"\"\"\n info = extract_sample_info(filepath)\n sample = info['sample']\n lane = info.get('lane', 'L001')\n\n # Include lane in key for multi-lane samples\n if lane != \"L001\":\n return f\"{sample}_{lane}\"\n return sample\n\n\ndef match_read_pairs(files) -> Dict[str, Dict]:\n \"\"\"\n Match R1/R2 read pairs using scored pattern matching.\n\n Args:\n files: List of FileInfo objects (from file_discovery)\n\n Returns:\n Dict mapping sample_key to {'r1': path, 'r2': path, 'info': dict}\n \"\"\"\n # Classify files\n r1_files = []\n r2_files = []\n\n for file in files:\n filename = file.name if hasattr(file, 'name') else os.path.basename(str(file))\n filepath = file.path if hasattr(file, 'path') else str(file)\n\n r1_score = _get_pattern_score(filename, R1_PATTERNS)\n r2_score = _get_pattern_score(filename, R2_PATTERNS)\n\n if r2_score > r1_score and r2_score > 0:\n r2_files.append((filepath, r2_score))\n elif r1_score > 0:\n r1_files.append((filepath, r1_score))\n else:\n # No clear indicator - assume R1 (single-end or non-standard naming)\n r1_files.append((filepath, 0))\n\n # Build pairs by matching sample keys\n pairs = {}\n\n # Process R1 files first\n for r1_path, score in r1_files:\n key = _get_sample_key(r1_path)\n info = extract_sample_info(r1_path)\n\n if key not in pairs:\n pairs[key] = {\n 'r1': r1_path,\n 'r2': None,\n 'info': info,\n 'score': score\n }\n else:\n # Multiple R1 files for same sample (should not happen)\n pairs[key]['r1'] = r1_path\n\n # Match R2 files\n for r2_path, score in r2_files:\n key = _get_sample_key(r2_path)\n info = extract_sample_info(r2_path)\n\n if key in pairs:\n pairs[key]['r2'] = r2_path\n else:\n # R2 without matching R1\n pairs[key] = {\n 'r1': None,\n 'r2': r2_path,\n 'info': info,\n 'score': score\n }\n\n return pairs\n\n\ndef infer_patient_groupings(sample_names: List[str]) -> Dict[str, str]:\n \"\"\"\n Infer patient groupings from sample names.\n\n Groups samples that share a common prefix pattern.\n\n Args:\n sample_names: List of sample identifiers\n\n Returns:\n Dict mapping sample_name to patient_id\n \"\"\"\n patient_map = {}\n\n for sample in sample_names:\n # Try to find a patient pattern\n for pattern in PATIENT_PATTERNS:\n match = re.match(pattern, sample, re.IGNORECASE)\n if match:\n patient_map[sample] = match.group(1)\n break\n\n if sample not in patient_map:\n # Default: each sample is its own patient\n patient_map[sample] = sample\n\n return patient_map\n","content_type":"text/x-python; charset=utf-8","language":"python","size":7848,"content_sha256":"3f2dcebbc540fa76e9285dd247cb0d6922d4fbe2588b7f7b3ad7ac7ab82b63af"},{"filename":"scripts/utils/validators.py","content":"\"\"\"\nSamplesheet validation utilities.\n\nValidates samplesheet rows against pipeline configuration before writing,\ncatching errors early with helpful messages.\n\"\"\"\n\nimport os\nfrom dataclasses import dataclass, field\nfrom pathlib import Path\nfrom typing import Dict, List, Optional\nimport yaml\n\n\n@dataclass\nclass ValidationResult:\n \"\"\"Result of samplesheet validation.\"\"\"\n valid: bool\n errors: List[str] = field(default_factory=list)\n warnings: List[str] = field(default_factory=list)\n suggestions: List[str] = field(default_factory=list)\n\n def __bool__(self):\n return self.valid\n\n def summary(self) -> str:\n \"\"\"Generate human-readable summary.\"\"\"\n lines = []\n if self.errors:\n lines.append(\"Errors:\")\n for e in self.errors:\n lines.append(f\" - {e}\")\n if self.warnings:\n lines.append(\"Warnings:\")\n for w in self.warnings:\n lines.append(f\" - {w}\")\n if self.suggestions:\n lines.append(\"Suggestions:\")\n for s in self.suggestions:\n lines.append(f\" - {s}\")\n return \"\\n\".join(lines)\n\n\ndef load_pipeline_config(pipeline: str) -> Optional[Dict]:\n \"\"\"Load pipeline configuration from YAML file.\"\"\"\n # Find config directory relative to this file\n script_dir = Path(__file__).parent.parent.parent\n config_path = script_dir / \"config\" / \"pipelines\" / f\"{pipeline}.yaml\"\n\n if not config_path.exists():\n return None\n\n with open(config_path) as f:\n return yaml.safe_load(f)\n\n\ndef validate_samplesheet(\n rows: List[Dict],\n pipeline: str,\n config: Optional[Dict] = None\n) -> ValidationResult:\n \"\"\"\n Validate samplesheet rows against pipeline requirements.\n\n Args:\n rows: List of row dictionaries\n pipeline: Pipeline name (e.g., 'rnaseq', 'sarek')\n config: Optional pre-loaded config dict\n\n Returns:\n ValidationResult with errors, warnings, and suggestions\n \"\"\"\n errors = []\n warnings = []\n suggestions = []\n\n # Load config if not provided\n if config is None:\n config = load_pipeline_config(pipeline)\n\n if config is None:\n errors.append(f\"Unknown pipeline: {pipeline}\")\n return ValidationResult(valid=False, errors=errors)\n\n columns = config.get(\"samplesheet\", {}).get(\"columns\", [])\n required_cols = [c[\"name\"] for c in columns if c.get(\"required\", False)]\n\n if not rows:\n errors.append(\"Samplesheet is empty - no samples found\")\n return ValidationResult(valid=False, errors=errors)\n\n # Validate each row\n for i, row in enumerate(rows):\n row_num = i + 2 # Account for header row\n\n # Check required columns\n for col_name in required_cols:\n col_config = next((c for c in columns if c[\"name\"] == col_name), None)\n\n # Skip columns with conditions that don't apply\n if col_config and \"condition\" in col_config:\n # Simple condition check - skip for now\n # Full implementation would evaluate conditions\n pass\n\n if col_name not in row or row[col_name] is None or row[col_name] == \"\":\n # Check if there's a default\n if col_config and \"default\" in col_config:\n continue\n errors.append(f\"Row {row_num}: Missing required column '{col_name}'\")\n\n # Validate path columns exist\n for col_name in [\"fastq_1\", \"fastq_2\", \"bam\", \"bai\"]:\n if col_name in row and row[col_name]:\n path = row[col_name]\n if not os.path.exists(path):\n errors.append(f\"Row {row_num}: File not found: {path}\")\n elif not os.path.isfile(path):\n errors.append(f\"Row {row_num}: Not a file: {path}\")\n\n # Validate enum values\n for col_config in columns:\n col_name = col_config[\"name\"]\n if col_name in row and row[col_name] and \"allowed\" in col_config:\n value = row[col_name]\n allowed = col_config[\"allowed\"]\n if value not in allowed:\n errors.append(\n f\"Row {row_num}: Invalid value '{value}' for '{col_name}'. \"\n f\"Allowed: {allowed}\"\n )\n\n # Check R1/R2 pairing consistency\n r1 = row.get(\"fastq_1\", \"\")\n r2 = row.get(\"fastq_2\", \"\")\n if r1 and not r2:\n warnings.append(f\"Row {row_num}: Single-end data (no R2 file)\")\n elif r2 and not r1:\n errors.append(f\"Row {row_num}: R2 present but R1 missing\")\n\n # Check for duplicate samples\n sample_col = \"sample\" if \"sample\" in rows[0] else \"patient\"\n if sample_col in rows[0]:\n samples = [r.get(sample_col, \"\") for r in rows]\n duplicates = [s for s in set(samples) if samples.count(s) > 1]\n if duplicates:\n warnings.append(f\"Duplicate sample names: {duplicates}\")\n suggestions.append(\n \"Duplicates may be intentional (multi-lane sequencing). \"\n \"Verify sample grouping is correct.\"\n )\n\n # Pipeline-specific validation\n if pipeline == \"sarek\":\n _validate_sarek_specific(rows, errors, warnings, suggestions)\n elif pipeline == \"atacseq\":\n _validate_atacseq_specific(rows, errors, warnings, suggestions)\n\n return ValidationResult(\n valid=len(errors) == 0,\n errors=errors,\n warnings=warnings,\n suggestions=suggestions\n )\n\n\ndef _validate_sarek_specific(\n rows: List[Dict],\n errors: List[str],\n warnings: List[str],\n suggestions: List[str]\n):\n \"\"\"Sarek-specific validation for tumor/normal pairing.\"\"\"\n # Group by patient\n patients = {}\n for row in rows:\n patient = row.get(\"patient\", \"\")\n status = row.get(\"status\")\n\n if patient not in patients:\n patients[patient] = {\"tumor\": 0, \"normal\": 0, \"unknown\": 0}\n\n if status == 1:\n patients[patient][\"tumor\"] += 1\n elif status == 0:\n patients[patient][\"normal\"] += 1\n else:\n patients[patient][\"unknown\"] += 1\n\n # Check pairing\n for patient, counts in patients.items():\n if counts[\"tumor\"] > 0 and counts[\"normal\"] == 0:\n warnings.append(\n f\"Patient '{patient}': Tumor sample(s) without matched normal. \"\n \"Somatic calling works best with paired tumor-normal.\"\n )\n suggestions.append(\n f\"For patient '{patient}': Add a normal sample or use tumor-only mode.\"\n )\n\n if counts[\"unknown\"] > 0:\n warnings.append(\n f\"Patient '{patient}': {counts['unknown']} sample(s) with unknown status. \"\n \"Set status column to 0 (normal) or 1 (tumor).\"\n )\n\n\ndef _validate_atacseq_specific(\n rows: List[Dict],\n errors: List[str],\n warnings: List[str],\n suggestions: List[str]\n):\n \"\"\"ATAC-seq specific validation for replicates.\"\"\"\n # Group by sample (condition)\n samples = {}\n for row in rows:\n sample = row.get(\"sample\", \"\")\n replicate = row.get(\"replicate\", 1)\n\n if sample not in samples:\n samples[sample] = []\n\n samples[sample].append(replicate)\n\n # Check replicates\n for sample, reps in samples.items():\n if len(reps) \u003c 2:\n warnings.append(\n f\"Sample '{sample}': Only {len(reps)} replicate(s). \"\n \"Consensus peaks require 2+ replicates.\"\n )\n\n # Check for duplicate replicate numbers\n if len(reps) != len(set(reps)):\n errors.append(\n f\"Sample '{sample}': Duplicate replicate numbers detected. \"\n \"Each replicate must have a unique number.\"\n )\n\n # Check all samples have R2 (ATAC-seq requires paired-end)\n for i, row in enumerate(rows):\n if not row.get(\"fastq_2\"):\n errors.append(\n f\"Row {i+2}: ATAC-seq requires paired-end data. R2 file missing.\"\n )\n\n\ndef validate_file_exists(path: str) -> bool:\n \"\"\"Check if file exists and is accessible.\"\"\"\n return os.path.isfile(path) and os.access(path, os.R_OK)\n\n\ndef validate_absolute_path(path: str) -> bool:\n \"\"\"Check if path is absolute.\"\"\"\n return os.path.isabs(path)\n","content_type":"text/x-python; charset=utf-8","language":"python","size":8419,"content_sha256":"2572671195244a3ac7e67a6ebe4c44bbd541fbaa1afe3ebab1f81a46b29feea1"}],"content_json":{"type":"doc","content":[{"type":"heading","attrs":{"level":1},"content":[{"text":"nf-core Pipeline Deployment","type":"text"}]},{"type":"paragraph","content":[{"text":"Run nf-core bioinformatics pipelines on local or public sequencing data.","type":"text"}]},{"type":"paragraph","content":[{"text":"Target users:","type":"text","marks":[{"type":"strong"}]},{"text":" Bench scientists and researchers without specialized bioinformatics training who need to run large-scale omics analyses—differential expression, variant calling, or chromatin accessibility analysis.","type":"text"}]},{"type":"heading","attrs":{"level":2},"content":[{"text":"Workflow Checklist","type":"text"}]},{"type":"code_block","attrs":{"wrap":false,"language":""},"content":[{"text":"- [ ] Step 0: Acquire data (if from GEO/SRA)\n- [ ] Step 1: Environment check (MUST pass)\n- [ ] Step 2: Select pipeline (confirm with user)\n- [ ] Step 3: Run test profile (MUST pass)\n- [ ] Step 4: Create samplesheet\n- [ ] Step 5: Configure & run (confirm genome with user)\n- [ ] Step 6: Verify outputs","type":"text"}]},{"type":"hr","attrs":{"markup":"---"}},{"type":"heading","attrs":{"level":2},"content":[{"text":"Step 0: Acquire Data (GEO/SRA Only)","type":"text"}]},{"type":"paragraph","content":[{"text":"Skip this step if user has local FASTQ files.","type":"text","marks":[{"type":"strong"}]}]},{"type":"paragraph","content":[{"text":"For public datasets, fetch from GEO/SRA first. See ","type":"text"},{"text":"references/geo-sra-acquisition.md","type":"text","marks":[{"type":"link","attrs":{"href":"references/geo-sra-acquisition.md","title":null}}]},{"text":" for the full workflow.","type":"text"}]},{"type":"paragraph","content":[{"text":"Quick start:","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"# 1. Get study info\npython scripts/sra_geo_fetch.py info GSE110004\n\n# 2. Download (interactive mode)\npython scripts/sra_geo_fetch.py download GSE110004 -o ./fastq -i\n\n# 3. Generate samplesheet\npython scripts/sra_geo_fetch.py samplesheet GSE110004 --fastq-dir ./fastq -o samplesheet.csv","type":"text"}]},{"type":"paragraph","content":[{"text":"DECISION POINT:","type":"text","marks":[{"type":"strong"}]},{"text":" After fetching study info, confirm with user:","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Which sample subset to download (if multiple data types)","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"Suggested genome and pipeline","type":"text"}]}]}]},{"type":"paragraph","content":[{"text":"Then continue to Step 1.","type":"text"}]},{"type":"hr","attrs":{"markup":"---"}},{"type":"heading","attrs":{"level":2},"content":[{"text":"Step 1: Environment Check","type":"text"}]},{"type":"paragraph","content":[{"text":"Run first. Pipeline will fail without passing environment.","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"python scripts/check_environment.py","type":"text"}]},{"type":"paragraph","content":[{"text":"All critical checks must pass. If any fail, provide fix instructions:","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Docker issues","type":"text"}]},{"type":"table","attrs":{"layout":null},"content":[{"type":"tr","content":[{"type":"th","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Problem","type":"text"}]}]},{"type":"th","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Fix","type":"text"}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Not installed","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Install from https://docs.docker.com/get-docker/","type":"text"}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Permission denied","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"sudo usermod -aG docker $USER","type":"text","marks":[{"type":"code_inline"}]},{"text":" then re-login","type":"text"}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Daemon not running","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"sudo systemctl start docker","type":"text","marks":[{"type":"code_inline"}]}]}]}]}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Nextflow issues","type":"text"}]},{"type":"table","attrs":{"layout":null},"content":[{"type":"tr","content":[{"type":"th","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Problem","type":"text"}]}]},{"type":"th","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Fix","type":"text"}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Not installed","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"curl -s https://get.nextflow.io | bash && mv nextflow ~/bin/","type":"text","marks":[{"type":"code_inline"}]}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Version \u003c 23.04","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"nextflow self-update","type":"text","marks":[{"type":"code_inline"}]}]}]}]}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Java issues","type":"text"}]},{"type":"table","attrs":{"layout":null},"content":[{"type":"tr","content":[{"type":"th","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Problem","type":"text"}]}]},{"type":"th","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Fix","type":"text"}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Not installed / \u003c 11","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"sudo apt install openjdk-11-jdk","type":"text","marks":[{"type":"code_inline"}]}]}]}]}]},{"type":"paragraph","content":[{"text":"Do not proceed until all checks pass.","type":"text","marks":[{"type":"strong"}]},{"text":" For HPC/Singularity, see ","type":"text"},{"text":"references/troubleshooting.md","type":"text","marks":[{"type":"link","attrs":{"href":"references/troubleshooting.md","title":null}}]},{"text":".","type":"text"}]},{"type":"hr","attrs":{"markup":"---"}},{"type":"heading","attrs":{"level":2},"content":[{"text":"Step 2: Select Pipeline","type":"text"}]},{"type":"paragraph","content":[{"text":"DECISION POINT: Confirm with user before proceeding.","type":"text","marks":[{"type":"strong"}]}]},{"type":"table","attrs":{"layout":null},"content":[{"type":"tr","content":[{"type":"th","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Data Type","type":"text"}]}]},{"type":"th","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Pipeline","type":"text"}]}]},{"type":"th","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Version","type":"text"}]}]},{"type":"th","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Goal","type":"text"}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"RNA-seq","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"rnaseq","type":"text","marks":[{"type":"code_inline"}]}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"3.22.2","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Gene expression","type":"text"}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"WGS/WES","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"sarek","type":"text","marks":[{"type":"code_inline"}]}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"3.7.1","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Variant 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Important: agents should read /llm.txt, /llms.txt, or /.well-known/skills.json to discover the public Skillopedia API.