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Bases2Fastq

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Demultiplexes and converts Element AVITI base calls into FASTQ files.

https://docs.elembio.io/docs/bases2fastq/introduction/

Bases2Fastq is Element Biosciences' secondary analysis software for demultiplexing sequencing data from AVITI systems and converting base calls into FASTQ files.

Data Flow Overview​

The module handles three distinct data hierarchy levels:

  1. Run Level: Single sequencing run with all samples in one output

    • Directory: <run_output>/
    • Files: RunStats.json, RunManifest.json
    • Samples identified by: {RunName}-{AnalysisID}__{SampleName}

  2. Project Level: Demultiplexing by project, samples split into project subdirectories

    • Directory: <run_output>/Samples/<ProjectName>/
    • Files: Project-specific RunStats.json
    • Run-level RunManifest.json accessed via ../../RunManifest.json
    • Samples identified by: {RunName}-{AnalysisID}__{SampleName}

  3. Combined Level: Both run and project data present (merged view)

Parsing Flow​

__init__()
    │
    ├─> _init_data_structures()     # Initialize empty dicts for all data levels
    │
    ├─> _parse_and_validate_data()  # Main parsing entry point
    │       │
    │       ├─> _parse_run_project_data("bases2fastq/run")     # Parse run-level RunStats.json
    │       │       └─> Populates: run_level_data, run_level_samples, run_level_samples_to_project
    │       │
    │       ├─> _parse_run_project_data("bases2fastq/project") # Parse project-level RunStats.json
    │       │       └─> Populates: project_level_data, project_level_samples, project_level_samples_to_project
    │       │
    │       └─> _determine_summary_path()  # Returns: "run_level" | "project_level" | "combined_level"
    │
    ├─> _select_data_by_summary_path()  # Route to appropriate data sources
    │       │
    │       ├─> _parse_run_manifest() or _parse_run_manifest_in_project()
    │       │       └─> Returns: manifest_data (lane settings, adapter info)
    │       │
    │       ├─> _parse_index_assignment() or _parse_index_assignment_in_project()
    │       │       └─> Returns: index_assignment_data (per-sample index stats)
    │       │
    │       └─> _parse_run_unassigned_sequences() (run_level only)
    │               └─> Returns: unassigned_sequences (unknown barcodes)
    │
    ├─> _setup_colors()             # Assign colors to runs/projects/samples
    │
    └─> _generate_plots()           # Create all report sections and plots

Data Structures​

  • run_level_data: Dict[run_name, run_stats] - Run-level QC metrics
  • run_level_samples: Dict[sample_id, sample_stats] - Sample metrics from run-level
  • project_level_data: Dict[project_name, project_stats] - Project-level QC metrics
  • project_level_samples: Dict[sample_id, sample_stats] - Sample metrics from project-level
  • *_samples_to_project: Dict[sample_id, project_name] - Maps samples to their projects

Sample Naming Convention​

Samples are uniquely identified as: {RunName}-{AnalysisID[0:4]}__{SampleName} This ensures uniqueness across multiple runs while keeping names readable.

Files Parsed​

  • RunStats.json: Run/project QC metrics, sample statistics, lane data
  • RunManifest.json: Sample sheet info, index sequences, adapter settings

Metrics Displayed​

  • Polony counts and yields
  • Base quality distributions (histogram and by-cycle)
  • Index assignment statistics
  • Per-sample sequence content and GC distribution
  • Adapter content analysis
  • Unassigned/unknown barcode sequences (run-level only)

File search patterns​

bases2fastq/manifest:
  contents: Settings
  fn: RunManifest.json
  num_lines: 100
bases2fastq/project:
  contents: SampleStats
  fn: '*_RunStats.json'
  num_lines: 100
bases2fastq/run:
  contents: SampleStats
  fn: RunStats.json
  num_lines: 100
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