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Use demultiplexed, quality-controlled FASTQ as the handoff between Illumina primary processing and your AI workflow. Preserve the run and sample context in a validated manifest, perform task-specific secondary analysis, build features with training-only transformations, and record every software, reference, model and quality-control decision needed to reproduce a prediction.
Illumina’s BaseSpace documentation describes the normal transition clearly: BCL files are converted into FASTQ files containing base-call and quality information, after which FASTQ can feed alignment and other secondary-analysis software. BCL Convert or BaseSpace can perform conversion and demultiplexing; DRAGEN, open workflows or both can perform secondary analysis; and an orchestrator such as Nextflow or an equivalent system can connect the stages.
What the Illumina-to-AI boundary should be
Illumina instruments produce primary-run data as base-call files (BCLs), organized by run, flow cell and lane. General-purpose machine-learning tools normally should not consume those files directly. The controlled interface is a set of demultiplexed, gzip-compressed FASTQ files plus a manifest that explains exactly which reads belong to which sample and library.
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FASTQ retains the nucleotide sequence and per-base quality information needed by downstream software. Illumina’s BaseSpace documentation says that BaseSpace Sequence Hub converts BCL files into FASTQ files “which contain base call and quality information for all reads that pass filtering.” Its Connected Software documentation likewise states that each system’s data must be converted from BCLs to FASTQs, either locally or in the cloud through BaseSpace Sequence Hub (BSSH) or Illumina Connected Analytics (ICA).
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That boundary is not merely a file-format preference. It separates instrument-specific primary processing from an auditable data product that can be checked, moved between environments and consumed by several analysis engines. Keep BCLs when retention, reprocessing or regulatory policy requires them, but make the demultiplexed FASTQ set the explicit input contract for the AI side.
Why feeding BCL directly is usually the wrong abstraction
- BCL layout and conversion depend on the run and instrument software, whereas FASTQ is accepted by common alignment, quantification, variant, single-cell and metagenomic tools.
- Demultiplexing assigns reads using the sample sheet and index sequences. An AI process should not attempt to infer sample identity from file names or raw lane folders.
- FASTQ-level quality metrics can be captured as structured records before expensive feature generation or model inference.
- A stable FASTQ manifest lets the same downstream workflow run on BaseSpace, a local server, a cloud bucket or a different sequencing run.
Reference architecture
A production design has seven linked stages. Each stage writes immutable or versioned artifacts and metadata rather than passing an untracked directory to the next stage.
- Capture run metadata. Register the run ID, instrument, flow cell, lane, sample sheet, index sequences, library-preparation method, organism, assay, consent and data-use restrictions.
- Convert and demultiplex. Run BCL Convert, BaseSpace or a validated local equivalent. Produce paired-end or single-end gzip-compressed FASTQ and a manifest mapping sample IDs to files, lanes, read pairs and indexes.
- Gate on read-level QC. Measure read counts, per-cycle quality, adapter and contaminant content, duplication, index balance and lane or sample failures. Store the values, thresholds, software version and pass/fail decision.
- Perform task-specific secondary analysis. Use a pinned reference and annotation to create alignments and variant records for DNA, expression quantities for RNA, cell-by-gene matrices for single-cell assays, or taxonomic and functional profiles for metagenomics.
- Create AI features. Apply transformations such as filtering, scaling, normalization or dimensionality reduction only after fitting them on the training partition. Retain provenance from each feature to its sample, read set, reference and pipeline version.
- Run inference and monitor. Record the model version, preprocessing checksum, calibration information, confidence, abstentions and QC flags alongside each prediction.
- Publish reproducible outputs. Package workflow code, parameters, container or environment digest, reference checksums, QC reports and model-card information with the result.
Step 1: Build a manifest before moving data
The manifest is the source of truth for identity and provenance. It should be generated from the sample sheet and run metadata, then validated before any AI feature is created. A filename convention can be useful for navigation, but it is not an identity system.
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Minimum manifest fields
| Category | Fields to retain | Why it matters |
|---|---|---|
| Run | Run ID, instrument, run date, flow-cell ID and lane | Links a read set to the physical sequencing event and permits lane-level failure analysis. |
| Sample and library | Stable sample ID, library ID, biological subject or study ID, library-preparation method and assay | Separates technical libraries from the biological unit used for model splitting. |
| Indexes | Index sequences, index names and the sample-sheet version | Documents demultiplexing decisions and makes index swaps or balance problems detectable. |
| Files | R1 and R2 paths, lane, compression type, byte or read counts and cryptographic checksums | Prevents a prediction from being tied to an accidentally replaced or truncated file. |
| Biology | Organism, genome build, annotation release and relevant phenotype labels | Allows the correct reference and label definition to be selected. |
| Governance | Consent or data-use category, retention class, location and access restrictions | Controls where data may be processed and who can retrieve it. |
Validate identity and file integrity
- Confirm that every expected sample and index appears exactly once, unless a deliberate technical replicate is recorded.
- Check that paired reads have matching sample, lane and pair identifiers and that the read counts are compatible.
- Compute and store a checksum for each FASTQ. Recalculate it after transfer into the analysis environment.
- Reject duplicate sample IDs, undocumented renames and files whose manifest path or checksum does not match the object being processed.
- Keep phenotype labels in a controlled data store with an explicit label version; do not embed sensitive labels in unrestricted filenames.
Step 2: Convert BCL and demultiplex
Choose one validated conversion path and record its version and parameters. BaseSpace Sequence Hub provides managed upload or transfer, demultiplexing, FASTQ generation, dataset aggregation for a biosample, workflow launch and automatic QC. BCL Convert or another validated local implementation is appropriate when data must remain in an organization’s environment or when local automation is required.
Operational sequence
- Verify that the run folder is complete and that the sample sheet matches the intended index sequences and read structure.
- Transfer BCLs and the sample sheet into the selected conversion environment. Preserve the original run identifier and record the transfer checksum.
- Run conversion and demultiplexing with the read lengths, adapter settings and index-mismatch policy appropriate to the assay.
- Collect demultiplexing statistics, undetermined-read counts, index balance and per-lane output. Treat a large undetermined fraction or an unexpected lane imbalance as a QC event, not as a reason to silently discard data.
- Write gzip-compressed FASTQ and the validated manifest to versioned storage. Do not let downstream jobs discover samples by scanning a directory.
BaseSpace can launch analyses when dependencies are met and can aggregate datasets for a biosample. Those conveniences do not remove the need for an external manifest: the manifest remains the portable record of how files, samples and biological subjects relate.
Step 3: Gate the read set with QC
QC should be a machine-readable gate with assay-specific thresholds. A report that is merely viewed once in a web interface is difficult to audit or use for model monitoring.
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Checks to record
- Yield: total reads and bases by sample, pair and lane, with expected-versus-observed values.
- Quality by cycle: per-cycle quality distributions and the fraction of bases below the project’s quality threshold.
- Adapters and contaminants: detected adapter sequences, overrepresented content and contaminant screening results.
- Duplication: duplicate-read estimates and any assay-specific interpretation, since high duplication can reflect either a failure or an intentionally targeted library.
- Index behavior: index balance, mismatch counts, undetermined reads and evidence of sample cross-talk.
- Lane and sample status: failed lanes, missing pairs, truncated files and samples that require resequencing or exclusion.
Store each metric with the tool version, reference database or adapter set, threshold, timestamp and decision. A model can then distinguish “low confidence because the biology is unusual” from “low confidence because the input failed read-level QC.”
Step 4: Select the representation for the biological task
AI should consume a representation that matches the question, not raw reads by default. Secondary analysis converts the FASTQ into a form in which biological and technical assumptions are explicit.
| Assay or question | Typical representation before AI | Required provenance |
|---|---|---|
| Germline or somatic DNA | Reference-aligned BAM or CRAM, coverage metrics and filtered variant records | Reference genome build, aligner and duplicate/quality policies, caller and filter versions, panel or region definition. |
| Bulk RNA sequencing | Gene- or transcript-level count or abundance matrix with sample-level QC | Genome and annotation release, strandedness, quantifier, normalization method and treatment of low-count features. |
| Single-cell RNA sequencing | Cell-by-gene matrix with cell-level QC and optional embeddings or clusters | Cell-calling thresholds, ambient-RNA handling, mitochondrial or ribosomal filters, batch labels and feature-selection version. |
| Metagenomics | Taxonomic, functional or pathway abundance profiles | Database release, classifier, read-filtering rules, host-depletion policy and normalization of compositional data. |
Illumina lists DRAGEN pipelines for DNA, RNA, single-cell and other workflows. DRAGEN can perform alignment, variant calling and related secondary analysis, but an open-source stack can be equally appropriate when its versions, references and parameters are pinned. The important decision is not brand loyalty; it is whether the resulting representation is validated for the assay and reproducible in the deployment environment.
Step 5: Engineer features without leaking information
Feature engineering is part of the model, not a harmless preprocessing detail. Fit every learned transformation—imputation, scaling, normalization parameters, feature selection, batch correction or dimensionality reduction—on training data only. Apply the frozen transformation to validation and test data.
Split at the biological unit
When several libraries, lanes, time points or technical replicates come from one person, tumor, family or study, keep them in the same partition. Splitting individual FASTQ files can let the model memorize subject-specific or batch-specific signals and produce an apparently strong but invalid score. For multi-study data, reserve an entire study or collection site as an external test where feasible.
Keep feature provenance
For every feature matrix, retain the source sample and read-set IDs, reference and annotation checksums, filtering rules, normalization parameters, software environment digest and feature-schema version. A model input should be reconstructable without relying on an analyst’s local notebook.
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Step 6: Run models with an auditable inference record
An inference service or batch job should accept a manifest-linked feature artifact, not an arbitrary path. The output record should include:
- model identifier and immutable version;
- preprocessing or feature-schema checksum;
- input artifact checksums and QC status;
- prediction, calibrated probability or interval, and confidence policy;
- abstention or “needs review” status when the model is outside its validated range;
- timestamp, software environment and operator or service identity.
Monitor the distribution of QC metrics, missing features, coverage, class balance, confidence and abstentions after deployment. A change in library preparation, instrument, reference build or population can cause drift even when the model binary has not changed.
Where BaseSpace, DRAGEN and Connected Analytics fit
Illumina offers several execution patterns. Select among them using data residency, throughput, cost, operational control, APIs, supported assays and the requirement that raw FASTQ remain inside your environment.
| Option | Strengths | Trade-offs and questions to resolve |
|---|---|---|
| BaseSpace Sequence Hub | Managed transfer and storage, demultiplexing, FASTQ generation, dataset aggregation, workflow launch and automatic QC. | Confirm residency, retention, access controls, export APIs and whether the required assay and downstream workflow are available in the account and region. |
| DRAGEN on instrument or on-premises | Local execution and operational control; suitable when raw data cannot leave the site. Supports secondary analysis for multiple assay types. | Requires local capacity, software administration and a plan for sharing references, containers, logs and results with the AI environment. |
| DRAGEN in cloud or through BaseSpace/Connected Analytics | Elastic execution and Illumina-supported secondary-analysis pipelines without maintaining every server component. | Validate transfer time, recurring compute and storage charges, residency, API behavior and the exact software version used for a run. |
| Illumina Connected Analytics | Cloud environment intended to manage and analyze large multiomic data sets and connect analysis stages. | Check tenancy, governance, supported integrations, export paths and whether the organization can retain required artifacts outside the service. |
| Open workflow layer | Nextflow, nf-core or an equivalent orchestrator can connect conversion, QC, feature generation and inference with portable parameters and containers. | The orchestrator does not validate an assay by itself; maintain references, images, credentials, provenance and workflow tests. |
Illumina’s Connected Software documentation describes BCL-to-FASTQ conversion followed by alignment, variant calling or other DRAGEN secondary analysis. It notes that secondary analysis may run on a local DRAGEN server or in the cloud through BaseSpace Sequence Hub or Connected Analytics. The same architecture can place an open-source secondary-analysis stage after FASTQ conversion, provided the organization validates it.
Cloud, on-premises and hybrid design decisions
Cloud-first
Cloud execution simplifies elastic storage and burst capacity. Define the region, encryption, identity roles, retention period, egress policy and deletion process before uploading a human dataset. Keep a local record of manifests, checksums and workflow metadata even when primary files remain in cloud storage.
On-premises
Local execution supports stricter residency and predictable network boundaries. Budget for disk throughput, temporary space, backup, reference distribution, GPU or CPU capacity where needed, patching and disaster recovery. A local DRAGEN server can perform secondary analysis, while a separate orchestrator can submit jobs and collect artifacts.
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Hybrid
A hybrid design commonly converts BCL locally, sends only approved FASTQ or derived representations to a cloud AI environment, and returns predictions and audit records. Document exactly which artifacts cross the boundary; “derived” does not automatically mean non-sensitive.
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Most Illumina workflows are naturally batch-oriented: a run completes, BCLs are converted, QC gates the dataset, and downstream analysis starts. Batch processing simplifies completeness checks and biological-subject splits.
Streaming or incremental ingestion can reduce time to first result when lanes or samples arrive at different times, but it adds state-management problems. You must prevent a partial lane from being treated as a complete sample, reconcile late-arriving mates, freeze the manifest for each model version and decide how predictions are revised when additional reads arrive. Unless latency is a validated requirement, use a completed-run gate and retain the original batch boundary.
Reproducibility package
At minimum, publish or archive the following with each model-ready dataset and prediction batch:
- run, lane, sample and library manifest;
- sample sheet and index sequences;
- FASTQ checksums and transfer records;
- demultiplexing and read-level QC reports;
- reference genome, annotation or database checksums;
- secondary-analysis software versions and parameters;
- feature schema, training-only transformation parameters and split definition;
- workflow source, container image or environment digest;
- model version, calibration method and model-card information;
- prediction records, QC flags, confidence and abstention decisions.
An orchestrator such as Nextflow, nf-core or an equivalent system can make these links explicit, but the cited Illumina documentation does not require a particular orchestrator. Choose one that can pin inputs, resume failed tasks, capture logs and export provenance in a form your organization can retain.
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Sample names are inferred from filenames
Symptom: a model receives a plausible file but the wrong phenotype or subject. Fix: stop the run, regenerate the manifest from the sample sheet and identity-controlled metadata, verify checksums and require manifest IDs at every workflow boundary.
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Cloud transfer or local storage fails midway
Symptom: truncated FASTQ, checksum mismatch or missing read pair. Fix: retain the failed artifact, restart from the original object with resumable transfer, recalculate checksums and do not release the sample to QC until both pairs and the manifest agree.
QC is displayed but not enforced
Symptom: low-quality samples reach feature generation because a human overlooked a report. Fix: represent thresholds and pass/fail decisions as structured data and make downstream tasks require a passing status or an explicit, logged override.
How to evaluate a vendor-reported benchmark
Illumina reports a 99.83% F1 result in a 2022 DRAGEN datasheet describing a PrecisionFDA V2 Truth Challenge context. That number is a vendor-reported result for a defined benchmark configuration. It is not a universal accuracy claim for every Illumina assay, organism, coverage level, reference, sample type or AI model. Reproduce performance on the intended cohort, report the split and preprocessing, and include calibration, failure and abstention behavior rather than relying on a single headline metric.
Implementation checklist
- Choose and validate the BCL-to-FASTQ conversion path.
- Freeze a manifest schema and identity-control process.
- Define assay-specific QC metrics, thresholds and override rules.
- Pin references, annotations, databases, containers and workflow versions.
- Choose a task-appropriate representation before designing model features.
- Split by biological subject or study when leakage is possible.
- Fit learned preprocessing on training data only.
- Record checksums, model versions, confidence, abstentions and QC flags.
- Decide cloud, on-premises or hybrid boundaries using residency, throughput, cost and control.
- Test recovery from incomplete transfers, failed lanes, changed references and revised labels.
- Archive enough metadata to recreate a prediction without an analyst’s workstation.
What cannot be assumed in advance
There is no universal independent accuracy, latency or cost figure for an Illumina-plus-AI workflow. Outcomes vary with assay, organism, coverage, reference, preprocessing, model architecture and validation cohort. DRAGEN, BaseSpace, Connected Analytics and instrument capabilities also change by product version and deployment. Verify current support, APIs, residency and licensing terms for the exact systems you will operate.
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