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Chapter 11: Cloud Execution: Google Cloud Batch & Azure CycleCloud

Tested Stacks: GATK 4.6.1 Samtools 1.21 Nextflow 24.10 Seurat v5.1 Python 3.12
Prerequisites: Basic Linux CLI & Sequencing Formats
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Chapter 11: Cloud Execution: Google Cloud Batch & Azure CycleCloud

In multi-cloud environments, organizations deploy Nextflow across Google Cloud Platform (GCP) and Microsoft Azure to avoid vendor lock-in and leverage specific cloud AI services.

Google Cloud Batch Integration

Google Cloud Batch replaces the legacy Life Sciences API. It schedules containerized jobs across Compute Engine Spot VMs, streaming data directly to and from Google Cloud Storage (GCS) buckets.

1. Configuring Google Cloud Batch in Nextflow

plugins {
    id 'nf-google'
}

profiles {
    googlebatch {
        process {
            executor = 'google-batch'
        }

        google {
            project  = 'my-genomics-project-123'
            location = 'us-central1'
        }

        workDir = 'gs://my-genomics-bucket/work'
    }
}

2. Microsoft Azure Batch Configuration

plugins {
    id 'nf-azure'
}

profiles {
    azure {
        process {
            executor = 'azurebatch'
        }

        azure {
            batch {
                location = 'eastus'
                accountName = 'myazurebatchaccount'
                accountKey = secrets.AZURE_BATCH_KEY
                autoPoolMode = true
            }
            storage {
                accountName = 'mystorageaccount'
                accountKey = secrets.AZURE_STORAGE_KEY
            }
        }

        workDir = 'az://mycontainer/work'
    }
}
⚠️

Common Pitfalls & Troubleshooting Checklist

  • Java Heap Exhaustion (GATK / Picard): If processes terminate with java.lang.OutOfMemoryError, set your environment variable explicitly: export _JAVA_OPTIONS="-Xmx16g -Djava.io.tmpdir=/tmp".
  • Index Timestamp Mismatch: If tools complain that a BAM index is missing or out of date, regenerate with samtools index -@ 4 sample.bam.
  • Conda Channel Conflicts: Avoid ABI incompatibilities between bioconda and conda-forge by setting strict priority: conda config --set channel_priority strict.
  • Chromosome Naming Contamination: Always verify whether reference FASTA headers use chr1 or 1; divergent naming causes silent alignment drops.
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