𝗗𝗲𝗽𝗹𝗼𝘆𝗶𝗻𝗴 𝗚𝗿𝗮𝗱𝗶𝗼 𝗼𝗻 𝗖𝗹𝗼𝘂𝗱 𝗥𝘂𝗻

𝗗𝗲𝗽𝗹𝗼𝘆𝗶𝗻𝗴 𝗚𝗿𝗮𝗱𝗶𝗼 𝗼𝗻 𝗖𝗹𝗼𝘂𝗱 𝗥𝘂𝗻

I am moving my bioinformatics workflows from Mac M4 to an RTX 3090. This shift allows me to test heavy genomic tools with actual GPU acceleration.

Here is my current testing and migration plan:

𝗠𝗶𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗦𝘁𝗮𝘁𝘂𝘀

• Scanpy Single-Cell Analysis: Complete. I finished the PBMC 3k analysis in 60 seconds on the RTX 3090.
• PrimateAI-3D: Pending. Moving from Mac M4 to RTX 3090 using Docker.
• Parabricks WES: Pending. Adding full performance data for the RTX 3090.

𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱 𝘃𝘀 𝗚𝗔𝗧𝗞

I am comparing Google Cloud infrastructure with traditional GATK methods.

1. Variant Calling

• GATK HaplotypeCaller: The traditional CPU method.
• Google DeepVariant: The AI-driven GPU method.
• I will compare accuracy, speed, and resource use.

2. Data Processing

• Google BigQuery Genomics: Uses SQL to query genomic data at scale.
• GATK + Spark: Uses traditional processing tools.
• I will test query speed and scalability for millions of variants.

3. Workflow Comparison

• Google Cloud: Uses Cloud Life Sciences API and Dataflow. It is pay-as-you-go and good for large, temporary tasks.
• GATK Best Practices: Uses Cromwell or WDL. It relies on fixed hardware or local clusters.

𝗔𝗜 𝗠𝗼𝗱𝗲𝗹𝘀 𝗶𝗻 𝗙𝗼𝗰𝘂𝘀

I am also testing several specialized DNA models:

• ProkBERT: For promoter prediction and phage detection in prokaryotes.
• DNABERT-2: For DNA sequence classification.
• Nucleotide Transformer: For multi-species genome embeddings.
• GeneGPT: For generative DNA sequence design.

𝗡𝗲𝘅𝘁 𝗦𝘁𝗲𝗽𝘀

My priority is the DeepVariant vs GATK performance test. This will take 2 to 3 days. I will then build an intelligent VCF interpreter using LLMs to explain clinical variants.

Source: https://dev.to/jh5_pulse/zai-cloud-run-shang-bu-shu-gradio-ying-yong-4le3

Optional learning community: https://t.me/GyaanSetuAi