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 how hardware changes performance for genomic analysis.

My current testing plan focuses on comparing traditional tools with AI-driven solutions.

𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗣𝗿𝗼𝗴𝗿𝗮𝗺

• Scanpy: Completed on RTX 3090. It finished PBMC 3k analysis in 60 seconds and identified 9 cell clusters.
• DeepVariant vs GATK: This is my top priority. I will compare GATK HaplotypeCaller (CPU) against Google DeepVariant (GPU) to measure speed and accuracy.
• PrimateAI-3D: Moving from Mac M4 to RTX 3090 using Docker.
• ProkBERT: Testing promoter prediction and phage detection using BERT architecture.

𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱 𝘃𝘀 𝗟𝗼𝗰𝗮𝗹 𝗛𝗮𝗿𝗱𝘄𝗮𝗿𝗲

I am also comparing Google Cloud infrastructure with local GPU setups.

• Google Cloud uses tools like BigQuery Genomics for SQL queries and Dataflow for parallel processing.
• Local RTX 3090 setups provide high speed for specific tasks like DeepVariant.
• Google Cloud scales better for massive datasets.
• Local hardware is better for controlled, repetitive testing.

𝗡𝗲𝘅𝘁 𝗦𝘁𝗲𝗽𝘀

I will spend the next few days running performance benchmarks. I want to see the exact difference in resource usage between CPU and GPU when calling variants.

I will also build a VCF Intelligent Interpreter. This tool will use Large Language Models to explain clinical significance in plain language.

Source: https://dev.to/jh5_pulse/zai-cloud-run-shang-bu-shu-gradio-ying-yong-3f35

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