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Transcriptome annotation in the cloud: complexity, best practices, and cost.

Roberto Vera Alvarez1, Leonardo Mariño-Ramírez1, David Landsman1

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Summary
This summary is machine-generated.

Public cloud platforms like AWS and GCP offer cost-effective solutions for computational biology. Researchers can perform transcriptome annotation using BLAST alignments in under two hours for approximately $200-$250.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Cloud Computing

Background:

  • The NIH Science and Technology Research Infrastructure for Discovery, Experimentation, and Sustainability (STRIDES) initiative facilitates access to commercial cloud platforms for researchers.
  • Transcriptome annotation, a complex computational biology task, traditionally relies on on-premises systems for BLAST sequence alignments.

Purpose of the Study:

  • To compare the cost and processing time of multiple BLAST sequence alignments on Amazon Web Services (AWS) and Google Cloud Platform (GCP).
  • To provide an estimate of the computational resources required for executing BLAST runs in a cloud environment.

Main Methods:

  • Utilized Jupyter Notebooks to script and submit batch computing jobs on AWS and GCP.
  • Evaluated the impact of varying numbers of query transcripts on cost and processing time.
  • Tested compute instances with 16, 32, and 64 vCPUs.
  • Collected timing data including total run time, database transfer time, script execution time, and instance setup time.

Main Results:

  • Public cloud platforms are a viable and cost-effective option for advanced computational biology experiments.
  • Transcriptome annotation using BLAST alignments for approximately 500,000 transcripts can be completed in under 2 hours at a cost of $200-$250.
  • The choice between cloud platforms depends on institutional accessibility, required technical expertise, and API availability, rather than the specific workflow.

Conclusions:

  • Cloud providers offer a practical and economical alternative for large-scale computational biology tasks.
  • The study provides valuable cost and time estimations for cloud-based BLAST workflows.
  • Platform selection should consider practical factors beyond workflow compatibility, such as ease of use and integration.