ArticleGigaScience2021
Transcriptome annotation in the cloud: complexity, best practices, and cost.
Article in GigaScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- Haplotype-resolved telomere-to-telomere assembly and haplotype-aware annotation pipeline enable high-quality reannotation of threeHorticulture research · 2026Article
- VAREANT: a bioinformatics application for gene variant reduction and annotation.Bioinformatics advances · 2025Article
- HPC-T-Annotator: an HPC tool for de novo transcriptome assembly annotation.BMC bioinformatics · 2024Article
- Medicare meets the cloud: the development of a secure platform for the storage and analysis of claims data.JAMIA open · 2024Article
- GTax: improving de novo transcriptome assembly by removing foreign RNA contamination.Genome biology · 2024Article
- Article
- Pharmacogenomics: Driving Personalized Medicine.Pharmacological reviews · 2023Review
- ElasticBLAST: accelerating sequence search via cloud computing.BMC bioinformatics · 2023Article
- ElasticBLAST: Accelerating Sequence Search via Cloud Computing.bioRxiv : the preprint server for biology · 2023Article
- Understanding enterprise data warehouses to support clinical and translational research: enterprise information technology relationships, data governance, workforce, and cloud computing.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- A simple guide to de novo transcriptome assembly and annotation.Briefings in bioinformatics · 2022Review
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundThe NIH Science and Technology Research Infrastructure for Discovery, Experimentation, and Sustainability (STRIDES) initiative provides NIH-funded researchers cost-effective access to commercial cloud providers, such as Amazon Web Services (AWS) and Google Cloud Platform (GCP). These cloud providers represent an alternative for the execution of large computational biology experiments like transcriptome annotation, which is a complex analytical process that requires the interrogation of multiple biological databases with several advanced computational tools. The core components of annotation pipelines published since 2012 are BLAST sequence alignments using annotated databases of both nucleotide or protein sequences almost exclusively with networked on-premises compute systems.
findingsWe compare multiple BLAST sequence alignments using AWS and GCP. We prepared several Jupyter Notebooks with all the code required to submit computing jobs to the batch system on each cloud provider. We consider the consequence of the number of query transcripts in input files and the effect on cost and processing time. We tested compute instances with 16, 32, and 64 vCPUs on each cloud provider. Four classes of timing results were collected: the total run time, the time for transferring the BLAST databases to the instance local solid-state disk drive, the time to execute the CWL script, and the time for the creation, set-up, and release of an instance. This study aims to establish an estimate of the cost and compute time needed for the execution of multiple BLAST runs in a cloud environment.
conclusionsWe demonstrate that public cloud providers are a practical alternative for the execution of advanced computational biology experiments at low cost. Using our cloud recipes, the BLAST alignments required to annotate a transcriptome with ∼500,000 transcripts can be processed in <2 hours with a compute cost of ∼$200-$250. In our opinion, for BLAST-based workflows, the choice of cloud platform is not dependent on the workflow but, rather, on the specific details and requirements of the cloud provider. These choices include the accessibility for institutional use, the technical knowledge required for effective use of the platform services, and the availability of open source frameworks such as APIs to deploy the workflow.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.