ReviewBiology direct2012
Bioinformatics clouds for big data manipulation.
Review in Biology direct, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
37 citing papers in PubMed.
- rMAP 2.0: a modular, reproducible, and scalable WDL-Cromwell-Docker workflow for genomic analysis of ESKAPEE pathogens.Bioinformatics advances · 2026Article
- Insights into the Datasets, Tools, and Training Needs of the AnVIL Community: 2024.bioRxiv : the preprint server for biology · 2025Article
- KmerCrypt: private k-mer search with homomorphic encryption.Briefings in bioinformatics · 2025Article
- Tick genomics through a Nanopore: a low-cost approach for tick genomics.BMC genomics · 2025Article
- Snowflake Data Warehouse for Large-Scale and Diverse Biological Data Management and Analysis.Genes · 2024Review
- Container Profiler: Profiling resource utilization of containerized big data pipelines.GigaScience · 2022Article
- Cardiovascular informatics: building a bridge to data harmony.Cardiovascular research · 2022Review
- Orchestrating an Optimized Next-Generation Sequencing-Based Cloud Workflow for Robust Viral Identification during Pandemics.Biology · 2021Article
- Cloud Computing Enabled Big Multi-Omics Data Analytics.Bioinformatics and biology insights · 2021Review
- Upscaling Statistical Patterns from Reduced Storage in Social and Life Science Big Datasets.Entropy (Basel, Switzerland) · 2020Article
- A Fast and Scalable Workflow for SNPs Detection in Genome Sequences Using Hadoop Map-Reduce.Genes · 2020Article
- A novel framework for horizontal and vertical data integration in cancer studies with application to survival time prediction models.Biology direct · 2019Article
- Cloud computing applications for biomedical science: A perspective.PLoS computational biology · 2018Article
- The High-Throughput Analyses Era: Are We Ready for the Data Struggle?High-throughput · 2018Article
- -Omic and Electronic Health Record Big Data Analytics for Precision Medicine.IEEE transactions on bio-medical engineering · 2017Review
- Sellers' Revisited: A Big Data Reassessment of Historical Outbreaks of Bluetongue and African Horse Sickness due to the Long-Distance Wind Dispersion ofFrontiers in veterinary science · 2017Article
- Precision Medicine: What Challenges Are We Facing?Genomics, proteomics & bioinformatics · 2016Article
- A Computational Solution to Automatically Map Metabolite Libraries in the Context of Genome Scale Metabolic Networks.Frontiers in molecular biosciences · 2016Article
- The need for novel informatics tools for integrating and planning research in molecular and cellular cognition.Learning & memory (Cold Spring Harbor, N.Y.) · 2015Review
- From big data analysis to personalized medicine for all: challenges and opportunities.BMC medical genomics · 2015Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
unlabelledAs advances in life sciences and information technology bring profound influences on bioinformatics due to its interdisciplinary nature, bioinformatics is experiencing a new leap-forward from in-house computing infrastructure into utility-supplied cloud computing delivered over the Internet, in order to handle the vast quantities of biological data generated by high-throughput experimental technologies. Albeit relatively new, cloud computing promises to address big data storage and analysis issues in the bioinformatics field. Here we review extant cloud-based services in bioinformatics, classify them into Data as a Service (DaaS), Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS), and present our perspectives on the adoption of cloud computing in bioinformatics. REVIEWERS: This article was reviewed by Frank Eisenhaber, Igor Zhulin, and Sandor Pongor.
Indexed as
Identifiers
What OpenQuestion holds
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.