ReviewGigaScience2018
Bioinformatics applications on Apache Spark.
Review in GigaScience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 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
33 citing papers in PubMed.
- Benchmarking Fast Healthcare Interoperability Resources-Based Analytics: Quantitative Study of RESTful Server Queries and Big Data Engines.JMIR medical informatics · 2026Article
- Applicability Assessment of Technologies for Predictive and Prescriptive Analytics of Nephrology Big Data.Proteomics · 2025Review
- Mechanisms and technologies in cancer epigenetics.Frontiers in oncology · 2024Review
- Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review.Global challenges (Hoboken, NJ) · 2024Review
- Negation recognition in clinical natural language processing using a combination of the NegEx algorithm and a convolutional neural network.BMC medical informatics and decision making · 2023Article
- Ten quick tips for bioinformatics analyses using an Apache Spark distributed computing environment.PLoS computational biology · 2023Article
- Fog-Based Smart Cardiovascular Disease Prediction System Powered by Modified Gated Recurrent Unit.Diagnostics (Basel, Switzerland) · 2023Article
- Framing Apache Spark in life sciences.Heliyon · 2023Review
- Article
- Article
- Cloud-native distributed genomic pileup operations.Bioinformatics (Oxford, England) · 2023Article
- Identifying key genes in milk fat metabolism by weighted gene co-expression network analysis.Scientific reports · 2022Article
- RGMQL: scalable and interoperable computing of heterogeneous omics big data and metadata in R/Bioconductor.BMC bioinformatics · 2022Article
- Halvade somatic: Somatic variant calling with Apache Spark.GigaScience · 2022Article
- Orchestrating privacy-protected big data analyses of data from different resources with R and DataSHIELD.PLoS computational biology · 2021Article
- Lactation Associated Genes Revealed in Holstein Dairy Cows by Weighted Gene Co-Expression Network Analysis (WGCNA).Animals : an open access journal from MDPI · 2021Article
- Cloud Computing Enabled Big Multi-Omics Data Analytics.Bioinformatics and biology insights · 2021Review
- Single-Cell Transcriptomics: Current Methods and Challenges in Data Acquisition and Analysis.Frontiers in neuroscience · 2021Review
- Identification of therapeutic targets and mechanisms of tumorigenesis in non-small cell lung cancer using multiple-microarray analysis.Medicine · 2020Article
- pmTM-align: scalable pairwise and multiple structure alignment with Apache Spark and OpenMP.BMC bioinformatics · 2020Article
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
With the rapid development of next-generation sequencing technology, ever-increasing quantities of genomic data pose a tremendous challenge to data processing. Therefore, there is an urgent need for highly scalable and powerful computational systems. Among the state-of-the-art parallel computing platforms, Apache Spark is a fast, general-purpose, in-memory, iterative computing framework for large-scale data processing that ensures high fault tolerance and high scalability by introducing the resilient distributed dataset abstraction. In terms of performance, Spark can be up to 100 times faster in terms of memory access and 10 times faster in terms of disk access than Hadoop. Moreover, it provides advanced application programming interfaces in Java, Scala, Python, and R. It also supports some advanced components, including Spark SQL for structured data processing, MLlib for machine learning, GraphX for computing graphs, and Spark Streaming for stream computing. We surveyed Spark-based applications used in next-generation sequencing and other biological domains, such as epigenetics, phylogeny, and drug discovery. The results of this survey are used to provide a comprehensive guideline allowing bioinformatics researchers to apply Spark in their own fields.
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.