The real cost of sequencing: scaling computation to keep pace with data generation
By Paul Muir, Shantao Li, S. Lou, Daifeng Wang, Daniel Spakowicz, L. Salichos, Jing Zhang, George M. Weinstock, Farren J. Isaacs, Joel S. Rozowsky and 1 more. 2016
307 citations recorded by the source corpus.
As the cost of sequencing continues to decrease and the amount of sequence data generated grows, new paradigms for data storage and analysis are increasingly important. The relative scaling behavior of these evolving technologies will impact genomics research moving forward.
Subfields: Cancer Research, Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cancer Research
- C. Théry
- A. Llorente
- Casey S. Greene
- G. Way
- Suresh Mathivanan
- Aled Clayton
- Jinbo Xu
- Daron M. Standley
In Molecular Biology
- Daron M. Standley
- Kazutaka Katoh
- C. Théry
- John Rozewicki
- Casey S. Greene
- Anthony Mathelier
- Wyeth W. Wasserman
- Suresh Mathivanan
Citations in the corpus
- Opportunities and obstacles for deep learning in biology and medicine (2017, background): “Cloud computing, which has already seen wide adoption in genomics [403], could facilitate easier sharing of the large datasets common to biology [404,405], and may be key to scaling deep learning.”
- Development of Bioinformatics Infrastructure for Genomics Research in H3Africa (2017, unknown): “To deal with such complex and voluminous data, existing data storage and transfer mechanisms as well as public repositories and data processing technologies have had to adapt, and skills in data science have had to be developed [Muir et al 2016].”
- JASPAR 2024: 20th anniversary of the open-access database of transcription factor binding profiles (2023, background): “…effort is the rapid expansion witnessed following the introduction of high-throughput sequencing assays such as ChIP-seq ( 21 ), DAP-seq ( 22 ), PBM ( 23 ), SMiLE-seq ( 24 ), and HT-SELEX ( 25 ), which accelerated the generation of datasets suitable for modelling TF-DNA interactions ( 26 ,27 ).”
- DNA Sequencing as a Tool to Monitor Marine Ecological Status (2017, background): “Despite the enormous opportunities posed by this technological revolution, the vast amount of data generated requires new solutions for data handling, storage, processing, documentation, visualization, and dissemination (Desai et al., 2012; Muir et al., 2016).”
- A novel community driven software for functional enrichment analysis of extracellular vesicles data (2017, background): “stantly developed by academic and commercial entities [4].”
- Lepbase: the Lepidopteran genome database (2016, background): “All results and datafiles are available at http://download.lepase.org”
- MAFFT-DASH: integrated protein sequence and structural alignment (2019, background): “In the highthroughput sequencing era, scalable and accurate sequence alignment is becoming more important, but also more challenging (5).”