Using DECIPHER v2.0 to Analyze Big Biological Sequence Data in R
By Erik S. Wright. The R Journal, 2016
845 citations recorded by the source corpus.
In recent years, the cost of DNA sequencing has decreased at a rate that has outpaced improvements in memory capacity. It is now common to collect or have access to many gigabytes of biological sequences. This has created an urgent need for approaches that analyze sequences in subsets without requiring all of the sequences to be loaded into memory at one time. It has also opened opportunities to improve the organization and accessibility of information acquired in sequencing projects. The DECIPHER package offers solutions to these problems by assisting in the curation of large sets of biological sequences stored in compressed format inside a database. This approach has many practical advantages over standard bioinformatics workflows, and enables large analyses that would otherwise be prohibitively time consuming.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/770a8a5c14ac4d6ec5fb609fd0f453738353a4b0
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In Molecular Biology
- Susan P. Holmes
- Paul J. McMurdie
- Benjamin J. Callahan
- Aniruddha Bhargava
- Adithya Murali
- Julia Fukuyama
- Kris Sankaran
- Erik S. Wright
Citations in the corpus
- IDTAXA: a novel approach for accurate taxonomic classification of microbiome sequences (2018, methodology): “Both functions are part of the R [18] package DECIPHER [19], which is distributed under the GPLv3 license as part of Bioconductor [20].”
- Bioconductor Workflow for Microbiome Data Analysis: from raw reads to community analyses (2016, methodology): “05 * nsamples(ps0) prevalenceThreshold ## [1] 18 # Execute prevalence filter, using `prune_taxa()` function keepTaxa = rownames(prevdf1)[(prevdf1$Prevalence >= prevalenceThreshold)] ps2 = prune_taxa(keepTaxa, ps0)”