rchime
Detect and Remove Chimeras from Amplicon Sequence Analysis Data
Detect and remove chimeras from your amplicon sequence analysis
using reference-based or de novo approaches. The 'rchime' package
implements the 'VSEARCH' algorithms described in Rognes et al. (2016)
rchime 
Overview
The rchime package allows you to detect and remove chimeras from your dataset using a de novo approach or alternatively a reference model. This package uses code from the vsearch tools.
rchime()detect and remove chimeras from your strollur dataset object or data.frame
Installation
You can install the CRAN version with:
install.packages("rchime")
Development version
You can install the development version of rchime from GitHub with:
pak::pak("mothur/rchime")
Usage
The rchime() function accepts strollur
objects or data.frames as inputs. Let’s create a
strollur::strollur
object using files from mothur’s
Miseq_SOP example analysis. Then
we will use the de novo method in rchime() to detect and remove the
chimeras from the dataset.
fasta_data <- readRDS(rchime_example("miseq_fasta.rds"))
abundance_data <- readRDS(rchime_example("miseq_abundance.rds"))
data <- strollur::new_dataset("rchime de novo example")
strollur::add(data, table = fasta_data, type = "sequence")
#> Added 6084 sequences.
strollur::assign(data, table = abundance_data, type = "sequence_abundance")
#> Assigned 6084 sequence abundances.
rchime(data)
#> ℹ The de novo method runs with a single processor.
#> → rchime removed `10039` chimeras from your dataset.
#> → It took `4.30051493644714` seconds to detect and remove the chimeras.
data
#> rchime de novo example:
#>
#> starts ends nbases ambigs polymers numns numseqs
#> Minimum: 1 249 249 0 3 0 1.00
#> 2.5%-tile: 1 252 252 0 4 0 2965.40
#> 25%-tile: 1 252 252 0 4 0 29654.00
#> Median: 1 253 253 0 4 0 59308.00
#> 75%-tile: 1 253 253 0 5 0 88962.00
#> 97.5%-tile: 1 254 254 0 6 0 115650.60
#> Maximum: 1 256 256 0 8 0 118616.00
#> Mean: 1 252 252 0 4 0 59308.14
#>
#> scrap_summary:
#> type trash_code unique total
#> 1 sequence rchime_chimeras 3332 10039
#>
#> Number of unique seqs: 2752
#> Total number of seqs: 118616
#>
#> Total number of samples: 20
#> Total number of custom reports: 1
References
Many thanks for the great work of the vsearch and uchime teams!
-
Rognes T, Flouri T, Nichols B, Quince C, Mahé F. (2016) VSEARCH: a versatile open source tool for metagenomics. PeerJ 4:e2584. doi: 10.7717/peerj.2584
-
Edgar,R.C., Haas,B.J., Clemente,J.C., Quince,C. and Knight,R. (2011), UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27:2194.
Code of Conduct
Please note that the rchime project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.