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Package {rchime}

Type: Package
Title: Detect and Remove Chimeras from Amplicon Sequence Analysis Data
Version: 0.1.0
Date: 2026-07-07
Maintainer: Pat Schloss <pschloss@umich.edu>
Description: 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) <doi:10.7717/peerj.2584>. 'VSEARCH' builds on the work of Edgar,R.C. et al. (2011) <doi:10.1093/bioinformatics/btr381>.
URL: https://github.com/mothur/rchime, https://mothur.org/rchime/
BugReports: https://github.com/mothur/rchime/issues
License: GPL (≥ 3)
Config/testthat/edition: 3
Imports: cli, parallelly, RcppThread, utils
Depends: Rcpp, R (≥ 4.5.0), strollur(≥ 0.1.3)
Suggests: knitr, rmarkdown, xml2, pak, testthat (≥ 3.0.0)
LinkingTo: Rcpp, RcppThread, RcppXsimd, cli
Encoding: UTF-8
VignetteBuilder: knitr
Config/roxygen2/version: 8.0.0
NeedsCompilation: yes
Packaged: 2026-07-16 17:00:27 UTC; swestcot
Author: Sarah Westcott ORCID iD [aut], Pat Schloss ORCID iD [cph, cre], Torbjorn Rognes [cph], Frederic Mahe [cph], Tomas Flouri [cph], Christopher Quince [cph], Ben Nichols [cph]
Repository: CRAN
Date/Publication: 2026-07-23 14:20:02 UTC

rchime: Detect and Remove Chimeras from Amplicon Sequence Analysis Data

Description

logo

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) doi:10.7717/peerj.2584. 'VSEARCH' builds on the work of Edgar,R.C. et al. (2011) doi:10.1093/bioinformatics/btr381.

Author(s)

Maintainer: Pat Schloss pschloss@umich.edu (ORCID) [copyright holder]

Authors:

Other contributors:

See Also

Useful links:

Detect and remove chimeras from your strollur object or data.frame using a de novo approach or alternatively a reference model.

Description

The rchime() function allows you to detect and remove chimeras from your data using a de novo approach or alternatively a reference model.

Our preferred way of doing this is to use the abundant sequences as our reference (de novo).

This function uses code from the vsearch tools.

Usage

rchime(
  data,
  reference = NULL,
  dereplicate = TRUE,
  verbose = TRUE,
  remove_chimeras = TRUE,
  rchime_options = NULL,
  table_names = list(sequence_name = "sequence_name", sequence = "sequence", abundance =
    "abundance", sample = "sample")
)

Arguments

data

a strollur dataset object or a data.frame containing your sequence data.

reference

a strollur dataset object or a data.frame containing reference sequence data.

dereplicate

logical. The dereplicate option allows you to remove chimeras by sample. When dereplicate=FALSE, if a sequence is flagged as chimeric in one sample, it is removed from all samples. Our experience suggests that this is a bit aggressive since we’ve seen rare sequences get flagged as chimeric when they’re the most abundant sequence in another sample. For a more conservative approach, we recommend using the default dereplicate=TRUE which will only remove sequences from the samples in which they are flagged as chimeric.

verbose

logical, allow console outputs. Default = TRUE.

remove_chimeras

Boolean, remove chimeras from dataset. Default = TRUE. Only used when data is a strollur object.

rchime_options

List, You can fine tune the vsearch specific options using the [rchime_options()] function. Default = NULL.

table_names

named list used to indicate the names of the columns in the data.frame. Only used when data or reference are a data.frames.

Value

list() containing a chimera report, and vector of the chimeric sequence's names.

The strollur dataset object will also be updated. The sequences flagged as chimeric are removed and the chimera report is added.

Author(s)

Sarah Westcott, swestcot@umich.edu

References

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.

See Also

rchime_options() to set vsearch specific parameters.

Examples


# Let's use a strollur object with 500 sequences

data <- strollur::load_dataset(
  rchime_example("strollur_multi_sample_small.rds")
)

chimera_report <- rchime(data)
data

Detect chimeras in your data.frames.

Description

The rchime() function allows you to detect chimeras from your data using a de novo approach or alternatively a reference model.

Our preferred way of doing this is to use the abundant sequences as our reference (de novo).

This function uses code from the vsearch tools.

Usage

## S3 method for class 'data.frame'
rchime(
  data,
  reference = NULL,
  dereplicate = TRUE,
  verbose = TRUE,
  remove_chimeras = NULL,
  rchime_options = NULL,
  table_names = list(sequence_name = "sequence_name", sequence = "sequence", abundance =
    "abundance", sample = "sample")
)

Arguments

data

a data.frame containing your sequence data.

reference

a data.frame containing reference sequence data.

dereplicate

logical. The dereplicate option allows you to flag chimeras by sample. When dereplicate=FALSE, if a sequence is flagged as chimeric in one sample, it is removed from all samples. Our experience suggests that this is a bit aggressive since we’ve seen rare sequences get flagged as chimeric when they’re the most abundant sequence in another sample. For a more conservative approach, we recommend using the default dereplicate=TRUE which will only remove sequences from the samples in which they are flagged as chimeric.

verbose

logical, allow console outputs. Default = TRUE.

remove_chimeras

Only used when data is a strollur object.

rchime_options

List, You can fine tune the vsearch specific options using the [rchime_options()] function. Default = NULL.

table_names

named list used to indicate the names of the columns in the data.frame. Only used when data is a data.frame. By default:

table_names <- list(sequence_name = "sequence_name", sequence = "sequence" abundance = "abundance", sample = "sample")

In table_names, 'sequence_name' is a string containing the name of the column in 'table' that contains the sequence names. Default column name is 'sequence_name'.

In table_names, 'sequence' is a string containing the name of the column in 'table' that contains the sequences. Default column name is 'sequence'.

In table_names, 'abundance' is a string containing the name of the column in 'table' that contains the abundances. Default column name is 'abundance'.

In table_names, 'sample' is a string containing the name of the column in 'table' that contains the samples. Default column name is 'sample'.

Value

list() containing a chimera report, and vector of the chimeric sequence's names. If running with multiple samples and dereplicate = TRUE, then a table containing the modified sequence abundances will also be returned.

Author(s)

Sarah Westcott, swestcot@umich.edu

References

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.

See Also

rchime_options() to set vsearch specific parameters.

Examples


# Detect chimeras from the dataset using de novo approach by sample
# (recommended)

data <- readRDS(rchime_example("miseq_data_frame_by_sample_small.rds"))

chimera_report <- rchime(data)

Detect and remove chimeras from your strollur dataset object.

Description

The rchime() function allows you to detect and remove chimeras from your data using a de novo approach or alternatively a reference model.

Our preferred way of doing this is to use the abundant sequences as our reference (de novo).

This function uses code from the vsearch tools.

Usage

## S3 method for class 'strollur'
rchime(
  data,
  reference = NULL,
  dereplicate = TRUE,
  verbose = TRUE,
  remove_chimeras = TRUE,
  rchime_options = NULL,
  table_names = list(sequence_name = "sequence_name", sequence = "sequence")
)

Arguments

data

a strollur dataset object containing your sequence data.

reference

a strollur dataset object or a data.frame containing reference sequence data.

dereplicate

logical. The dereplicate option allows you to remove chimeras by sample. When dereplicate=FALSE, if a sequence is flagged as chimeric in one sample, it is removed from all samples. Our experience suggests that this is a bit aggressive since we’ve seen rare sequences get flagged as chimeric when they’re the most abundant sequence in another sample. For a more conservative approach, we recommend using the default dereplicate=TRUE which will only remove sequences from the samples in which they are flagged as chimeric.

verbose

logical, allow console outputs. Default = TRUE.

remove_chimeras

logical, remove chimeras from dataset. Default = TRUE.

rchime_options

List, You can fine tune the vsearch specific options using the [rchime_options()] function. Default = NULL.

table_names

Only used when reference is a data.frame. By default:

table_names <- list(sequence_name = "sequence_name", sequence = "sequence")

In table_names, 'sequence_name' is a string containing the name of the column in 'table' that contains the sequence names. Default column name is 'sequence_name'.

In table_names, 'sequence' is a string containing the name of the column in 'table' that contains the sequences. Default column name is 'sequence'.

Value

list() containing a chimera report, and vector of the chimeric sequence's names.

The strollur dataset object will also be updated. The sequences flagged as chimeric are removed and the chimera report is added.

Author(s)

Sarah Westcott, swestcot@umich.edu

References

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.

See Also

rchime_options() to set vsearch specific parameters.

Examples


# Let's load a strollur object with 500 sequences

data <- strollur::load_dataset(
  rchime_example("strollur_multi_sample_small.rds")
)

# Detect and remove chimeras from the dataset using de novo approach by
# sample (recommended)

chimera_report <- rchime(data)
data

Get path to rchime example files

Description

rchime comes bundled with some example files in its inst/extdata directory. This function make them easy to access.

Usage

rchime_example(file = NULL)

Arguments

file

Name of file. If NULL, the example files will be listed.

Value

string, full path to file requested

Examples

rchime_example()
rchime_example("silva.gold.rds")

The rchime_options() function allows you to set vsearch specific optional parameters.

Description

The rchime_options() function allows you to set parameters for the various chimera detection functions. With the exception of dereplicate, we recommend using the defaults, unless you have a compelling reason to do otherwise.

Usage

rchime_options(
  processors = parallelly::availableCores(),
  dereplicate = TRUE,
  abskew = 2,
  minh = 0.28,
  mindiv = 0.8,
  xn = 8,
  dn = 1.4,
  maxp = 3
)

Arguments

processors

Integer, number of cores to use. Default = all available

dereplicate

Boolean, The dereplicate option allows you to remove chimeras by sample. For example, if dereplicate parameter is FALSE, then if one group finds the sequence to be chimeric, it will be removed from all groups. If dereplicate is set to TRUE, sequences found to be chimeric are only removed from the sample they are found to be chimeric in. Default = TRUE.

abskew

Float, the minimum abundance skew (de novo only). Default = 2.0. abskew <- min (abund(parent1), abund(parent2)) / abund(query)

minh

Float, Mininum score to report chimera. Default 0.28. Values from 0.1 to 5 might be reasonable. Lower values increase sensitivity but may report more false positives. If you decrease –xn, you may need to increase –minh, and vice versa.

mindiv

Float, Minimum divergence ratio, default 0.8. Div ratio is 100%% %%identity between query sequence and the closest candidate for being a parent. If you don't care about very close chimeras, then you could increase –mindiv to, say, 1.0 or 2.0, and also decrease –min h, say to 0.1, to increase sensitivity. How well this works will depend on your data. Best is to tune parameters on a good benchmark.

xn

Float, Weight of a no vote, also called the beta parameter. Default 8.0. Decreasing this weight to around 3 or 4 may give better performance on denoised data.

dn

Float, Pseudo-count prior on number of no votes. Default 1.4. Probably no good reason to change this unless you can retune to a good benchmark for your data. Reasonable values are probably in the range from 0.2 to 2.

maxp

Integer, Maximum number of candidate parents to consider. Default 3.

Value

List containing selected rchime parameters and their values.

Author(s)

Sarah Westcott, swestcot@umich.edu

silva_gold

Description

The silva_gold() function creates a silva based reference for use with the rchime() function in reference mode. It contains 5,181 sequences.

Usage

silva_gold()

Value

data.frame containing selected rchime parameters and their values.

Author(s)

Sarah Westcott, swestcot@umich.edu

Examples


reference <- silva_gold()

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