Skip to contents

Abundance based binning on metagenomic samples

Source: R/abundance_based_binning.R
abundance_based_binning.Rd

This function performs abundance based binning on metagenomic samples, directly from FASTA or FASTQ files, by long kmer analysis (k>8). See doi:10.1186/s12859-016-1186-3 for more details.

Usage

abundance_based_binning(
  ...,
  eMin = 1,
  eMax = 0,
  kMerSizeAB = 10,
  numOfClustersAB = 3,
  outputAB = "AB.cluster",
  keepQuality = FALSE,
  dryRun = FALSE,
  gzip = FALSE,
  numOfThreads = BiocParallel::bpworkers()
)

Arguments

...

Input sequences. Either character paths to FASTA/FASTQ files (uncompressed or gzip compressed), a DNAStringSet / QualityScaledDNAStringSet, or a ShortReadQ object. Non-file inputs are staged to a temporary FASTA/FASTQ file for the Java backend.

eMin

Exclude kmers of less or equal count.

eMax

Exclude kmers of more or equal count.

kMerSizeAB

kmer length for Abundance based Binning.

numOfClustersAB

Number of Clusters for Abundance based Binning.

outputAB

Output Abundance based Binning Clusters files location and prefix.

keepQuality

Keep fastq qualities on the output files. (will produce .fastq)

dryRun

Don't write any output files.

gzip

Gzip output files.

numOfThreads

Number of threads to use. Defaults to bpworkers().

Value

A MetabinResult object. Its assignments slot is a DataFrame with numOfClustersAB + 2 columns:

  • read_id : read identifier from fasta header

  • AB : read was assigned to this AB cluster index

  • AB.n : read to cluster AB.n distance

For backwards-compatible data.frame output use as.data.frame(result).

References

https://github.com/gkanogiannis/metabinR

Author

Anestis Gkanogiannis, anestis@gkanogiannis.com

Examples

res <- abundance_based_binning(
    system.file("extdata", "reads.metagenome.fasta.gz", package = "metabinR"),
    dryRun = TRUE, kMerSizeAB = 8
)
res
#> MetabinResult (AB)
#>   reads:     26,664
#>   clusters:  3
#>   inputs:    1 file(s)
#>     - /home/runner/work/_temp/Library/metabinR/extdata/reads.metagenome.fasta.gz
head(as.data.frame(res))
#>   read_id AB       AB.1       AB.2        AB.3
#> 1  S0R0/1  1 0.09945798 0.04015390 0.014257520
#> 2  S0R0/2  1 0.09309136 0.04982843 0.009528324
#> 3  S0R1/1  1 0.07955002 0.03811272 0.025665234
#> 4  S0R1/2  1 0.06132334 0.04118644 0.029910034
#> 5  S0R2/1  1 0.04883219 0.03985131 0.037270951
#> 6  S0R2/2  2 0.01154256 0.06789413 0.029774552