# De novo Chimera Detection

## Overview

The `rchime()` function allows you to detect and remove chimeric sequences using the dataset as it’s own reference (de novo). The denovo approach is our preferred method for removing chimeras. `rchime()` can be used with strollur objects or data.frames as inputs. Let’s look at examples of both data types using sequence data from mothur’s MiSeq_SOP example analysis.

### Creating strollur objects

    fasta_data <- readRDS(rchime_example("miseq_fasta.rds"))
    abundance_data <- readRDS(rchime_example("miseq_abundance.rds"))

    strollur <- strollur::new_dataset("rchime de novo example")

    strollur::add(strollur, table = fasta_data, type = "sequence")
    #> Added 6084 sequences.
    strollur::assign(strollur, table = abundance_data, type = "sequence_abundance")
    #> Assigned 6084 sequence abundances.

    strollur
    #> 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   3216.38
    #> 25%-tile:        1  252    252      0        4     0  32163.75
    #> Median:          1  253    253      0        4     0  64327.50
    #> 75%-tile:        1  253    253      0        5     0  96491.25
    #> 97.5%-tile:      1  254    254      0        6     0 125438.62
    #> Maximum:         1  256    256      0        8     0 128655.00
    #> Mean:            1  252    252      0        4     0  64327.64
    #>
    #> Number of unique seqs: 6084
    #> Total number of seqs: 128655
    #>
    #> Total number of samples: 20

### Loading data.frames

    df <- readRDS(rchime_example("miseq_data_frame_by_sample.rds"))

    str(df)
    #> 'data.frame':    11039 obs. of  4 variables:
    #>  $ sequence_name: chr  "M00967_43_000000000-A3JHG_1_1101_10133_8460" "M00967_43_000000000-A3JHG_1_1101_10133_8460" "M00967_43_000000000-A3JHG_1_1101_10133_8460" "M00967_43_000000000-A3JHG_1_1101_10133_8460" ...
    #>  $ sequence     : chr  "TACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGGCGGCCATGCAAGTCAGAAGTGAAAACCCGGGGCTCAACCCTGGGAGTGCTTTTGAAACT"| __truncated__ "TACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGGCGGCCATGCAAGTCAGAAGTGAAAACCCGGGGCTCAACCCTGGGAGTGCTTTTGAAACT"| __truncated__ "TACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGGCGGCCATGCAAGTCAGAAGTGAAAACCCGGGGCTCAACCCTGGGAGTGCTTTTGAAACT"| __truncated__ "TACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGGCGGCCATGCAAGTCAGAAGTGAAAACCCGGGGCTCAACCCTGGGAGTGCTTTTGAAACT"| __truncated__ ...
    #>  $ sample       : chr  "F3D2" "F3D146" "F3D149" "F3D150" ...
    #>  $ abundance    : int  222 1 1 1 127 17 32 13 95 86 ...

## Removing chimeras

When removing chimeras using the de novo method, the potential parents are chosen from more abundant sequences in your dataset.

Before we remove the chimeras let’s discuss the `dereplicate` parameter. 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. Let’s use the de novo method to remove the chimeras.

    rchime(strollur)
    #> ℹ The de novo method runs with a single processor.
    #> → rchime removed `10453` chimeras from your dataset.
    #> → It took `4.40263795852661` seconds to detect and remove the chimeras.
    strollur
    #> 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   2955.05
    #> 25%-tile:        1  252    252      0        4     0  29550.50
    #> Median:          1  253    253      0        4     0  59101.00
    #> 75%-tile:        1  253    253      0        5     0  88651.50
    #> 97.5%-tile:      1  254    254      0        6     0 115246.95
    #> Maximum:         1  256    256      0        8     0 118202.00
    #> Mean:            1  252    252      0        4     0  59101.14
    #>
    #> scrap_summary:
    #>       type      trash_code unique total
    #> 1 sequence rchime_chimeras   3588 10453
    #>
    #> Number of unique seqs: 2496
    #> Total number of seqs: 118202
    #>
    #> Total number of samples: 20
    #> Total number of custom reports: 1

    data <- rchime(df)
    #> ℹ The de novo method runs with a single processor.
    #> → rchime removed `10453` chimeras from your dataset.
    #> → It took `4.41122698783875` seconds to detect and remove the chimeras.
    data
    #>             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   2955.05
    #> 25%-tile:        1  252    252      0        4     0  29550.50
    #> Median:          1  253    253      0        4     0  59101.00
    #> 75%-tile:        1  253    253      0        5     0  88651.50
    #> 97.5%-tile:      1  254    254      0        6     0 115246.95
    #> Maximum:         1  256    256      0        8     0 118202.00
    #> Mean:            1  252    252      0        4     0  59101.14
    #>
    #> scrap_summary:
    #>       type      trash_code unique total
    #> 1 sequence rchime_chimeras   3588 10453
    #>
    #> Number of unique seqs: 2496
    #> Total number of seqs: 118202
    #>
    #> Total number of samples: 20
    #> Total number of custom reports: 1

## Results

The `rchime()` function returns a strollur object. Let’s take a closer look at the `chimera_report` generated by `rchime()`. The chimera_report is a data.frame with a row for each sequence in your dataset. Let’s take a look at the first 5 chimeric sequences in the report:

    chimera_report <- strollur::report(data, type = "chimera_report")

    chimera_report[chimera_report$Chimeric_Status == "Y", ] |> head(n = 5)
    #>        Score                                        Query
    #> 66 0.3805621 M00967_43_000000000-A3JHG_1_1106_11629_14238
    #> 70 0.5261480 M00967_43_000000000-A3JHG_1_1103_26580_14708
    #> 80 0.5580357 M00967_43_000000000-A3JHG_1_2101_21700_24164
    #> 89 0.3348214 M00967_43_000000000-A3JHG_1_1112_23980_19089
    #> 91 0.6377551 M00967_43_000000000-A3JHG_1_2110_20944_24019
    #>                                         ParentA
    #> 66 M00967_43_000000000-A3JHG_1_1107_15750_18592
    #> 70 M00967_43_000000000-A3JHG_1_2110_12856_16229
    #> 80 M00967_43_000000000-A3JHG_1_1113_11294_24024
    #> 89 M00967_43_000000000-A3JHG_1_1107_15750_18592
    #> 91 M00967_43_000000000-A3JHG_1_2101_22400_13416
    #>                                         ParentB
    #> 66 M00967_43_000000000-A3JHG_1_2101_22400_13416
    #> 70 M00967_43_000000000-A3JHG_1_1107_15750_18592
    #> 80 M00967_43_000000000-A3JHG_1_2110_12856_16229
    #> 89 M00967_43_000000000-A3JHG_1_1109_25348_18015
    #> 91  M00967_43_000000000-A3JHG_1_1112_6862_18037
    #>                                      Top_Parent        QM       QA       QB
    #> 66 M00967_43_000000000-A3JHG_1_1107_15750_18592  99.60317 98.01587 94.44444
    #> 70 M00967_43_000000000-A3JHG_1_2110_12856_16229 100.00000 97.61905 95.63492
    #> 80 M00967_43_000000000-A3JHG_1_1113_11294_24024 100.00000 98.01587 94.44444
    #> 89 M00967_43_000000000-A3JHG_1_1107_15750_18592 100.00000 98.80952 94.44444
    #> 91 M00967_43_000000000-A3JHG_1_2101_22400_13416 100.00000 98.01587 93.65079
    #>         QAB       QT LY LN LA RY RN RA      Div Chimeric_Status
    #> 66 93.25397 98.01587 13  0  0  4  0  1 1.587302               Y
    #> 70 93.25397 97.61905 11  0  0  6  0  0 2.380952               Y
    #> 80 92.46032 98.01587 14  0  0  5  0  0 1.984127               Y
    #> 89 93.25397 98.80952 14  0  0  3  0  0 1.190476               Y
    #> 91 91.66667 98.01587 16  0  0  5  0  0 1.984127               Y