The chimera_report generated by the
rchime() function is a data.frame with 18 columns and a row
for each sequence in your dataset. Here’s a brief description of the
various columns.
1. Score: a higher score means a more likely chimeric alignment.
2. Query: query sequence name.
3. ParentA: parent A sequence name.
4. ParentB: parent B sequence name.
5. Top_Parent: top parent sequence name (i.e. parent most similar to the query).
6. QM: percentage of similarity of query (Q) and model (M) constructed as a part of parent A and a part of parent B.
7. QA: percentage of similarity of query (Q) and parent A.
8. QB: percentage of similarity of query (Q) and parent B.
9. QAB: percentage of similarity of parent A and parent B.
10. QT percentage of similarity of query (Q) and top parent (T)
11. LY : yes votes in the left part of the model.
12. LN: no votes in the left part of the model.
13. LA: abstain votes in the left part of the model.
14. RY: yes votes in the right part of the model.
15. RN: no votes in the right part of the model.
16. RA: abstain votes in the right part of the model.
17. Div: divergence, defined as (QM - QT).
18. Chimeric_Status: query is chimeric (Y), or not (N), or is a borderline case (?).Let’s run rchime() with the de novo approach to generate
the chimera report, and take a closer look.
library(rchime)
#> Loading required package: Rcpp
#> Loading required package: strollur
#>
#> Attaching package: 'strollur'
#> The following objects are masked from 'package:base':
#>
#> assign, names, summary
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.
results <- rchime(data, dereplicate = TRUE)
#> ℹ The de novo method runs with a single processor.
#> Added a chimera_report report.
#> → rchime removed `10453` chimeras from your dataset.
#> → It took `4.50990509986877` seconds to detect and remove the chimeras.
results <- strollur::new_dataset("rchime de novo example") |>
strollur::add(table = fasta_data, type = "sequence") |>
strollur::assign(
table = abundance_data,
type = "sequence_abundance"
) |>
rchime(dereplicate = TRUE)
#> Added 6084 sequences.
#> Assigned 6084 sequence abundances.
#> ℹ The de novo method runs with a single processor.
#> Added a chimera_report report.
#> → rchime removed `10453` chimeras from your dataset.
#> → It took `4.47902488708496` seconds to detect and remove the chimeras.
results$chimera_report[60:70, ]
#> Score Query
#> 60 0.01736930 M00967_43_000000000-A3JHG_1_1112_24606_18511
#> 61 0.04669018 M00967_43_000000000-A3JHG_1_2103_25809_24518
#> 62 0.08630113 M00967_43_000000000-A3JHG_1_2106_17516_3721
#> 63 0.16534392 M00967_43_000000000-A3JHG_1_2104_12407_10383
#> 64 0.02075598 M00967_43_000000000-A3JHG_1_2108_19081_20723
#> 65 0.01122486 M00967_43_000000000-A3JHG_1_2101_6377_14009
#> 66 0.38056206 M00967_43_000000000-A3JHG_1_1106_11629_14238
#> 67 0.03046349 M00967_43_000000000-A3JHG_1_1107_12904_20713
#> 68 0.21344934 M00967_43_000000000-A3JHG_1_1109_22008_18955
#> 69 0.05562478 M00967_43_000000000-A3JHG_1_1103_19870_21567
#> 70 0.52614796 M00967_43_000000000-A3JHG_1_1103_26580_14708
#> ParentA
#> 60 M00967_43_000000000-A3JHG_1_1112_25719_18946
#> 61 M00967_43_000000000-A3JHG_1_2108_22507_11051
#> 62 M00967_43_000000000-A3JHG_1_1112_25719_18946
#> 63 M00967_43_000000000-A3JHG_1_1101_10133_8460
#> 64 M00967_43_000000000-A3JHG_1_1109_16432_18573
#> 65 M00967_43_000000000-A3JHG_1_1105_5158_15329
#> 66 M00967_43_000000000-A3JHG_1_1107_15750_18592
#> 67 M00967_43_000000000-A3JHG_1_1113_18037_24127
#> 68 M00967_43_000000000-A3JHG_1_2101_15190_13450
#> 69 M00967_43_000000000-A3JHG_1_1113_5336_24219
#> 70 M00967_43_000000000-A3JHG_1_2110_12856_16229
#> ParentB
#> 60 M00967_43_000000000-A3JHG_1_1113_18037_24127
#> 61 M00967_43_000000000-A3JHG_1_1109_16432_18573
#> 62 M00967_43_000000000-A3JHG_1_1113_18037_24127
#> 63 M00967_43_000000000-A3JHG_1_2108_22507_11051
#> 64 M00967_43_000000000-A3JHG_1_2108_22507_11051
#> 65 M00967_43_000000000-A3JHG_1_1107_10661_18652
#> 66 M00967_43_000000000-A3JHG_1_2101_22400_13416
#> 67 M00967_43_000000000-A3JHG_1_1113_5336_24219
#> 68 M00967_43_000000000-A3JHG_1_1112_13142_18436
#> 69 M00967_43_000000000-A3JHG_1_1107_10661_18652
#> 70 M00967_43_000000000-A3JHG_1_1107_15750_18592
#> Top_Parent QM QA QB
#> 60 M00967_43_000000000-A3JHG_1_1113_18037_24127 90.72581 89.11290 89.91935
#> 61 M00967_43_000000000-A3JHG_1_2108_22507_11051 95.65217 94.46640 94.07115
#> 62 M00967_43_000000000-A3JHG_1_1113_18037_24127 92.09486 88.14229 90.51383
#> 63 M00967_43_000000000-A3JHG_1_2108_22507_11051 95.65217 92.49012 92.88538
#> 64 M00967_43_000000000-A3JHG_1_1109_16432_18573 86.16601 84.98024 84.18972
#> 65 M00967_43_000000000-A3JHG_1_1105_5158_15329 94.86166 94.46640 93.28063
#> 66 M00967_43_000000000-A3JHG_1_1107_15750_18592 99.60317 98.01587 94.44444
#> 67 M00967_43_000000000-A3JHG_1_1113_5336_24219 86.93878 84.89796 86.12245
#> 68 M00967_43_000000000-A3JHG_1_1112_13142_18436 90.51383 84.98024 86.16601
#> 69 M00967_43_000000000-A3JHG_1_1113_5336_24219 97.62846 96.04743 96.04743
#> 70 M00967_43_000000000-A3JHG_1_2110_12856_16229 100.00000 97.61905 95.63492
#> QAB QT LY LN LA RY RN RA Div Chimeric_Status
#> 60 91.12903 89.91935 4 2 11 7 3 7 0.8064516 N
#> 61 91.69960 94.46640 4 0 1 9 6 4 1.1857708 N
#> 62 90.51383 90.51383 6 2 11 10 0 7 1.5810277 N
#> 63 90.90909 92.88538 10 3 8 8 0 0 2.7667984 N
#> 64 91.69960 84.98024 9 4 23 4 1 7 1.1857708 N
#> 65 97.23320 94.46640 5 1 4 1 0 8 0.3952569 N
#> 66 93.25397 98.01587 13 0 0 4 0 1 1.5873016 Y
#> 67 86.53061 86.12245 11 9 16 5 0 7 0.8163265 N
#> 68 84.18972 86.16601 15 4 14 14 0 6 4.3478261 N
#> 69 95.25692 96.04743 5 1 2 5 1 2 1.5810277 N
#> 70 93.25397 97.61905 11 0 0 6 0 0 2.3809524 Y