Post-processing tools

Note

All post-processing tools accessible under ADD STEP -> POSTPROCESSING

Important

Note that the input ASV/OTU table must contain “Sequence” column for some postprocessing modules. Read the tooltips too see which ones.

---

Filter tag-jumps

Filter out putative tag-jumps with UNCROSS2.

Input data is tab delimited OTU table and corresponding fasta file (representative sequences of ASV/OTUs).

Note

To START, specify working directory under SELECT WORKDIR, but the file formats do not matter here (just click ‘Next’).

Outputs
*_TagJumpFilt.txt	output table where tag-jumps have been filtered out
TagJump_plot.pdf	illustration about the presence of tag-jumps based on the selected parameters
TagJump_stats.txt	tag-jump statistics (Total_reads, Number_of_TagJump_Events, TagJump_reads, ReadPercent_removed)

Setting	Tooltip
table	select tab-delimited OTU/ASV table, where the 1st column is the OTU/ASV IDs and the following columns represent samples; 2nd column may be Sequence column, with the colName ‘Sequence’ [file must be in the SELECT WORKDIR directory]
fasta file	select corresponding fasta file for OTU/ASV table [fasta file must be in the SELECT WORKDIR directory]
f value	f-parameter of UNCROSS2, which defines the expected tag-jumps rate. Default is 0.03 (equivalent to 3%). A higher value enforces stricter filtering
p value	p-parameter, which controls the severity of tag-jump removal. It adjusts the exponent in the UNCROSS formula. Default is 1. Opt for 0.5 or 0.3 to steepen the curve

---

ASV to OTU

Cluster ASVs (zOTUs) to OTUs using vsearch.

Input data is tab delimited ASV table and ASV sequences in fasta format.

2nd column of ASV table MUST BE ‘Sequences’ (1st column is ASV IDs; default pipecraft output table).

For clustering, the ASV size annotation is obtained from the ASV table.

Outputs ASVs2OTUs_out
OTUs.fasta	FASTA formated representative OTU sequences
OTU_table.txt	OTU distribution table per sample (tab delimited file)
OTUs.uc	uclust-like formatted clustering results for OTUs
ASVs.size.fasta	size annotated input sequences

---

LULU post-clustering

Perform OTU post-clustering with LULU to merge co-occurring ‘daughter’ OTUs.

LULU description from the LULU repository: the purpose of LULU is to reduce the number of erroneous OTUs in OTU tables to achieve more realistic biodiversity metrics. By evaluating the co-occurence patterns of OTUs among samples LULU identifies OTUs that consistently satisfy some user selected criteria for being errors of more abundant OTUs and merges these. It has been shown that curation with LULU consistently result in more realistic diversity metrics.

Additional information:

• LULU repository

• LULU paper

Input data is tab delimited OTU table (table) and OTU sequences (rep_seqs) in fasta format (see input examples below).

EXAMPLE table here (from LULU repository)

EXAMPLE fasta here (from LULU repository)

Note

To START, specify working directory under SELECT WORKDIR, but the file formats do not matter here (just click ‘Next’).

Outputs lulu_out
OTU_table.lulu.txt	curated table in tab delimited txt format
OTUs.lulu.fasta	fasta file for the molecular units (OTUs or ASVs) in the curated table
match_list.lulu	match list file that was used by LULU to merge ‘daughter’ molecular units
discarded_units.lulu	molecular units (OTUs or ASVs) that were merged with other units based on specified thresholds

Setting	Tooltip
table	select OTU/ASV table. If no file is selected, then PipeCraft will look OTU_table.txt or ASV_table.txt in the working directory. EXAMPLE table here
fasta_file	select fasta formatted sequence file containing your OTU/ASV reads. EXAMPLE file here
min_ratio_type	sets whether a potential error must have lower abundance than the parent in all samples ‘min’ (default), or if an error just needs to have lower abundance on average ‘avg’
min_ratio	set the minimim abundance ratio between a potential error and a potential parent to be identified as an error
min_match	specify minimum threshold of sequence similarity for considering any OTU as an error of another
min_rel_cooccurence	minimum co-occurrence rate. Default = 0.95 (meaning that 1 in 20 samples are allowed to have no parent presence)
match_list_soft	use either ‘blastn’ or ‘vsearch’ to generate match list for LULU. Default is ‘vsearch’ (much faster)
vsearch_similarity_type	applies only when ‘vsearch’ is used as ‘match_list_soft’. Pairwise sequence identity definition (–iddef)
perc_identity	percent identity cutoff for match list. Excluding pairwise comparisons with lower sequence identity percentage than specified threshold
coverage_perc	percent query coverage per hit. Excluding pairwise comparisons with lower sequence coverage than specified threshold
strands	query strand to search against database. Both = search also reverse complement
cores	number of cores to use for generating match list for LULU

---

DADA2 collapse ASVs

DADA2 collapseNoMismatch function collapses identical ASVs with no internal mismatches (~greedy 100% clustering with end-gapping ignored). Representative sequence of a collapsed ASV will be the most abundant one. and ASVs filtering based on minimum accepted sequence length (custom R functions).

To START, specify working directory under SELECT WORKDIR, but the file formats do not matter here (just click ‘Next’).

Outputs filtered_table
ASVs_table_collapsed.txt	ASV table after collapsing identical ASVs
ASVs_collapsed.fasta	ASV sequences after collapsing identical ASVs
ASV_table_collapsed.rds	ASV table in RDS format after collapsing identical ASVs
If length filtering was applied
ASV_table_lenFilt.tx	ASV table after filtering out ASVs with shorther than specified sequence length
ASVs_lenFilt.fasta	ASV sequences after filtering out ASVs with shorther than specified sequence length

Setting	Tooltip
DADA2 table	select the RDS file (ASV table), output from DADA2 workflow; usually in ASVs_out.dada2/ASVs_table.denoised-merged.rds
collapseNoMismatch	collapses ASVs that are identical up to shifts or length variation, i.e. that have no mismatches or internal indels
by_length	discard ASVs from the ASV table that are shorter than specified value (in base pairs). Value 0 means OFF, no filtering by length
minOverlap	collapseNoMismatch setting. Default = 20. The minimum overlap of base pairs between ASV sequences required to collapse them together
vec	collapseNoMismatch setting. Default = TRUE. Use the vectorized aligner. Should be turned off if sequences exceed 2kb in length

---

metaMATE

Determine and filter out putative NUMTs (from mitochondrial coding amplicon genes) and and other erroneous sequences based on relative read abundance thresholds within libraries, phylogenetic clades and/or taxonomic groupings.

Additional information:

• metaMATE repository

• metaMATE paper

---

ORF-Finder

Filter out putative pseudogenes (NUMTs) from protein coding amplicon dataset (such as COI, rbcL) using NCBI’s ORFfinder (Sayers et al 2022). This process translates sequences to open reading frames (ORFs) and retaines the longest ORF per sequence if the length of the ORF is between the specified range of min length and max length.

---

DEICODE

DEICODE (Martino et al., 2019) is used to perform beta diversity analysis by applying robust Aitchison PCA on the OTU/ASV table. To consider the compositional nature of data, it preprocesses data with rCLR transformation (centered log-ratio on only non-zero values, without adding pseudo count). As a second step, it performs dimensionality reduction of the data using robust PCA (also applied only to the non-zero values of the data), where sparse data are handled through matrix completion.

Additional information:

• DEICODE repository

• DEICODE paper

Input data is tab delimited OTU table and optionally subset of OTU ids to generate results also for the selected subset (see input examples below).

Note

To START, specify working directory under SELECT WORKDIR, but the file formats do not matter here (just click ‘Next’).

Output directory DEICODE_out
otutab.biom	full OTU table in BIOM format
rclr_subset.tsv	rCLR-transformed subset of OTU table *
full/distance-matrix.tsv	distance matrix between the samples, based on full OTU table
full/ordination.txt	ordination scores for samples and OTUs, based on full OTU table
full/rclr.tsv	rCLR-transformed OTU table
subs/distance-matrix.tsv	distance matrix between the samples, based on a subset of OTU table *
subs/ordination.txt	ordination scores for samples and OTUs, based a subset of OTU table *
* files are present only if ‘subset_IDs’ variable was specified

Setting	Tooltip
table	select OTU/ASV table. If no file is selected, then PipeCraft will look OTU_table.txt or ASV_table.txt in the working directory. See OTU table example below
subset_IDs	select list of OTU/ASV IDs for analysing a subset from the full table see subset_IDs file example below
min_otu_reads	cutoff for reads per OTU/ASV. OTUs/ASVs with lower reads then specified cutoff will be excluded from the analysis
min_sample_reads	cutoff for reads per sample. Samples with lower reads then specified cutoff will be excluded from the analysis

Example of input table (tab delimited text file):

OTU_id	sample1	sample2	sample3	sample4
00fc1569196587dde	106	271	584	20
02d84ed0175c2c79e	81	44	88	14
0407ee3bd15ca7206	3	4	3	0
042e5f0b5e38dff09	20	83	131	4
07411b848fcda497f	1	0	2	0
07e7806a732c67ef0	18	22	83	7
0836d270877aed22c	1	1	0	0
0aa6e7da5819c1197	1	4	5	0
0c1c219a4756bb729	18	17	40	7

Example of input subset_IDs:

07411b848fcda497f
042e5f0b5e38dff09
0836d270877aed22c
0c1c219a4756bb729
...

PERMANOVA and PERMDISP example using the robust Aitchison distance

library(vegan)

## Load distance matrix
dd <- read.table(file = "distance-matrix.tsv")

## You will also need to load the sample metadata
## However, for this example we will create a dummy data
meta <- data.frame(
  SampleID = rownames(dd),
  TestData = rep(c("A", "B", "C"), each = ceiling(nrow(dd)/3))[1:nrow(dd)])

## NB! Ensure that samples in distance matrix and metadata are in the same order
meta <- meta[ match(x = meta$SampleID, table = rownames(dd)), ]

## Convert distance matrix into 'dist' class
dd <- as.dist(dd)

## Run PERMANOVA
adon <- adonis2(formula = dd ~ TestData, data = meta, permutations = 1000)
adon

## Run PERMDISP
permdisp <- betadisper(dd, meta$TestData)
plot(permdisp)

Example of plotting the ordination scores

library(ggplot2)

## Load ordination scores
ord <- readLines("ordination.txt")

## Skip PCA summary
ord <- ord[ 8:length(ord) ]

## Break the data into sample and species scores
breaks <- which(! nzchar(ord))
ord <- ord[1:(breaks[2]-1)]               # Skip biplot scores
ord_sp <- ord[1:(breaks[1]-1)]            # species scores
ord_sm <- ord[(breaks[1]+2):length(ord)]  # sample scores

## Convert scores to data.frames
ord_sp <- as.data.frame( do.call(rbind, strsplit(x = ord_sp, split = "\t")) )
colnames(ord_sp) <- c("OTU_ID", paste0("PC", 1:(ncol(ord_sp)-1)))

ord_sm <- as.data.frame( do.call(rbind, strsplit(x = ord_sm, split = "\t")) )
colnames(ord_sm) <- c("Sample_ID", paste0("PC", 1:(ncol(ord_sm)-1)))

## Convert PCA to numbers
ord_sp[colnames(ord_sp)[-1]] <- sapply(ord_sp[colnames(ord_sp)[-1]], as.numeric)
ord_sm[colnames(ord_sm)[-1]] <- sapply(ord_sm[colnames(ord_sm)[-1]], as.numeric)

## At this step, sample and OTU metadata could be added to the data.frame

## Example plot
ggplot(data = ord_sm, aes(x = PC1, y = PC2)) + geom_point()

---