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Merging Chimeras & Taxonomy

Merging Reads

  • For paired-end data there is a good deal of overlap between the forward and reverse read
  • To resolve this redundancy, these reads are collapsed into contigs

Let's do this with code now!

# Merge Read Pairs

# so far we have "denoised", so to speak, 
# these sequence variants. We now need to merge the
# forward and reverse strands
mergers <- mergePairs(
  dadaForward,
  filtForward,
  dadaReverse, 
  filtReverse, 
  verbose=TRUE)

619 paired-reads (in 18 unique pairings) successfully merged out of 807 (in 82 pairings) input.
570 paired-reads (in 29 unique pairings) successfully merged out of 815 (in 136 pairings) input.
619 paired-reads (in 28 unique pairings) successfully merged out of 868 (in 128 pairings) input.
713 paired-reads (in 18 unique pairings) successfully merged out of 860 (in 76 pairings) input.
609 paired-reads (in 29 unique pairings) successfully merged out of 851 (in 133 pairings) input.
620 paired-reads (in 30 unique pairings) successfully merged out of 810 (in 115 pairings) input.
679 paired-reads (in 28 unique pairings) successfully merged out of 845 (in 104 pairings) input.
616 paired-reads (in 28 unique pairings) successfully merged out of 830 (in 106 pairings) input.

Info

Here we see that for each sample we get the number of reads that were able to be successfully merged out of the total number of reads that could be merged.

ASVs vs. OTUs

  • Now that we have finally merged our sequence variants we are left with an Amplicon Sequence Variant.
  • Traditional 16S metagenomic approaches use OTUs or operational taxonomic units instead of ASVs.
  • Operational taxonomic units (OTUs): clusters of reads that differ by less than a fixed sequence dissimilarity threshold, most commonly 3% Instead of clustering sequences, methods that resolve amplicon sequence variants (ASVs) distinguish sequence variants differing by as little as one nucleotide. ASVs represent a biological reality and provide nucleotide-level resolution. Callahan, MucMurdie, & Holmes (2017) have demonstrated that “ASVs capture all biological variation present in the data, and ASVs inferred from a given data set can be reproduced in future data sets and validly compared between data sets.”

ASV Table

Now that our sequences are merged we can create an ASV counts table, basically telling us how which samples contain which ASV's:

# Making a Sequence Table

# now that we have merged sequences we can construct
# an Amplicon Sequence Variant (ASV) table
seqtab <- makeSequenceTable(mergers)

Chimera Removal

  • During Sequencing microbial DNA is subjected to PCR to amplify DNA
  • During PCR it is possible for two unrelated templates to form a non-biological hybrid sequence
  • DADA2 finds these chimeras by:
    • aligning each sequence to more abundant sequences
    • now check low abundant sequences and determine:
      • can this sequence be created if we mix the left and right sides of the abundant sequences

Now in code:

# Removing Chimeras

# Chimeric sequences occur as errors during PCR 
# when two unrelated templates for a hybrid sequence
# we will need to remove them before going forward

seqtab.nochim <- removeBimeraDenovo(seqtab, method="consensus", verbose=TRUE)

Now let's check if any chimeric sequences are removed:

## check to see if the dimensions are different
## between the chimera filtered and unfiltered
## ASV tables

dim(seqtab)
dim(seqtab.nochim)

[1]   8 119
[1]   8 117

Info

We can see here that 2 chimeric sequences were removed because our before and after sequence count matrices differ by two columns.

Pipeline Quality Control

We will also take a moment to do some final QC:

# Final QC

## we have performed quite a few steps 
## and it would be nice to get a final qc check 
## before assigning taxonomy
getN <- function(x) sum(getUniques(x))
finalQC <- cbind(
  out, 
  sapply(dadaForward, getN),
  sapply(dadaReverse, getN),
  sapply(mergers, getN),
  rowSums(seqtab.nochim))
colnames(finalQC) <- c("input", "filtered", "denoisedF", "denoisedR", "merged", "nonchim")
rownames(finalQC) <- sampleNames
finalQC

           input filtered denoisedF denoisedR merged nonchim
SRR5690809  1000      905       840       855    619     611
SRR5690810  1000      937       853       885    570     549
SRR5690811  1000      937       880       910    619     594
SRR5690812  1000      924       886       888    713     700
SRR5690819  1000      938       872       906    609     609
SRR5690820  1000      916       870       844    620     620
SRR5690821  1000      921       883       879    679     679
SRR5690822  1000      940       865       891    616     616

Info

Here we see that we start with 1000 sequences per sample, end up with around 900 after filtering, around 800 after denoising to find unique sequences, and around 600-700 sequences after merging sequences and removing chimeric sequences.

Assigning Taxonomy

  • To determine which taxon each ASV belongs to DADA2 uses a naïve bayes classifier
  • This classifier uses a set of reference sequences with known taxonomy, here we use the SILVA database, as the training set and and outputs taxonomic assignments with bootstrapped confidence
# Assigning Taxonomy

# dada2 uses a naive Bayes classifier when
# assigning taxonomy. This means we need a training
# set of sequences with known taxonomy information.
# here we use the silva database

taxa <- assignTaxonomy(seqtab.nochim, "../data/silva_nr99_v138.1_train_set.fa.gz")

Databases

While we use the SILVA database here, there are other options databases:

Time for a break!