Missing Fusobacterium in colon cancer 16S RNAseq data

General Discussion
#1

Hi,

We analyzed 16S RNAseq from colon cancer samples where we expect to see Fusobacterium.
Indeed, we found them from DADA2, but they were not found in Qimme2 outcomes.
Do I need to set some particular parameters to detect Fusobacterium?
It would be great if some one can give me some hints about this.

Thanks!

#2

Hi @compbio,

It’s hard to answer this question without more information.

  1. Can you give a brief summary of your wet lab process? (Hypervariable region, sequencing strategy)?
  2. What steps did you apply in QIIME 2? (Denoising, taxonomic classification and database)
  3. What steps did you use in DADA2 (presumably in R)?
  4. How did you look for the Fusobacterium in R?
  5. How did you look for the Fusobacterium in your QIIME 2 results?

Best,
Justine

2 Likes
#3

Hi Justine,

Thank you for the reply. Here are my answers for your questions:

  1. Can you give a brief summary of your wet lab process? (Hypervariable region, sequencing strategy)?:
    [Answer] Total genomic DNAs were extracted using Maxwell® 16 FFPE Tissue LEV DNA Purification Kit from Promega (Madison, Wisconsin, USA). 16S rRNA gene amplicon library preparation was carried out according to the 16S Metagenomic Sequencing Library Preparation protocol developed by Illumina (San Diego, California, USA). Briefly, 200ng of mucosal DNA was amplified using primers targeting the V3/V4 variable region of the 16S rRNA gene: 16S amplicon PCR forward primer (V3 region): 5’ TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACG GGNGGCWGCAG; 16S amplicon PCR reverse primer (V4 region): 5’ GTCTCGTGGGCTCG GAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC. Equimolar amounts of DNA per amplicon were then pooled and sequenced on an Illumina MiSeq instrument V3 Illumina kit (2x300 bp paired-end reads).

  2. What steps did you apply in QIIME 2? (Denoising, taxonomic classification and database)
    [Answer] Overall, the steps taken with qiime are importing sequence data, joining paired-ends, quality filtering with Deblur, then taxonomic analysis. Here is a detailed list of commands:
    qiime tools import --type ‘SampleData[PairedEndSequencesWithQuality]’ --input-path {} --input-format CasavaOneEightSingleLanePerSampleDirFmt --output-path demux-paired-end.qza
    qiime vsearch join-pairs --i-demultiplexed-seqs demux-paired-end.qza --o-joined-sequences demux-joined.qza
    qiime demux summarize --i-data demux-joined.qza --o-visualization demux.qzv
    qiime quality-filter q-score --i-demux demux-joined.qza --o-filtered-sequences demux-filtered.qza --o-filter-stats demux-filter-stats.qza
    qiime metadata tabulate --m-input-file demux-filter-stats.qza --o-visualization demux-filter-stats.qzv
    qiime deblur denoise-16S --i-demultiplexed-seqs demux-filtered.qza --p-trim-length {} --o-representative-sequences rep-seqs.qza --o-table table.qza --p-sample-stats --o-stats deblur-stats.qza
    qiime deblur visualize-stats --i-deblur-stats deblur-stats.qza --o-visualization deblur-stats.qzv
    qiime feature-table summarize --i-table table.qza --o-visualization table.qzv
    qiime feature-table tabulate-seqs --i-data rep-seqs.qza --o-visualization rep-seqs.qzv
    qiime feature-classifier classify-sklearn --i-classifier SILVA-138-SSURef-full-length-classifier.qza --i-reads rep-seqs.qza --o-classification taxonomy.qza
    qiime metadata tabulate --m-input-file taxonomy.qza --o-visualization taxonomy.qzv
    qiime taxa barplot --i-table table.qza --i-taxonomy taxonomy.qza --m-metadata-file sample-metadata.tsv --o-visualization taxa-bar-plots.qzv

  3. What steps did you use in DADA2 (presumably in R)?
    [Answer] We use DADA2 R package version 1.6. After trimming adaptors and primers, reads with ambiguous bases were removed or truncated at “filterAndTrim” step: filterAndTrim(fnFs_samples, filtFs_samples, fnRs_samples, filtRs_samples, maxN=0, truncLen=0, truncQ=0, rm.phix=TRUE, compress=TRUE, multithread=TRUE). We did not remove any part of forward/reverse reads because it made significant reads lost at merging step. Except for this, we just followed through DADA2 flow. use Silva database to assign Taxonomy.

  4. What steps did you use in DADA2 (presumably in R)?
    [Answer] We use DADA2 R package version 1.6. After trimming adaptors and primers, reads with ambiguous bases were removed or truncated at “filterAndTrim” step: filterAndTrim(fnFs_samples, filtFs_samples, fnRs_samples, filtRs_samples, maxN=0, truncLen=0, truncQ=0, rm.phix=TRUE, compress=TRUE, multithread=TRUE). We did not remove any part of forward/reverse reads because it made significant reads lost at merging step. Except for this, we just followed through DADA2 flow. use Silva database to assign Taxonomy.

  5. How did you look for the Fusobacterium in your QIIME 2 results?
    We just checked if Fusobacterium is in output of QIIME 2.

I hope this information would be good enough for you to give us some feedback.

Thanks,
HoJoon

1 Like
#4

Hi @compbio,

Thanks for the very comprehensive description. It looks like the two major differences here are that you deblur in QIIME 2 and then ran DADA2 in R. Im not sure what Silva version DADA2 uses (132 or 138) but I dont think that should make as much of a difference. But, I think the DADA2 classifier tends to be slightly more permissive.

With the checking Fusobacterium, what level were you looking at? Phylum? Genus? A specific species? Becuase that can make a difference in identification.

Best,
Justine