HiFi Full-length 16S analysis with pb-16S-nf

• Table of Contents
  • Workflow overview and output
  • Installation and usage
  • HPC and job scheduler usage
  • Speeding up the denoising process
  • Run time and compute requirements
  • Frequently asked questions (FAQ)
  • References
  • DISCLAIMER

The pipeline is currently under active development; we welcome your feedback to help improve it.

Workflow overview and output

This Nextflow pipeline is designed to process PacBio HiFi full-length 16S data into high- quality amplicon sequence variants (ASVs) using QIIME 2 and DADA2. The pipeline provides a set of visualizations using the QIIME 2 framework for interactive plotting. The pipeline generates an HTML report for the important statistics and top taxonomies. The outputs and stages of this pipeline are documented here.

We provide a sample report generated using this pipeline based on 8 replicates from the ATCC MSA-1003 mock community sequenced on a Sequel II system (Link). Right-click this link, save it on your computer, then double-click to open the sample report. All other important outputs from the pipeline are available in the examples folder when you clone this repository.

Installation and usage

This pipeline runs using Nextflow Version 22 and later. If you have Singularity or Docker on your cluster, we recommend using Singularity or Docker to run the pipeline by specifying -profile singularity or -profile docker when running the pipeline. Singularity will pull the docker images to the folder $HOME/nf_conda/singularity.

By default, all software dependencies are managed using Conda. Nextflow will use Conda to build the required environment so there is no need to manually build environments. You can install Nextflow by following the steps here (documentation) or by using Conda itself:

conda install -c bioconda nextflow

# If this is your first time using conda
conda init

After installing Nextflow, clone the repository and download databases using the following commands. To update the pipeline in the future, type git pull.

git clone https://github.com/PacificBiosciences/pb-16S-nf.git
cd pb-16S-nf
nextflow run main.nf --download_db
# With docker (If you use docker, add -profile docker to all Nextflow-related command)
nextflow run main.nf --download_db -profile docker

After downloading the databases, run the following command in the cloned folder to see the options for the pipeline:

nextflow run main.nf --help

  Usage:
  This pipeline takes in the standard sample manifest and metadata file used in
  QIIME 2 and produces QC summary, taxonomy classification results and visualization.

  For samples TSV, two columns named "sample-id" and "absolute-filepath" are
  required. For metadata TSV file, at least two columns named "sample_name" and
  "condition" to separate samples into different groups.

  nextflow run main.nf --input samples.tsv --metadata metadata.tsv \\
    --dada2_cpu 8 --vsearch_cpu 8

  By default, sequences are first trimmed with cutadapt. If adapters are already trimmed, you can skip 
  cutadapt by specifying "--skip_primer_trim".

  Other important options:
  --front_p    Forward primer sequence. Default to F27. (default: AGRGTTYGATYMTGGCTCAG)
  --adapter_p    Reverse primer sequence. Default to R1492. (default: AAGTCGTAACAAGGTARCY)
  --filterQ    Filter input reads above this Q value (default: 20).
  --downsample    Limit reads to a maximum of N reads if there are more than N reads (default: off)
  --max_ee    DADA2 max_EE parameter. Reads with number of expected errors higher than
              this value will be discarded (default: 2)
  --minQ    DADA2 minQ parameter. Reads with any base lower than this score 
            will be removed (default: 0)
  --min_len    Minimum length of sequences to keep (default: 1000)
  --max_len    Maximum length of sequences to keep (default: 1600)
  --pooling_method    QIIME 2 pooling method for DADA2 denoise see QIIME 2 
                      documentation for more details (default: "pseudo", alternative: "independent") 
  --maxreject    max-reject parameter for VSEARCH taxonomy classification method in QIIME 2
                 (default: 100)
  --maxaccept    max-accept parameter for VSEARCH taxonomy classification method in QIIME 2
                 (default: 100)
  --min_asv_totalfreq    Total frequency of any ASV must be above this threshold
                         across all samples to be retained. Set this to 0 to disable filtering
                         (default 5)
  --min_asv_sample    ASV must exist in at least min_asv_sample to be retained. 
                      Set this to 0 to disable. (default 1)
  --vsearch_identity    Minimum identity to be considered as hit (default 0.97)
  --rarefaction_depth    Rarefaction curve "max-depth" parameter. By default the pipeline
                         automatically select a cut-off above the minimum of the denoised 
                         reads for >80% of the samples. This cut-off is stored in a file called
                         "rarefaction_depth_suggested.txt" file in the results folder
                         (default: null)
  --dada2_cpu    Number of threads for DADA2 denoising (default: 8)
  --vsearch_cpu    Number of threads for VSEARCH taxonomy classification (default: 8)
  --cutadapt_cpu    Number of threads for primer removal using cutadapt (default: 16)
  --outdir    Output directory name (default: "results")
  --vsearch_db  Location of VSEARCH database (e.g. silva-138-99-seqs.qza can be
                downloaded from QIIME database)
  --vsearch_tax    Location of VSEARCH database taxonomy (e.g. silva-138-99-tax.qza can be
                   downloaded from QIIME database)
  --silva_db   Location of Silva 138 database for taxonomy classification 
  --gtdb_db    Location of GTDB r202 for taxonomy classification
  --refseq_db    Location of RefSeq+RDP database for taxonomy classification
  --skip_primer_trim    Skip all primers trimming (switch off cutadapt and DADA2 primers
                        removal) (default: trim with cutadapt)
  --skip_nb    Skip Naive-Bayes classification (only uses VSEARCH) (default: false)
  --colorby    Columns in metadata TSV file to use for coloring the MDS plot
               in HTML report (default: condition)
  --run_picrust2    Run PICRUSt2 pipeline. Note that pathway inference with 16S using PICRUSt2
                    has not been tested systematically (default: false)
  --download_db    Download databases needed for taxonomy classification only. Will not
                   run the pipeline. Databases will be downloaded to a folder "databases"
                   in the Nextflow pipeline directory.
  --publish_dir_mode    Outputs mode based on Nextflow "publishDir" directive. Specify "copy"
                        if requires hard copies. (default: symlink)
  --version    Output version

To test the pipeline, run the example below. Note that the database paths should be changed to their respective locations on your server if they are different. (See the parameters above.) If you follow the command above, the databases will be downloaded into a databases folder in the pb-16S-nf folder and you do not need to specify the path. The conda environment will be created by default in the $HOME/nf_conda folder unless changed in the nextflow.config file. Once the conda environment is created, it will be reused by any future run.

# Create sample TSV for testing
echo -e "sample-id\tabsolute-filepath\ntest_data\t$(readlink -f test_data/test_1000_reads.fastq.gz)" > test_data/test_sample.tsv

nextflow run main.nf --input test_data/test_sample.tsv \
    --metadata test_data/test_metadata.tsv -profile conda \
    --outdir results

# To test using Singularity or docker (change singularity to docker)
nextflow run main.nf --input test_data/test_sample.tsv \
    --metadata test_data/test_metadata.tsv -profile singularity \
    --outdir results

To run this pipeline on your data, create the sample TSV and metadata TSV following the test data format (for metadata, if you do not have any grouping, put any words in the "condition" column) and run the workflow similar to the above. Remember to specify the --outdir directory to avoid overwriting existing results.

HPC and job scheduler usage

The pipeline uses "Local" by default to run jobs on HPC. This can be changed in the nextflow.config file under executor to use HPC schedulers such as Slurm, SGE and so on using Nextflow's native support. For example, to use Slurm, open the nextflow.config file and change executor = 'Local' to executor = 'slurm' and specify the partition to be used using queue = PARTITION. See the Nextflow documentation for the available executors and parameters. CPUs for VSEARCH, DADA2 and cutadapt can be specified as command-line parameters. For all the other processes, they use any of the default labels in nextflow.config and can be changed according to your needs.

Note that the nextflow.config file, by default, will generate the workflow DAG and resources reports to help in benchmarking the resources required. See the report_results folder created after the pipeline finishes running for the DAG and resources report.

Speeding up DADA2 denoise

By default, the pipeline pools all samples into one single qza file for DADA2 denoise (using the default pseudo-pooling approach by DADA2). This is designed to maximize the sensitivity to low frequency ASVs. For example, an ASV with just 2 reads in sample 1 may be discarded, but if the same exact ASV is seen in another sample, this gives the algorithm higher confidence that it is real. However, when the samples are highly diverse (such as with environmental samples), this can become very slow.

If a (possibly) minor loss in sensitivity is acceptable, the pipeline allows you to "split" the input samples into different groups that will be denoised separately. This is done using a pool column in the metadata.tsv input. Example:

sample_name     condition       pool
bc1005_bc1056   RepA    RepA
bc1005_bc1057   RepA    RepA
bc1005_bc1062   RepA    RepA
bc1005_bc1075   RepA    RepA
bc1005_bc1100   RepB    RepB
bc1007_bc1075   RepB    RepB
bc1020_bc1059   RepB    RepB
bc1024_bc1111   RepB    RepB

The TSV above will split the 8 samples into two groups (RepA and RepB) and denoise them separately. After denoising, all denoised ASVs and statistics are merged again for downstream filtering and processing. This allows you to maximize sensitivity within a group of samples and speed up the pipeline considerably. On the other hand, if each sample has been sequenced deeply, you can denoise each sample individually by setting a unique group for each sample (e.g. replicating the sample_name column as the pool column) to process the samples quickly.

Run time and compute requirements

We recommend at least 32 CPUs for most sample types. The run time highly depends on the complexity of the samples in addition to the total number of reads. Shown here are examples of run times for data tested with this pipeline using 32 CPUs:

Frequently asked questions (FAQ)

• Can I restart the pipeline?

  Yes! The Nextflow pipeline can be resumed after interruption by adding the -resume option in the nextflow run command when you run the pipeline. Nextflow is intelligent enough to not rerun any steps if it does not need to. For example, if you want to manually provide the rarefaction/sampling depth after the pipeline finishes, rerun by adding -resume --rarefaction_depth 5000 and only the steps that uses sampling/rarefaction depth will be rerun. Of course, any downstream steps will also be rerun.

• Why cutadapt?

  The naive Bayes classified in QIIME 2 requires the ASVs to be in the same sequence orientation. PacBio's CCS reads have random orientations out of the instrument, hence they need to to be oriented first; this can be done using either lima or cutadapt. Technically, lima has this capability too but it requires BAM input. There are many public datasets on SRA in FASTQ format and lima will not orient them. Due to the accuracy of HiFi reads, the performance difference between lima and cutadapt should be minimal in our experience.

  Without the read orientation, you will notice that the taxonomy assignments can produce strange results, such as archea assignment only at the highest taxonomy level.

• Many of my reads are lost in the denoise stage - what's going on?

  This can happen in extremely diverse communities such as soil where the ASVs are of very low abundance. In each sample, the reads supporting the ASV are very low and may not pass the DADA2 threshold to qualify as a cluster. In addition, DADA2 has a strict reads quality filter (maxEE parameter) that will filter away reads with relatively low accuracy. See here and here for discussions on DADA2 algorithm and reads loss.

• I'm getting Conda "Safety" error indicating a corrupted package or that some pipeline steps are not able to find specific command-line tools (e.g. qiime).

  Sometimes the conda cache can become corrupted if you run many workflows in parallel before the environment was created, thus causing conflicts between different workflow competing to create the same environment. We recommend running the test dataset above and then wait for it to finish first so the conda environment is created successfully. Subsequent runs will use the same environment, and will not need to recreate the environment. If the errors still occur, try running conda clean -a and remove the offending conda package cache in the cache directory. For example, if the error happens for QIIME, delete any folder in conda info "package cache" containing QIIME.

  You can try to install the QIIME 2 environment directly to inspect any error messages:

  conda env create -n q2_test -f qiime2-2022.2-py38-linux-conda.yml

• I've received/downloaded 16S FASTQ files that already have the primers trimmed. Can I skip primers removal?

  We recommend using the pipeline to trim the primers as it works well for HiFi sequencing data. However, there are many public dataset that may already have the full length primers trimmed, in which case you can specify --skip_primer_trim to skip primer trimming. If unsure, run with the default pipeline and the cutadapt demultiplexing rate (in the file results/samples_demux_rate.tsv) should be close to zero for all samples if the primers are already trimmed.

• How does the taxonomy classification work?

  The "besttax" assignment uses the assignTaxonomy Naive-Bayes classifier function from DADA2 to carry out taxonomy assignment. It uses 3 databases to classify the ASVs (requiring a minimum bootstrap of 80 using the minBoot parameter) and the priority of assignment is GTDB r207, followed by Silva v138, then lastly RefSeq + RDP. This means, for example, if an ASV is not assigned at Species level using GTDB, it will check if it can be assigned with Silva. This ensures that we assign as many ASVs as possible.

  This process is done first at Species level, then at Genus level. In addition, if any ASV is assigned as "uncultured" or "metagenome" (there are many entries like this in Silva), it will go through the iterative assignment process just like with the unclassified ASVs. Note that while this method will assign a high amount of ASVs, there may be issues such as how the taxonomy is annotated in different databases.

  There is also a VSEARCH taxonomy classification using the GTDB database (r207) only in the file called results/vsearch_merged_freq_tax.tsv that may provide a more consistent annotation. This uses the classify-consensus-vsearch plugin from QIIME 2 and we use the "top-hits" approach with a stringent default hit criteria (97% identity) to classify the taxonomy of ASVs.

  The final report will contain statistics from either types of assignment. If you notice a large discrepancy, it can be because one method fails to assign a large amount of ASVs from the same genus/species. This is likely a database-related bias.

• Some species in the MSA 1003 demo data are missing!

  If you run this pipeline by default with PacBio's publicly available 192-plex replicates ATCC-MSA1003, some 0.02% bacteria may be missing depending on which replicates you use due to the default min_asv_sample and min_asv_totalfreq parameters. These bacteria may only have a few reads in 1/2 samples, so they are prone to getting filtered out. You can set the two parameters to 0 to disable filtering and the bacteria should pop out. However, in a real dataset this may result in more false-positives.

• The percentage reads classified at species is higher than genus!

  You have likely bumped into strange issues with the database. For example, there are some microbes that have the taxonomy populated at species level, but all the other levels are empty. Unfortunately, database curation is out of the scope of this pipeline.

• Can I manually download the databases for taxonomic classification?

  The pipeline taxonomy classification step requires a few databases that will be downloaded with the --download_db parameters into a "databases" folder. All the databases are also collected on Zenodo. These databases can also be downloaded manually from the following links if the download script above does not work. The GTDB database for VSEARCH will require some processing using the QIIME 2 package. See scripts/download_db.sh for details.

  The links for VSEARCH here are for SILVA 138 databases provided by QIIME 2 and do not require further processing. You can also use these if you do not want to use GTDB; this is the default if you run the --download_db command above.

  • --vsearch_db and --vsearch_tax provided by the QIIME 2 community
  • silva-138-99-seqs.qza
  • silva-138-99-tax.qza
  • --silva_db provided by DADA2
  • silva_nr99_v138.1_wSpecies_train_set.fa.gz
  • --gtdb_db provided by DADA2
  • GTDB_bac120_arc122_ssu_r202_fullTaxo.fa.gz
  • --refseq_db provided by DADA2
  • RefSeq_16S_6-11-20_RDPv16_fullTaxo.fa.gz

• I want to understand more about OTU versus ASV.

  Zymo Research provides a good article on the difference between ASV and OTU here: (https://www.zymoresearch.com/blogs/blog/microbiome-informatics-otu-vs-asv). In addition, this thread on the QIIME 2 forum discusses the difference in numbers through traditional OTU compared to the ASV approach.

• Can I classify with X database?

  With the current implementation, it is straightforward to import any database to use with VSEARCH. You will need the X.fasta sequences and the corresponding taxonomy for each sequence in the FASTA file. The taxonomy format should be in a 2-columns TSV file. Example:

  seq1  d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Enterobacter;s__Enterobacter asburiae_B
  seq2  d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Enterobacter;s__Enterobacter asburiae_B

  Then, import both the sequences and taxonomy into the QZA format for QIIME 2:

  qiime tools import --type 'FeatureData[Sequence]' --input-path 'X.fasta' --output-path X.qza
  qiime tools import --type 'FeatureData[Taxonomy]' --input-format HeaderlessTSVTaxonomyFormat --input-path X.taxonomy.tsv --output-path X.taxonomy.qza

  Then supply X.qza to --vsearch_db, X.taxonomy.qza to --vsearch_tax. It is not straightforward to use it with the Naive-Bayes approach, yet, so please also set --skip_nb to use only VSEARCH for classification.

• The pipeline failed in report generation with error code 137 (e.g. issue #21).

  If you have many samples with diverse sample types such as environmental samples, it is possible that DADA2 will generate a very large number of ASVs. The script to produce the report may subsequently fail due to running out of memory trying to process all the ASVs. You may want to consider splitting the different sample types into individual Nextflow runs to avoid this issue. Alternatively, if you have a cluster with a lot of memory, you can assign higher memory to the step that fails using nextflow.config.

• Can I get the output in hard copy instead of symlinks? (Issue #22)

  By default, Nextflow provides output in absolute symlinks (linked to files in the work folder) to avoid duplicating files. This is controlled by the publishDir directive (see here: https://www.nextflow.io/docs/latest/process.html#publishdir) in each process. The pipeline implements a global --publish_dir_mode that allows user to specify a global publishDir mode. For example, nextflow run main.nf --publish_dir_mode copy will provide all outputs in hard copy. Note that the files will still exist as duplicates in the work folder. You may delete the work folder when the pipeline finishes successfully.

References

QIIME 2

• Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 37, 852–857 (2019).
• For individual citations of plugins, you can use the --citations command for the relevant plugins. For example, if you want to cite VSEARCH plugin, type qiime feature-classifier classify-consensus-vsearch --citations after activating the conda environment. You can activate the environment installed by the pipelines by typing conda activate $HOME/nf_conda/$ENV (Change $ENBV to the name of the environment you want to activate).

  DADA2

• Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods 13, 581–583 (2016).

  Seqkit

• Shen, W., Le, S., Li, Y. & Hu, F. SeqKit: A Cross-Platform and Ultrafast Toolkit for FASTA/Q File Manipulation. PLOS ONE 11, e0163962 (2016).

  Cutadapt

• Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12 (2011).

  GTDB database

• Parks, D. H. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol 36, 996–1004 (2018).
• Parks, D. H. et al. A complete domain-to-species taxonomy for Bacteria and Archaea. Nat Biotechnol 38, 1079–1086 (2020).

  SILVA database

• Yilmaz, P. et al. The SILVA and “All-species Living Tree Project (LTP)” taxonomic frameworks. Nucleic Acids Research 42, D643–D648 (2014).
• Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Research 41, D590–D596 (2013).

  RDP database

• Cole, J. R. et al. Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res 42, D633–D642 (2014).

  RefSeq database

• O’Leary, N. A. et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res 44, D733-745 (2016).

  Krona plot

• Bd, O., Nh, B. & Am, P. Interactive metagenomic visualization in a Web browser. BMC bioinformatics 12, (2011).
• We use the QIIME 2 plugin implementation here: https://github.com/kaanb93/q2-krona

  Phyloseq and Tidyverse (for HTML report visualization)

• McMurdie, P. J. & Holmes, S. phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLOS ONE 8, e61217 (2013).
• Wickham, H. et al. Welcome to the Tidyverse. Journal of Open Source Software 4, 1686 (2019).

  PICRUSt2

• Douglas, G. M. et al. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol 38, 685–688 (2020).

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