Data generated from microbiome experiments tends to require a distinct analytical approach, one that takes into account the many different organisms which may be present within a single biological sample. The considerations of sample preparation also tend to be fairly specific to the microbiome, e.g. 16S rRNA gene amplicon sequencing tends to be used only for the purpose of performing taxonomic identification for a mixture of bacteria.
The Microbiome Research Initiative was started at Fred Hutch to support and provide a community for scientists researching the microbiome, and part of that effort includes some work to develop a relatively standardized toolkit of bioinformatic analysis tools which can be used by multiple investigators across the center. This page is provided to host a description of analysis tools that are available, and will be updated as more are made available.
The tools below happen to use Nextflow as a system for running reproducible and portable analytical workflows. See this documentation for more details on running Nextflow at Fred Hutch, as well as the docs for more details on Nextflow itself.
For any questions on the tools presented here, either in trying to get them running on your data, or if you would like access to additional functionality (or would like to offer your own utilities), please contact Sam Minot (firstname.lastname@example.org).
Ribosomal 16S Amplicon Analysis
One of the primary tools used by microbiome researchers to detect organisms present in a microbiome sample is 16S amplicon sequencing. This technique takes advantage of a highly conserved gene present in bacterial genomes which can be targeted by PCR with well-designed primers, and then processed with high-thoughput sequencing. The analysis of 16S datasets is a highly-developed analytical process with a long series of steps, and therefore is highly amenable to automation with a formalized workflow.
Dr. Jonathan Golob is a physician-scientist at the University of Michigan, and he
has developed a highly accurate and effective workflow for analyzing 16S datasets.
This workflow uses
dada2 to identify exact sequence varients and ultimately
performs taxonomic identification using a phylogenetic approach which is very much
on the cutting edge of the 16S field (using
pplacer for phylogenetic assignment).
GitHub repository: jgolob/maliampi/
All input files are specified in a single file (
manifest.csv) with columns used to
read__2. You may also include a
column to indicate which samples were processed for sequencing together.
set -e ml nextflow REF_FOLDER="s3://fh-ctr-public-reference-data/tool_specific_data/maliampi/ya16sdb_20190821/dedup/1200bp/named/filtered" nextflow \ run \ jgolob/maliampi \ --manifest manifest.csv \ --repo_fasta $REF_FOLDER/seqs.fasta \ --repo_si $REF_FOLDER/seq_info.csv \ --email <EMAIL> \ --output <OUTPUT_FOLDER> \ -w <WORK_DIR> \ -resume
See the MaLiAmPi documentation for more details on running the tool and interpreting the output.
Microbial Genome Assembly
One common task in microbiology is sequencing the genome of microbial isolates. With the advent of single-molecule long-read PacBio sequencing, it is now possible to routinely generate fully closed genome assemblies. To automate this process, we have implemented the UniCycler assembler in an easy-to-use workflow. This assembler provides the advantage of accommodating both short- and long-reads, performing hybrid assembly when both are provided.
GitHub Repository: FredHutch/unicycler-nf/
Usage: nextflow run fredhutch/unicycler-nf <ARGUMENTS> Arguments: --sample_sheet CSV file listing samples to analyze --output_folder Folder to place outputs Options: --short_reads Sample sheet contains short read data (`short_R1` and `short_R2`) --long_reads Sample sheet contains long read data (`long_reads`) --min_fasta_length Minimum contig length (default: 100) --help Display this message Sample Sheet: The sample_sheet is a CSV with a header indicating which samples correspond to which files. The file must contain the column `name`, and `long_reads`, `short_R1`, `short_R2` as appropriate.
Microbial Pan-Genome Analysis
Microbial researchers often need to compare multiple genomes in order to identify similarities and differences. The best tool available in the community for this analysis is the anvi’o software suite developed by the Meren Lab at the University of Chicago. The anvi’o software does many things, but we wanted to provide an easy point of entry with a workflow that imports a set of bacterial genomes into the anvi’o database format, and then launches a graphical viewer which allows the user to explore their pan-genome collection.
The guidance and instructions for running this tool can be found on the FredHutch/nf-anvio-pangenome GitHub repository
One application of whole-genome shotgun sequencing (WGS) for microbiome research
is the analysis of microbial mixtures on the basis of what microbes are present
(DNA) or are transcriptionally active (RNA). To address this analytical need, we
developed an analysis tool which takes a set of WGS input data and aligns it against
a set of whole microbial genomes. With the orientation towards RNAseq, the tool
takes a parameter
--min_cov_pct which limits the analysis to those organisms
which have greater than the specified level of coverage across the rRNA genes found
in their genomes. Using just those organisms, the pipeline will then measure the
depth of sequencing across all genes for all organisms across all samples, and provide
those results to the user in the form of a set of CSV files.
GitHub repository: FredHutch/microbial-rnaseq
All input files are specified in a single file
batchfile.csv which notes each
sample with the columns
fastq2 for paired-end
nextflow \ run \ fredhutch/microbial-rnaseq \ --batchfile batchfile.csv \ --host_genome "s3://fh-ctr-public-reference-data/tool_specific_data/microbial-rnaseq/2019-06-10/Homo_sapiens_assembly38.fasta.tar" \ --database_folder "s3://fh-ctr-public-reference-data/tool_specific_data/microbial-rnaseq/2019-07-03/" \ --database_prefix 2019-07-03-rnaseq-database \ --min_cov_pct 90 \ --output_folder results/ \ --output_prefix 2019-05-08-test \ -work-dir work/ \ -resume
When studying viruses in the human microbiome, one fruitful approach can simply be to perform whole-genome shotgun sequencing (WGS) and then align all reads against a set of viruses from some reference database. This is an extremely parallelizable process, and therefore benefits heavily from execution systems like Nextflow which can distribute tasks to cloud computing services (like AWS).
This utility takes a set of input files (FASTQ format) and aligns them all against a set
of viral genomes (specified by NCBI
accession in a provided CSV file).
GitHub repository: FredHutch/nf-viral-metagenomics
<INPUT_DIRECTRY> contains the set of FASTQ files to analyze
<OUTPUT_DIRECTRY> is the location where all outputs will be placed
<VIRAL_GENOME_CSV> is a CSV with a column
accession containing the set of viral genomes to align against (as NCBI Nucleotide accessions)
<NAME_OF_OUTPUT_CSV> is the name of the output file to be placed in the
nextflow \ run \ FredHutch/nf-viral-metagenomics \ --input_directory <INPUT_DIRECTORY> \ --output_directory <OUTPUT_DIRECTORY> \ --viral_genome_csv <VIRAL_GENOME_CSV> \ --output_csv <NAME_OF_OUTPUT_CSV>
Updated: October 2, 2019Edit this Page via GitHub Comment by Filing an Issue Have Questions? Ask them here.