[TOC]
--unknown_estimation--index latestMetaPhlAn2 is a computational tool for profiling the composition of microbial communities (Bacteria, Archaea and Eukaryotes) from metagenomic shotgun sequencing data (i.e. not 16S) with species-level. With the newly added StrainPhlAn module, it is now possible to perform accurate strain-level microbial profiling.
MetaPhlAn2 relies on ~1.5M unique clade-specific marker genes (the latest marker information file mpa_v29_CHOCOPhlAn_201901_marker_info.txt.bz2 can be found in the Download page here) identified from ~100,000 reference genomes (~99,500 bacterial and archaeal and ~500 eukaryotic), allowing:
If you use MetaPhlAn version 1, please cite:
Metagenomic microbial community profiling using unique clade-specific marker genes. Nicola Segata, Levi Waldron, Annalisa Ballarini, Vagheesh Narasimhan, Olivier Jousson, & Curtis Huttenhower. Nature Methods 9, 811-814 (2012)
If you use MetaPhlAn2, please cite:
MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Duy Tin Truong, Eric A Franzosa, Timothy L Tickle, Matthias Scholz, George Weingart, Edoardo Pasolli, Adrian Tett, Curtis Huttenhower & Nicola Segata. Nature Methods 12, 902-903 (2015)
If you use StrainPhlAn, please cite the MetaPhlAn2 paper and the following StrainPhlAn paper:
Microbial strain-level population structure and genetic diversity from metagenomes. Duy Tin Truong, Adrian Tett, Edoardo Pasolli, Curtis Huttenhower, & Nicola Segata. Genome Research 27:626-638 (2017)
In addition to the information on this page, you can refer to the following additional resources.
The MetaPhlAn2 and StrainPhlAn Google Group (metaphlan-users@googlegroups.com)
Related tools including PanPhlAn (and its tutorial), GraPhlAn (and it tutorial), PhyloPhlAn2 (and its tutorial), HUMAnN2 (and its tutorial).
The related bioBakery workflows
MetaPhlAn2 requires python 2.7 or newer with argparse, tempfile, numpy, and Biopython libraries installed (apart for numpy and Biopython, the others are usually installed together with the python distribution).
If you provide the SAM output of BowTie2 as input, there are no additional prerequisite.
If you would like to use the BowTie2 integrated in MetaPhlAn2, you need to have BowTie2 version 2.0.0 or higher and perl installed (bowtie2 needs to be in the system path with execute and read permission)
If you want to produce the output as “biom” file you also need biom installed
MetaPhlAn2 is integrated with advanced heatmap plotting with hclust2 and cladogram visualization with GraPhlAn. If you use such visualization tool please refer to their prerequisites.
The best way to install MetaPhlAn2 2.9.20 is through conda:
#!bash
$ conda install -c bioconda metaphlan2It is recommended to create an isolated conda environment and install MetaPhlAn2 into it.
#!bash
$ conda create --name mpa2 -c bioconda metaphlan2This allow to have the correct version of all the dependencies isolated from the system’s python installation.
Before using MetaPhlAn2, you should activate the mpa environment:
#!bash
$ conda activate mpa2You can also install and run MetaPhlAn2 through Docker
#!bash
$ docker pull quay.io/biocontainers/metaphlan2:2.9.20Alternatively, you can manually download from Bitbucket or clone the repository using the following command $ hg clone https://bitbucket.org/biobakery/metaphlan2.
If you choose this way, you’ll need to install manually all the dependencies!
MetaPhlAn2 needs the clade markers and the database to be downloaded locally. To obtain them:
#!bash
$ metaphlan2.py --install By default, the latest MetaPhlAn2 database is downloaded and built. You can download a specific version with the --index parameter
#!bash
$ metaphlan2.py --install --index mpa_v29_CHOCOPhlAn_201901This section presents some basic usages of MetaPhlAn2, for more advanced usages, please see at its wiki.
We assume here that metaphlan2 is in the system path.
Here is the basic example to profile a metagenome from raw reads (requires BowTie2 in the system path with execution and read permissions, Perl installed).
#!bash
$ metaphlan2.py metagenome.fastq --input_type fastq -o profiled_metagenome.txtIt is highly recommended to save the intermediate BowTie2 output for re-running MetaPhlAn2 extremely quickly (--bowtie2out), and use multiple CPUs (--nproc) if available:
#!bash
$ metaphlan2.py metagenome.fastq --bowtie2out metagenome.bowtie2.bz2 --nproc 5 --input_type fastq -o profiled_metagenome.txtIf you already mapped your metagenome against the marker DB (using a previous MetaPhlAn2 run), you can obtain the results in few seconds by using the previously saved --bowtie2out file and specifying the input (--input_type bowtie2out):
#!bash
$ metaphlan2.py metagenome.bowtie2.bz2 --nproc 5 --input_type bowtie2out -o profiled_metagenome.txtbowtie2out files generated with MetaPhlAn2 versions below 2.9 are not compatibile. Starting from MetaPhlAn2 2.9, the BowTie2 ouput now includes the size of the profiled metagenome. If you want to re-run MetaPhlAn2 using these file you should provide the metagenome size via --nreads
#!bash
$ metaphlan2.py metagenome.bowtie2.bz2 --nproc 5 --input_type bowtie2out --nreads 520000 -o profiled_metagenome.txtYou can also provide an externally BowTie2-mapped SAM if you specify this format with --input_type. Two steps here: first map your metagenome with BowTie2 and then feed MetaPhlAn2 with the obtained sam:
#!bash
$ bowtie2 --sam-no-hd --sam-no-sq --no-unal --very-sensitive -S metagenome.sam -x metaphlan_databases/mpa_v29_CHOCOPhlAn_201901 -U metagenome.fastq
$ metaphlan2.py metagenome.sam --input_type sam -o profiled_metagenome.txtMetaPhlAn 2 can also natively handle paired-end metagenomes (but does not use the paired-end information), and, more generally, metagenomes stored in multiple files (but you need to specify the –bowtie2out parameter):
#!bash
$ metaphlan2.py metagenome_1.fastq,metagenome_2.fastq --bowtie2out metagenome.bowtie2.bz2 --nproc 5 --input_type fastq -o profiled_metagenome.txtYou can provide the specific database version with --index.
By default MetaPhlAn2 is run with --index latest: the latest version of the database is used; if it is not available, MetaPhlAn2 will try to download from BitBucket.
For advanced options and other analysis types (such as strain tracking) please refer to the full command-line options.
usage: metaphlan2.py --input_type
{fastq,fasta,multifasta,multifastq,bowtie2out,sam}
[--mpa_pkl MPA_PKL] [--force]
[--bowtie2db METAPHLAN_BOWTIE2_DB] [-x INDEX]
[--bt2_ps BowTie2 presets] [--bowtie2_exe BOWTIE2_EXE]
[--bowtie2_build BOWTIE2_BUILD] [--bowtie2out FILE_NAME]
[--no_map] [--tmp_dir] [--tax_lev TAXONOMIC_LEVEL]
[--min_cu_len] [--min_alignment_len]
[--ignore_eukaryotes] [--ignore_bacteria]
[--ignore_archaea] [--stat_q] [--perc_nonzero]
[--ignore_markers IGNORE_MARKERS] [--avoid_disqm]
[--stat] [-t ANALYSIS TYPE] [--nreads NUMBER_OF_READS]
[--pres_th PRESENCE_THRESHOLD] [--clade] [--min_ab]
[-o output file] [--sample_id_key name]
[--sample_id value] [-s sam_output_file]
[--legacy-output] [--CAMI_format_output]
[--unknown_estimation] [--biom biom_output]
[--mdelim mdelim] [--nproc N] [--install]
[--force_download] [--read_min_len READ_MIN_LEN] [-v]
[-h]
[INPUT_FILE] [OUTPUT_FILE]
DESCRIPTION
MetaPhlAn version 2.9.20 (14 Aug 2019):
METAgenomic PHyLogenetic ANalysis for metagenomic taxonomic profiling.
AUTHORS: Nicola Segata (nicola.segata@unitn.it), Duy Tin Truong, Francesco Asnicar (f.asnicar@unitn.it), Francesco Beghini (francesco.beghini@unitn.it)
COMMON COMMANDS
We assume here that metaphlan2.py is in the system path and that mpa_dir bash variable contains the
main MetaPhlAn folder. Also BowTie2 should be in the system path with execution and read
permissions, and Perl should be installed)
========== MetaPhlAn 2 clade-abundance estimation =================
The basic usage of MetaPhlAn 2 consists in the identification of the clades (from phyla to species and
strains in particular cases) present in the metagenome obtained from a microbiome sample and their
relative abundance. This correspond to the default analysis type (-t rel_ab).
* Profiling a metagenome from raw reads:
$ metaphlan2.py metagenome.fastq --input_type fastq -o profiled_metagenome.txt
* You can take advantage of multiple CPUs and save the intermediate BowTie2 output for re-running
MetaPhlAn extremely quickly:
$ metaphlan2.py metagenome.fastq --bowtie2out metagenome.bowtie2.bz2 --nproc 5 --input_type fastq -o profiled_metagenome.txt
* If you already mapped your metagenome against the marker DB (using a previous MetaPhlAn run), you
can obtain the results in few seconds by using the previously saved --bowtie2out file and
specifying the input (--input_type bowtie2out):
$ metaphlan2.py metagenome.bowtie2.bz2 --nproc 5 --input_type bowtie2out -o profiled_metagenome.txt
* bowtie2out files generated with MetaPhlAn2 versions below 2.9 are not compatibile.
Starting from MetaPhlAn2 2.9, the BowTie2 ouput now includes the size of the profiled metagenome.
If you want to re-run MetaPhlAn2 using these file you should provide the metagenome size via --nreads:
$ metaphlan2.py metagenome.bowtie2.bz2 --nproc 5 --input_type bowtie2out --nreads 520000 -o profiled_metagenome.txt
* You can also provide an externally BowTie2-mapped SAM if you specify this format with
--input_type. Two steps: first apply BowTie2 and then feed MetaPhlAn2 with the obtained sam:
$ bowtie2 --sam-no-hd --sam-no-sq --no-unal --very-sensitive -S metagenome.sam -x ${mpa_dir}/metaphlan_databases/mpa_v25_CHOCOPhlAn_201901 -U metagenome.fastq
$ metaphlan2.py metagenome.sam --input_type sam -o profiled_metagenome.txt
* We can also natively handle paired-end metagenomes, and, more generally, metagenomes stored in
multiple files (but you need to specify the --bowtie2out parameter):
$ metaphlan2.py metagenome_1.fastq,metagenome_2.fastq --bowtie2out metagenome.bowtie2.bz2 --nproc 5 --input_type fastq
-------------------------------------------------------------------
========== Marker level analysis ============================
MetaPhlAn 2 introduces the capability of charachterizing organisms at the strain level using non
aggregated marker information. Such capability comes with several slightly different flavours and
are a way to perform strain tracking and comparison across multiple samples.
Usually, MetaPhlAn 2 is first ran with the default -t to profile the species present in
the community, and then a strain-level profiling can be performed to zoom-in into specific species
of interest. This operation can be performed quickly as it exploits the --bowtie2out intermediate
file saved during the execution of the default analysis type.
* The following command will output the abundance of each marker with a RPK (reads per kilo-base)
higher 0.0. (we are assuming that metagenome_outfmt.bz2 has been generated before as
shown above).
$ metaphlan2.py -t marker_ab_table metagenome_outfmt.bz2 --input_type bowtie2out -o marker_abundance_table.txt
The obtained RPK can be optionally normalized by the total number of reads in the metagenome
to guarantee fair comparisons of abundances across samples. The number of reads in the metagenome
needs to be passed with the '--nreads' argument
* The list of markers present in the sample can be obtained with '-t marker_pres_table'
$ metaphlan2.py -t marker_pres_table metagenome_outfmt.bz2 --input_type bowtie2out -o marker_abundance_table.txt
The --pres_th argument (default 1.0) set the minimum RPK value to consider a marker present
* The list '-t clade_profiles' analysis type reports the same information of '-t marker_ab_table'
but the markers are reported on a clade-by-clade basis.
$ metaphlan2.py -t clade_profiles metagenome_outfmt.bz2 --input_type bowtie2out -o marker_abundance_table.txt
* Finally, to obtain all markers present for a specific clade and all its subclades, the
'-t clade_specific_strain_tracker' should be used. For example, the following command
is reporting the presence/absence of the markers for the B. fragulis species and its strains
the optional argument --min_ab specifies the minimum clade abundance for reporting the markers
$ metaphlan2.py -t clade_specific_strain_tracker --clade s__Bacteroides_fragilis metagenome_outfmt.bz2 --input_type bowtie2out -o marker_abundance_table.txt
-------------------------------------------------------------------
positional arguments:
INPUT_FILE the input file can be:
* a fastq file containing metagenomic reads
OR
* a BowTie2 produced SAM file.
OR
* an intermediary mapping file of the metagenome generated by a previous MetaPhlAn run
If the input file is missing, the script assumes that the input is provided using the standard
input, or named pipes.
IMPORTANT: the type of input needs to be specified with --input_type
OUTPUT_FILE the tab-separated output file of the predicted taxon relative abundances
[stdout if not present]
Required arguments:
--input_type {fastq,fasta,multifasta,multifastq,bowtie2out,sam}
set whether the input is the multifasta file of metagenomic reads or
the SAM file of the mapping of the reads against the MetaPhlAn db.
[default 'automatic', i.e. the script will try to guess the input format]
Mapping arguments:
--mpa_pkl MPA_PKL The metadata pickled MetaPhlAn file [deprecated]
--force Force profiling of the input file by removing the bowtie2out file
--bowtie2db METAPHLAN_BOWTIE2_DB
Folder containing the MetaPhlAn database. Used if --input_type is fastq, fasta, multifasta, or multifastq [default /shares/CIBIO-Storage/CM/scratch/users/francesco.beghini/hg/metaphlan2_dev/metaphlan_databases]
-x INDEX, --index INDEX
Specify the id of the database version to use. If "latest", MetaPhlAn2 will get the latest version. If the database
files are not found on the local MetaPhlAn2 installation they
will be automatically downloaded [default latest]
--bt2_ps BowTie2 presets
Presets options for BowTie2 (applied only when a multifasta file is provided)
The choices enabled in MetaPhlAn are:
* sensitive
* very-sensitive
* sensitive-local
* very-sensitive-local
[default very-sensitive]
--bowtie2_exe BOWTIE2_EXE
Full path and name of the BowTie2 executable. This option allowsMetaPhlAn to reach the executable even when it is not in the system PATH or the system PATH is unreachable
--bowtie2_build BOWTIE2_BUILD
Full path to the bowtie2-build command to use, deafult assumes that 'bowtie2-build is present in the system path
--bowtie2out FILE_NAME
The file for saving the output of BowTie2
--no_map Avoid storing the --bowtie2out map file
--tmp_dir The folder used to store temporary files [default is the OS dependent tmp dir]
Post-mapping arguments:
--tax_lev TAXONOMIC_LEVEL
The taxonomic level for the relative abundance output:
'a' : all taxonomic levels
'k' : kingdoms
'p' : phyla only
'c' : classes only
'o' : orders only
'f' : families only
'g' : genera only
's' : species only
[default 'a']
--min_cu_len minimum total nucleotide length for the markers in a clade for
estimating the abundance without considering sub-clade abundances
[default 2000]
--min_alignment_len The sam records for aligned reads with the longest subalignment
length smaller than this threshold will be discarded.
[default None]
--ignore_eukaryotes Do not profile eukaryotic organisms
--ignore_bacteria Do not profile bacterial organisms
--ignore_archaea Do not profile archeal organisms
--stat_q Quantile value for the robust average
[default 0.1]
--perc_nonzero Percentage of markers with a non zero relative abundance for misidentify a species
[default 0.33]
--ignore_markers IGNORE_MARKERS
File containing a list of markers to ignore.
--avoid_disqm Deactivate the procedure of disambiguating the quasi-markers based on the
marker abundance pattern found in the sample. It is generally recommended
to keep the disambiguation procedure in order to minimize false positives
--stat EXPERIMENTAL! Statistical approach for converting marker abundances into clade abundances
'avg_g' : clade global (i.e. normalizing all markers together) average
'avg_l' : average of length-normalized marker counts
'tavg_g' : truncated clade global average at --stat_q quantile
'tavg_l' : trunated average of length-normalized marker counts (at --stat_q)
'wavg_g' : winsorized clade global average (at --stat_q)
'wavg_l' : winsorized average of length-normalized marker counts (at --stat_q)
'med' : median of length-normalized marker counts
[default tavg_g]
Additional analysis types and arguments:
-t ANALYSIS TYPE Type of analysis to perform:
* rel_ab: profiling a metagenomes in terms of relative abundances
* rel_ab_w_read_stats: profiling a metagenomes in terms of relative abundances and estimate the number of reads coming from each clade.
* reads_map: mapping from reads to clades (only reads hitting a marker)
* clade_profiles: normalized marker counts for clades with at least a non-null marker
* marker_ab_table: normalized marker counts (only when > 0.0 and normalized by metagenome size if --nreads is specified)
* marker_counts: non-normalized marker counts [use with extreme caution]
* marker_pres_table: list of markers present in the sample (threshold at 1.0 if not differently specified with --pres_th
[default 'rel_ab']
--nreads NUMBER_OF_READS
The total number of reads in the original metagenome. It is used only when
-t marker_table is specified for normalizing the length-normalized counts
with the metagenome size as well. No normalization applied if --nreads is not
specified
--pres_th PRESENCE_THRESHOLD
Threshold for calling a marker present by the -t marker_pres_table option
--clade The clade for clade_specific_strain_tracker analysis
--min_ab The minimum percentage abundace for the clade in the clade_specific_strain_tracker analysis
Output arguments:
-o output file, --output_file output file
The output file (if not specified as positional argument)
--sample_id_key name Specify the sample ID key for this analysis. Defaults to '#SampleID'.
--sample_id value Specify the sample ID for this analysis. Defaults to 'Metaphlan2_Analysis'.
-s sam_output_file, --samout sam_output_file
The sam output file
--legacy-output Old two columns output
--CAMI_format_output Report the profiling using the CAMI output format
--unknown_estimation Ignore estimation of reads mapping to unkwnown clades
--biom biom_output, --biom_output_file biom_output
If requesting biom file output: The name of the output file in biom format
--mdelim mdelim, --metadata_delimiter_char mdelim
Delimiter for bug metadata: - defaults to pipe. e.g. the pipe in k__Bacteria|p__Proteobacteria
Other arguments:
--nproc N The number of CPUs to use for parallelizing the mapping [default 4]
--install Only checks if the MetaPhlAn2 DB is installed and installs it if not. All other parameters are ignored.
--force_download Force the re-download of the latest MetaPhlAn2 database.
--read_min_len READ_MIN_LEN
Specify the minimum length of the reads to be considered when parsing the input file with 'read_fastx.py' script, default value is 70
-v, --version Prints the current MetaPhlAn version and exit
-h, --help show this help message and exitMetaPhlAn’s repository features a few utility scripts to aid in manipulation of sample output and its visualization. These scripts can be found under the utils folder in the metaphlan2 directory.
The script merge_metaphlan_tables.py allows to combine MetaPhlAn2 output from several samples to be merged into one table Bugs (rows) vs Samples (columns) with the table enlisting the relative normalized abundances per sample per bug.
To merge multiple output files, run the script as below
#!bash
$ python3 utils/merge_metaphlan_tables.py metaphlan_output1.txt metaphlan_output2.txt metaphlan_output3.txt output/merged_abundance_table.txtWildcards can be used as needed:
#!bash
$ python3 utils/merge_metaphlan_tables.py metaphlan_output*.txt output/merged_abundance_table.txtOutput files can be merged only if the profiling was performed with the same version of the MetaPhlAn2 database.
There is no limit to how many files you can merge.
The hclust2 script generates a hierarchically-clustered heatmap from MetaPhlAn2 abundance profiles. To generate the heatmap for a merged MetaPhlAn2 output table (as described above), you need to run the script as below:
#!bash
hclust2.py \
-i HMP.species.txt \
-o HMP.sqrt_scale.png \
--skip_rows 1 \
--ftop 50 \
--f_dist_f correlation \
--s_dist_f braycurtis \
--cell_aspect_ratio 9 \
-s --fperc 99 \
--flabel_size 4 \
--metadata_rows 2,3,4 \
--legend_file HMP.sqrt_scale.legend.png \
--max_flabel_len 100 \
--metadata_height 0.075 \
--minv 0.01 \
--no_slabels \
--dpi 300 \
--slinkage completeThe tutorial of using GraPhlAn can be found from the MetaPhlAn2 wiki.
In order to add a marker to the database, the user needs the following steps:
#!bash
bowtie2-inspect metaphlan_databases/mpa_v29_CHOCOPhlAn_201901 > metaphlan_databases/mpa_v29_CHOCOPhlAn_201901_markers.fasta#!bash
cat new_marker.fasta >> metaphlan_databases/mpa_v29_CHOCOPhlAn_201901_markers.fasta#!bash
bowtie2-build metaphlan_databases/mpa_v29_CHOCOPhlAn_201901_markers.fasta metaphlan_databases/mpa_v25_CHOCOPhlAn_NEW#!/usr/bin/python3
import pickle
import bz2
db = pickle.load(bz2.open('metaphlan_databases/mpa_v29_CHOCOPhlAn_201901.pkl', 'r'))
# Add the taxonomy of the new genomes
db['taxonomy']['taxonomy of genome1'] = ('NCBI taxonomy id of genome1', length of genome1)
db['taxonomy']['taxonomy of genome2'] = ('NCBI taxonomy id of genome1', length of genome2)
# Add the information of the new marker as the other markers
db['markers'][new_marker_name] = {
'clade': the clade that the marker belongs to,
'ext': {the GCA of the first external genome where the marker appears,
the GCA of the second external genome where the marker appears,
},
'len': length of the marker,
'taxon': the taxon of the marker
}
# To see an example, try to print the first marker information:
# print db['markers'].items()[0]
# Save the new mpa_pkl file
with bz2.BZ2File('metaphlan_databases/mpa_v25_CHOCOPhlAn_NEW.pkl', 'w') as ofile:
pickle.dump(db, ofile, pickle.HIGHEST_PROTOCOL)
StrainPhlAn is a computational tool for tracking individual strains across large set of samples. The input of StrainPhlAn is a set of metagenomic samples and for each species, the output is a multiple sequence alignment (MSA) file of all species strains reconstructed directly from the samples. From this MSA, StrainPhlAn calls RAxML (or other phylogenetic tree builders) to build the phylogenetic tree showing the strain evolution of the sample strains. For each sample, StrainPhlAn extracts the strain of a specific species by merging and concatenating all reads mapped against that species markers in the MetaPhlAn2 database.
In detail, let us start from a toy example with 6 HMP gut metagenomic samples (SRS055982-subjectID_638754422, SRS022137-subjectID_638754422, SRS019161-subjectID_763496533, SRS013951-subjectID_763496533, SRS014613-subjectID_763840445, SRS064276-subjectID_763840445) from 3 three subjects (each was sampled at two time points) and one Bacteroides caccae genome G000273725. We would like to:
Running StrainPhlAn on these samples, we will obtain the Bacteroides caccae phylogentic tree and its multiple sequence alignment in the following figure (produced with ete2 and Jalview):
We can see that the strains from the same subject are grouped together. The tree also highlights that the strains from subject “763840445” (red color) do not change between two sampling time points whereas the strains from the other subjects have slightly evolved. From the tree, we also know that the strains of subject “763496533” is closer to the reference genome than those of the others. In addition, the table below shows the number of snps between the sample strains and the reference genome based on the strain alignment returned by MetaPhlAn_Strainer.
In the next sections, we will illustrate step by step how to run MetaPhlAn_Strainer on this toy example to reproduce the above figures.
StrainPhlAn requires python 2.7 or later and the libraries pysam, biopython, msgpack, pandas, numpy and scipy, dendropy. Besides, StrainPhlAn also needs the following programs in the executable path:
If MetaPhlAn2 was installed through conda and you have the mpa activated, all the pre-requisites are satisfied.
Otherwise, all dependence binaries on Linux 64 bit can be downloaded in the folder “bin” from this link.
The script files in folder “strainphlan_src” should be changed to executable mode by:
chmod +x strainphlan_src/*.pyand add to the executable path:
export PATH=$PATH:$(pwd -P)/strainphlan_srcLet’s reproduce the toy example result in the introduction section. Note that all the commands to run the below steps are in the "strainphlan_tutorial/step?*.sh" files (? corresponding to the step number). All the below steps are excuted under the “strainphlan_tutorial” folder. The steps are as follows:
Step 1. Download 6 HMP gut metagenomic samples, the metadata.txt file and one reference genome from the folder “fastqs” and “reference_genomes” in this link and put these folders under the “strainer_tutorial” folder.
Step 2. Obtain the sam files from these samples by mapping them against MetaPhlAn2 database:
This step will run MetaPhlAn2 to map all metagenomic samples against the MetaPhlAn2 marker database and produce the sam files (*.sam.bz2). Each sam file (in SAM format) corresponding to each sample contains the reads mapped against the marker database of MetaPhlAn2. The commands to run are:
mkdir -p sams
for f in $(ls fastqs/*.bz2)
do
echo "Running metaphlan2 on ${f}"
bn=$(basename ${f} | cut -d '.' -f 1)
../metaphlan2.py --index mpa_v29_CHOCOPhlAn_201901 --input_type multifastq --nproc 10s -s sams/${bn}.sam.bz2 --bowtie2out sams/${bn}.bowtie2_out.bz2 -o ssams/${bn}.profile ${f}
doneAfter this step, you will have a folder “sams” containing the sam files (*.sam.bz2) and other MetaPhlAn2 output files. This step will take around 270 minutes. If you want to skip this step, you can download the sam files from the folder “sams” in this link.
Step 3. Produce the consensus-marker files which are the input for StrainPhlAn:
For each sample, this step will reconstruct all species strains found in it and store them in a marker file (*.markers). Those strains are referred as sample-reconstructed strains. Additional details in generating consensus sequences can be found here. The commands to run are:
mkdir -p consensus_markers
cwd=$(pwd -P)
export PATH=${cwd}/../strainphlan_src:${PATH}
python3 ../strainphlan_src/sample2markers.py --ifn_samples sams/*.sam.bz2 --input_type sam --output_dir consensus_markers --nprocs 10 &> consensus_markers/log.txtThe result is the same if you want run several sample2markers.py scripts in parallel with each run for a sample (this maybe useful for some cluster-system settings). After this step, you will have a folder “consensus_markers” containing all sample-marker files (*.markers). This steps will take around 44 minutes. If you want to skip this step, you can download the consensus marker files from the folder “consensus_markers” in this link.
Step 4. Extract the markers of Bacteroides_caccae from MetaPhlAn2 database (to add its reference genome later):
This step will extract the markers of Bacteroides_caccae in the database and then StrainPhlAn will identify the sequences in the reference genomes that are closet to them (in the next step by using blast). Those will be concatenated and referred as reference-genome-reconstructed strains. The commands to run are:
mkdir -p db_markers
bowtie2-inspect ../metaphlan_databases/mpa_v29_CHOCOPhlAn_201901 > db_markers/all_markers.fasta
python3 ../strainphlan_src/extract_markers.py --mpa_pkl ../metaphlan_databases/mpa_v29_CHOCOPhlAn_201901.pkl --ifn_markers db_markers/all_markers.fasta --clade s__Bacteroides_caccae --ofn_markers db_markers/s__Bacteroides_caccae.markers.fastaNote that the “all_markers.fasta” file consists can be reused for extracting other reference genomes. After this step, you should have two files in folder “db_markers”: “all_markers.fasta” containing all marker sequences, and “s__Bacteroides_caccae.markers.fasta” containing the markers of Bacteroides caccae. This step will take around 1 minute and can skipped if you do not need to add the reference genomes to the phylogenetic tree. Those markers can be found in the folder “db_markers” in this link.
Before building the trees, we should get the list of all clades detected from the samples and save them in the “output/clades.txt” file by the following command:
python3 ../strainphlan.py --mpa_pkl ../metaphlan_databases/mpa_v29_CHOCOPhlAn_201901.pkl --ifn_samples consensus_markers/*.markers --output_dir output --nprocs_main 10 --print_clades_only > output/clades.txtThe clade names in the output file “clades.txt” will be used for the next step.
Step 5. Build the multiple sequence alignment and phylogenetic tree:
This step will align and clean the sample-reconstructed strains (stored in the marker files produced in step 3) and reference-genome-reconstructed strains (extracted based on the database markers in step 4) to produce a multiple sequence alignment (MSA) and store it in the file “clade_name.fasta”. From this MSA file, StrainPhlAn will call RAxML to build the phylogenetic tree. Note that: all marker files (*.markers) must be used together as the input for the strainphlan.py script because StrainPhlAn needs to align all of the strains at once.
The commands to run are:
mkdir -p output
python3 ../strainphlan.py --mpa_pkl ../metaphlan_databases/mpa_v29_CHOCOPhlAn_201901.pkl --ifn_samples consensus_markers/*.markers --ifn_markers db_markers/s__Bacteroides_caccae.markers.fasta --ifn_ref_genomes reference_genomes/G000273725.fna.bz2 --output_dir output --nprocs_main 10 --clades s__Bacteroides_caccae | tee output/log_full.txtThis step will take around 2 minutes. After this step, you will find the tree “output/RAxML_bestTree.s__Bacteroides_caccae.tree”. All the output files can be found in the folder “output” in this link. You can view it by Archaeopteryx or any other viewers.
By default, if you do not specify reference genomes (by –ifn_ref_genomes) and any specific clade (by –clades), strainphlan.py will build the phylogenetic trees for all species that it can detect.
In order to add the metadata, we also provide a script called “add_metadata_tree.py” which can be used as follows:
python3 ../strainphlan_src/add_metadata_tree.py --ifn_trees output/RAxML_bestTree.s__Bacteroides_caccae.tree --ifn_metadatas fastqs/metadata.txt --metadatas subjectIDThe script “add_metadata_tree.py” can accept multiple metadata files (space separated, wild card can also be used) and multiple trees. A metadata file is a tab separated file where the first row is the meta-headers, and the following rows contain the metadata for each sample. Multiple metadata files are used in the case where your samples come from more than one dataset and you do not want to merge the metadata files. For more details of using “add_metadata_tree.py”, please see its help (with option “-h”). An example of a metadata file is the “fastqs/metadata.txt” file with the below content:
sampleID subjectID
SRS055982 638754422
SRS022137 638754422
SRS019161 763496533
SRS013951 763496533
SRS014613 763840445
SRS064276 763840445
G000273725 ReferenceGenomesNote that “sampleID” is a compulsory field.
After adding the metadata, you will obtain the tree files "*.tree.metadata" with metadata and view them by Archaeopteryx as in the previous step.
If you have installed graphlan, you can plot the tree with the command:
python3 ../strainphlan_src/plot_tree_graphlan.py --ifn_tree output/RAxML_bestTree.s__Bacteroides_caccae.tree.metadata --colorized_metadata subjectIDand obtain the following figure (output/RAxML_bestTree.s__Bacteroides_caccae.tree.metadata.png):
Step 6. If you want to remove the samples with high-probability of containing multiple strains, you can rebuild the tree by removing the multiple strains:
python3 ../strainphlan_src/build_tree_single_strain.py --ifn_alignments output/s__Bacteroides_caccae.fasta --nprocs 10 --log_ofn output/build_tree_single_strain.log
python3 ../strainphlan_src/add_metadata_tree.py --ifn_trees output/RAxML_bestTree.s__Bacteroides_caccae.remove_multiple_strains.tree --ifn_metadatas fastqs/metadata.txt --metadatas subjectIDYou will obtain the refined tree “output/RAxML_bestTree.s__Bacteroides_caccae.remove_multiple_strains.tree.metadata”. This tree can be found in the folder “output” in this link.
All option details can be viewed by strainphlan.py help:
python3 ../strainphlan.py -hThe default setting can be stringent for some cases where you have very few samples left in the phylogenetic tree. You can relax some parameters to add more samples back:
In the output folder, you can find the following files: