# metaphlan 3.0.14 # MetaPhlAn: Metagenomic Phylogenetic Analysis [![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/metaphlan/README.html) [![PyPI - Downloads](https://img.shields.io/pypi/dm/metaphlan?label=MetaPhlAn%20on%20PyPi)](https://pypi.org/project/MetaPhlAn/) [![MetaPhlAn on DockerHub](https://img.shields.io/docker/pulls/biobakery/metaphlan?label=MetaPhlAn%20on%20DockerHub)](https://hub.docker.com/r/biobakery/metaphlan) [![Build MetaPhlAn package](https://github.com/biobakery/MetaPhlAn/workflows/Build%20MetaPhlAn%20package/badge.svg?branch=3.0)](https://github.com/biobakery/MetaPhlAn/actions?query=workflow%3A%22Build+MetaPhlAn+package%22) ## What's new in version 3 * New MetaPhlAn marker genes extracted with a newer version of ChocoPhlAn based on UniRef * Estimation of metagenome composed by unknown microbes with parameter `--unknown_estimation` * Automatic retrieval and installation of the latest MetaPhlAn database with parameter `--index latest` * Virus profiling with `--add_viruses` * Calculation of metagenome size for improved estimation of reads mapped to a given clade * Inclusion of NCBI taxonomy ID in the ouput file * CAMI (Taxonomic) Profiling Output Format included * Removal of reads with low MAPQ values ------------- ## Description MetaPhlAn 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. MetaPhlAn relies on ~1.1M unique clade-specific marker genes (the latest marker information file `mpa_v30_CHOCOPhlAn_201901_marker_info.txt.bz2` can be found [here](https://www.dropbox.com/sh/7qze7m7g9fe2xjg/AAAlyQITZuUCtBUJxpxhIroIa/mpa_v30_CHOCOPhlAn_201901_marker_info.txt.bz2?dl=1)) identified from ~100,000 reference genomes (~99,500 bacterial and archaeal and ~500 eukaryotic), allowing: * unambiguous taxonomic assignments; * accurate estimation of organismal relative abundance; * species-level resolution for bacteria, archaea, eukaryotes and viruses; * strain identification and tracking * orders of magnitude speedups compared to existing methods. * metagenomic strain-level population genomics If you use MetaPhlAn, please cite: [**Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3**](https://elifesciences.org/articles/65088) *Francesco Beghini, Lauren J McIver, Aitor Blanco-Míguez, Leonard Dubois, Francesco Asnicar, Sagun Maharjan, Ana Mailyan, Paolo Manghi, Matthias Scholz, Andrew Maltez Thomas, Mireia Valles-Colomer, George Weingart, Yancong Zhang, Moreno Zolfo, Curtis Huttenhower, Eric A Franzosa, Nicola Segata*. eLife (2021) If you use StrainPhlAn, please cite the MetaPhlAn paper and the following StrainPhlAn paper: [**Microbial strain-level population structure and genetic diversity from metagenomes.**](http://genome.cshlp.org/content/27/4/626.full.pdf) *Duy Tin Truong, Adrian Tett, Edoardo Pasolli, Curtis Huttenhower, & Nicola Segata*. Genome Research 27:626-638 (2017) ## Activating the conda environment ```console bash source /local/cluster/metaphlan3/activate.sh ``` *Note:* to use in SGE_Batch, generate a shell script that runs the `source` command as above prior to running your metaphlan3 commands. Location and version: ```console $ which metaphlan /local/cluster/metaphlan3/bin/metaphlan $ metaphlan --version MetaPhlAn version 3.0.14 (19 Jan 2022) ``` Database location: ```console /nfs1/CGRB/databases/metaphlan3 ``` Specify the DB location with the `--bowtie2db` flag. help message: ```console $ metaphlan--help usage: metaphlan --input_type {fastq,fasta,bowtie2out,sam} [--force] [--bowtie2db METAPHLAN_BOWTIE2_DB] [-x INDEX] [--bt2_ps BowTie2 presets] [--bowtie2_exe BOWTIE2_EXE] [--bowtie2_build BOWTIE2_BUILD] [--bowtie2out FILE_NAME] [--min_mapq_val MIN_MAPQ_VAL] [--no_map] [--tmp_dir] [--tax_lev TAXONOMIC_LEVEL] [--min_cu_len] [--min_alignment_len] [--add_viruses] [--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] [--use_group_representative] [--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 3.0.14 (19 Jan 2022): METAgenomic PHyLogenetic ANalysis for metagenomic taxonomic profiling. AUTHORS: Francesco Beghini (francesco.beghini@unitn.it),Nicola Segata (nicola.segata@unitn.it), Duy Tin Truong, Francesco Asnicar (f.asnicar@unitn.it), Aitor Blanco Miguez (aitor.blancomiguez@unitn.it) COMMON COMMANDS We assume here that MetaPhlAn is installed using the several options available (pip, conda, PyPi) Also BowTie2 should be in the system path with execution and read permissions, and Perl should be installed) ========== MetaPhlAn clade-abundance estimation ================= The basic usage of MetaPhlAn consists in the identification of the clades (from phyla to species ) 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: $ metaphlan 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: $ metaphlan metagenome.fastq --bowtie2out metagenome.bowtie2.bz2 --nproc5 --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): $ metaphlan metagenome.bowtie2.bz2 --nproc 5 --input_type bowtie2out -o profiled_metagenome.txt * bowtie2out files generated with MetaPhlAn versions below 3 are not compatibile. Starting from MetaPhlAn 3.0, the BowTie2 ouput now includes the size of the profiled metagenome and the average read length. If you want to re-run MetaPhlAn using these file you should provide the metagenome size via --nreads: $ metaphlan 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 MetaPhlAn with the obtained sam: $ bowtie2 --sam-no-hd --sam-no-sq --no-unal --very-sensitive -S metagenome.sam -x ${mpa_dir}/metaphlan_databases/mpa_v30_CHOCOPhlAn_201901 -U metagenome.fastq $ metaphlan 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): $ metaphlan metagenome_1.fastq,metagenome_2.fastq --bowtie2out metagenome.bowtie2.bz2 --nproc 5 --input_type fastq ------------------------------------------------------------------- ========== Marker level analysis ============================ MetaPhlAn introduces the capability of characterizing 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 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 aRPK (reads per kilo-base) higher 0.0. (we are assuming that metagenome_outfmt.bz2 has been generated before as shown above). $ metaphlan -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' $ metaphlan -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. $ metaphlan -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. fragilis species and its strains the optional argument --min_ab specifies the minimum clade abundance for reporting the markers $ metaphlan -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 metagenomegenerated by a previous MetaPhlAn run If the input file is missing, the script assumesthat 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,bowtie2out,sam} set whether the input is the FASTA file of metagenomic reads or the SAM file of the mapping of the reads againstthe MetaPhlAn db. Mapping arguments: --force Force profiling of the input file by removing the bowtie2out file --bowtie2db METAPHLAN_BOWTIE2_DB Folder containing the MetaPhlAn database. You can specify the location by exporting the DEFAULT_DB_FOLDER variable in theshell.[default /local/cluster/metaphlan3/lib/python3.7/site-packages/metaphlan/metaphlan_databases] -x INDEX, --index INDEX Specify the id of the database version to use. If "latest", MetaPhlAn will get the latest version. If an index name is provided, MetaPhlAn will tryto use it, if available, and skip the online check. If the database files are not found on the localMetaPhlAn installation they will be automatically downloaded [default latest] --bt2_ps BowTie2 presets Presets options for BowTie2 (applied only when aFASTA 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 --min_mapq_val MIN_MAPQ_VAL Minimum mapping quality value (MAPQ) [default 5] --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] --add_viruses Allow the profiling of viral organisms --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 0.2] --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. Itis generally recommended to keep the disambiguation procedure in order tominimize false positives --stat 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' : truncated 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 (onlyreads 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 inthe sample (threshold at 1.0 if not differently specified with --pres_th * clade_specific_strain_tracker: list of markers present for a specific clade, specified with --clade, and all its subclades [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 thelength-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 -tmarker_pres_table option --clade The clade for clade_specific_strain_tracker analysis --min_ab The minimum percentage abundance 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'. --use_group_representative Use a species as representative for species groups. --sample_id value Specify the sample ID for this analysis. Defaults to 'Metaphlan_Analysis'. -s sam_output_file, --samout sam_output_file The sam output file --legacy-output Old MetaPhlAn2 two columns output --CAMI_format_output Report the profiling using the CAMI output format --unknown_estimation Scale relative abundances to the number of readsmapping to known clades in order to estimate unknowness --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 MetaPhlAn DB is installed andinstalls it if not. All other parameters are ignored. --force_download Force the re-download of the latest MetaPhlAn 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, defaultvalue is 70 -v, --version Prints the current MetaPhlAn version and exit -h, --help show this help message and exit ``` software ref: software ref: research ref: