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SameStr was developed as a bioinformatic tool for the identification of shared microbial strains in metagenomic shotgun sequencing data. SameStr uses clade-specific markers from MetaPhlan or mOTUs to identify and compare SNV profiles. In this, SameStr is not constrained to comparing the majority variants but considers all possible variants in the alignments.
SameStr's shared strains are specific to related but not unrelated sample pairs and can therefore be used to track strains across biological samples. As demonstrated with strain co-occurrence networks, this enables further applications such as for the quality screening of mislabelled data and possible contamination, or personal identification which raises further questions regarding study participant privacy.
While the program does not reconstruct conspecific marker sequences, SameStr's outputs (numpy format) can be used for strain composition modelling with probabilistic algorithms.
SameStr databases generated from mOTUs and MetaPhlan markers are now available on Zenodo in the Zenodo SameStr Community:
Instead of generating the database files from scratch with samestr db
, you can download the database version that you used for alignment, extract it, and start processing your data with samestr convert
or samestr extract
.
Podlesny, D., Arze, C., Dörner, E., Verma, S., Dutta, S., Walter, J., & Fricke, W. F. (2022). Metagenomic strain detection with SameStr: identification of a persisting core gut microbiota transferable by fecal transplantation. Microbiome, 10(1), 53. https://doi.org/10.1186/s40168-022-01251-w
Podlesny, D., Durdevic, M., Paramsothy, S., Kaakoush, N. O., Högenauer, C., Gorkiewicz, G., Walter, J., & Fricke, W. F. (2022). Identification of clinical and ecological determinants of strain engraftment after fecal microbiota transplantation using metagenomics. Cell Reports Medicine, 3(8), 100711. https://doi.org/10.1016/j.xcrm.2022.100711
Podlesny, D., & Fricke, W. F. (2021). Strain inheritance and neonatal gut microbiota development: A meta-analysis. International journal of medical microbiology : IJMM, 311(3), 151483. https://doi.org/10.1016/j.ijmm.2021.151483
SameStr was inspired by StrainPhlAn in initially using the MetaPhlAn markers to identify and compare species-specific SNV profiles. Difference lies in the handling of SNVs, as StrainPhlAn processes only the majority variant at each position, whereas SameStr considers all possible variants in the alignments.
We wrapped tools or borrowed code from these repositories:
SameStr identifies shared strains between pairs of metagenomic samples based on the similarity of SNV profiles. Here, we present a pipeline to process data starting from raw single or paired-end metagenomic shotgun sequencing files to output tables summarizing shared strain calls for individual clades and overall strain co-occurrence between sample pairs.
SameStr must be used from the command line and encompasses multiple modules which can be called by using the following syntax: samestr <command>
. Help for specific command-line usage is available by using the --help
option with the samestr
command (samestr --help
) or any of its modules (samestr convert --help
).
markers-pkl
sam
to SNV profiles npy
npy
from reference genomes fasta
npy
+ npy
from multiple sourcesnpy
tsv
for SNV profiles npy
npy
to determine their similarity and overlapping coverage tsv
tsv
It is highly recommended to install SameStr in a conda environment, which will install the required dependencies automatically.
SameStr requires python>=3.9 and has been tested with the following software versions:
SameStr is fully compatible with the databases from mOTUs (v2, 2.5, 3, +) and MetaPhlAn (mpa_vJun23_CHOCOPhlAnSGB_202307, mpa_vOct22_CHOCOPhlAnSGB_202212, mpa_vJun23_CHOCOPhlAnSGB_202307, +).
SameStr can be install through conda by using the following command:
conda install -c bioconda -c conda-forge samestr
You can also clone this repository, recreate the environment with conda and install SameStr with pip:
git clone https://github.com/danielpodlesny/samestr.git
cd samestr
conda env create -f environment.yml
conda activate samestr
pip install .
SameStr can be installed from pypi with pip by using the following command:
pip install samestr
For the pip installation to work, make sure to install the following dependencies manually:
blast>=2.6.0
glibc>=2.3
muscle==3.8.1551 and/or mafft==7.515
python>=3.9
samtools==0.1.19
You can refer to the command-line help at any of the following commands:
samestr --help
usage: samestr [-h] [--version] [--citation [STR]] [--verbosity STR] {db,convert,extract,merge,filter,stats,compare,summarize} ...
Welcome to SameStr! SameStr identifies shared strains between pairs of metagenomic samples based on the similarity of their Single Nucleotide Variant (SNV) profiles.
optional arguments:
-h, --help show this help message and exit
--version Show version of the program and exit.
--citation [STR] Print citation and exit. Options: Text, BibTex, Endnote, RIS, DOI. (default: None)
--verbosity STR Set the verbosity of the program. Options: DEBUG, INFO, WARNING, ERROR, CRITICAL. (default: INFO)
commands:
{db,convert,extract,merge,filter,stats,compare,summarize}
Use one of the following commands for different tasks:
db Make database from MetaPhlAn or mOTUs markers.
convert Convert sequence alignments to SNV Profiles.
extract Extract SNV Profiles from Reference Genomes.
merge Merge SNV Profiles from multiple sources.
filter Filter SNV Profiles.
stats Report alignment statistics.
compare Calculate pairwise sequence similarity.
summarize Summarize Taxonomic Co-Occurrence.
The module samestr db
has to be used after installation of SameStr in order to generate database files from MetaPhlAn pickle
file (e.g. mpa_vJun23_CHOCOPhlAnSGB_202307.pkl
) and marker
file (e.g. mpa_vJun23_CHOCOPhlAnSGB_202307.fna.bz2
). Database files are required for further processing and can be generated for individual species or all MetaPhlAn species that are available.
Obtain the latest MetaPhlAn database from Segata Lab's FTP server and extract it. As of writing, this is mpa_vJun23_CHOCOPhlAnSGB_202307.
# download
wget -P db_MetaPhlAn/ http://cmprod1.cibio.unitn.it/biobakery4/metaphlan_databases/mpa_vJun23_CHOCOPhlAnSGB_202307.tar
# untar
tar -xvf db_MetaPhlAn/mpa_vJun23_CHOCOPhlAnSGB_202307.tar
You might want to combine the two separate marker files that have been published with the recent versions of MetaPhlAn to generate a database for all species. This can be done with the following commands:
# concatenate
cat db_MetaPhlAn/mpa_vJun23_CHOCOPhlAnSGB_202307_SGB.fna.bz2 \
db_MetaPhlAn/mpa_vJun23_CHOCOPhlAnSGB_202307_VSG.fna.bz2 > \
db_MetaPhlAn/mpa_vJun23_CHOCOPhlAnSGB_202307.fna.bz2
Note that you don't need to decompress the fna.bz2
file before concatenating or using it with SameStr.
Generate a SameStr database from the source files.
samestr db \
--markers-info db_MetaPhlAn/mpa_vJun23_CHOCOPhlAnSGB_202307.pkl \
--markers-fasta db_MetaPhlAn/mpa_vJun23_CHOCOPhlAnSGB_202307.fna.bz2 \
--db-version db_MetaPhlAn/mpa_latest \
--output-dir samestr_db/
Obtain the latest mOTUs database from the mOTUs Zenodo link and extract it. As of writing, this is mpa_vJun23_CHOCOPhlAnSGB_202307.
# download
wget https://zenodo.org/records/7778108/files/db_mOTU_v3.1.0.tar.gz
# untar
tar -xzvf db_mOTU_v3.1.0.tar.gz
Generate a SameStr database from the source files.
samestr db \
--markers-info db_mOTU/db_mOTU_taxonomy_ref-mOTUs.tsv db_mOTU/db_mOTU_taxonomy_meta-mOTUs.tsv \
--markers-fasta db_mOTU/db_mOTU_DB_CEN.fasta \
--db-version db_mOTU/db_mOTU_versions \
--output-dir samestr_db/
Deprecated. The samestr align
command previously wrapped kneaddata
, fastq-stats
, and MetaPhlAn2
and you could use it to first perform basic quality control measures such as read length trimming and host-genome mapping, gather qc and fastq statistics, and finally map cleaned sequence reads against MetaPhlAn2's species-specific marker sequences.
Please refer to the respective tools to conduct QC preprocessing and alignment. Although it is not required to follow this exact pre-processing scheme for the SameStr analysis, we highly recommend quality processing of your raw sequencing data. For example with Kneaddata:
Please refer to the kneaddata documentation for the latest commands.
kneaddata \
-i ${ID}.R1.fastq.gz \
-i ${ID}.R2.fastq.gz \
-db /path-to-db/Homo_sapiens_Bowtie2_v0.1/Homo_sapiens \
-p 2 \
-t 15 \
--max-memory ${RAM} \
--output-prefix ${ID} \
--remove-intermediate-output \
-o QC/
When using MetaPhlAn as the source database, make sure to align sequences further specifying the -s
or --samout
option to save the alignments in sam
format. MetaPhlAn sam
files are required for downstream processing.
metaphlan QC/${ID}.R1.fastq.gz,QC/${ID}.R2.fastq.gz \
--bowtie2db db_MetaPhlAn/ \
--input_type fastq \
--nproc 30 \
-t rel_ab \
--bowtie2out out_align/${ID}.bowtie2out \
--samout out_align/${ID}.sam.bz2 \
-o out_align/${ID}.profile.txt
When using mOTUs as the source database, make sure to generate a mOTUs profile with its mOTUs profile
command, and further align sequences with mOTUs map_snv
to generate and save alignments in bam
format. mOTUs bam
files are required for downstream processing.
# generate profile
motus profile \
-f QC/${ID}.R1.fastq.gz -r QC/${ID}.R2.fastq.gz \
-g 1 \
-t 30 \
-y insert.raw_counts \
-o out_align/${ID}.profile.txt
# generate alignment
motus map_snv \
-f QC/${ID}.R1.fastq.gz -r QC/${ID}.R2.fastq.gz \
-t 30 \
-o out_align/${ID}.bam
Convert MetaPhlAn or mOTUs marker alignments to nucleotide variant profiles.
The input to samestr convert
are marker alignments with the file extension .sam
, .sam.bz2
, or .bam
. For parallel processing, specify --nprocs
. Note: This step requires a SameStr database regenerated with samestr db.
samestr convert \
--input-files out_align/*sam.bz2 \
--marker-dir samestr_db/ \
--nprocs 30 \
--min-vcov 5 \
--output-dir out_convert/
This step converts the marker alignment to a nucleotide variant profile. The outputs are per-clade numpy files (.npy , .npz , .np.gz are recognized throughout the script) with the following format: |
Nucleotide | Marker 1 (Pos 1) | Marker 1 (Pos n) | Marker 2 (Pos 1) | Marker 2 (Pos n) | Marker n (Pos n) |
---|---|---|---|---|---|---|
A | 0 | 6 | 0 | 0 | .. | |
C | 12 | 4 | 0 | 0 | .. | |
G | 0 | 0 | 0 | 0 | .. | |
T | 0 | 0 | 14 | 0 | .. |
The module samestr extract
obtains MetaPhlAn or mOTUs marker sequences from reference genomes by using BLASTN.
The input to samestr extract
are genomic sequences in fasta
format. For parallel processing, specify --nprocs
. Note: This step requires a MetaPhlAn database regenerated with samestr db.
samestr extract \
--input-files reference_genomes/*.fasta \
--marker-dir samestr_db/ \
--nprocs 30 \
--output-dir out_extract/
Use samestr merge
to merge nucleotide variant profiles obtained from metagenomic samples (convert
) and/or reference genomes (extract
) for further filtering and pairwise comparisons.
For parallel processing, specify --nprocs
.
samestr merge \
--input-files out_extract/*.npz out_convert/*.npz \
--marker-dir samestr_db_metaphlan/ \
--nprocs 30 \
--output-dir out_merge/
The module samestr filter
provides extensive options for global, per sample, marker, and position filtering of nucleotide variant profiles based on absolute (including n, mean, sd) and relative alignment horizontal and vertical alignment coverage. Refer to the command line --help
for more information.
For parallel processing, specify --nprocs
.
samestr filter \
--input-files out_merge/*.npy \
--input-names out_merge/*.names.txt \
--marker-dir samestr_db/ \
--clade-min-n-hcov 5000 \
--clade-min-samples 2 \
--marker-trunc-len 20 \
--global-pos-min-n-vcov 2 \
--sample-pos-min-n-vcov 5 \
--sample-var-min-f-vcov 0.1 \
--nprocs 30 \
--output-dir out_filter/
The module samestr stats
can be used to generate statistics related to coverage and nucleotide diversity in nucleotide variant profiles at any step in the analysis pipeline.
samestr stats \
--input-files out_filter/*.npy \
--input-names out_filter/*.names.txt \
--marker-dir samestr_db/ \
--nprocs 30 \
--output-dir out_stats_/
The module samestr compare
can be used to perform the pairwise clade-specific alignment comparison. We recommend using filter
to compare alignments only at meaningful sites.
Similarity can be determined by calculating the maximum variant profile similarity MVS
or consensus similarity CVS
for all species and sample pairs. For parallel processing, specify --nprocs
.
samestr compare \
--input-files out_filter/*.npy \
--input-names out_filter/*.names.txt \
--marker-dir samestr_db/ \
--nprocs 30 \
--output-dir out_compare/
Difference to StrainPhlAn when using MetaPhlAn as the source database: Due to the nature of the consensus rule, StrainPhlAn's accuracy is only given when the dominant strain has above 50% relative abundance in the species population (e.g. 70%/20%/10%). Genotypes reconstructed with StrainPhlAn are confounded when the dominant strain does not hold the majority at all positions, such as in more balanced mixtures (e.g. 40%/20%/20%/20%) or in low-coverage regions where subdominant strains can hold the majority (40% < 20%+20%+20%) over the most abundant strain.
This can lead to false-negatives when assessing strain co-occurrence between samples in which strain mixtures should be expected (such as FMT). By considering all nucleotide variants, SameStr's shared strain calls remain sensitive to more complex strain mixtures without compromising specificity by requiring at least 5kb coverage overlap between alignment pairs and limiting comparisons to variants with ≥10% variant frequency at a given position.
Use samestr summarize
to call and summarize shared strains based on defined criteria. This module also generates taxonomic co-occurrence tables at the kingdom, phylum, class, order, family, genus, species, and strain-level.
Specify the minimum required overlap (aln-pair-min-overlap
) and similarity (aln-pair-min-similarity
) to define shared strains between nucleotide variant profiles.
samestr summarize \
--input-dir out_compare/ \
--tax-profiles-dir out_align/ \
--marker-dir samestr_db/
--output-dir out_summarize/