Skip to content
std
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
bin
 
 
 
 
 
 
src
 
 
 
 
 
 
 
 
 
 
 
 

Meta-Storms 2

Version Release date

Contents

Introduction

Meta-Storms 2 is the standalone implementation of the Microbiome Search Engine (MSE; http://mse.ac.cn). MSE is a search engine designed to efficiently search a database of microbiome samples and identify similar samples based on phylogenetic or functional relatedness. Meta-Storms 2 consists of the following steps: (i) creating a database composed of reference microbiome samples, and (ii) searching for similar samples in the database with given query microbiome sample(s) via phylogenetic similarities. Meta-Storms 2 relies on an advanced indexing algorithm, providing a fast, and constant, search speed in very large databases. Now Meta-Storms 2 supports OTU-based (for 16S rRNA), Species-based (for WGS) and KEGG-Ontology-based (for function) search, which is compatible with profiling tools of Parallel-META 3, QIIME/QIIME2 and MetaPhlAn2.

System Requirement and dependency

Hardware Requirements

Meta-Storms 2 only requires a standard computer with sufficient RAM to support the operations defined by a user. For typical users, this would be a computer with about 2 GB of RAM. For optimal performance, we recommend a computer with the following specs:

RAM: 8+ GB
CPU: 4+ cores, 3.3+ GHz/core

Software Requirements

OpenMP library is the C/C++ parallel computing library. Most Linux releases have OpenMP already been installed in the system. In Mac OS X, to install the compiler that supports OpenMP, we recommend using the Homebrew package manager:

brew install gcc --without-multilib

Installation guide

Automatic Installation (recommended)

At present, Meta-Storms 2 provides a fully automatic installer for easy installation.

a. Download the package:

git clone https://github.com/qibebt-bioinfo/meta-storms.git

b. Install by installer:

cd meta-storms
source install.sh

The package should take less than 1 minute to install on a computer with the specifications recommended above.

Tips for Automatic Installation

  1. The automatic installer configures the environment variables to the default configuration specified in the file of "~/.bashrc" or "~/.bash_profile". If you prefer to configure the environment variables to other configuration file, please choose the option of manual installation below.

  2. If the environment variables are not activated automatically, please enable them manually by running the command "source ~/.bashrc".

  3. If the automatic installer fails, Meta-Storms 2 can still be installed manually by the following options.

Manual Installation

If the automatic installer fails, Meta-Storms 2 can still be installed manually.

a. Download the package:

git clone https://github.com/qibebt-bioinfo/meta-storms.git

b. Configure the environment variables (the default environment variable configuration file is "~/.bashrc"):

export MetaStorms=Path to Meta-Storms 2
export PATH="$PATH:$MetaStorms/bin/"
source ~/.bashrc

c. Compile the source code (this is required only when installing the source code package):

cd meta-storms
make

Pre-computing

Meta-Storms 2 accepts microbiome samples profiled into OTUs (for 16S) or species (for shotgun) or KEGG Orthologies (KO, for both 16S and shotgun).

OTU (for 16S sequences)

16S rRNA amplicon sequences can be picked into OTUs against GreenGenes 13-8 (97% level) reference by Parallel-META 3 (recommended) or QIIME. For a give sequence file (FASTA or FASTQ format, eg. sample1.fa), OTUs can be profiled from 16S sequences in the three alternative methods:

a. by Parallel-META 3 (recommended):

PM-parallel-meta -f F -r sample1.fa -o sample1.out

Then the output file "sample1.out/classification.txt" is qualified as the input for Meta-Storms 2 (refer to Single sample). For multiple samples as input, samples should be listed in the sample list (refer to Sample list).

b. by QIIME

pick_otus.py -m uclust_ref --suppress_new_clusters -i sample1.fa -o sample1.out
MetaDB-parse-qiime-otu -i sample1.out/sample1_otus.txt -o sample1.out/classification.txt

Then the output file "sample1.out/classification.txt" is qualified as the input for Meta-Storms 2 (refer to Single sample). For multiple samples as input, samples should be listed in the sample list (refer to Sample list).

c. by QIIME2: integrate Meta-Storms2 as QIIME2-plug-in.

Species (for shotgun sequences)

Metagenomic shotgun sequences can be annotated into species by MetaPhlAn2 (recommended). With a input sequence file "sample1.fa", to get the species using MetaPhlAn2 by:

metaphlan2.py sample_1.fa --input_type fasta --tax_lev s --ignore_viruses --ignore_eukaryotes --ignore_archaea > profiled_sample_1.sp.txt

Then the output file "profiled_sample_1.sp.txt" is qualified as the input for Meta-Storms 2 (refer to Single sample). For multiple samples as input, samples should be listed in the sample list (refer to Sample list).

Functions (KEGG Orthology, for both 16S and shotgun)

Both 16S and shotgun sequences can be annotated into KEGG Orthologies (KO) by Parallel-META 3 (recommended for 16S) or Humann2 (recommended for shotgun). With a input sequence file "sample1.fa":

a. 16S sequences by Parallel-META 3

PM-parallel-meta -r sample1.fa -o sample1.out

in the output directory "sample1.out", the result file "functions.txt" is qualified as the input for Meta-Storms 2 (refer to Single sample). For multiple samples as input, samples should be listed in the sample list (refer to Sample list).

b. Shotgun sequences by Humann2

humann2 --input sample.fa --output sample1.out

Example dataset

Here we provide a demo dataset with 20 human oral microbiome samples in two different healthy statuses from Huang, et al., 2014. The pre-computing result (in the OTU table format and derived from Parallel-META 3 and the meta-data are in the "example" folder in the installation package. We use this dataset to demonstrate all the following example commands.

Please change your work directory to the "example" folder by

cd example
sh Readme

* Huang, S., et al., Predictive modeling of gingivitis severity and susceptibility via oral microbiota. ISME J, 2014. 8(9): p. 1768-80.

Build a MSE database

The command "MetaDB-make-otu" builds a new MSE database for Meta-Storms 2 based search from the given samples. Samples are listed in either (i) single sample list (for Parallel- META 3 format, by -i or -l with optional –p,), or (ii) OTU table (OTU table format, by -T). It outputs a database file (*.mdb).

Usage:

	MetaDB-make-otu [-option] value
	
	[Input options]
		-i or -l Input filename list
		-p List file path prefix for '-i' or '-l' [Optional for -i and -l]
	or
		-T (upper) Input OTU table (*.OTU.Count)
	or
		-d (*.mdb) Make the HDD mode data files for a database
	
	[Output options]
		-o Output database name, default is "database.mdb"
		-H (upper) If enable the HDD mode (low RAM usage), T(rue) or F(alse), default is F
	
	[Other options]
		-h Help

Example (make sure you are in "example" path):

MetaDB-make-otu -T taxa.OTU.Count -o database

You can also build a MSE database by species (MetaDB-make-sp) or function (MetaDB-make-func) .

HDD mode

The HDD (Hard Drive Disk) mode uses the re-encoding technique to minimize the RAM usage for database search (although the mode is slower). When the HDD mode is enabled via "–H t", MetaDB-make-otu/func/sp will generate accessory data named as *.mdb.hdd under the same directory of the output database (*.mdb). For extremely large databases (e.g., sample number > 10,000), we strongly recommend users to enable the HDD mode to minimize the RAM consumption.

For an existing database (*.mdb), HDD mode can also be enabled by making its HDD files via the command below. Then the *.mdb.hdd would be generated and stored under the same directory as the database.

Example (make sure you are in "example" path):

MetaDB-make-otu -d database.mdb

Merge MSE databases

The command "MetaDB-merge" merges two existing databases (*.mdb) into one. Usage:

MetaDB-merge [-option] value

[Input and Output options]
	-1 The 1st database name [Required]
	-2 The 2nd database name [Required]
	-o Merged output database name, default is "database_merge.mdb"
	
[Other options]
	-h Help

Example: Here you can make another database named as "database_2.mdb"

MetaDB-merge -1 database.mdb -2 database_2.mbd -o database_merged

Search the MSE database

Search via Meta-Storms 2

Meta-Storms 2 supports the index-based query, which features an extremely fast and constant search speed against very large microbiome databases.

The query sample(s) can be provided via either (i) single sample (for a single sample in Parallel-META 3 format, by -i), or (ii) single sample list (for multiple samples in Parallel- META 3 format, by -l with optional -p), or (iii) OTU table (for OTU table format, by -T).

We also recommend users to enable the HDD mode for large databases to minimize the RAM consumption (e.g., sample number > 10,000) (See HDD mode).

Usage:

	MetaDB-search-otu [Options] Value
	[Database options]
		-d Database file (*.mdb) [Required]
		-H (upper) If enable the HDD mode (low RAM usage), T(rue) or F(alse), default is F
		-P (upper) Path for the HDD mode data files [Optional for '-H T']
	
	[Input options]
		-i Single input file name
	or
		-l Input filename list
		-p Input List file path prefix for '-l' [Optional for -l]
	or
		-T (upper) Input OTU table (*.Count)
	
	[Output options]
		-o Output file, default is "query.out"
	
	[Advanced options]
		-n Number of the matched sample(s), default is 10
		-m Minimum similarity of the matched sample(s), range (0.0 ~ 1.0], default is 0
		-e If enable the exhaustive search (low speed), T(rue) or F(alse), default is F
		-w Abundance weighted or unweighted, T(rue) or F(alse), default is T
	
	[Other options]
		-t CPU core number, default is auto
		-h Help

Example(make sure you are in "example" path):

MetaDB-search-otu -d database.mdb -T taxa.OTU.Count -o query.out

Meta-Storms 2 also supports search by species or function with the commands "MetaDB-search-sp" and "MetaDB-search-func".

Search output

The search result contains a number of matches, each with its sample ID and its similarity score (always between 0 and 1) to the query. In the output, for each of the query samples, all of its matches are listed in tandem in a single line, e.g.

# Query Match Similarity Match Similarity
Query: q_id_0 ref_id_x 0.9823 ref_id_y 0.9758
Query: q_id_1 Ref_id_m 0.9541 ref_id_n 0.9386

In the output above, the first query sample (q_id_0) matches against the reference sample (ref_id_x) with a similarity of 0.9823. In addition, q_id_0 also matches ref_id_v with a similarity of 0.9758. The number of matches is assigned by the parameter -n, and default is 10.

The similarity between query sample(s) and matched sample(s) is a phylogeny-based similarity that is computed using the Meta-Storms 2 scoring function. This algorithm takes the relative abundance of OTUs and their binary phylogeny between two samples as input, and output their quantitative similarities (always between 0 and 1). For high performance and parallel computing, this algorithm is optimized by non-recursive transformation, memory recycling and variable reallocation. Please also refer to "Meta-Storms: efficient search for similar microbial communities based on a novel indexing scheme and similarity score for metagenomic data, Bioinformatics, 2012" for details.

Multiple classification based on search result

Meta-data prediction

Important features of the query sample, such as the meta-data of habitat, status, etc., can potentially be predicted based on the meta-data of its matches. From the search output generated by MetaDB-search-*, the meta-data of the query sample can be predicted by:

Usage:

	MetaDB-parse-meta [Options] Value
	[Input and Output options]
		-i Input file name (the output of MetaDB-search) [Required]
		-m Input meta-data file name (meta-data of the database in MetaDB-search) [Required]
		-l Meta-data column, default is 1 (exclude the ID column)
		-o Output file name, default is "query.out.meta"
	
	[Advanced options]
		-r Number of predictd meta-data, default is 1
		-b Base of the similarity in the input file, default is 0
		-n Max number of matches in the input file, default is 10
		-s Number of skipped matches in the input file, default is 0
	
	[Other options]
		-h Help

Usage for the -s:

When the query sample has already been included in the database, the search result must contain the query samples itself as the top hit since they have the 100% similarity, which causes the bias in meta-data prediction. Here we can use the parameter –s 1 to exclude this top hit in the meta-data prediction to avoid such bias.

Example(make sure you are in "example" path):

MetaDB-parse-meta -i query.out -m meta.txt -o query.out.meta

Multiple classification output

MetaDB-parse-meta generates the predicted meta-data with the assigned scores (always between 0 and 1). In the output, for each of the query samples, all of its predicted meta-data are listed in tandem in a single line, e.g.

#ID Meta-data Score Meta-data Score
q_id_0 Healthy 0.75 Disease 0.25
q_id_1 Disease 0.72 Healthy 0.28

In the output above, the first query sample (q_id_0) is predicted as "Healthy" with a score of 0.75, and "Disease" with a score of 0.25. The predicted meta-data are sorted by their scores.

The number of predicted meta-data is assigned by parameter -r, and default is 1 (i.e., only reporting the predicted meta-data with the highest score).

Microbiome Novelty Score (MNS) based on search results

Calculate the Microbiome Novelty Score (MNS)

With the search output generated by MetaDB-search, the Microbiome Novelty Score (MNS) of each sample can be calculated by:

Usage:

MetaDB-parse-mns [Options] Value

[Input and Output options]
	-i Input file name (the output of MetaDB-search) [Required]
	-o Output file name, default is "query.out.mns"

[Advanced options]
	-b Base of the similarity in the input file, default is 0
	-n Max number of matches in the input file, default is 10
	-s Number of skipped matches in the input file, default is 0

[Other options]
	-h Help

Usage for the -s:

When the query sample has already been included in the database, the search result must contain the query samples itself as the top hit since they have the 100% similarity, which causes bias in calculating the MNS. Here we can use the parameter –s 1 to exclude this top hit in calculating the MNS.

Example (make sure you are in "example" path):

MetaDB-parse-mns -i query.out -o query.out.mns

Microbiome Novelty Score (MNS) output

MetaDB-parse-mns generates the MNS (always between 0 and 1) of each query sample in a single line, e.g.

#ID MNS
q_id_0 0.06
q_id_1 0.12

In the output above, the first query sample (q_id_0) reports a MNS of 0.06.

Input and output format

Meta-Storms 2 accepts the two alternative formats as input.

Single sample file and sample list

A single sample is the OTU/species/KO information of a single microbiome sample profiled by Parallel-META 3, QIIME or MetaPhlAn2 from the 16S/shotgun sequences (refer to Pre-computing for details). It is a plain-text file. An example of the single sample is below:

#Database_OTU Count
OTU_1 10
OTU_2 17
OTU_3 38

A sample list is a plain-text file for listing multiple samples (by –l) as Meta-Storms 2 input, which consists of two columns: the sample IDs and the directories of samples’ single input files, e.g.

Sample_1	/home/data/single_sample/Sample_1/classification.txt
Sample_2	/home/data/single_sample/Sample_2/classification.txt
Sample_3	/home/data/single_sample/Sample_3/classification.txt
……
Sample_N	/home/data/single_sample/Sample_N/classification.txt

The directory can be either absolute directory or relative directory. Meta-Storms 2 also provides an optional parameter –p to add a prefix for the all the directories in the sample list in case of a relative directory is preferred.

OTU/species/KO table

An OTU/species/KO table is a plain-text file that contains the features (OTU/species/KO) and their richness for each of multiple samples. An example of the OTU table is bellow:

#Sample_ID OTU_1 OTU_2 OTU_3 OTU_4 OTU_5
Sample_1 10 17 38 2 2
Sample_2 0 5 57 0 0
Sample_3 2 35 7 0 0
Sample_4 58 30 23 3 0
Sample_5 95 5 5 4 0

The output file format can be found at Search output.

Supplementary

The test code and datasets for reproducing the results of manuscript "Multiple-Disease Detection and Classification across Cohorts via Microbiome Search" is available here (Linux X86_64 / Mac OS X, ~ 892 MB). See “Readme.txt" in the package for usage and details.

Citation

X. Su*, G. Jing, D. McDonald, H. Wang, Z. Wang, A. Gonzalez, Z. Sun, S. Huang, J. Navas, R. Knight* and J. Xu*. Identifying and predicting novelty in microbiome studies, mBio, 2018.

Contact

Any problem please contact MSE development team:

JING Gongchao    Email: jinggc@qibebt.ac.cn

About

Meta-Storms 2 is the standalone implementation of the Microbiome Search Engine (MSE; http://mse.ac.cn). This is the official software repository of Meta-Storms 2.

Resources

License

Releases

No releases published

Packages

No packages published

Languages