| Name | woltka JSON |
| Version |
0.1.7
JSON |
| download |
| home_page | None |
| Summary | versatile meta-omic data classifier |
| upload_time | 2024-10-08 20:55:55 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.6 |
| license | BSD 3-Clause License Copyright (c) 2020--, Qiyun Zhu All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| keywords |
bioinformatics
metagenomics
microbiome
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
|
# Woltka
[](https://opensource.org/licenses/BSD-3-Clause)
[](https://github.com/qiyunzhu/woltka/actions)
[](https://coveralls.io/github/qiyunzhu/woltka?branch=main)
**Woltka** is a versatile program for determining the structure and functional capacity of microbiomes. It mainly works with shotgun metagenomic data. It bridges first-pass sequence aligners with advanced analytical platforms (such as QIIME 2). It takes full advantage of, and is not limited by, the [WoL](https://biocore.github.io/wol/) reference database. Its scope and highlights are:
Woltka ships with a **QIIME 2 plugin**. [See here for instructions](woltka/q2).
## Contents
- [Overview](#overview)
- [Installation](doc/install.md)
- [Example usage](#example-usage)
- Tutorials
- [Working with WoL](doc/wol.md), [The OGU analysis](doc/ogu.md), [Sequence alignment](doc/align.md)
- Main workflow
- [Input files](doc/input.md), [Output files](doc/output.md), [Classification systems](doc/hierarchy.md), [Classification methods](doc/classify.md), ["Coord-match" functional profiling](doc/ordinal.md), [Stratification](doc/stratify.md)
- Table utilities
- [Collapse](doc/collapse.md), [Coverage](doc/coverage.md), [Normalize](doc/normalize.md), [Filter](doc/filter.md), [Merge](doc/merge.md)
- For users of
- [QIIME 2](woltka/q2), [Qiita](doc/qiita.md), [Bowtie2](doc/align.md#alignment-with-bowtie2), [SHOGUN](doc/align.md#the-shogun-protocol), [RefSeq](doc/refseq.md), [GTDB](doc/gtdb.md), [MetaCyc](doc/metacyc.md), [KEGG](doc/kegg.md)
- References
- [Command-line interface](doc/cli.md), [Test datasets](woltka/tests/data), [Computational efficiency](doc/perform.md)
- [FAQs](doc/faq.md)
- [Citation](#citation)
- [Contact](#contact)
## Overview
### Where does Woltka fit in a pipeline
Woltka is a **classifier**. It fits in between sequence alignment and microbiome analyses.
### What does Woltka do
Woltka processes [**alignments**](doc/input.md) -- the mappings of microbiome sequencing data against reference sequences (such as genomes or genes), and [infers the best placement](doc/classify.md) of the queries in a hierarchical [classification system](doc/hierarchy.md). One query could have simultaneous matches in multiple references. Woltka finds the most suitable classification unit(s) to describe the query accordingly the criteria specified by the user. Woltka generates [**profiles**](doc/output.md) (feature tables) -- the abundances of classification units which describe the structure or function of microbial communities.
### What else does Woltka do
Woltka provides several utilities for handling feature tables, including [normalizing](doc/normalize.md) data, [collapsing](doc/collapse.md) a table to higher-level features, calculating feature group [coverage](doc/coverage.md), [filtering](doc/filter.md) features based on per-sample abundance, and [merging](doc/merge.md) tables.
### What does Woltka not do
Woltka does NOT **align** sequences. You need to align your sequencing data (FastQ, etc.) against a reference database (we recommend [WoL](wol.md)) using an aligner of your choice (e.g., [Bowtie2](doc/align.md#alignment-with-bowtie2)). The resulting alignment files can be fed into Woltka.
Woltka does NOT **analyze** profiles. We recommend using [QIIME 2](https://qiime2.org/) for robust downstream analyses of the profiles to decode the relationships among microbial communities and with their environments.
Flowchart of Woltka's main classification workflow:
## Installation
Requirement: [Python](https://www.python.org/) 3.6 or above.
```bash
pip install woltka
```
See more details about [installation](doc/install.md).
## Example usage
Woltka provides several small test datasets under [woltka/tests/data](woltka/tests/data). To access them, [download](https://github.com/qiyunzhu/woltka/archive/main.zip) this GitHub repo, unzip, and navigate to this directory.
One can execute the following commands to make sure that Woltka functions correctly, and to get an impression of the basic usage of Woltka.
(Note: a more complete list of commands is provided [here](woltka/tests/data). Alternatively, you can skip this test dataset and check out the [instruction](doc/wol.md) for working with WoL.)
### 1\. OGU (operational genomic unit) table generation ([details](doc/ogu.md)):
```bash
woltka classify -i align/bowtie2 -o ogu.biom
```
The input path, [`align/bowtie2`](woltka/tests/data/align/bowtie2), is a directory containing five Bowtie2 alignment files (`S01.sam.xz`, `S02.sam.xz`,... `S05.sam.xz`) (SAM format, xzipped), each representing the mapping of metagenomic sequencing reads per sample against a reference genome database (here are [guidlines](doc/align.md) for performing alignment).
The output file, `table.biom`, is a feature table in BIOM format, which can then be analyzed using various bioformatics programs such as [QIIME 2](https://qiime2.org/).
### 2\. Taxonomic profiling at the ranks of phylum, genus and species ([details](doc/hierarchy.md)):
```bash
woltka classify \
--input align/bowtie2 \
--map taxonomy/taxid.map \
--nodes taxonomy/nodes.dmp \
--names taxonomy/names.dmp \
--rank phylum,genus,species \
--output output_dir
```
The mapping file ([`taxid.map`](woltka/tests/data/taxonomy/taxid.map)) translates genome IDs to taxonomy IDs, which then allow Woltka to classify query sequences based on the NCBI taxonomy ([`nodes.dmp`](woltka/tests/data/taxonomy/nodes.dmp) and [`names.dmp`](woltka/tests/data/taxonomy/names.dmp)).
The output directory (`output_dir`) will contain three feature tables: `phylum.biom`, `genus.biom` and `species.biom`, each representing a taxonomic profile at one of the three ranks.
### 3\. Functional profiling by UniRef entries, then by GO molecular processes ([details](doc/ordinal.md)):
```bash
woltka classify \
--input align/bowtie2 \
--coords function/coords.txt.xz \
--map function/uniref/uniref.map.xz \
--map function/go/process.tsv.xz \
--rank uniref,process \
--output output_dir
```
Here, the input files are still **read-to-genome** alignments, rather than read-to-gene ones. Woltka matches reads with genes based on their coordinates on genomes using an efficient algorithm (["coord-match"](doc/ordinal.md)). The gene coordinates are given by the database file [`coords.txt`](woltka/tests/data/function/coords.txt.xz) (see [details](doc/ordinal.md)). The read coordinates are extracted from the alignment files. This ensures consistency between structural and functional analyses.
Subsequently, Woltka is able to assign query sequences to functional units, as defined in mapping files ([`uniref.map`](woltka/tests/data/function/uniref/uniref.map.xz) and [`process.tsv`](woltka/tests/data/function/go/process.tsv.xz)). As you can see, compressed files are supported and auto-detected.
Similarly, the output files are two functional profiles: `uniref.biom` and `process.biom`.
### 4\. Combined taxonomic/functional profiling by GO molecular processes of individual genera of organisms ([details](doc/stratify.md)).
Two steps. _First_, perform taxonomic classification. The `--outmap` parameter writes a read-to-genus mapping file per sample to the directory `genus_map/`. The `--name-as-id` flag replaces NCBI TaxIDs with real taxon names in the output.
```bash
woltka classify \
--input align/bowtie2 \
--map taxonomy/taxid.map \
--nodes taxonomy/nodes.dmp \
--names taxonomy/names.dmp \
--name-as-id \
--rank genus \
--output genus.biom \
--outmap genus_map
```
_Second_, perform functional classification. The `--stratify` parameter imports the genus mappings from the last analysis, and groups functional units (GO processes) by the genus of the source genome.
```bash
woltka classify \
--input align/bowtie2 \
--stratify genus_map \
--coords function/coords.txt.xz \
--map function/uniref/uniref.map.xz \
--map function/go/process.tsv.xz \
--rank process \
--output genus_by_process.biom
```
In the output profile (see below), each feature is a combination of taxonomy and function. This "**stratified**" profile lets the researcher explore the functional capacities of individual microbial components.
Feature ID | S01 | S02 | S03 | S04 | S05
--- | --- | --- | --- | --- | ---
Aeromonas\|GO:0000917 | 4 | 20 | 3 | 0 | 7
Aeromonas\|GO:0005975 | 0 | 12 | 5 | 2 | 0
Bacteroides\|GO:0006260 | 105 | 0 | 0 | 0 | 0
Bacteroides\|GO:0006281 | 10 | 6 | 2 | 0 | 3
Lactobacillus\|GO:0045454 | 2 | 0 | 0 | 34 | 3
Lactobacillus\|GO:0055085 | 0 | 0 | 7 | 0 | 0
... |
## Citation
The first paper describing Woltka was published at:
- Zhu Q, Huang S, Gonzalez A, McGrath I, McDonald M, Haiminen N, Armstrong G, et al. [Phylogeny-aware analysis of metagenome community ecology based on matched reference genomes while bypassing taxonomy.](https://journals.asm.org/doi/10.1128/msystems.00167-22) _mSystems_. 2022. e00167-22.
Note: This paper focuses on the [OGU analysis](doc/ogu.md). Although it does not discuss other functions of Woltka, it is so far the only citable paper if you use Woltka in your studies.
## Contact
Please forward any questions to the project leader: **Dr. Qiyun Zhu** (qiyunzhu@gmail.com).
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"description": "# Woltka\n\n[](https://opensource.org/licenses/BSD-3-Clause)\n[](https://github.com/qiyunzhu/woltka/actions)\n[](https://coveralls.io/github/qiyunzhu/woltka?branch=main)\n\n\n\n**Woltka** is a versatile program for determining the structure and functional capacity of microbiomes. It mainly works with shotgun metagenomic data. It bridges first-pass sequence aligners with advanced analytical platforms (such as QIIME 2). It takes full advantage of, and is not limited by, the [WoL](https://biocore.github.io/wol/) reference database. Its scope and highlights are:\n\n\n\nWoltka ships with a **QIIME 2 plugin**. [See here for instructions](woltka/q2).\n\n## Contents\n\n- [Overview](#overview)\n- [Installation](doc/install.md)\n- [Example usage](#example-usage)\n- Tutorials\n - [Working with WoL](doc/wol.md), [The OGU analysis](doc/ogu.md), [Sequence alignment](doc/align.md)\n- Main workflow\n - [Input files](doc/input.md), [Output files](doc/output.md), [Classification systems](doc/hierarchy.md), [Classification methods](doc/classify.md), [\"Coord-match\" functional profiling](doc/ordinal.md), [Stratification](doc/stratify.md)\n- Table utilities\n - [Collapse](doc/collapse.md), [Coverage](doc/coverage.md), [Normalize](doc/normalize.md), [Filter](doc/filter.md), [Merge](doc/merge.md)\n- For users of\n - [QIIME 2](woltka/q2), [Qiita](doc/qiita.md), [Bowtie2](doc/align.md#alignment-with-bowtie2), [SHOGUN](doc/align.md#the-shogun-protocol), [RefSeq](doc/refseq.md), [GTDB](doc/gtdb.md), [MetaCyc](doc/metacyc.md), [KEGG](doc/kegg.md)\n- References\n - [Command-line interface](doc/cli.md), [Test datasets](woltka/tests/data), [Computational efficiency](doc/perform.md)\n- [FAQs](doc/faq.md)\n- [Citation](#citation)\n- [Contact](#contact)\n\n\n## Overview\n\n### Where does Woltka fit in a pipeline\n\nWoltka is a **classifier**. It fits in between sequence alignment and microbiome analyses.\n\n### What does Woltka do\n\nWoltka processes [**alignments**](doc/input.md) -- the mappings of microbiome sequencing data against reference sequences (such as genomes or genes), and [infers the best placement](doc/classify.md) of the queries in a hierarchical [classification system](doc/hierarchy.md). One query could have simultaneous matches in multiple references. Woltka finds the most suitable classification unit(s) to describe the query accordingly the criteria specified by the user. Woltka generates [**profiles**](doc/output.md) (feature tables) -- the abundances of classification units which describe the structure or function of microbial communities.\n\n### What else does Woltka do\n\nWoltka provides several utilities for handling feature tables, including [normalizing](doc/normalize.md) data, [collapsing](doc/collapse.md) a table to higher-level features, calculating feature group [coverage](doc/coverage.md), [filtering](doc/filter.md) features based on per-sample abundance, and [merging](doc/merge.md) tables.\n\n### What does Woltka not do\n\nWoltka does NOT **align** sequences. You need to align your sequencing data (FastQ, etc.) against a reference database (we recommend [WoL](wol.md)) using an aligner of your choice (e.g., [Bowtie2](doc/align.md#alignment-with-bowtie2)). The resulting alignment files can be fed into Woltka.\n\nWoltka does NOT **analyze** profiles. We recommend using [QIIME 2](https://qiime2.org/) for robust downstream analyses of the profiles to decode the relationships among microbial communities and with their environments.\n\nFlowchart of Woltka's main classification workflow:\n\n\n\n\n## Installation\n\nRequirement: [Python](https://www.python.org/) 3.6 or above.\n\n```bash\npip install woltka\n```\n\nSee more details about [installation](doc/install.md).\n\n\n## Example usage\n\nWoltka provides several small test datasets under [woltka/tests/data](woltka/tests/data). To access them, [download](https://github.com/qiyunzhu/woltka/archive/main.zip) this GitHub repo, unzip, and navigate to this directory.\n\nOne can execute the following commands to make sure that Woltka functions correctly, and to get an impression of the basic usage of Woltka.\n\n(Note: a more complete list of commands is provided [here](woltka/tests/data). Alternatively, you can skip this test dataset and check out the [instruction](doc/wol.md) for working with WoL.)\n\n### 1\\. OGU (operational genomic unit) table generation ([details](doc/ogu.md)):\n\n```bash\nwoltka classify -i align/bowtie2 -o ogu.biom\n```\n\nThe input path, [`align/bowtie2`](woltka/tests/data/align/bowtie2), is a directory containing five Bowtie2 alignment files (`S01.sam.xz`, `S02.sam.xz`,... `S05.sam.xz`) (SAM format, xzipped), each representing the mapping of metagenomic sequencing reads per sample against a reference genome database (here are [guidlines](doc/align.md) for performing alignment).\n\nThe output file, `table.biom`, is a feature table in BIOM format, which can then be analyzed using various bioformatics programs such as [QIIME 2](https://qiime2.org/).\n\n### 2\\. Taxonomic profiling at the ranks of phylum, genus and species ([details](doc/hierarchy.md)):\n\n```bash\nwoltka classify \\\n --input align/bowtie2 \\\n --map taxonomy/taxid.map \\\n --nodes taxonomy/nodes.dmp \\\n --names taxonomy/names.dmp \\\n --rank phylum,genus,species \\\n --output output_dir\n```\n\nThe mapping file ([`taxid.map`](woltka/tests/data/taxonomy/taxid.map)) translates genome IDs to taxonomy IDs, which then allow Woltka to classify query sequences based on the NCBI taxonomy ([`nodes.dmp`](woltka/tests/data/taxonomy/nodes.dmp) and [`names.dmp`](woltka/tests/data/taxonomy/names.dmp)).\n\nThe output directory (`output_dir`) will contain three feature tables: `phylum.biom`, `genus.biom` and `species.biom`, each representing a taxonomic profile at one of the three ranks.\n\n### 3\\. Functional profiling by UniRef entries, then by GO molecular processes ([details](doc/ordinal.md)):\n\n```bash\nwoltka classify \\\n --input align/bowtie2 \\\n --coords function/coords.txt.xz \\\n --map function/uniref/uniref.map.xz \\\n --map function/go/process.tsv.xz \\\n --rank uniref,process \\\n --output output_dir\n```\n\nHere, the input files are still **read-to-genome** alignments, rather than read-to-gene ones. Woltka matches reads with genes based on their coordinates on genomes using an efficient algorithm ([\"coord-match\"](doc/ordinal.md)). The gene coordinates are given by the database file [`coords.txt`](woltka/tests/data/function/coords.txt.xz) (see [details](doc/ordinal.md)). The read coordinates are extracted from the alignment files. This ensures consistency between structural and functional analyses.\n\nSubsequently, Woltka is able to assign query sequences to functional units, as defined in mapping files ([`uniref.map`](woltka/tests/data/function/uniref/uniref.map.xz) and [`process.tsv`](woltka/tests/data/function/go/process.tsv.xz)). As you can see, compressed files are supported and auto-detected.\n\nSimilarly, the output files are two functional profiles: `uniref.biom` and `process.biom`.\n\n### 4\\. Combined taxonomic/functional profiling by GO molecular processes of individual genera of organisms ([details](doc/stratify.md)).\n\nTwo steps. _First_, perform taxonomic classification. The `--outmap` parameter writes a read-to-genus mapping file per sample to the directory `genus_map/`. The `--name-as-id` flag replaces NCBI TaxIDs with real taxon names in the output.\n\n```bash\nwoltka classify \\\n --input align/bowtie2 \\\n --map taxonomy/taxid.map \\\n --nodes taxonomy/nodes.dmp \\\n --names taxonomy/names.dmp \\\n --name-as-id \\\n --rank genus \\\n --output genus.biom \\\n --outmap genus_map\n```\n\n_Second_, perform functional classification. The `--stratify` parameter imports the genus mappings from the last analysis, and groups functional units (GO processes) by the genus of the source genome.\n\n```bash\nwoltka classify \\\n --input align/bowtie2 \\\n --stratify genus_map \\\n --coords function/coords.txt.xz \\\n --map function/uniref/uniref.map.xz \\\n --map function/go/process.tsv.xz \\\n --rank process \\\n --output genus_by_process.biom\n```\n\nIn the output profile (see below), each feature is a combination of taxonomy and function. This \"**stratified**\" profile lets the researcher explore the functional capacities of individual microbial components.\n\nFeature ID | S01 | S02 | S03 | S04 | S05\n--- | --- | --- | --- | --- | ---\nAeromonas\\|GO:0000917 | 4 | 20 | 3 | 0 | 7\nAeromonas\\|GO:0005975 | 0 | 12 | 5 | 2 | 0\nBacteroides\\|GO:0006260 | 105 | 0 | 0 | 0 | 0\nBacteroides\\|GO:0006281 | 10 | 6 | 2 | 0 | 3\nLactobacillus\\|GO:0045454 | 2 | 0 | 0 | 34 | 3\nLactobacillus\\|GO:0055085 | 0 | 0 | 7 | 0 | 0\n... |\n\n\n## Citation\n\nThe first paper describing Woltka was published at:\n\n- Zhu Q, Huang S, Gonzalez A, McGrath I, McDonald M, Haiminen N, Armstrong G, et al. [Phylogeny-aware analysis of metagenome community ecology based on matched reference genomes while bypassing taxonomy.](https://journals.asm.org/doi/10.1128/msystems.00167-22) _mSystems_. 2022. e00167-22.\n\nNote: This paper focuses on the [OGU analysis](doc/ogu.md). Although it does not discuss other functions of Woltka, it is so far the only citable paper if you use Woltka in your studies.\n\n\n## Contact\n\nPlease forward any questions to the project leader: **Dr. Qiyun Zhu** (qiyunzhu@gmail.com).\n",
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