<commit_before>Deblur
======

[![Build Status](https://travis-ci.org/biocore/deblur.png?branch=master)](https://travis-ci.org/biocore/deblur)
[![Coverage Status](https://coveralls.io/repos/github/biocore/deblur/badge.svg?branch=master)](https://coveralls.io/github/biocore/deblur?branch=master)

Deblur is a greedy deconvolution algorithm for amplicon sequencing based on Illumina Miseq/Hiseq error profiles.

Install
=======
- Deblur requires Python 3.5. If Python 3.5 is not installed, you can create a [conda](http://conda.pydata.org/docs/install/quick.html) environment for Deblur using:
```
conda create -n deblurenv python=3.5 numpy
```

and activate it using:
```
source activate deblurenv
```

(note you will need to activate this environment every time you want to use Deblur)

Install Deblur dependencies and Deblur itself:
```
conda install -c bioconda -c biocore VSEARCH=2.0.3 MAFFT=7.221 biom-format SortMeRNA==2.0 deblur
```

N.B. Some dependencies are version restricted at the moment but for different reasons. SortMeRNA 2.1 has a different output format which Deblur is not compatible with yet. VSEARCH 2.0.3 and MAFFT 7.221 yield slightly different results in testing than their respective latest versions (at the time of release of Deblur 1.0.0). A review of their changelogs did not reveal any remarkable notes (e.g., bugs) about the reasons for the differences. In testing, the differences affected <0.1% of the sOTUs. As a precaution, we are advising the use of these specific versions for consistency with the manuscript. 

Example usage
=============

We recommend using Deblur via the QIIME2 plugin [q2-deblur](https://github.com/wasade/q2-deblur). Examples of its use can be found within the plugin itself. However, Deblur itself does not depend on QIIME2.

If you are running Deblur directly, we recommend focusing on the `workflow` subcommand. Detailed help can be obtained with:

```
deblur workflow --help
```

As a simple example, let's specify an input FASTA file, an output path and a sequence trim length of 150. This command will trim all sequences in `all_samples.fna` to 150nt in length; any read that is shorter will be omitted. This execution mode assumes that `all_samples.fna` is demultiplexed such that the sequence IDs are compatible with QIIME 1.9.1. On completion, a new directory `output` will be created with multiple output files (see the Input and Output Files section for more detail). <commit_msg>DOC: Updates regarding MAFFT version change<commit_after>Deblur
======

[![Build Status](https://travis-ci.org/biocore/deblur.png?branch=master)](https://travis-ci.org/biocore/deblur)
[![Coverage Status](https://coveralls.io/repos/github/biocore/deblur/badge.svg?branch=master)](https://coveralls.io/github/biocore/deblur?branch=master)

Deblur is a greedy deconvolution algorithm for amplicon sequencing based on Illumina Miseq/Hiseq error profiles.

Install
=======
- Deblur requires Python 3.5. If Python 3.5 is not installed, you can create a [conda](http://conda.pydata.org/docs/install/quick.html) environment for Deblur using:
```
conda create -n deblurenv python=3.5 numpy
```

and activate it using:
```
source activate deblurenv
```

(note you will need to activate this environment every time you want to use Deblur)

Install Deblur dependencies and Deblur itself:
```
conda install -c bioconda -c biocore VSEARCH=2.0.3 MAFFT=7.310 biom-format SortMeRNA==2.0 deblur
```

N.B. Some dependencies are version restricted at the moment but for different reasons. SortMeRNA 2.1 has a different output format which Deblur is not compatible with yet. VSEARCH 2.0.3 yields slightly different results in testing than the latest version (at the time of release of Deblur 1.0.0). A review of the changelog did not reveal any remarkable notes (e.g., bugs) about the reasons for the differences. In testing, the differences affected <0.1% of the sOTUs. As a precaution, we are advising the use of these specific versions for consistency with the manuscript.

Example usage
=============

We recommend using Deblur via the QIIME2 plugin [q2-deblur](https://github.com/wasade/q2-deblur). Examples of its use can be found within the plugin itself. However, Deblur itself does not depend on QIIME2.

If you are running Deblur directly, we recommend focusing on the `workflow` subcommand. Detailed help can be obtained with:

```
deblur workflow --help
```

As a simple example, let's specify an input FASTA file, an output path and a sequence trim length of 150. This command will trim all sequences in `all_samples.fna` to 150nt in length; any read that is shorter will be omitted. This execution mode assumes that `all_samples.fna` is demultiplexed such that the sequence IDs are compatible with QIIME 1.9.1. On completion, a new directory `output` will be created with multiple output files (see the Input and Output Files section for more detail). 
{'fraction_non_alphanumeric': 0.08910688739014919, 'fraction_numerical': 0.01634988759452279, 'mean_word_length': 4.7172897196261685, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 3, 'https://': 10, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '3883366', 'n_tokens_mistral': 1508, 'n_tokens_neox': 1368, 'n_words': 629}