<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 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 environment for deblur using:

At the moment, the install is a two stage process as we do not currently have deblur staged in a conda channel.

```
conda install -c biocore VSEARCH MAFFT SortMeRNA==2.0 
conda install -c bioconda biom-format
pip install deblur
```

- Install Deblur:
```
pip install deblur
```

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

The input to deblur workflow is a directory of fasta files (1 per sample) or a demultiplexed FASTA or FASTQ file. The output is a biom table with sequences as the OTU ids (final.biom in the output directory).

The simple use case just specifies the input fasta file (or directory) and output directory name:

```
deblur workflow --seqs-fp all_samples.fna --output-dir output
```

If starting from a barcode and read file, you can first use the qiime [split_libraries_fastq.py](http://qiime.org/scripts/split_libraries_fastq.html) command (we recommend using -q 19 to remove low quality reads):

```
split_libraries_fastq.py -i XXX_R1_001.fastq -m map.txt -o split -b XXXX_I1_001.fastq -q 19
```<commit_msg>add python 3 conda env. creation and activation<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 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 environment for deblur using:
```
conda create -n deblurenv python=3 numpy
```

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

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

At the moment, the install is a two stage process as we do not currently have deblur staged in a conda channel.

```
conda install -c biocore VSEARCH MAFFT SortMeRNA==2.0 
conda install -c bioconda biom-format
pip install deblur
```

- Install Deblur:
```
pip install deblur
```

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

The input to deblur workflow is a directory of fasta files (1 per sample) or a demultiplexed FASTA or FASTQ file. The output is a biom table with sequences as the OTU ids (final.biom in the output directory).

The simple use case just specifies the input fasta file (or directory) and output directory name:

```
deblur workflow --seqs-fp all_samples.fna --output-dir output
```

If starting from a barcode and read file, you can first use the qiime [split_libraries_fastq.py](http://qiime.org/scripts/split_libraries_fastq.html) command (we recommend using -q 19 to remove low quality reads):

```
split_libraries_fastq.py -i XXX_R1_001.fastq -m map.txt -o split -b XXXX_I1_001.fastq -q 19
```
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