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Deblur

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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 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.

  • install deblur dependencies
conda install -c bioconda VSEARCH MAFFT biom-format SortMeRNA==2.0
  • 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 are three biom tables (in the output directory) with sequences as the OTU ids:

  • all.biom : contains all deblurred reads.

  • reference-hit.biom : contains only deblurred reads matching the positive filtering database (default is only 16S reads)

  • reference-non-hit.biom : contains only deblurred reads not matching the positive filtering database (default is all non-16S reads)

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

deblur workflow --seqs-fp all_samples.fna --output-dir output -t 150

If starting from a barcode and read file, you can first use the qiime split_libraries_fastq.py 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

and use the split/seqs.fna as the input to the deblur workflow.

Important options

  • Since deblur cannot associate together sequences with different lengths, trimming is automaticallly performed as the first step in the deblur pipeline. The sequence trim length is specified by the -t NNN flag, where NNN denotes the length all sequences will be trimmed to. Note that all reads shorter than this length will be discarded.

  • In order to run in parallel, the number of threads can be specified by the -O NNN flag (default it 1). Note that running more threads than available cores will not speed up performance.

  • To get a full list of options, use:

deblur workflow --help

Positive and Negative Filtering

Deblur uses two types of filtering on the sequences:

  • negative mode - removes known artifact sequences (i.e. sequences aligning to PhiX or Adapter with >=95% identity and coverage).

  • positive mode - keeps only sequences similar (sortmerna e-value<=10) to a reference database (by default known 16S sequences). Note that deblur also outputs a biom table without this positive filtering step (named all.biom).

The fasta files for both of these filtering steps can be supplied via the --neg-ref-fp and --pos-ref-fp options, and by default are supplied for 16S sequences.

Deblur uses negative mode filtering to remove known artifact (i.e. PhiX and Adapter sequences) prior to denoising. The output of deblur contains three files: all.biom, which includes all sOTUs, reference-hit.biom, which contains the output of positive filtering of the sOTUs (default only sOTUs similar to 16S sequences), and reference-non-hit.biom, which contains only sOTUs failing the positive filtering (default only non-16S sOTUs).

Minimal Reads Filtering

Deblur runs on each sample independently. However, sometimes there is also additional information based on the total number of times an sOTU is observed in all samples (e.g. an sOTU which is observed only in one sample at low read count is more likely to be pcr/read error as opposed to an sOTU present in many samples). The --min-reads option allows to use this information by removing sOTUs with a total read count (across all samples) lower than the given threshold. The default value is set to 10, and should be useful for most cases. However, if such filtering is not wanted (e.g. if all samples in an experiment are expected not to contain the same bacteria, so no additional information is gained by combining the information from multiple samples), --min-reads can be set to 0 to skip this final filtering step.

Troubleshooting

  • Mac users: if you get the following error:

RuntimeError: Python is not installed as a framework. The Mac OS X backend will not be able to function correctly if Python is not installed as a framework. See the Python documentation for more information on installing Python as a framework on Mac OS X. Please either reinstall Python as a framework, or try one of the other backends. If you are Working with Matplotlib in a virtual enviroment see 'Working with Matplotlib in Virtual environments' in the Matplotlib FAQ

You can solve it by the following commands:

cd ~/.matplotlib
echo "backend: TkAgg" >> ~/.matplotlib/matplotlibrc
  • "Too many open files" : This error indicates deblur is trying to split a single fasta/q file into per-sample files, and the OS does not allow so many open simultaneous open files. Current solution is to use the qiime1.9 command split_sequence_file_on_sample_ids.py or the equivalent qiime2 command to split the single fasta/q file into a directory of per sample fasta/q files and then run deblur with this directory as the input to deblur (--seqs-fp).

Code Development Note

Some of the code in the package deblur has been derived from QIIME. The contributors to these specific QIIME modules have granted permission for this porting to take place and put under the BSD license.