q2-deblur

This QIIME 2 plugin wraps the Deblur software for performing sequence quality control.

version: 2024.10.0

website: https://github.com/biocore/deblur

user support:

Please post to the QIIME 2 forum for help with this plugin: https://forum.qiime2.org

citations:

Amir et al., 2017

Actions¶

Artifact Classes¶

Formats¶

deblur denoise-16S¶

Perform sequence quality control for Illumina data using the Deblur workflow with a 16S reference as a positive filter. Only forward reads are supported at this time. The specific reference used is the 88% OTUs from Greengenes 13_8. This mode of operation should only be used when data were generated from a 16S amplicon protocol on an Illumina platform. The reference is only used to assess whether each sequence is likely to be 16S by a local alignment using SortMeRNA with a permissive e-value; the reference is not used to characterize the sequences.

Citations¶

Amir et al., 2017

Inputs¶

demultiplexed_seqs: SampleData[SequencesWithQuality | PairedEndSequencesWithQuality | JoinedSequencesWithQuality]

The demultiplexed sequences to be denoised.[required]

Parameters¶

trim_length: Int

Sequence trim length, specify -1 to disable trimming.[required]

left_trim_len: Int % Range(0, None)

Sequence trimming from the 5' end. A value of 0 will disable this trim.[default: 0]

sample_stats: Bool

If true, gather stats per sample.[default: False]

mean_error: Float

The mean per nucleotide error, used for original sequence estimate.[default: 0.005]

indel_prob: Float

Insertion/deletion (indel) probability (same for N indels).[default: 0.01]

indel_max: Int

Maximum number of insertion/deletions.[default: 3]

min_reads: Int

Retain only features appearing at least min_reads times across all samples in the resulting feature table.[default: 10]

min_size: Int

In each sample, discard all features with an abundance less than min_size.[default: 2]

jobs_to_start: Threads

Number of jobs to start (if to run in parallel).[default: 1]

hashed_feature_ids: Bool

If true, hash the feature IDs.[default: True]

Outputs¶

table: FeatureTable[Frequency]

The resulting denoised feature table.[required]

representative_sequences: FeatureData[Sequence]

The resulting feature sequences.[required]

stats: DeblurStats

Per-sample stats if requested.[required]

Examples¶

denoise_16S¶

wget -O 'demux-filtered.qza' \
  'https://amplicon-docs.qiime2.org/en/latest/data/examples/deblur/denoise-16S/1/demux-filtered.qza'

qiime deblur denoise-16S \
  --i-demultiplexed-seqs demux-filtered.qza \
  --p-trim-length 120 \
  --p-sample-stats \
  --o-representative-sequences representative-sequences.qza \
  --o-table table.qza \
  --o-stats denoising-stats.qza

from qiime2 import Artifact
from urllib import request
import qiime2.plugins.deblur.actions as deblur_actions

url = 'https://amplicon-docs.qiime2.org/en/latest/data/examples/deblur/denoise-16S/1/demux-filtered.qza'
fn = 'demux-filtered.qza'
request.urlretrieve(url, fn)
demux_filtered = Artifact.load(fn)

table, representative_sequences, denoising_stats = deblur_actions.denoise_16S(
    demultiplexed_seqs=demux_filtered,
    trim_length=120,
    sample_stats=True,
)

library(reticulate)

Artifact <- import("qiime2")$Artifact
deblur_actions <- import("qiime2.plugins.deblur.actions")
request <- import("urllib")$request

url <- 'https://amplicon-docs.qiime2.org/en/latest/data/examples/deblur/denoise-16S/1/demux-filtered.qza'
fn <- 'demux-filtered.qza'
request$urlretrieve(url, fn)
demux_filtered <- Artifact$load(fn)

action_results <- deblur_actions$denoise_16S(
    demultiplexed_seqs=demux_filtered,
    trim_length=120L,
    sample_stats=TRUE,
)
representative_sequences <- action_results$representative_sequences
table <- action_results$table
denoising_stats <- action_results$stats

from q2_deblur._examples import denoise_16S_example

denoise_16S_example(use)

deblur denoise-other¶

Perform sequence quality control for Illumina data using the Deblur workflow, including positive alignment-based filtering. Only forward reads are supported at this time. This mode of execution is particularly useful when operating on non-16S data. For example, to apply Deblur to 18S data, you would want to specify a reference composed of 18S sequences in order to filter out sequences which do not appear to be 18S. The assessment is performed by local alignment using SortMeRNA with a permissive e-value threshold.

Citations¶

Amir et al., 2017

Inputs¶

demultiplexed_seqs: SampleData[SequencesWithQuality | PairedEndSequencesWithQuality | JoinedSequencesWithQuality]

The demultiplexed sequences to be denoised.[required]

reference_seqs: FeatureData[Sequence]

Positive filtering database. Keep all sequences aligning to these sequences.[required]

Parameters¶

trim_length: Int

Sequence trim length, specify -1 to disable trimming.[required]

left_trim_len: Int % Range(0, None)

Sequence trimming from the 5' end. A value of 0 will disable this trim.[default: 0]

sample_stats: Bool

If true, gather stats per sample.[default: False]

mean_error: Float

The mean per nucleotide error, used for original sequence estimate.[default: 0.005]

indel_prob: Float

Insertion/deletion (indel) probability (same for N indels).[default: 0.01]

indel_max: Int

Maximum number of insertion/deletions.[default: 3]

min_reads: Int

Retain only features appearing at least min_reads times across all samples in the resulting feature table.[default: 10]

min_size: Int

In each sample, discard all features with an abundance less than min_size.[default: 2]

jobs_to_start: Threads

Number of jobs to start (if to run in parallel).[default: 1]

hashed_feature_ids: Bool

If true, hash the feature IDs.[default: True]

Outputs¶

table: FeatureTable[Frequency]

The resulting denoised feature table.[required]

representative_sequences: FeatureData[Sequence]

The resulting feature sequences.[required]

stats: DeblurStats

Per-sample stats if requested.[required]

deblur visualize-stats¶

Display Deblur statistics per sample

Citations¶

Amir et al., 2017

Inputs¶

deblur_stats: DeblurStats

Summary statistics of the Deblur process.[required]

Outputs¶

visualization: Visualization

<no description>[required]

Examples¶

visualize_stats¶

wget -O 'deblur-stats.qza' \
  'https://amplicon-docs.qiime2.org/en/latest/data/examples/deblur/visualize-stats/1/deblur-stats.qza'

qiime deblur visualize-stats \
  --i-deblur-stats deblur-stats.qza \
  --o-visualization deblur-stats-viz.qzv

from qiime2 import Artifact
from urllib import request
import qiime2.plugins.deblur.actions as deblur_actions

url = 'https://amplicon-docs.qiime2.org/en/latest/data/examples/deblur/visualize-stats/1/deblur-stats.qza'
fn = 'deblur-stats.qza'
request.urlretrieve(url, fn)
deblur_stats = Artifact.load(fn)

deblur_stats_viz_viz, = deblur_actions.visualize_stats(
    deblur_stats=deblur_stats,
)

library(reticulate)

Artifact <- import("qiime2")$Artifact
deblur_actions <- import("qiime2.plugins.deblur.actions")
request <- import("urllib")$request

url <- 'https://amplicon-docs.qiime2.org/en/latest/data/examples/deblur/visualize-stats/1/deblur-stats.qza'
fn <- 'deblur-stats.qza'
request$urlretrieve(url, fn)
deblur_stats <- Artifact$load(fn)

action_results <- deblur_actions$visualize_stats(
    deblur_stats=deblur_stats,
)
deblur_stats_viz_viz <- action_results$visualization

from q2_deblur._examples import visualize_stats_example

visualize_stats_example(use)