Shotgun report for demo_report
This report has been generated by the Zymo-Research/aladdin-shotgun analysis pipeline. For information about how to interpret these results, please see the report documentation.
General Statistics
By default, all read count columns are displayed as millions (M) of reads.Showing 20/20 rows and 8/25 columns.
| Sample Name | No. Input Reads | Length Input Reads | % Dups Input Reads | % GC Input Reads | % Pass QC Reads | No. Processed Reads | % Low-complexity Reads | % Kmers w/ Taxonomy |
|---|---|---|---|---|---|---|---|---|
| SRR5935740 | 10.5 | 92 bp | 21.3% | 46% | 99.2% | 10.5 | 0.0 | 83.22% |
| SRR5935744 | 15.3 | 93 bp | 39.7% | 46% | 99.3% | 15.2 | 0.0 | 91.85% |
| SRR5935746 | 11.0 | 93 bp | 26.0% | 45% | 99.4% | 10.9 | 0.0 | 91.48% |
| SRR5935752 | 14.3 | 93 bp | 60.0% | 43% | 99.3% | 14.2 | 0.0 | 96.41% |
| SRR5935753 | 14.2 | 93 bp | 33.0% | 44% | 99.3% | 14.1 | 0.0 | 93.29% |
| SRR5935754 | 10.5 | 91 bp | 25.9% | 47% | 99.1% | 10.4 | 0.0 | 88.36% |
| SRR5935755 | 6.1 | 89 bp | 23.3% | 48% | 99.0% | 6.0 | 0.0 | 88.83% |
| SRR5935756 | 16.1 | 93 bp | 34.1% | 45% | 99.3% | 15.9 | 0.0 | 91.71% |
| SRR5935757 | 16.3 | 92 bp | 47.1% | 45% | 99.3% | 16.2 | 0.0 | 82.86% |
| SRR5935759 | 15.3 | 93 bp | 43.8% | 46% | 99.3% | 15.2 | 0.0 | 69.19% |
| SRR5935760 | 10.8 | 90 bp | 35.7% | 44% | 99.0% | 10.7 | 0.0 | 93.12% |
| SRR5935764 | 11.7 | 90 bp | 28.2% | 47% | 99.1% | 11.6 | 0.0 | 92.35% |
| SRR5935768 | 15.7 | 91 bp | 30.8% | 46% | 99.2% | 15.6 | 0.0 | 85.02% |
| SRR5935769 | 11.4 | 90 bp | 24.8% | 47% | 99.1% | 11.3 | 0.0 | 88.39% |
| SRR5935770 | 16.2 | 93 bp | 37.0% | 45% | 99.4% | 16.1 | 0.0 | 92.52% |
| SRR5935771 | 9.2 | 91 bp | 29.5% | 45% | 99.2% | 9.2 | 0.0 | 89.81% |
| SRR5935774 | 12.3 | 90 bp | 24.9% | 46% | 99.0% | 12.1 | 0.0 | 82.39% |
| SRR5935778 | 10.6 | 92 bp | 22.9% | 46% | 99.2% | 10.5 | 0.0 | 91.62% |
| SRR5935781 | 8.5 | 95 bp | 47.5% | 45% | 99.5% | 8.4 | 0.0 | 91.14% |
| SRR5935784 | 14.8 | 93 bp | 31.1% | 45% | 99.4% | 14.7 | 0.0 | 95.08% |
FastQC / Falco (pre-Trimming)
FastQC / Falco (pre-Trimming) is a quality control tool for high throughput sequence data, written by Simon Andrews at the Babraham Institute in Cambridge.
If used in this run, Falco is a drop-in replacement for FastQC producing the same output, written by Guilherme de Sena Brandine and Andrew D. Smith.Sequence Counts
Sequence counts for each sample. Duplicate read counts are an estimate only.
This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).
You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:
Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.
The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.
Sequence Quality Histograms
The mean quality value across each base position in the read.
To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).
Taken from the FastQC help:
The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.
Per Sequence Quality Scores
The number of reads with average quality scores. Shows if a subset of reads has poor quality.
From the FastQC help:
The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.
Per Base Sequence Content
The proportion of each base position for which each of the four normal DNA bases has been called.
To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.
To see the data as a line plot, as in the original FastQC graph, click on a sample track.
From the FastQC help:
Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.
In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.
It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.
Rollover for sample name
Per Sequence GC Content
The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.
From the FastQC help:
This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.
In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.
An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.
Per Base N Content
The percentage of base calls at each position for which an N was called.
From the FastQC help:
If a sequencer is unable to make a base call with sufficient confidence then it will
normally substitute an N rather than a conventional base call. This graph shows the
percentage of base calls at each position for which an N was called.
It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.
Sequence Length Distribution
The distribution of fragment sizes (read lengths) found. See the FastQC help
Sequence Duplication Levels
The relative level of duplication found for every sequence.
From the FastQC Help:
In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (eg PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.
Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.
The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.
In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.
Overrepresented sequences
The total amount of overrepresented sequences found in each library.
FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as over represented.
Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.
From the FastQC Help:
A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.
FastQC lists all of the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.
Adapter Content
The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.
Note that only samples with ≥ 0.1% adapter contamination are shown.
There may be several lines per sample, as one is shown for each adapter detected in the file.
From the FastQC Help:
The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.
Status Checks
Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).
FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).
It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.
Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.
In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.
fastp
fastp An ultra-fast all-in-one FASTQ preprocessor (QC, adapters, trimming, filtering, splitting...).DOI: 10.1093/bioinformatics/bty560.
Filtered Reads
Filtering statistics of sampled reads.
Insert Sizes
Insert size estimation of sampled reads.
Sequence Quality
Average sequencing quality over each base of all reads.
GC Content
Average GC content over each base of all reads.
N content
Average N content over each base of all reads.
FastQC / Falco (post-Trimming)
FastQC / Falco (post-Trimming) is a quality control tool for high throughput sequence data, written by Simon Andrews at the Babraham Institute in Cambridge.
If used in this run, Falco is a drop-in replacement for FastQC producing the same output, written by Guilherme de Sena Brandine and Andrew D. Smith.Sequence Counts
Sequence counts for each sample. Duplicate read counts are an estimate only.
This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).
You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:
Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.
The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.
Sequence Quality Histograms
The mean quality value across each base position in the read.
To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).
Taken from the FastQC help:
The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.
Per Sequence Quality Scores
The number of reads with average quality scores. Shows if a subset of reads has poor quality.
From the FastQC help:
The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.
Per Base Sequence Content
The proportion of each base position for which each of the four normal DNA bases has been called.
To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.
To see the data as a line plot, as in the original FastQC graph, click on a sample track.
From the FastQC help:
Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.
In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.
It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.
Rollover for sample name
Per Sequence GC Content
The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.
From the FastQC help:
This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.
In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.
An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.
Per Base N Content
The percentage of base calls at each position for which an N was called.
From the FastQC help:
If a sequencer is unable to make a base call with sufficient confidence then it will
normally substitute an N rather than a conventional base call. This graph shows the
percentage of base calls at each position for which an N was called.
It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.
Sequence Length Distribution
The distribution of fragment sizes (read lengths) found. See the FastQC help
Sequence Duplication Levels
The relative level of duplication found for every sequence.
From the FastQC Help:
In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (eg PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.
Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.
The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.
In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.
Overrepresented sequences
The total amount of overrepresented sequences found in each library.
FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as over represented.
Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.
From the FastQC Help:
A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.
FastQC lists all of the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.
Adapter Content
The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.
Note that only samples with ≥ 0.1% adapter contamination are shown.
There may be several lines per sample, as one is shown for each adapter detected in the file.
From the FastQC Help:
The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.
Status Checks
Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).
FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).
It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.
Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.
In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.
BBDuk
BBDuk is a tool performing common data-quality-related trimming, filtering, and masking operations with a kmer based approach.
BBDuk: Filtered Reads
The number of reads removed by various BBDuk filters
Kmer composition (sourmash)
This plot depicts the composition of kmers identified (or not) by sourmash. It includes percentages of kmers that were identified as common host organisms, common eukaryotic pathogens/parasites, and other microbes. Unidentified kmers are those that do not match with the database or those with a match but fail to reach the base-pair threshold set during the sourmash step of the pipeline. Please refer to plots in sections below for detailed compositions of other microbes. sourmash only outputs percentages of kmers identified, the numbers of reads you see here are estimated using percentages and total numbers of reads that are input into sourmash.
Composition Barplots
Taxonomy Abundance Heatmap
The taxonomy abundance heatmap is a plot of the relative abundance of taxa (ranging from 0-1) for each sample and is a quick way to identify patterns of microbial distribution amongst samples. Each row represents the relative abundance of each taxon, with the taxonomy ID shown on the right. Each column represents a sample, with the sample ID shown at the bottom. Each box, or tile, on the heatmap represents relative abundance, colored according to the key scale on the far right of the plot.
Hierarchical clustering is performed on samples and taxa based on the Euclidean distance of log-transformed relative abundance. More similar samples and taxa are closer to each other. The heatmaps are made using the top 20 most abundant taxa of each sample group. Heatmaps are only made when there are >= 5 taxa at a taxonomic rank.
Please note that long taxa names (right of plot) may be partially obscured, however full details can be observed by hovering the mouse cursor over each tile.
Heatmaps at different taxonomic ranks can be accessed by clicking the buttons above the plot. By default, the plot shows relative abundance values, however results can be displayed as log-transformed and normalized. Relative abundances are log10 transformed and centered on each row. This view can be activated by selecting 'Log_normalized' from the drop down-list that appears when clicking the taxonomic rank buttons at the top of the plot.
Diversity Analysis
In this section, alpha diversity, alpha rarefaction, and beta diversity results by group are displayed in each subsection. Samples may be removed from diversity analysis for low read counts (default: 1,000 reads) or if samples belong to groups that lack any other replicates.
Beta Diversity
Beta diversity is a measurement of microbial diversity differences between samples. The figure below is the 3-dimensional principle coordinate analysis (PCoA) plot created using Bray-Curtis or Jaccard dissimilarity to calculate a matrix of paired-wise distances between samples. Use the dropdown menus below to access interactive 3-dimensional plots of beta diversity with different matrices. Each dot on the beta diversity plot represents the whole microbial composition profile. Samples with similar microbial composition profiles are closer to each other, while samples with different profiles are farther away from each other.
Alpha Diversity
Alpha diversity is a measurement of the microbial diversity of each group. For analyses with group comparison, a box-and-whisker plot of observed species in each group is shown. Alpha diversity computed by different metrics can be found by clicking on the dropdown menu to the left of the plot.
Alpha Rarefaction
This section shows an interactive alpha rarefaction plot, which is the alpha diversity (species richness) as a function of the sampling depth (sequencing read counts). Rarefaction is a technique to correct for uneven sampling when performing diversity analyses. Rarefaction curves can help to identify if sampling depth is sufficient to capture the true diversity of the community. Rarefaction curves are generated by randomly subsampling sequencing reads over a range of values and determining the alpha diversity at each read depth (at an average of 5 iterations). Curves computed by different metrics can be found by clicking on the dropdown menu to the left of the plot.
Comparative Diversity Analysis
In this section, a comparative diversity analysis has been conducted using the selected reference dataset. All diversity analyses have been recomputed to include the reference dataset, so results may appear differently than in the Diversity Analysis section. Please note that comparisons across different studies should be interpreted with caution, as many different factors from sample collection, DNA isolation, library preparation and sequencing can impact the results obtained. We always try to use the original group labels for comparisons to the reference dataset. However, when original group labels are not provided or lack the replicates required for this comparison, we use 'user-samples' as the new group label for all user samples.
Beta Diversity
This section displays the beta diversity of user and reference data, determined by the Bray-Curtis and Jaccard metrics. Click on the icon for each group in the legend to hide or reveal samples.
Alpha Diversity
This section displays the alpha diversity of user and reference data, determined by the Observed features and Shannon diversity index metrics. Click on the icon for each group in the legend to hide or reveal samples.
Resistome composition (AMRplusplus)
This plot depicts the composition of reads of antimicrobial resistance gene classes identified (or not) by AMRplusplus. The 'Read counts' tab of this plot includes counts of anti-microbial resistant gene classes detected in each shotgun sample. Each class contains multiple genes. The plotted class-level read counts are the sum of read counts of all genes in that class. Read counts have been normalized to counts per one million reads that passed trimming filters for each sample. 'Relative percentage' in this stacked bargraph represents the share of antimicrobial gene reads for a certain class over the total number of antimicrobial gene reads detected in the entire sample. For gene level data that has not been normalized, please refer to downloadable table genes_rawcounts_AMR_analytic_matrix.
Top 20 Genes Composition of Each Sample (AMRplusplus)
This plot depicts the composition of reads of antimicrobial resistance genes identified by AMRplusplus. It highlights the top 20 genes by mean relative abundance among all AMR genes. Read counts have been normalized to counts per one million reads that passed trimming filters for each sample. All other genes are labeled as 'Other'.
Zymo-Research/aladdin-shotgun Software Versions
are collected at run time from the software output.
| Process Name | Software | Version |
|---|---|---|
| BBMAP_BBDUK | bbmap | 39.01 |
| BWA_ALIGN | bwa | 0.7.17-r1188 |
| samtools | 1.11+htslib-1.11-4 | |
| CUSTOM_DUMPSOFTWAREVERSIONS | python | 3.10.6 |
| yaml | 6.0 | |
| FASTP_PAIRED | fastp | 0.23.4 |
| FASTQC | fastqc | 0.11.9 |
| FASTQC_PROCESSED | fastqc | 0.11.9 |
| QIIME_ALPHADIVERSITY | qiime | 2023.2.0 |
| QIIME_ALPHARAREFACTION | qiime | 2023.2.0 |
| QIIME_BARPLOT | qiime | 2023.2.0 |
| QIIME_BETAGROUPCOMPARE | qiime | 2023.2.0 |
| QIIME_DATAMERGE | biom | 2.1.12 |
| qiime | 2023.2.0 | |
| QIIME_DIVERSITYCORE | qiime | 2023.2.0 |
| QIIME_FILTER_SINGLETON_SAMPLE | biom | 2.1.12 |
| qiime | 2023.2.0 | |
| QIIME_IMPORT | qiime | 2023.2.0 |
| SAMPLESHEET_CHECK | python | 3.8.17 |
| SOURMASH_GATHER | sourmash | 4.8.2 |
| SOURMASH_QIIMEPREP | biom | 2.1.14 |
| SOURMASH_SKETCH | sourmash | 4.8.2 |
| Workflow | Nextflow | 22.10.4 |
| Zymo-Research/aladdin-shotgun | 0.0.4 |
Zymo-Research/aladdin-shotgun Workflow Summary
This section summarizes important parameters used in the pipeline. Only parameters that differ from the default are shown.
- aladdin_ref_dataset
- Aladdin-healthy_gut
- run_amr
- true
Report generated on 2024-04-30, 21:18 UTC.