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Configuration Reference

This document provides a comprehensive reference for all configuration parameters in Maple. They are defined in the config.yaml file, which should be found at the top level of the Maple working directory.

Required Configuration

runs

Path to CSV file defining experimental runs to be analyzed.

Type: String (file path)
Default: None (must be specified)
Example: runs: tags.csv

The tags.csv file must be located within the metadata directory and contains columns defining each experimental run:

  • tag: A unique identifier for the run that will be used in all output filenames (Note: Tags may not contain underscores)
  • reference_csv: Path to a reference CSV file in the metadata directory (see Reference CSV Format below)
  • one of the following for sequence import:
    • runname: Directory or individual file name containing FASTQ files/sequences, must be within the config-defined sequences_dir
    • bs_project_ID and sample_ID: For pulling pre-demultiplexed data from Illumina's BaseSpace. Downloads the specified project ID and grabs the two paired fastq.gz files for the specified sample ID.
    • fwdReads and rvsReads: For paired-end read merging
  • optional columns:
    • barcode_info_csv: CSV file defining barcode types and contexts. See 'Demultiplexing'
    • partition_barcode_groups_csv: CSV file defining names to assign to barcode groups for demultiplexing and partitioning sequences to distinct files. See 'Demultiplexing'
    • label_barcode_groups_csv: CSV file defining names to assign to barcode groups for demultiplexing and labeling sequences (in the 'BC' tag of a sequence in BAM file output). See 'Demultiplexing'
    • splint: Splint sequence for RCA consensus generation
    • UMI_contexts: Context patterns for UMI extraction, in order of appearance in the reference, separated by , (comma)
    • timepoint: A CSV file used for time series analysis (see below)
sequences_dir

Path to directory containing raw sequencing data.

Type: String (directory path)
Default: data
Example: sequences_dir: /path/to/sequences

This directory will be searched for subdirectories matching the runname values from tags.csv. Best practice is to set this to be an absolute path to a central sequence storage location that doesn't need to change when a new analysis is being set up.

fastq_dir (required)

Comma-separated list of folders within run directories to pull FASTQ files from.

Type: String (comma-separated)
Default: fastq_pass, fastq_fail
Example: fastq_dir: fastq_pass

Common configurations:

  • fastq_pass: Only reads that passed filter (Nanopore)
  • fastq_pass, fastq_fail: All reads (Nanopore)
  • raw: Custom directory name
metadata

Directory name for metadata files, located in working directory.

Type: String (directory name)
Default: metadata
Example: metadata: metadata

This directory should contain all files that define a Maple run except for the config file and sequencing data. For example, CSV files defining runs or barcode info and fasta files defining barcodes.

Reference CSV Format

The reference CSV file defines reference sequences for alignment and mutation analysis. Each row represents a distinct reference sequence that reads can align to.

Required columns:

  • ref_seq_name: Unique identifier for this reference
  • ref_seq_alignment: Full sequence used for alignment, including Ns for variable regions (barcodes, UMIs)

Optional columns (for mutation analysis):

  • ref_seq_NT: Nucleotide sequence range for NT mutation counting (must be a subsequence of ref_seq_alignment or its reverse complement). If not provided but ref_seq_coding is provided, ref_seq_coding will be used for NT analysis
  • ref_seq_coding: Coding sequence for amino acid mutation analysis (must be a subsequence of ref_seq_NT or ref_seq_alignment)
  • use_longest_ORF: Boolean (TRUE/FALSE). If TRUE, automatically identifies and uses the longest open reading frame for both NT and AA analysis. Overrides the global use_longest_orf_default setting
  • structure_file: Path to a PDB structure file for structure-based analysis

Example:

ref_seq_name,ref_seq_alignment,ref_seq_NT,ref_seq_coding,use_longest_ORF,structure_file
TrpB,AGGNNNN...CTGA,ATGAAA...TAA,ATGAAA...TAA,FALSE,metadata/structure.pdb

Notes:

  • Multiple references can be defined in a single CSV file, allowing reads to be analyzed against whichever reference they align best to
  • If only ref_seq_alignment is provided, the pipeline can still be used for demultiplexing and enrichment analysis without mutation analysis
  • Barcode and UMI contexts (if used) must appear exactly once in each reference's alignment sequence
use_longest_orf_default

Default behavior for identifying protein coding sequences in references.

Type: Boolean Default: FALSE Example: use_longest_orf_default: TRUE

Controls whether the pipeline automatically identifies and uses the longest open reading frame (ORF) as the coding sequence for protein analysis. This can be overridden per-reference by including a use_longest_ORF column in the reference CSV file. When enabled, the pipeline will:

  • Identify the longest ORF in each reference sequence
  • Use it for amino acid mutation analysis
  • Validate that manually provided ref_seq_coding values match the longest ORF (if both are provided)

Consensus Generation

RCA Consensus

Parameters for rolling circle amplification consensus using C3POa. Adding the splint parameter to a run tag will trigger RCA consensus generation. To view available flags and other documentation for this tool, use 'python -m C3POa --help'.

peak_finder_settings

  • Type: String
  • Default: '23,3,27,2'
  • Description: Settings used to identify splint alignment locations for splitting reads into subreads
  • Example: peak_finder_settings: '23,3,27,2'

RCA_batch_size

  • Type: Integer
  • Default: 10000
  • Description: Number of sequences to batch together in each subprocess. Lower this number if RCA processing crashes
  • Example: RCA_batch_size: 5000

RCA_consensus_minimum

  • Type: Integer
  • Default: 3
  • Description: Inclusive minimum number of complete subreads required to generate an RCA consensus read. Subreads at the end that do not include the splint will qualify
  • Example: RCA_consensus_minimum: 3

RCA_consensus_maximum

  • Type: Integer
  • Default: 20
  • Description: Inclusive maximum number of complete subreads used to generate an RCA consensus read. Reads with more subreads will not be used
  • Example: RCA_consensus_maximum: 15
UMI Consensus

Parameters for UMI-based consensus generation using medaka and umicollapse.

UMI_mismatches

  • Type: Integer
  • Default: None
  • Description: Maximum allowable number of mismatches that UMIs can contain and still be grouped together
  • Example: UMI_mismatches: 1

UMI_consensus_minimum

  • Type: Integer
  • Default: None
  • Description: Inclusive minimum number of subreads required to generate a UMI consensus read
  • Example: UMI_consensus_minimum: 5

UMI_consensus_maximum

  • Type: Integer
  • Default: None
  • Description: Inclusive maximum number of subreads used to generate a UMI consensus read. Groups with more subreads will be downsampled to this number. Note that this behavior differs slightly from that of RCA_consensus_maximum. Setting this parameter to 1 will trigger 'deduplication' behavior in which consensus sequence construction is skipped
  • Example: UMI_consensus_maximum: 20

UMI_medaka_batches

  • Type: Integer
  • Default: None
  • Description: Number of files to split BAM file into prior to running medaka. Increase if medaka throws memory errors
  • Example: UMI_medaka_batches: 10

UMI_distribution_log_x

  • Type: Boolean
  • Default: False
  • Description: If True, uses logarithmic scale for x-axis in UMI distribution plots
  • Example: UMI_distribution_log_x: True

UMI_distribution_log_y

  • Type: Boolean
  • Default: False
  • Description: If True, uses logarithmic scale for y-axis in UMI distribution plots
  • Example: UMI_distribution_log_y: True
Medaka

Parameters for medaka consensus generation (Nanopore only).

medaka_model

  • Type: String
  • Default: 'r104_e81_sup_variant_g610'
  • Description: Model for medaka to use. Use medaka smolecule --help to see all available options
  • Example: medaka_model: 'r103_sup_variant_g507'

medaka_flags

  • Type: String
  • Default: '--quiet'
  • Description: Additional flags to add to medaka smolecule command. Threads and model flags are already added
  • Example: medaka_flags: '--quiet --chunk-len 10000'

medaka_use_gpu

  • Type: Boolean
  • Default: True
  • Description: Controls thread allocation for medaka consensus to optimize for GPU or CPU usage. When True (GPU mode), uses all available cores per batch to prevent GPU memory overload from parallel jobs. When False (CPU mode), uses 1 thread per batch to enable parallel processing of multiple batches simultaneously with -j flag.
  • Example: medaka_use_gpu: False

Alignment and Processing

Alignment

Parameters for sequence alignment using minimap2 and samtools.

alignment_minimap2_flags

  • Type: String or Dictionary
  • Default: '-a -A2 -B4 -O4 -E2 --end-bonus=30 --secondary=no'
  • Description: Command line flags for minimap2 DNA alignment. Default options are optimized for targeted sequencing. can also supply a dict with tags as keys and flags as values for cases where you want to use different alignment settings for different tags. To view available flags and other documentation for this tool, use 'minimap2 --help'
  • Example (string): alignment_minimap2_flags: '-a -A2 -B4 -O4 -E2 --secondary=no'
  • Example (dict): 
    alignment_minimap2_flags:
  tag1: '-a -A2 -B4 -O4 -E2 --secondary=no'
  tag2: '-a -A2 -B4 -O10 -E4 --secondary=no'

alignment_samtools_flags

  • Type: String
  • Default: ''
  • Description: Additional flags for samtools during alignment processing. To view available flags and other documentation for this tool, use 'samtools --help'
  • Example: alignment_samtools_flags: '-F 4'
Thread Configuration

Thread allocation for different pipeline steps.

threads_medaka

  • Type: Integer
  • Default: 2
  • Description: Threads per medaka processing batch
  • Example: threads_medaka: 4

threads_alignment

  • Type: Integer
  • Default: 4
  • Description: Threads for alignment step. Recommended to provide maximum available threads, minimum 3
  • Example: threads_alignment: 8

threads_samtools

  • Type: Integer
  • Default: 1
  • Description: Threads for samtools operations
  • Example: threads_samtools: 2

threads_demux

  • Type: Integer
  • Default: 4
  • Description: Threads for demultiplexing operations
  • Example: threads_demux: 8

Demultiplexing

Demultiplexing

Parameters controlling barcode detection and sequence sorting. Demultiplexing is enabled for a tag by providing, in the runs CSV file, a CSV file in the barcode_info_csv column for a tag. This CSV file contains columns that to describe how demultiplexing should be performed:

  • barcode_name: str, some descriptive name for the barcode.
  • context: str, the nucleotide sequence within the reference fasta sequence that includes the location where the barcode will align. The barcode itself should be replaced with the ambiguous nucleotide N. Sufficient additional unambiguous nucleotides should be included to distinguish the barcode from any others. If there is only one barcode, this can just be the ambiguous nucleotides alone. However, if there is more than one barcode, then the ambiguous nucleotides alone will not be sufficient to distinguish the two or more barcode locations, so additional adjacent unambiguous nucleotides should be included.
  • fasta: str, a fasta formatted file located in the metadata directory that contains the barcode sequences that you wish to demultiplex. The names of each sequence will be used to name output files if a partition_barcode_groups_csv is not provided for the tag. If generate is not set to True, this fasta file must already exist.
  • optional columns:
    • reverse_complement: Boolean, whether the sequences in the barcode fasta are the reverse complement of the barcodes that are expected to be found in the top strand at the context position. Defaults to False if not provided explicitly
    • generate: False (default) or an integer. If set to False, the provided fasta file must already exist, and that fasta file will be used as the source for barcode sequences to search for during demultiplexing. If set to an integer, then prior to demultiplexing the provided fasta file will instead be constructed: barcodes will be extracted from the position defined by context and will be added to the fasta file, with the most frequently appearing barcodes appearing first. The provided integer is the maximum number of unique barcode sequences to add to the fasta file
    • label_only: Boolean, whether the provided barcode should be used to 'label' a sequence (i.e., add the barcode as a label to the BAM file entry for the sequence in the demultiplexed output) and not to partition sequences that differ in this barcode into different output files

To name demultiplexed files and/or label demultiplexed sequences based on combinations of barcodes, a CSV file name should be provided in the partition_barcode_groups_csv and/or label_barcode_groups_csv columns, respectively in the runs CSV file. These CSV files have identical structure, differing only in whether names are assigned to demultiplexed files or individual sequences in the BAM file outputs:

  • barcode_group: string, the name to be assigned to a file or sequence with barcodes that match those defined in the other columns
  • All other columns must match one of the barcode_names defined in the barcode_info_csv, and values in each column will be one of the expected barcodes defined in the fasta column of the barcode_info_csv

demux_screen_no_group

  • Type: Boolean
  • Default: True
  • Description: Set to True if sequences not assigned to a named barcode group should be blocked from subsequent analysis
  • Example: demux_screen_no_group: False

demux_screen_failures

  • Type: Boolean
  • Default: False
  • Description: Set to True if sequences that fail barcode detection should be blocked from subsequent analysis. Also used for enrichment calculations to exclude failed barcodes.
  • Example: demux_screen_failures: True

demux_threshold

  • Type: Float
  • Default: 0.01
  • Description: Minimum proportion of total reads required for a demultiplexed file to be processed further
  • Example: demux_threshold: 0.05

demux_max_references

  • Type: Integer or Boolean
  • Default: False
  • Description: Maximum number of reference sequences to keep for downstream analysis. If set to an integer N, only sequences aligning to the top N most abundant references will be written to output BAM files. Sequences aligning to filtered references still appear in demultiplexing statistics but not in output files. Set to False to keep all references
  • Example: demux_max_references: 100

Paired-End Read Processing

NGmerge

Parameters for paired-end read merging using NGmerge.

merge_paired_end

  • Type: Boolean
  • Default: False
  • Description: Set to True if merging of paired-end reads is needed
  • Example: merge_paired_end: True

NGmerge_flags

  • Type: String
  • Default: ''
  • Description: Command line flags for NGmerge. -m X sets minimum allowable overlap to X. Examine NGmerge documentation for usage if amplicons are shorter than both mates of a paired end read. If you see 'Error! Quality scores outside of set range', then including the flags '-u 41 -g' may help
  • Example: NGmerge_flags: '-m 10'

Mutation Analysis

Mutation Analysis

Parameters controlling mutation detection and analysis.

mutation_analysis_quality_score_minimum

  • Type: Integer
  • Default: 5
  • Description: Minimum quality score needed for mutation to be counted. For amino acid analysis, all nucleotides in the codon must be above threshold
  • Example: mutation_analysis_quality_score_minimum: 10

sequence_length_threshold

  • Type: Float
  • Default: ''
  • Description: Proportion of sequence length used as threshold for discarding aberrant-length sequences. e.g. if set to 0.1 and length of trimmed reference sequence is 1000 bp, then all sequences either below 900 or above 1100 bp will not be analyzed
  • Example: sequence_length_threshold: 0.1

analyze_seqs_with_indels

  • Type: Boolean
  • Default: True
  • Description: Set to True if sequences containing insertions or deletions should be analyzed
  • Example: analyze_seqs_with_indels: False

mutations_frequencies_raw

  • Type: Boolean
  • Default: False
  • Description: If True, outputs mutation frequencies as raw counts instead of proportions
  • Example: mutations_frequencies_raw: True

mutations_frequencies_number_of_positions

  • Type: Integer
  • Default: 20
  • Description: Number of mutations to include in most/least frequent mutations plots
  • Example: mutations_frequencies_number_of_positions: 30

mutations_frequencies_heatmap

  • Type: Boolean
  • Default: True
  • Description: If True, frequencies plot will be a heatmap; otherwise a stacked bar chart
  • Example: mutations_frequencies_heatmap: False

uniques_only

  • Type: Boolean
  • Default: False
  • Description: If True, only uses unique mutations to determine mutation spectrum
  • Example: uniques_only: True
Genotype Analysis

Parameters for genotype identification and analysis.

highest_abundance_genotypes

  • Type: Integer
  • Default: 10
  • Description: Number of most frequently appearing genotypes to find representative sequences for
  • Example: highest_abundance_genotypes: 20

genotype_ID_alignments

  • Type: Integer or String
  • Default: 0
  • Description: Comma-separated list of genotype IDs to include in output, or 0 if not desired
  • Example: genotype_ID_alignments: '1,5,10'

unique_genotypes_count_threshold

  • Type: Integer
  • Default: 5
  • Description: Minimum number of reads of a genotype for it to be included in unique genotypes count
  • Example: unique_genotypes_count_threshold: 10

Plotting and Visualization

Plot Configuration

Parameters controlling plot generation and appearance.

colormap

  • Type: String
  • Default: 'kbc_r'
  • Description: Colormap for plots. Options include 'kbc', 'fire', 'bgy', 'bgyw', 'bmy', 'gray', 'rainbow4', and their reverse versions with '_r'
  • Example: colormap: 'fire'

distribution_x_range

  • Type: Boolean or String
  • Default: False
  • Description: Comma-separated pair of values for x-axis range of distribution plots, or False for auto-range
  • Example: distribution_x_range: '0,100'

distribution_y_range

  • Type: Boolean or String
  • Default: False
  • Description: Comma-separated pair of values for y-axis range of distribution plots, or False for auto-range
  • Example: distribution_y_range: '0,0.5'

distribution_split_by_reference

  • Type: Boolean or Integer
  • Default: False
  • Description: Controls how mutation distribution plots handle multiple references. False: aggregate data across all references into a single plot. True: plot all references separately in a grid layout. Integer N: plot only the top N most abundant references (rows: samples, columns: references)
  • Example: distribution_split_by_reference: 5

export_SVG

  • Type: Boolean or String
  • Default: False
  • Description: Controls SVG export. False=no export, True=export all, string=export plots containing string. Requires a chrome installation on your machine. Plots will be exported individually so may require manually setting x/y ranges. SVG outputs are not tracked by the pipeline. Colorbars are not included in exports.
  • Example: export_SVG: 'mutation'
Plot Enable/Disable

Boolean flags to enable or disable inclusion of specific plot outputs in the targets rule.

plot_demux

  • Type: Boolean
  • Default: True
  • Description: Generate demultiplexing statistics plots
  • Example: plot_demux: False

plot_mutation-distribution

  • Type: Boolean
  • Default: True
  • Description: Generate mutation distribution plots
  • Example: plot_mutation-distribution: False

plot_mutations-aggregated

  • Type: Boolean
  • Default: True
  • Description: Generate mutations aggregated plots
  • Example: plot_mutations-aggregated: False

mutations_aggregated_split_by_reference

  • Type: Integer
  • Default: 10
  • Description: Number of top references (by sequence abundance) to include in mutations-aggregated plots. Each reference will be plotted as a separate column. References with fewer sequences will be excluded from the plot.
  • Example: mutations_aggregated_split_by_reference: 5

plot_hamming-distance-distribution

  • Type: Boolean
  • Default: True
  • Description: Generate hamming distance distribution plots
  • Example: plot_hamming-distance-distribution: False

plot_mutation-distribution-violin

  • Type: Boolean
  • Default: True
  • Description: Generate violin plots for mutation distributions
  • Example: plot_mutation-distribution-violin: False

plot_genotypes2D

  • Type: Boolean
  • Default: False
  • Description: Generate 2D genotype plots (also depends on other genotypes2D options)
  • Example: plot_genotypes2D: True

Advanced Features

Enrichment Analysis

Parameters for enrichment score calculation and filtering.

enrichment_SE_filter

  • Type: Float
  • Default: 0
  • Description: Proportion of standard errors to filter out (0-1). 0 or 1 disables filter
  • Example: enrichment_SE_filter: 0.1

enrichment_t0_filter

  • Type: Float
  • Default: 0
  • Description: Proportion of timepoint 0 counts to filter out (0-1). 0 or 1 disables filter
  • Example: enrichment_t0_filter: 0.1

enrichment_score_filter

  • Type: Boolean or Float
  • Default: False
  • Description: Enrichment score threshold. Scores below this value are removed. False disables filter
  • Example: enrichment_score_filter: 0.5

enrichment_missing_replicates_filter

  • Type: Boolean
  • Default: True
  • Description: Whether to filter out barcodes without enrichment scores for all replicates
  • Example: enrichment_missing_replicates_filter: False

enrichment_reference

  • Type: String
  • Default: 'all'
  • Description: Entity to use as reference for normalization. 'all' normalizes to all entities. If '' or False, a single entity that is abundant within all samples will be chosen as the reference. Can also specify a specific entity ID to use as reference
  • Example: enrichment_reference: 'all'

do_enrichment

  • Type: Boolean
  • Default: False
  • Description: Enable enrichment score calculation for timepoint samples. When True, enrichment scores are calculated based on the specified enrichment_type. Requires a timepoints CSV file
  • Example: do_enrichment: True

enrichment_type

  • Type: String
  • Default: 'genotype'
  • Description: Type of enrichment analysis to perform. Options: 'genotype' (mutation-based enrichment using genotype CSV files) or 'demux' (barcode-based enrichment using demux-stats.csv)
  • Example: enrichment_type: 'genotype'
Genotypes2D Plotting

Parameters for 2D genotype visualization using dimensionality reduction.

genotypes2D_plot_all

  • Type: Boolean
  • Default: False
  • Description: Generate 2D genotype plots for all samples individually
  • Example: genotypes2D_plot_all: True

genotypes2D_plot_groups

  • Type: Boolean
  • Default: False
  • Description: Generate 2D genotype plots for groups of samples (e.g. tags, timepoints)
  • Example: genotypes2D_plot_groups: True

genotypes2D_plot_downsample

  • Type: Integer
  • Default: 10000
  • Description: Maximum number of genotypes to include in 2D plots (downsampling threshold)
  • Example: genotypes2D_plot_downsample: 5000

genotypes2D_plot_AA

  • Type: Boolean
  • Default: True
  • Description: Use protein sequence for dimension reduction if available
  • Example: genotypes2D_plot_AA: False

genotypes2D_plot_point_size_col

  • Type: String
  • Default: count
  • Description: Genotypes column to use for point size (must be numerical)
  • Example: genotypes2D_plot_point_size_col: 'frequency'

genotypes2D_plot_point_size_range

  • Type: String
  • Default: '30, 60'
  • Description: Comma-separated pair of integers for minimum and maximum point sizes. If all values in the size column are the same, the minimum size will be used
  • Example: genotypes2D_plot_point_size_range: '20, 80'

genotypes2D_plot_point_color_col

  • Type: String (optional)
  • Default: None (depends on genotypes.csv file being used)
  • Description: Genotypes column to use for coloring data points. Any genotypes column is valid, though some choices work better than others. Numerical columns will be colored continuously from white to deep blue. Categorical columns will be colored as rainbow
  • Example: genotypes2D_plot_point_color_col: 'NT_substitutions_count'
Hamming Distance Analysis

Parameters for hamming distance distribution analysis.

hamming_distance_distribution_downsample

  • Type: Integer or Boolean
  • Default: 1000
  • Description: Maximum number of genotypes to use for hamming distance calculation. False uses all genotypes
  • Example: hamming_distance_distribution_downsample: 500

hamming_distance_distribution_raw

  • Type: Boolean
  • Default: False
  • Description: If True, y-axis shows raw counts instead of proportions
  • Example: hamming_distance_distribution_raw: True
Dashboard Configuration

Parameters for the interactive dashboard.

dashboard_input

  • Type: String
  • Default: TrpB
  • Description: Tag, timepoint name, or tag_barcodeGroup to use for the dashboard
  • Example: dashboard_input: 'experiment1'

dashboard_port

  • Type: Integer
  • Default: 3366
  • Description: Port number for running the dashboard
  • Example: dashboard_port: 8080
Time Series Analysis

Parameters for time series and evolution experiments.

timepoints

  • Type: String
  • Default: timepoints.csv
  • Description: CSV file providing tag and barcode combinations for experiment timepoints. This file is specified in the timepoint column of tags.csv and must be located in the metadata directory. The timepoints CSV links sample labels to specific demultiplexed samples across a time series, enabling enrichment calculations.

File Format:

  • First column header: sample_label (required)
  • Subsequent column headers: Timepoint values (numerical, all samples must share the same timepoints)
  • First column values: Sample labels used in analysis outputs and plots
  • Cell values: tag_barcodeGroup identifiers in the format tag_barcodeGroup, where:
    • tag matches a tag from tags.csv
    • barcodeGroup matches a barcode_group from the partition_barcode_groups_csv

Example CSV:

sample_label,0,24,48,72
TrpB-rep1,TrpB_P0,TrpB_P1,TrpB_P2,TrpB_P3
TrpB-rep2,TrpB_P0,TrpB_P4,TrpB_P5,TrpB_P6

In this example:

  • Two sample labels (biological replicates) are tracked across the same timepoints (0, 24, 48, 72 hours)
  • Both samples use tag "TrpB" with different barcode groups (P0-P6) at each timepoint
  • The first timepoint column (0) is used as the reference for enrichment calculations

Usage: Set in tags.csv using the timepoint column, or globally in config.yaml. When do_enrichment: True, enrichment scores are calculated by comparing each timepoint to the initial timepoint (first column) for each sample label.

  • Example: timepoints: my_timepoints.csv

timepoints_units

  • Type: String
  • Default: 'timepoint'
  • Description: Units for timepoint measurements (e.g., 'generations', 'hours', 'days', 'passages'). Used for labeling plots and enrichment analysis outputs
  • Example: timepoints_units: 'generations'
NanoPlot Integration

Parameters for NanoPlot quality control visualization. To view available flags and other documentation for this tool, use 'NanoPlot --help'

nanoplot

  • Type: Boolean
  • Default: False
  • Description: Enable NanoPlot for sequence quality visualization
  • Example: nanoplot: True

nanoplot_flags

  • Type: String
  • Default: '--plots dot'
  • Description: Command line flags for NanoPlot. -o (output) and -p (prefix) are already added
  • Example: nanoplot_flags: '--plots kde --format png'