sub1:assertion {
sub1:config-1 dcterms:identifier "config.yml" ;
schema:text """## General Workflow Parameters:
# sample information and experimental design
samples: samples.csv
## Workflow-specific Parameters:
# Define reference genome/transcriptome
ref:
species: \"Drosophila melanogaster\"
# Local reference data
# Genome file path (supported extensions: .fa, .fna, .fasta, case-insensitive)
# may be a path or left empty, if download using an accession number is preferred
genome: \"\"
# Annotation file (supported extensions: .gff, .gtf, case-insensitive)
# may be a path or left empty, if download using an accession number is preferred
annotation: \"\"
# Remote reference data
# NCBI accession number of the reference data set; can be left empty if both reference files are available locally\"
#accession: \"GCF_000001215.2\"
accession: \"GCF_000001215.4\"
ensembl_species: \"\" # e.g., \"homo_sapiens\"
build: \"\" # e.g., \"GRCh38\"
release: \"\" # e.g., \"105\"
read_filter:
# Minimum read length; set 0 to keep all reads.
min_length: 200
# minimap2 alignment parameters
minimap2:
# Minimap2 indexing options
index_opts: \"\"
# Minimap2 mapping options
opts: \"\"
# Maximum secondary alignments
maximum_secondary: 100
# Secondary score ratio (-p for minimap2)
secondary_score_ratio: 1.0
# samtools processing parameters
samtools:
# Samtools view opts, \"-b\" creates BAM from SAM.
samtobam_opts: \"-b\"
# Samtools sort opts,
bamsort_opts: \"\"
# Samtools index opts,
bamindex_opts: \"\"
# Samtools stats opts
bamstats_opts: \"\"
# salmon quantification parameters
quant:
# Salmon library type (Default: U)
salmon_libtype: \"U\"
# This section defines the pyDESeq2 plot and data handling parameters
deseq2:
# normalization fit type, must be 'parametric' or 'mean'
fit_type: \"\"
# the \"design factors\" are the confounding variables to be adjusted for
# during normalization. They must be given in the configuration (samples.csv).
design_factors:
- \"condition\"
#
# the \"continuous factors\" are non-categorial factors to be considered
#continuous_factors:
# -
#
# The (log2) log fold change under the null hypothesis. (default: 0).
lfc_null: 1.0
#
# The alternative hypothesis for computing wald p-values. By default,
# the normal Wald test assesses deviation of the estimated log fold
# change from the null hypothesis, as given by lfc_null.
# One of [\"greaterAbs\", \"lessAbs\", \"greater\", \"less\"] or None.
# The alternative hypothesis corresponds to what the user wants to
# find rather than the null hypothesis. (default: None).
alt_hypothesis: \"greaterAbs\"
#
# The marker size in points**2 (typographic points are 1/72 in.).
# Default is rcParams['lines.markersize'] ** 2.# minimum count to
# be considered for subsequent analysis
point_width: 20
#
# we disregard loci with count number lower 'mincount'
mincount: 10
#
# Type I error cutoff value:
alpha: 0.05
#
# in addition to the full heatmap, plot the top number of different
# values, ranked by the top ratio between the two traits
threshold_plot: 10
#
# the heatmap color map
# see https://seaborn.pydata.org/tutorial/color_palettes.htm for an overview
colormap: \"Blues\"
#plot figure type
figtype: \"png\"
batch_effect:
- \"\"
#
## Differential Isoform Analysis
# The FLAIR splice-isoform analysis pipeline includes resource-intensive computations and only works with additional constraints.
# 1. In 'samples.csv: The 'condition' column must contain exactly two distinct values. For example 'control' and 'treated'.
# 2. In 'samples.csv: Refrain from using underscores when naming samples. The 'sample' column may contain underscores, but be aware that underscores will be removed from the name for isoform quantification steps.
# 3. In this file: the variable 'FLAIR' below must be:'true'. This is a check to determine if users are aware of the constraints and wish to proceed.
isoform_analysis:
# Enables FLAIR Isoform Analysis if 'true'
FLAIR: true
# Minimum MAPQ of read assignment to an isoform (default: 1).
qscore: 1
# min read count expression threshold. Isoforms which contain fewer than 'exp_thresh' (Default=10) reads in both conditions are filtered out.
exp_thresh: 10
# 'flair_collapse' options
# '--annotation-reliant' makes FLAIR align reads to the annotation before identifying novel transcripts for the remaining reads
# '--generate-map' to generate a txt file of read-isoform assignments
# '--stringent' for full-length supporting reads (>=80% coverage)
col_opts: \"--annotation_reliant generate --generate_map --stringent\"
# Query genes to identify similar proteins using \"lambda\"
protein_annotation:
# Enables lambda sequence alignment if 'true'
lambda: false
# Pre-formatted UniProt Reference Cluster database (default: UniRef50)
uniref: \"https://ftp.imp.fu-berlin.de/pub/lambda/index/lambda3/gen_0/uniref50_20230713.lba.gz\"
# maximum number of protein matches returned per sequence (default: 3)
num_matches: 3
# Enrichment Analysis Parameters
#enrichment:
# # Enable enrichment analysis if 'true'
# perform_enrichment: true
# # minimum number of genes to consider per pathway (default: 3)
# min_genes: 3
""" .
sub1:dataset a schema:Dataset ;
ns1:describesWorkflow "RAjHDlPDghZzc9ZvQ3uJQNJ9Jd_KAYzZt7dk5PXKgjRyE" ;
ns1:description """This workflow performs differential expression analysis of RNA-seq data obtained from Oxford Nanopore long-read sequencing technology.
First a transcriptome FASTA is constructed using gffread. Reads are then mapped to the transcriptome with the long-read optimized alignment tool minimap2.
Next quantification is performed using salmon before normalization and differential expression analysis are conducted by PyDESeq2.
The workflow can optionally analyze splice-isoforms through integrating the FLAIR workflow.
Additionaly, NanoPlot is employed to analyze initial sequencing data and QualiMap is used to evaluate mapping results.""" ;
ns1:generatedAt "2026-04-16T12:55:36.287586+00:00"^^
xsd:dateTime ;
ns1:hasConfigurationSection sub1:workflow-configuration .
sub1:workflow-configuration rdfs:label "from workflow configuration" ;
ns1:hasConfigurationFile sub1:config-1 .
}