Hi, salmon quant is giving me some headache. I am processing total RNA-seq with star_salmon on PBS, I've set the igenome GRCm38, but salmon quant always complains because it wants more RAM, like up to 384GB. I've tried to reduce the number of threads to 4, but nothing. After reads being processed it goes into std::bad_alloc, as it exhaust all memory. To reduce the input complexity I also remove ribo with sortmerna, using the default db + the pre-rRNA 45S. The sample that's failing doesn't even have a lot of ribo:

mus_musculus_prerrna_45S_v1.fasta           0.60
    rfam-5.8s-database-id98.fasta               0.00
    rfam-5s-database-id98.fasta         0.56
    silva-arc-16s-id95.fasta            0.00
    silva-arc-23s-id98.fasta            0.00
    silva-bac-16s-id90.fasta            0.16
    silva-bac-23s-id98.fasta            0.02
    silva-euk-18s-id95.fasta            0.63
    silva-euk-28s-id98.fasta            0.31

Have you already seen this? Do you have any suggestion? Thanks! EDIT: this is the actual command:

Command executed:

  salmon quant \
      --geneMap genes.filtered.gtf \
      --threads 4 \
      --libType=ISR \
      -t genome.transcripts.fa \
      -a IIT_363_E5_B5_S6.Aligned.toTranscriptome.out.bam \
      --reduceGCMemory \
      -o IIT_363_E5_B5_S6

  if [ -f IIT_363_E5_B5_S6/aux_info/meta_info.json ]; then
      cp IIT_363_E5_B5_S6/aux_info/meta_info.json "IIT_363_E5_B5_S6_meta_info.json"
  fi
  if [ -f IIT_363_E5_B5_S6/lib_format_counts.json ]; then
      cp IIT_363_E5_B5_S6/lib_format_counts.json "IIT_363_E5_B5_S6_lib_format_counts.json"
  fi

Command exit status:
  134

Command output:
  (empty)

Command error:
  # [ threads ] => { 4 }
  # [ libType ] => { ISR }
  # [ targets ] => { genome.transcripts.fa }
  # [ alignments ] => { IIT_363_E5_B5_S6.Aligned.toTranscriptome.out.bam }
  # [ reduceGCMemory ] => { }
  # [ output ] => { IIT_363_E5_B5_S6 }
  Logs will be written to IIT_363_E5_B5_S6/logs
  Library format { type:paired end, relative orientation:inward, strandedness:(antisense, sense) }
  [2026-08-13 01:00:02.937] [jointLog] [info] setting maxHashResizeThreads to 4
  [2026-08-13 01:00:02.937] [jointLog] [info] Fragment incompatibility prior below threshold.  Incompatible fra>
  [2026-08-13 01:00:02.955] [jointLog] [info] numQuantThreads = 2
  parseThreads = 2
  Checking that provided alignment files have consistent headers . . . done
  Populating targets from aln = "IIT_363_E5_B5_S6.Aligned.toTranscriptome.out.bam", fasta = "genome.transcripts>






  processed 0 reads in current round[2026-08-13 01:00:05.272] [jointLog] [info] replaced 3 non-ACGT nucleotides>


  processed 1000000 reads in current round

  processed 2000000 reads in current round

  processed 3000000 reads in current round[2026-08-13 01:00:26.023] [jointLog] [info]

  The alignment group queue pool has been exhausted.  3133501 extra fragments were allocated on the heap to sat>




  processed 4000000 reads in current round

  processed 5000000 reads in current round

  processed 6000000 reads in current round

  processed 7000000 reads in current round

  processed 8000000 reads in current round

  processed 9000000 reads in current round

  processed 9335963 reads in current round
  terminate called after throwing an instance of 'std::bad_alloc'
    what():  std::bad_alloc

hey is Dupradar still in the latest versions of nf-core rnaseq? I thought it was removed in lieu of the newer QC module

Hi! I'm trying to run rnaseq the first time, using fastp for trimming and then kallisto pseudoalignment, but get stuck on an error. I run as a slurm script containing these input parameters:

module purge
module load PDC apptainer
module load nextflow/25.04.7

nextflow run nf-core/rnaseq -r 3.26.0 \
    -resume \
    --input /cfs/klemming/projects/snic/uppstore2017202/CSPR/CSPR_nfcore/samplesheet_SUBSET.csv \
    --gtf /cfs/klemming/projects/snic/uppstore2017202/CSPR/CSPR_nfcore/references/Cricetulus_griseus_picr.CriGri-PICRH-1.0.115.ADDONS.gtf.gz \
    --transcript_fasta /cfs/klemming/projects/snic/uppstore2017202/CSPR/CSPR_nfcore/references/Cricetulus_griseus_picr.CriGri-PICRH-1.0.cdna.all.ADDONS.fa.gz \
    --outdir /cfs/klemming/projects/snic/uppstore2017202/CSPR/CSPR_nfcore/CSPR_KALLISTO_SUBSET \
    --save_reference true \
    --trimmer fastp \
    --extra_fastp_args="--trim_poly_g" \
    --skip_alignment true \
    --pseudo_aligner kallisto \
    -profile pdc_kth

the NBIS supporters suggested I post this as the relevant part of the .nextflow.log:

"Aug-18 10:10:39.662 [Task monitor] DEBUG n.processor.TaskPollingMonitor - Task completed > TaskHandler[jobId: 23591666; id: 7; name: NFCORE_RNASEQ:RNASEQ:FASTQ_QC_TRIM_FILTER_SETSTRANDEDNESS:FASTQ_FAST QC_UMITOOLS_FASTP:FASTQC_RAW (P15053_155); status: COMPLETED; exit: 0; error: -; workDir: /cfs/klemming/projects/supr/uppstore2017202/CSPR/CSPR_nfcore/work/32/3a62dee4391d5da0ce1d5ba32f1c94 started: 17 87040184069; exited: 2026-08-18T08:10:34Z; ] Aug-18 10:13:14.672 [Task monitor] DEBUG n.processor.TaskPollingMonitor - !! executor slurm > tasks to be completed: 2 -- submitted tasks are shown below ~> TaskHandler[jobId: 23591911; id: 10; name: NFCORE_RNASEQ:RNASEQ:FASTQ_QC_TRIM_FILTER_SETSTRANDEDNESS:FQ_LINT_AFTER_TRIMMING (P15053_155); status: RUNNING; exit: -; error: -; workDir: /cfs/klemming/p rojects/supr/uppstore2017202/CSPR/CSPR_nfcore/work/75/f9ece9da17b459f7f5d252ce77a712 started: 1787040554642; exited: -; ] ~> TaskHandler[jobId: 23591912; id: 9; name: NFCORE_RNASEQ:RNASEQ:FASTQ_QC_TRIM_FILTER_SETSTRANDEDNESS:FASTQ_FASTQC_UMITOOLS_FASTP:FASTQC_TRIM (P15053_155); status: RUNNING; exit: -; error: -; workDir: /cfs/klemming/projects/supr/uppstore2017202/CSPR/CSPR_nfcore/work/25/4a70771faf8ae215578db8361afc4e started: 1787040554644; exited: -; ] Aug-18 10:14:04.781 [Task monitor] DEBUG n.processor.TaskPollingMonitor - Task completed > TaskHandler[jobId: 23591911; id: 10; name: NFCORE_RNASEQ:RNASEQ:FASTQ_QC_TRIM_FILTER_SETSTRANDEDNESS:FQ_LINT_A FTER_TRIMMING (P15053_155); status: COMPLETED; exit: 0; error: -; workDir: /cfs/klemming/projects/supr/uppstore2017202/CSPR/CSPR_nfcore/work/75/f9ece9da17b459f7f5d252ce77a712 started: 1787040554642; ex ited: 2026-08-18T08:14:01Z; ] Aug-18 10:14:05.056 [Actor Thread 46] DEBUG nextflow.Nextflow - FQ/SUBSAMPLE found 2 FASTQ files Aug-18 10:14:05.228 [Actor Thread 41] DEBUG nextflow.processor.TaskProcessor - Handling unexpected condition for task: name=NFCORE_RNASEQ:RNASEQ:FASTQ_QC_TRIM_FILTER_SETSTRANDEDNESS:FASTQ_SUBSAMPLE_FQ_SALMON:SALMON_INDEX; work-dir=null error [nextflow.exception.ProcessUnrecoverableException]: Not a valid path value type: java.util.LinkedHashMap ([id:P15053_155, strandedness:auto, seq_platform:[], seq_center:[], single_end:false, ha s_genome_bam:false, has_transcriptome_bam:false, original_genome_bam:null, original_transcriptome_bam:null]) Aug-18 10:14:05.239 [Task submitter] DEBUG nextflow.executor.GridTaskHandler - [SLURM] submitted process NFCORE_RNASEQ:RNASEQ:FASTQ_QC_TRIM_FILTER_SETSTRANDEDNESS:FASTQ_SUBSAMPLE_FQ_SALMON:FQ_SUBSAMPLE (P15053_155) > jobId: 23592206; workDir: /cfs/klemming/projects/supr/uppstore2017202/CSPR/CSPR_nfcore/work/4b/8a46a0aec53ac37c7d5db0fb2766ea Aug-18 10:14:05.240 [Task submitter] INFO nextflow.Session - [4b/8a46a0] Submitted process > NFCORE_RNASEQ:RNASEQ:FASTQ_QC_TRIM_FILTER_SETSTRANDEDNESS:FASTQ_SUBSAMPLE_FQ_SALMON:FQ_SUBSAMPLE (P15053_15 5) Aug-18 10:14:05.284 [Actor Thread 41] ERROR nextflow.processor.TaskProcessor - Execution aborted due to an unexpected error java.lang.NullPointerException: Cannot get property 'containerEngine' on null object"

Would be very grateful if anyone has any insight what might be going wrong. Thanks you so much!

Hi! I have a fairly basic question, but could not find the answer by searching in here, so maybe someone can help me out with this. We have a large Lexogen QuantSeq 3' end RNAseq dataset of a human cohort based on blood samples. Due to the size, the dataset was sequenced on an Illumina NovaSeq in two batches (Batch 1: 1056 samples, Batch 2: 135 samples). Independent of all downstream analytical batch handling considerations, does it make any difference whether we run all 1191 samples together in one nf-core rnaseq run on our HPC cluster or use two separate runs? Would it be advised to use different pipeline runs for different sequencing batches? Most steps in the pipeline seem to run within-sample processing operations, but we'd like to make sure that we are not overlooking any steps that may make use e.g. of any between-sample information or distribution estimates - apart from overall statistics, of course. I'd appreciate any feedback, thanks!

Hey everyone! I’m not sure if this is the appropriate place to ask, but we successfully ran an nf-core pipeline on our RNA-seq data. We’re quite new to this type of analysis and are struggling to interpret some parts of the MultiQC report generated from a subset of our RNA-seq data. Would anyone be willing to take a look? I can share some of the plots we have questions about in the thread. Thank you in advance!

Hey all, I'm receiving an error when I am running the pipeline that i'm not quite sure what to make of it. Here is my command: nextflow run nf-core/rnaseq -r 3.20.0 -profile docker --input /home/stefanie/Biggie/JF10_HIV_r4/sample_sheet.csv --outdir /home/stefanie/Biggie/nf_output --gtf /home/shared/GRCh38/gencode.v43.primary_assembly.annotation.gtf --fasta /home/shared/GRCh38/GRCh38.primary_assembly.genome.fa here is the error: Execution cancelled -- Finishing pending tasks before exit ERROR ~ Error executing process > 'NFCORE_RNASEQRNASEQFASTQ_QC_TRIM_FILTER_SETSTRANDEDNESS:FQ_LINT_AFTER_TRIMMING (HIV_RAL_6h)' Caused by: Process

NFCORE_RNASEQ:RNASEQ:FASTQ_QC_TRIM_FILTER_SETSTRANDEDNESS:FQ_LINT_AFTER_TRIMMING (HIV_RAL_6h)

terminated with an error exit status (1) Command executed: fq lint \ --disable-validator P001 \ HIV_RAL_6h_trimmed_1_val_1.fq.gz HIV_RAL_6h_trimmed_2_val_2.fq.gz > HIV_RAL_6h.fq_lint.txt cat <<-END_VERSIONS > versions.yml "NFCORE_RNASEQRNASEQFASTQ_QC_TRIM_FILTER_SETSTRANDEDNESS:FQ_LINT_AFTER_TRIMMING": fq: $(echo $(fq lint --version | sed 's/fq-lint //g')) END_VERSIONS Command exit status: 1 Command output: (empty) Command error: Error: I/O error Caused by: corrupt deflate stream Work dir: /mnt/biggie/Signaling_Systems_Drive/Users/Stefanie_Brizuela/JF10_HIV_r4/work/79/1330069ee4f77da8d17dcebf782816 Container: quay.io/biocontainers/fq:0.12.0--h9ee0642_0 Tip: you can try to figure out what's wrong by changing to the process work dir and showing the script file named

.command.sh

-- Check '.nextflow.log' file for details ERROR ~ Pipeline failed. Please refer to troubleshooting docs: nf-co.re/docs/usage/troubleshooting -- Check '.nextflow.log' file for details

just to follow up on my last message: (base) stefanie@bibo:*~/Biggie/JF10_HIV_r4/work/79/1330069ee4f77da8d17dcebf782816*$ cat .command.sh #!/usr/bin/env bash -C -e -u -o pipefail fq lint \ --disable-validator P001 \ HIV_RAL_6h_trimmed_1_val_1.fq.gz HIV_RAL_6h_trimmed_2_val_2.fq.gz > HIV_RAL_6h.fq_lint.txt cat <<-END_VERSIONS > versions.yml "NFCORE_RNASEQRNASEQFASTQ_QC_TRIM_FILTER_SETSTRANDEDNESS:FQ_LINT_AFTER_TRIMMING": fq: $(echo $(fq lint --version | sed 's/fq-lint //g')) END_VERSIONS

How are multimapping reads handled right now by the pipeline by default? Are they used for count matrix generation? One of my conditions has far more multimapping reads than the other. Can I just use the output from the default settings and take it into differential abundance?

I'm running this pipeline on azure and 1 process (star_genomeparams_upgade) is giving me trouble because azcopy (version 10.8.0) doesn't follow the symlinks. The task succeeds but subsequent tasks fail because the symlinked files aren't copied to the output blob. Has anyone ran into or resolved this issue yet?

Just in case I'm missing anything obvious: I'm trying to run this pipeline on some SmartSeq2 data and ran into this issue github.com/nf-core/rnaseq/issues/1912 possibly related to https://github.com/nextflow-io/nextflow/issues/7247 (Apologies for cross-posting)

Hi, is there a way to run only specific processes/subworkflows of an nf-core/rnaseq pipeline (specifically RSEM_MERGE_COUNTS and MULTIQC), without executing the full pipeline (The per-sample outputs already exist)? Context: I ran

nf-core/rnaseq v3.11.1

on a combined sample cohort on Seqera. I now want to re-run just the merge/report step without re-doing alignment/quantification (the most time consuming + cost consuming step) as the cohort came from 7 different datasets, so original 1 merged metrics doesn't work. I tried

-resume

the run with the the updated sample sheet , but looks like it still run from scratch instead of hitting cache. Is there a recommended approach for this kind of partial re-run? Thanks!

I’m curious if anyone has a valdiated set of flags for running on Plasmidsaurus “raw” FASTQs. This service saves a ton of money based on our in-house sequencing but they are a bit opaque on methods. Based on their documentation, it seems that ideally: 1. Length correction should be disabled (because it is 3' sequencing) 2. PolyA trimming should maybe be performed 3. UMI deduplication should be performed So something along these lines?

extflow run nf-core/rnaseq -r 3.26.0 \
    --input samplesheet.csv \
    --outdir results \
    --fasta genome.fa.gz \
    --gtf annotation.gtf.gz \
    --aligner star_salmon \
    --extra_salmon_quant_args "--noLengthCorrection" \
    --extra_star_align_args "--outFilterMismatchNoverLmax 0.1 --clip3pAdapterSeq AAAAAAAA" \
    --with_umi \
    --umi_dedup_tool umicollapse \
    <PLASMIDSAURUS_SPECIFIC_UMI_ARGUMENTS>

If anyone has a working submission script I would very much appreciate!

Hi nf-core/rnaseq community, I have run the latest version v3.26.0-ge7ca462 and observe that there are still polyG strings in every sample, although less. Some polyA's are also present. I tried to remove them supplying " --extra_trimgalore_args '--nextseq 20 -a G{10} -a A{10} -a2 G{10} -a2 A{10}' " on the command line. The polyG's and polyA's are removed - but the primers are still there!

How can use automatic primer recognition And remove explicitly the the polyG/A tracts?

This is what I have found: "# before trimming GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG in 8 samples, 1633758 occurences # after trimming GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG in 8 samples, 653694 occurences"

Also, could you suggest a faster way of testing that vs. running the complete pipeline? Is there a way to stop it after the QC of the trimmed reads, and still get the multiQC report?

We are testing more nf-core pipelines and aiming to use them instead of our in-house pipelines.

Thank you very much!

PS. I compared the polyG-content after trimming between v.3.22.2 and 3.26.0 - they are almost exactly the same.

🦅 nf-core/rnaseq 3.27.0 (Vanadium Vulture) is out! Bigger release this time - new pipeline options, a stack of edge-case fixes, and two template updates. Highlights: 🧬 New contaminant screening + rRNA index options •

--contaminant_screening_input trim_only|raw

lets you screen for contaminants right after adapter trimming (before BBSplit/rRNA removal) or on raw reads as a baseline, catching species a narrow BBSplit index would otherwise silently drop (thanks @Elad Herz) •

--bowtie2_rrna_index

accepts a pre-built Bowtie2 index for rRNA removal, skipping on-the-fly index construction from

--ribo_database_manifest

📦 STAR index fix for object-storage work directories •

STAR_GENOMEPARAMS_UPGRADE

(the same step that got legacy iGenomes indices running on ARM last release) was symlinking index files instead of copying them. Fine on a local/NFS work dir, but it breaks on object-storage-backed ones like Azure Batch via azcopy, which can't represent symlinks - indices could come out broken or missing. Now copies instead 🚀 GPU STAR (Parabricks) bumped to 4.7.1 • Thanks @Gary Burnett (NVIDIA) for keeping the Parabricks rna_fq2bam integration current 📊 Fixes for crashes and quiet correctness bugs: • StringTie merge now completes with warnings instead of crashing when every sample fails

--min_mapped_reads

, and its `--fr`/`--rf` strandedness flags are back after being dropped with no replacement in that same module update • Salmon auto-strandedness inference no longer crashes on kallisto-only runs with no

--fasta

• Biotype QC now warns instead of crashing when a GTF has no per-transcript biotype attribute, e.g. NCBI annotations •

--use_rustqc

works with UMI-deduplicated BAMs instead of aborting on the duplicate-marking check — UMI dedup removes PCR duplicates outright, so there's nothing left to flag (thanks @Felix Krueger) • TrimGalore gets a real 4 GB memory allocation instead of the 1 GB

process_low_memory

default that was OOM-killing every sample on every retry (thanks @Felix Krueger) • MultiQC's mapping-rate warning now names the aligner actually in use instead of always saying "STAR" 🔧 Other notable changes: • Nextflow floor raised to 25.10.4,

nf-schema

bumped to 2.7.2 • Sync nf-core template to v4.0.3 and v4.1.0 • New

--extra_hisat2_align_args

and

--extra_salmon_index_args

passthrough options (thanks @Maxime Garcia) •

manifest.diagram

now points at the metro map (thanks @Phil Ewels) • Module bumps:

trimgalore

2.3.0,

tximeta/tximport

,

quantify_rsem

, StringTie, HISAT2 and more 🙏 Thanks to everyone who contributed - bug reports, feature requests, reviews and code all very appreciated. 📋 Full changelog and release: github.com/nf-core/rnaseq/…/3.27.0 🐛 Bugs and feature requests to github.com/nf-core/rnaseq/issues!

Could I clarify if star_salmon and salmon (alignment free) both filter out reads of unexpected lib types? E.g for a reverse stranded library, it removes forward/unstranded reads?

I've had an issue with getting ribodetector to work in Apptainer/Singularity (using latest pipeline release 3.27.0 or prior). I'm aware of a previous issue with ONNX multiprocessing hang, that had to do with previous ribodetector versions but supposedly this has been fixed for ribodetector 0.3.3. Even when adjusting '--chunk_size' to smaller values, running the pipeline with gpu ribodetector always tries to allocate around ~ 75GB to my GPU when I only have ~50GB available. here is part of my config: // withName: '.*RIBODETECTOR' { // queue = 'gpu_h200' // clusterOptions = '--gres=gpu:1' // containerOptions = { workflow.containerEngine == 'singularity' || workflow.containerEngine == 'apptainer' ? '--nv' : // ( workflow.containerEngine == 'docker' ? '--gpus all' : null ) } // memory = { 120.GB * task.attempt } // cpus = 8 // time = 24.h // // // This prevents the H200 from hitting a CUDA OOM // ext.args = '-l 99 -m 180 --chunk_size 4096' // } For the cpu version of ribodetector, it just goes on forever and never finishes (which makes no sense). Should I try to allocate more GPU? sortmerna works fine in every run, which I have defaulted to because ribodetector has never worked for me.

Hi! I just came accross this tool which claims to infer strandedness without the need of a genome annotation (and apparently not even the genome sequence): biorxiv.org/content/10.64898/… I don't know what it's worth, but I thought you might be interested 😉 Cheers!

I am struggling with downloading a recent version of the rnaseq pipeline with nf-core tools and could use some inspiration how to fix it. I am using this Pixi environment

pixi.toml

on Debian Linux 13:

[workspace]
authors = ["Matthias Zepper <MatthiasZepper@users.noreply.github.com>"]
channels = ["conda-forge", "bioconda"]
name = "nf-core-tools"
platforms = ["linux-64"]
version = "0.1.0"

[tasks]

[activation.env]
NXF_APPTAINER_CACHEDIR = "${PIXI_PROJECT_ROOT}/.apptainer-cache"
NXF_SINGULARITY_CACHEDIR = "${PIXI_PROJECT_ROOT}/.apptainer-cache"

[dependencies]
python = ">=3.14.7,<3.15"
nextflow = ">=26.04.6"
nf-core = ">=4.1.0,<5"
apptainer = ">=1.5.4,<2"

When I try downloading with

pixi run nf-core pipelines download rnaseq -r "3.27.0" -r "3.26.0" -o "rnaseq" -s "singularity" -x "none"

I get:

,--./,-.
          ___     __   __   __   ___     /,-._.--~\
    |\ | |__  __ /  ` /  \ |__) |__         }  {
    | \| |       \__, \__/ |  \ |___     \`-._,-`-,
                                          `._,._,'

    nf-core/tools version 4.1.0 - <https://nf-co.re>


WARNING  Could not find GitHub authentication token. Some API requests may fail.
INFO     Detected Nextflow version 26.04.6
WARNING  Deleting existing output directory: 'rnaseq'
INFO     Saving 'nf-core/rnaseq'
         Pipeline revision: '3.27.0, 3.26.0'
         Use containers: 'singularity'
         Container library: '<http://quay.io|quay.io>'
         Using NXF_SINGULARITY_CACHEDIR': /home/matthias/Coding/Bioinfo/nf-core-tools/.apptainer-cache'
         Output directory: 'rnaseq'
         Include default institutional configuration: 'False'
INFO     Downloading workflow
INFO     Downloading workflow files from GitHub
INFO     Fetching container names for workflow revision 3.27.0 using nextflow inspect. This might take a while.
INFO     Processing workflow revision 3.27.0, found 49 container images in total.
ERROR    Could not parse Nextflow config for 'rnaseq/3_27_0'. Possibly because of a Nextflow version mismatch?
         Command 'nextflow config -o json rnaseq/3_27_0' failed with exit code 1
         WARN: Unrecognized config option 'manifest.diagram'
         ERROR ~ No such variable: task

Indeed, running nextflow config manually gives the same error

pixi run nextflow config -o json rnaseq/3_27_0

:

WARN: Unrecognized config option 'manifest.diagram'
ERROR ~ No such variable: task

This does not happen when I am running it directly against the GitHub version

pixi run nextflow config -o json nf-core/rnaseq -r "3.27.0"

, which prints a nicely formatted JSON output. For pipeline version 3.26.0, the warnings differ, but the issue persists:

ERROR    Could not parse Nextflow config for 'rnaseq/3_26_0'. Possibly because of a Nextflow version mismatch?
         Command 'nextflow config -o json rnaseq/3_26_0' failed with exit code 1
         WARN: Unrecognized config option 'validation.defaultIgnoreParams'
         WARN: Unrecognized config option 'validation.monochromeLogs'
         ERROR ~ No such variable: task

Strangely, the CI test didn't catch this problem, even though the Nextflow version 26.04.6 is the same. nf-core tools is admittedly still 4.0.3 in the test, but since it seems to be an issue of

nextflow config

, I reckon the tools version is irrelevant here. Pinning Nextflow at 25.10.04 resolves the issue, but gives other issues with recent pipelines such as plugins that require more recent versions. Any idea what the underlying reason could be?

Hi everyone! I have a basic question, is there currently version of the rnaseq pipeline to process 3' based RNA sequencing? Like just for gene expression quantification

Hi! I am having an issue with the pipeline finding duplicate names for the sample files. I am not sure why, I have cleared singularity and the nextflow cache and deleted the files anywhere but the folder I told the pipeline they are in. This is the error output: Command error: INFO: Environment variable SINGULARITYENV_TMPDIR is set, but APPTAINERENV_TMPDIR is preferred INFO: Environment variable SINGULARITYENV_NXF_TASK_WORKDIR is set, but APPTAINERENV_NXF_TASK_WORKDIR is preferred INFO: Environment variable SINGULARITYENV_NXF_DEBUG is set, but APPTAINERENV_NXF_DEBUG is preferred FixedFiles27_L1_R1.fastq.gz835708011: [S007] DuplicateNameValidator: duplicate name: '@AV250604148Run3 08 26 2026 SEQ WANG253251457611010200360031'

depictio Depictio dashboard for rnaseq: your feedback wanted Hi all! 👋 I am building Depictio, an open-source platform from EMBL to explore pipeline results without writing a Shiny or Streamlit app each time. A pipeline gets a dashboard template: a short definition that turns the outputs of any run into an interactive dashboard. I am now sharing it more widely, so I would like the view of rnaseq users and developers. ✨ What it does • MultiQC reports (aggregated across runs) next to result tables, images, phylogenetic trees, maps... with linked filters • Reusable: point the template at any new run of the same pipeline and you get the same dashboard • Fully editable: modify any dashboard from the GUI or in YAML, so the template is a starting point, not a constraint • Versioned, with comments and annotations on any component • Runs as a Python package, with Docker Compose or on Kubernetes, and can serve as a companion web app for a publication 📊 For rnaseq: per-library mapping, duplication and read distribution, sample PCA and distances, the most variable genes by condition and a per-gene expression explorer, next to the MultiQC report. ⚖️ Compared with shinyngs: for the differential expression part, the usual route is nf-core/differentialabundance, which builds an R/Shiny app (shinyngs) from a matrix and a design. Depictio is not tied to R or to one data type: it shows the same kind of results (PCA, volcano, MA, GSEA) next to the MultiQC report and the other outputs of the run, behind one link, with versioning and comments. I see it as complementary: shinyngs is made for exploring expression data, Depictio for sharing the whole run. 👀 What I need: I have built 25 templates and want people who really use these pipelines to review and criticise them. What is useful? What is misleading? 👉 Dashboard: rnaseq dashboard (no account needed) 💬 Comment on the dashboard (Google sign-in) or in the GitHub discussion dedicated to rnaseq. ⭐ A star on the repo is welcome if you find it useful. Thanks! 🙏 🎤 In you're interested in the project, I was speaking about Depictio at the Nextflow Summit (session page), and the recording will be available in the coming weeks.

Is it jıust me or does the full test data (the 8 cell lines) give some serious QC issues?

Hi all, just a note that it appears Salmon changed how

--noLengthCorrection

behaves in the latest Rust-based release (v2.7.0), which appears to be the version used in the updated nf-core workflow: github.com/COMBINE-lab/salmon/issues/1243. We expect this will change Salmon output in 3' data (e.g., QuantSeq) but need to evaluate the results.

I was perusing the changes that bumped rnaseq to use Salmon 2.*. According to the docs, in Salmon 2.*, the "classic error-model" is opt in and turned off by default. In alignment mode, the default behaviour now reads off alignment scores with from the

AS

tag and falls back to a uniform error model. According to this PR, the fallback is faster but performs worse github.com/COMBINE-lab/salmon/issues/1140. Performance can be rescued by opting-in into

--errorModel

. I noticed today that the STAR-Salmon route's transcriptome aligned bam records input to Salmon does not have AS tags, and

--errorModel

is not provided by default in the STAR-Salmon route. Has anyone here thought about or has taken a look at this quirk of the Salmon 1->2 migration?