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As soon as we start zooming out further, additional levels of precalculated

bins

within the bigWig files start averaging over the underlying (identical) values, which now means that the 0-values start bringing the averages down, whereas the `n/a`s are simply ignored for the average calculation. In essence this means that with

skipNAs

the floor value a bin can have is the scale factor (or value for exactly the coverage of 1 read), whereas for

0-value

bins the minimum value can be as low as scale factor divided by #bp in the bin, so for 50 bp bins scale factor/50. This will eventually change the values for bins with a coverage close to background level, and since this is related to the Zoom level in IGV, it gets rendered differently, appearing as a massively increased background for what used to be identical coverage values:

If we use as an example a 50bp bin, of which only one single bp is covered by 1 read, and assume that 1 read coverage has a value of 5. For

--skipNAs

the value of the bin would be calculated as 1(bp) *5 = 5 (49 positions are skipped/ignored). For

0-value

bins, the value of the bin would be calculated as [1(bp) * 5 + 49(bp) * 0] / 50 = 0.1

I suppose our question now is: does this warrant changing the default behaviour of

bedtools genomecov

within ChIP-seq, and

bamCoverage

within CUT&RUN to result in more realistic background levels? PS: As an aside, since the underlying bedGraph and bigWig files are not wrong at the individual position level, one can hope that peak callers do the right thing and NOT USE any binning prior to peal calling… Does someone want to check?

Thanks @Felix Krueger for reporting, I will try to take a look to this when I found a moment, of course if anyone else wants to help you are more than welcomed

Hello, I noticed that the

minimum_alignment_q_score

parameter is clearly defined (nextflow_schema.json)in the nf-core/cutandrun pipeline (default: 20), which helps filter low-quality alignments. However, I don’t see a similar threshold option in the nf-core/chipseq pipeline. Is MAPQ filtering handled differently there? @Jose Espinosa-Carrasco

Why is it showing me this error when I try to run the nfcore/chipseq

Pipeline completed with errors- ERROR ~ Error executing process > 'NFCORE_CHIPSEQPREPARE GENOMEGTF2BED (genes.gtf)' Caused by: Missing output file(s)

*.bed

expected by process

NFCORE_CHIPSEQ:PREPARE_GENOME:GTF2BED (genes.gtf)

Command executed: gtf2bed \ genes.gtf \ > genes.bed cat <<-END_VERSIONS > versions.yml "NFCORE_CHIPSEQPREPARE GENOMEGTF2BED": perl: $(echo $(perl --version 2>&1) | sed 's/.*v\(.*\)) built.*/\1/') END_VERSIONS Command exit status: 0 Command output: (empty)

Are the bigwig files in the output of the chipseq pipeline normalized to their matching inputs after depth normalization or is that only for peak calling?

This is a bit off topic, but is there anyone here that uses ChEC-seq, or is interested in using ChEC-seq data, from yeast? There is a lab that has produced an enormous amount, but the analysis step is "in custom MATLAB scripts". I'm tentatively interested in a nf ChEC-seq pipeline, but I'd rather not have another solo maintained workflow (I did the #C04M2HGBBK5 workflow, which is another binding assay). The data I'm looking at is in the first table supplement of this paper

Hey! I have been trying to run the run

nf-core/chipseq

pipeline, but constantly get an Java related error, even when I do load the Java using

module load Java/21.0.5

. Does anyone know what is happening? Error message:

-- Check '.nextflow.log' file for details
ERROR ~ Cannot invoke "java.nio.file.Path.getFileSystem()" because "path" is null

 -- Check script '/user/gent/479/vsc47925/.nextflow/assets/nf-core/chipseq/./subworkflows/local/utils_nfcore_chipseq_pipeline/../../nf-core/utils_nfcore_pipeline/main.nf' at line: 106 or see '.nextflow.log' file for more details
ERROR ~ Pipeline failed. Please refer to troubleshooting docs: <https://nf-co.re/docs/usage/troubleshooting>

 -- Check '.nextflow.log' file for details
-[nf-core/chipseq] Pipeline completed with errors-

Hi all, I am trying to test the nf-core/chipseq -profile test and the pipeline is failing anyone recently used this pipeline successfully? I am attaching the nextflow log file and error report if anyone can help that would be great . Thanks Ramya

Hi, I am running the latest chipseq pipeline (V 2.1.0), and the pipeline gets completed successfully. However, the MACS3 and DeSeq2 do not run (Attached screenshot). My arguments for running it is

nextflow run nf-core/chipseq --input ./input.csv --outdir ./results --genome GRCh38 -profile singularity --read_length 100 --max_cpus 5

Can someone tell me what went wrong? and what I should do to include MACS3 and DESEQ2 to run?

Hello, I run nf-core/chipseq 2.1.0 and found that the Mark Duplicates of the MERGED LIB: Picard (unfiltered) section in the MultiQC seems incorrect. The following H3K27Ac library was sequenced twice and merged. In a results/bowtie2/merged_library/picard_metrics/H3K27Ac_REP1.mLb.mkD.sorted.MarkDuplicates.metrics.txt, the metrics of the technical replicates were written in the two rows separately: ## METRICS CLASS picard.sam.DuplicationMetrics LIBRARY UNPAIRED_READS_EXAMINED READ_PAIRS_EXAMINED SECONDARY_OR_SUPPLEMENTARY_RDS UNMAPPED_READS UNPAIRED_READ_DUPLICATES READ_PAIR_DUPLICATES READ_PAIR_OPTICAL_DUPLICATES PERCENT_DUPLICATION ESTIMATED_LIBRARY_SIZE H3K27Ac_REP1_T1 147989 6164554 0 224851 37578 759418 516670 0.124742 63806670 H3K27Ac_REP1_T2 1333127 44048308 0 1918643 498651 6312181 3683105 0.146741 296264718 However, in the MultiQC yaml file, the metrics of only the technical replicate 1 was collected and displayed in the MultiQC html. H3K27Ac_REP1: Duplicate Pairs Nonoptical: 485496 Duplicate Pairs Optical: 1033340 Duplicate Unpaired: 37578 Unique Pairs: 10810272 Unique Unpaired: 110411 Unmapped: 224851 Is this a bug or specification of the pipeline?

I have been having issues running the test profile of V2.1.0 of chipseq pipeline. I tested the V 2.0.0 and it works fine.. not sure if there is a bug that needs fixing. the test for V2.1.0 failed at the multiqc step which is odd as rnaseq pipeline worked fine for me which also used that

To whom it may concern, I am Simone Romagnoli, I am attempting to run the nf-core/chipseq pipeline (v2.0.0 and v2.1.0). I have correctly prepared the sample sheet and configured my environment to avoid using the default /tmp directory, as it does not have sufficient space. To redirect temporary files, I’ve added the following lines to my .bashrc: export TMP=/scratch/tmp_sromagnoli/ export SINGULARITY_TMPDIR=/scratch/tmp_sromagnoli export JAVA_OPTS="-Djava.io.tmpdir=/scratch/tmp_sromagnoli/" export NXF_OPTS="-Djava.io.tmpdir=/scratch/tmp_sromagnoli/" However, during pipeline execution—possibly during the phantompeak process—I still encounter a "No space left on device" error. The error message indicates that the pipeline is attempting to write to /tmp, for example /tmp/RtmpL1lT5P/, instead of the designated scratch directory. I have observed this issue with both v2.0.0 and v2.1.0 of the pipeline. Could you please advise on how to ensure that all temporary files, including those created by R processes, are correctly redirected to the specified scratch location? Thank you for your assistance. Best regards,

Hello, I hope you are dowing well. I have a question concerning the alignement postprocessing with bowtie2. I am currently analyzing chipseq and rnaseq of bacteiral genome. do you have any idea about how can I see the percentage of uniquely mapped reads? and unmapped reads? shall I proceed only with uniquely mapped and elemniate multimapped and unmapped reads? Is is based on MAPQ score ? thank you

Hello! I was wondering is there any way to run a ChIPseq sample that is paired end with a read length of 75 nt with an Input sample that is 150 nt single end in the same pipeline run?

Hey everyone, I am attempting to run the nf-core/chipseq pipeline and I'm running into this issue during the MACS3 peak calling step: ValueError: cannot resize this array: it does not own its data I previously ran the fetchngs pipeline to obtain the fastq files needed for the chipseq pipeline and made sure the samplesheet was in the format required by chipseq as well. Has anyone seen this issue before? Thank you!

Hello! I am trying to obtain MACS peaks of an older (2010) published datasets, for internal comparison. The fastqs from this dataset fail a few QC checks for 2 antibodies, so the nextflow pipeline does not return peaks for these. Can I bypass the QC filtering to force MACS to be run?

Hello guys!! I am a begginer in the nextflow universe. I was wondering if anyone has tried the chipseq (chipseq: Introduction) pipeline for cell-free ChIP-seq (cfChIP-seq) analysis. Is it even possible?

Hi All, What is the recommended sequencing depth, read length, and library layout (se/pe) for: (1) TF (FOXA2), (2) broad histone mods (h3k27me3), and (3) narrow (H3k4me3)? Chat GPT gave me the attached table, but I would really appreciate getting some human feedback. Thanks!!

Any reason why the pipeline metromap isn't on the website?

Hi everyone! My name is Taimor I am a bioinformatics RA at BCH. I am trying to run Nextflow chipseq pipeline for some of the researchers in my lab and am encountering some issuses. Currently the error i’m getting is related to the sample sheet i don’t understand how to make it. I’m currently using,

(env_nf) **\[**\~/Nextflow**\]**

taimor**@**RDT01154 $ cat chengxin_samplesheet.csv
sample,fastq_1,fastq_2,replicate,antibody,control,control_replicate
WT_BCATENIN_IP,/storage2/researchers/chengxian/ChIP_seq/AltN468/trimmed_fastq/CXL1239_R1.trimmed.fastq.gz,,1,BCATENIN,WT_INPUT,1
WT_BCATENIN_IP,/storage2/researchers/chengxian/ChIP_seq/AltN468/trimmed_fastq/CXL1239_R2.trimmed.fastq.gz,,2,BCATENIN,WT_INPUT,2

and the error i get is,

Command output:

  ERROR: Please check samplesheet -> Control identifier and replicate has to match a provided sample identifier and replicate!

  Control: 'WT_INPUT_REP1'

Command error:

  WARNING: Skipping mount /usr/local/var/singularity/mnt/session/etc/resolv.conf \[files\]: /etc/resolv.conf doesn't exist in container

  ERROR: Please check samplesheet -> Control identifier and replicate has to match a provided sample identifier and replicate!

  Control: 'WT_INPUT_REP1'

The exact command i run is,

nextflow run nf-core/chipseq \\
    -profile singularity \\
    --input chengxin_samplesheet.csv \\
    --outdir chengxin_out \\
    --fasta /storage/genomes/mm9_AJ851868ins/mm9_AJ851868ins.fa \\
    --gtf /storage/genomes/mm9_AJ851868ins/annotation/genes.mm9_AJ851868ins.gtf \\
    --read_length 100 \\
    --save_reference

I am new to this as this is my 1st time any help would be appreciated! I understand the problem is my sample sheet but I don't understand how to make a correct 1. both the fastq are WT. The 1st is read 1 the 2nd is read 2 best, Taimor

Hi all, Are there any plans to work on the

chipseq

pipeline during the Hackathon next month? I guess the pipeline need some updates to be compatible with newer nextflow versions (e.g the strict syntax, the adaption to workflow outputs etc.). On the other hand, I read the discussion regarding

spike-in

normalization and a milestone for version 2.2. I will need to process some spike-in ChIPs in the near future, so I can offer to implement it. Any thoughts? - @Jose Espinosa-Carrasco (you contributed last), or other core maintainers? If there are more people interested in working on the pipeline, I can offer hosting an online project. Cheers, Kevin

Hi all, In case you are planning to participating in the hackathon and want to work on the chipseq pipeline. Could you quickly react (nf core duck) to this post, so we / I have a rough idea how many people want to join us.

Hi just created the #C0AL42B2CMP as some people would be involved in the pipeline during the hackathon, we can use this channel to discuss the development of the pipeline during the event and beyond 🚀

Hi all! Please see this comment in case you have strong opinions on how the pipeline should treat control and ip replicates, see below https://nfcore.slack.com/archives/C0AL42B2CMP/p1777361940305409

depictio Depictio dashboard for chipseq: feedback wanted Depictio (open source, from EMBL) turns the output of a workflow run into an interactive dashboard you can share, so nobody has to write a Shiny or Streamlit app for each project. Point a template at any run of a pipeline and you get the same dashboard, which you can edit (UI & YAML), version and comment. 🧬 The chipseq dashboard template (no account needed) shows signal and library complexity, called peaks with their gene annotation and the consensus peaks replicates agree on. 🙌 We need your help! Feedback and ideas welcome in #C0AFS06G9HR (all templates and details there) or in the chipseq GitHub discussion.

Hi, I have a question about the peak calling step. Broad peaks are called by default, I was wondering what is the recommendation for peak calling settings if there are different marks (some have narrow and some broad)? Does one need to run the pipeline separately per mark?