Dear all,
What are the most widely used peak calling tools for ChIP-seq data at the moment?
Thanks,
Eunice
What are the most widely used peak calling tools for ChIP-seq data at the moment?
Thanks,
Eunice
-
-
MACS or Homer. Homer has a few peak calling modes that control the type and size of peaks it finds. MACS is more of a straight ahead peak caller that just looks for peaks of any width. at least that's how I think of the two./* Shawn Driscoll, Gene Expression Laboratory, Pfaff
Salk Institute for Biological Studies, La Jolla, CA, USA */ -
Hi all,
And what about MeDIP-seq (not ChIP-seq)? I guess the answer would be the same, but what parameters keep/change?
I'm working on a Illumina IP library (50 bp SE) made after inmunoprecipitate genomic DNA fragments with antibodies which detect methylated bases. I've seen one paper using MACS with these kind of data, thought no specify any parameters used. Right now, I don't have control/input.
Eg. after using MACS, it says no paired peaks were found so I should go with the --nomodel option, but I'm guessing about --shiftsize since there is no TF I'm looking for (just nucleotide methylation); would it be important that --shiftsize number?
One more thing I'd like to ask is, not having control/input I've heard I should use the --nolambda option. Actually that's from this ppt tutorial:
Any thought/inputs would be appreciated. ThanksLast edited by cascoamarillo; 01-14-2016, 01:38 PM.Comment
-
Hi,
I am trying to analyze data for factor which does not behave typically as transcription factor. I used macs and homer for peak calling. macs2 is giving me hardly 20-30 peaks while Homer is giving about 20000 peaks. macs2 is not picking up peaks which i can easily visualize in IGV. How can i be sure which peak caller to use and how can I filter peaks from Homer data (based on what criteria)Comment
-
CRISPR/Cas9 sparked the gene editing revolution for both research and therapeutics.1 But this system still showed severe issues that limited its applications. The most prominent were the heavy reliance on PAM sequences, delivery limitations, double-stranded breaks that prompt unintended edits and cell death, and editing inefficiency (both in targeting and in knock-in reliability).
Despite this, “CRISPR helped turn genome editing from a specialized technique into...07-31-2026, 11:01 AM -
Proteomics platforms are evolving rapidly, with advances in mass spectrometry and affinity-based approaches expanding what researchers can detect and at what scale. As the field moves toward deeper proteome coverage and clinical applications, scientists face an increasingly complex landscape of tools. This article will explore how researchers are navigating these choices to find the right platform for their work.
The systematic characterization of the human proteome has...07-20-2026, 11:48 AM -
Genomics studies in neuroscience face a special challenge due to the brain’s complexity and scarcity of samples. Mapping changes in cell type and state using conventional next-generation sequencing methods remains challenging. Advances in technologies like single-cell sequencing, spatial transcriptomics, and long-read sequencing have opened the door to deeper studies of the brain and diseases like Alzheimer’s, amyotrophic lateral sclerosis (ALS), and schizophrenia.
...07-09-2026, 11:10 AM
Comment