Software and datasets

A. Computational and experimental resources

This section provides an overview of the computational and experimental resources used in the protocol, including system requirements, software tools, yeast strains, reference genomes, and key experimental parameters. Table 1 lists the operating systems used for implementation and testing. Table 2 details the primary programs required to execute the workflow, along with recommended versions for compatibility. Table 3 describes the yeast strains used for ChIP-seq data generation (see General note 11), specifying their genotypes and references. This protocol uses data from an engineered system in which FLAG epitope sequences were integrated with S. cerevisiae HHO1 (histone H1) [18], S. cerevisiae HMO1 (chromatin-associated high mobility group protein), and S. pombe abp1 (ARS binding protein 1, a CENP-B homolog) [19,20], resulting in FLAG-tagged proteins. This system promotes uniform antibody affinity across endogenous (S. cerevisiae) and spike-in (S. pombe) chromatin, minimizing variability in binding efficiency (discussed in later sections and notes). All yeast strains used in this protocol are available upon request from the Tsukiyama Lab (ttsukiya@fredhutch.org). Table 4 presents the reference genomes used for read alignment and data processing. Table 5 documents ChIP-seq experimental parameters for S. cerevisiae samples, including input and IP volumes, chromatin masses, and cell cycle states, which are needed to compute siQ-ChIP α proportionality constants. ChIP-seq data are available in the Gene Expression Omnibus (GEO) via accession number GSE288548.

Table 1. Operating systems (os) and respective versions used to implement, test, and run the protocol.

Table 2. Programs used to implement, test, and run the protocol, excluding dependencies and libraries. The table also includes program version numbers, associated operating systems (os), and relevant references. While most program versions do not need strict adherence, the following version guidelines are recommended for compatibility: Atria [21] 4.0.0 or later, installed with Julia [22,23] 1.8.5; Bash 3.2.0 or later; GNU Parallel [24] 20150222 or later; Python [25] 3.6.0 or later.

Table 3. S. cerevisiae and S. pombe strains used for ChIP-seq data generation. “Organism” indicates whether the strain is S. cerevisiae or S. pombe. “strain_full” represents the complete strain identifier; for S. cerevisiae, that includes a three-letter prefix indicating the lab of origin (e.g., “yTT” represents “yeast Toshio Tsukiyama”). “genotype_full” specifies that the S. cerevisiae HHO1 and HMO1 genes were separately tagged with sequences encoding 3×FLAG epitopes, including flexible linkers (2L-3FLAG), generating HHO1-2L-3FLAG and HMO1-2L-3FLAG strains; and the S. pombe gene abp1 was tagged with a 3×FLAG epitope sequence, generating abp1-3FLAG. The final column lists the studies in which the strains were described. For more details, see General note 11.

Table 4. Species genomes used for alignment, data processing, and signal visualization. “Species” specifies whether the genome is for S. cerevisiae or S. pombe. “Genome” refers to the reference genome assembly strain/name. “Version” indicates the assembly version used. “Database” refers to the source of the genome assembly. Assembly versions do not need to be strictly followed. For more information, refer to Data analysis A, B, E, and J.

Table 5. S. cerevisiae ChIP-seq experimental parameters, including those needed to compute siQ-ChIP α proportionality constants. Parameters include input volumes (“vol_in”) and total volumes before the removal of input (“vol_all”) in microliters, as well as input and IP chromatin masses (“mass_in” and “mass_ip”) in nanograms, measured during ChIP-seq benchwork [5,16]. Average fragment lengths in base pairs (bp; “length_in” and “length_ip”) are required but do not need to be included in the table, as they can be calculated from sample BAM files during α computation (see Data analysis H). “genotype_full” and “genotype”: Genetic constitution of S. cerevisiae sample. As there is no indication that the tag compromises the function of either Hho1 or Hmo1, these strains are labeled “WT” (wild type) in filenames. “State”: Cell-cycle phase. “G₁” represents cells in the first gap phase of the cell cycle, preparing for DNA synthesis. “G₂/M” includes cells in the second gap phase (G₂), undergoing DNA synthesis, and mitosis (M), representing the cell cycle substages leading to and including cell division. “Q” represents quiescent cells in a reversible state of cell cycle withdrawal. “Factor”: Protein immunoprecipitated using mouse monoclonal anti-FLAG M2 antibody (Sigma, F1804). “Hho1” corresponds to S. cerevisiae histone H1, and “Hmo1” to S. cerevisiae chromatin-associated high mobility group family member protein. “strain_full”: Full S. cerevisiae strain identifier. “Strain”: Abbreviated S. cerevisiae strain identifier, which may be substituted with “replicate” (or “rep”).

B. Companion GitHub repository for protocol implementation

This protocol is accompanied by the GitHub repository protocol_chipseq_signal_norm (github.com/kalavattam/protocol_chipseq_signal_norm), which contains tools, scripts, and resources for implementing the workflow described in this tutorial. protocol_chipseq_signal_norm includes driver and utility scripts, functions, and a Markdown notebook, workflow.md, that provides code examples with explanatory text for the steps outlined in the Procedure and Data analysis sections. All content is organized and documented following the principles in [38,39]. Key features include the following:

• Initializing variables for directory paths, files, and computational environments, among other things.

• Defining script arguments.

• Organizing input and output files within structured directory systems.

• Validating paths, files, dependencies, etc.

• Automating experiments with driver scripts that coordinate processes like read trimming, alignment, post-processing, and signal track generation. Most driver scripts accept serialized lists (e.g., comma-delimited strings) of FASTQ, BAM, or bedGraph input files, which can be generated using the utility script find_files.sh (see General note 12).

• Parallelizing tasks on high-performance computing clusters configured to use SLURM [37] or on local or remote systems using GNU Parallel [24].

• Capturing detailed logs for all commands to support troubleshooting and reproducibility.

Additionally, the protocol_chipseq_signal_norm repository includes tab-separated value (TSV) files for downloading experimental datasets (see Data analysis C) and a TSV file with metadata and parameters required for running siQ-ChIP (see Data analysis H).

Procedure