Transcription Factor ChIP-seq Data Standards and Processing Pipeline

Pipeline Overview

The ChIP-seq transcription factor pipeline was developed as a part of the ENCODE Uniform Processing Pipelines series. The ENCODE Consortium has developed two analysis pipelines to study the different classes of protein-chromatin interactions. Both ChIP-seq piplines share the same mapping steps, but differ in the methods for signal and peak calling and in the subsequent statistical treatment of replicates. The full ChIP-seq pipeline code is available on Github and can be run on DNAnexus (link requires account creation) at their current pricing.

Pipeline for Mapping of FASTQs

View the current mapping pipeline instance.

Pipeline for Transcription Factor ChIP-seq

Transcription factor ChIP-seq (TF ChIP-seq) specifically looks at proteins which are thought to associate with specific DNA sequences to influence the rate of transcription.

Replicated experiments

View the current replicated TF pipeline instance. 

Unreplicated experiments

View the current unreplicated TF pipeline instance. 

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Inputs:

Inputs for mapping

File format	Information contained in file	File description	Notes
fastq	reads	G-zipped reads, paired-ended or single ended, stranded or unstranded	Multiple fastqs from a single biological replicate or library are concatenated before mapping. Reads must meet the criteria outlined under the Uniform Processing Pipeline Restrictions.
fasta	genome indices	Indices are dependent on the assembly being used for mapping

 

Inputs for peak calling

File format	Information contained in file	File description	Notes
bam	alignments (filtered)	Produced by mapping reads to the genome
bam	alignments	Filtered bam file from the control experiment	See the  Uniform Processing Pipeline Restrictions for more on controls.

Outputs:

Transcription factor pipeline (replicated)

File format	Information contained in file	File description	Notes
bigWig	fold change over control, signal p-value	Two versions of nucleotide resolution signal coverage tracks.	The signal is expressed in two ways: as fold-over control at each position, and as a p-value to reject the null hypothesis that the signal at that location is present in the control.
bed and bigBed (narrowPeak)	peaks	Relaxed peak calls for each replicate individually and for both replicates' reads pooled together.	These peaks are thresholded to sample enough noise in the experiment for efficient statistical comparison of replicates in subsequent steps; as such, many false positives are expected to be present. They are not meant to be interpreted as definitive binding events, but are rather intended to be used as input for subsequent statistical comparison of replicates.
bed and bigBed (narrowPeak)	conservative IDR peaks	Peaks derived from IDR analysis of biological replicates.	IDR, or Irreproducible Discovery Rate, measures the reproducibility of the assay in question. The optimal set of peaks is more sensitive, especially when one replicate has substantially lower data quality than the other.
bed and bigBed (narrowPeak)	optimal IDR peaks	The largest set of peaks derived from IDR analysis of biological replicates and pseudoreplicates.
Quality control metrics are collected to determine library complexity, read depth, FRiP score, and reproducibility.

 

Transcription factor pipeline (unreplicated)

 

File format	Information contained in file	File description	Notes
bigWig	fold change over control, signal p-value	Two versions of nucleotide resolution signal coverage tracks.	The signal is expressed in two ways: as fold-over control at each position, and as a p-value to reject the null hypothesis that the signal at that location is present in the control.
bed and bigBed (narrowPeak)	peaks	Relaxed peak calls from all reads in the single replicate	These peaks are thresholded to sample enough noise in the experiment for efficient statistical comparison of pseudoreplicates in subsequent steps; as such, many false positives are expected to be present. They are not meant to be interpreted as definitive binding events, but are rather intended to be used as input for subsequent statistical comparison of replicates.
bed and bigBed (narrowPeak)	pseudoreplicated IDR thresholded peaks	The set of peak calls from two partitions, or "pseudoreplicates"	In the "unreplicated IDR" step, an IDR-based strategy is used to identify stable peaks across pseudoreplicates (instead of across true replicates). Peaks from the relaxed set (from all reads) as well as from pseudoreplicates are ranked, and those peaks that are well-supported in both pseudoreplicates (i.e. cross the same IDR threshold as for replicated experiments) are reported. There are no corresponding optimal and conservative peak sets in the unreplicated case because there are no true replicates.
Quality control metrics are collected to determine library complexity, read depth, FRiP score, and reproducibility.

References

Genomic References

View the mapping assembly and genome annotation reference files used in this pipeline

Links and Publications

Find data generated by the transcription factor pipeline for replicated experiments
Find data generated by the transcription factor pipeline for unreplicated experiments 
Explore all ChIP-seq related publications on the ENCODE portal

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Uniform Processing Pipeline Restrictions

Current Restrictions

• The read length should be a minimum of 50 base pairs, though longer read lengths are encouraged; the pipeline can process read lengths as low as 25 base pairs. Sequencing may be paired- or single-ended.
• The sequencing platform used should be indicated.
• Replicates should match in terms of read length and run type. 
• Pipeline files are mapped to either the GRCh38 or mm10 sequences.

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Current Standards

Experimental guidelines for ChIP-seq and epitope-tagged ChIP-seq experiments can be found here.

• Experiments should have two or more biological replicates, isogenic or anisogenic. Assays performed using EN-TEx samples may be exempted due to limited availability of experimental material.
• Antibodies must be characterized according to standards set by the ENCODE Consortium. Please see the linked documents for transcription factor standards (May 2016), histone modification and chromatin-associated protein standards (October 2016), and RNA binding protein standards (November 2016).
• Each ChIP-seq experiment should have a corresponding input control experiment with matching run type, read length, and replicate structure.
• Library complexity is measured using the Non-Redundant Fraction (NRF) and PCR Bottlenecking Coefficients 1 and 2, or PBC1 and PBC2. Preferred values are as follows: NRF>0.9, PBC1>0.9, and PBC2>10.
• The experiment must pass routine metadata audits in order to be released.

Target-specific Standards

• Each replicate should have 20 million usable fragments.
  • low read depth: 10 million to 20 million usable fragments
  • insufficient read depth: 5 million to 10 million usable fragments
  • extremely low read depth: < 5 million usable fragments
• For transcription factor experiments, replicate concordance is measured by calculating IDR values (Irreproducible Discovery Rate). The experiment passes if both rescue and self consistency ratios are less than 2.

Additional Metrics

• Additional metrics are calculated without defined thresholds, such as fraction of reads in peaks (FRiP), and are useful in comparing similar experiments.  These metrics are described here.

Previous Standards (ENCODE 2)

Data quality standards for ENCODE2 are outlined in ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia.

• Experiments should have two or more biological replicates, isogenic or anisogenic.
• Antibodies must be characterized according to standards set by the consortium for ENCODE2 data.
• Each ChIP-seq experiment should have a corresponding input or IgG control experiment with matching run type, read length, and replicate structure.
• Library complexity is measured using the Non-Redundant Fraction (NRF) and PCR Bottlenecking Coefficients 1 and 2, or PBC1 and PBC2. Preferred values are as follows: NRF>0.9, PBC1>0.9, and PBC2>10.
• The experiment should pass routine metadata audits in order to be released.

Target-specific Standards

• Each replicate should have 10 million usable fragments.
• For transcription factor experiments, replicate concordance is measured by calculating IDR values (Irreproducible Discovery Rate). The experiment passes if both rescue and self consistency ratios are less than 2.

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