Platform Characterization

#### What is Platform Characterization?

The ENCODE and modENCODE projects have collected thousands of datasets to identify functional elements, each consisting of at least two replicates. Biochemical validation of every individual dataset is an impractical prospect. As an alternative, the consortia are characterizing the platforms used to comprehensively identify these functional elements. In some cases, this involves orthogonal testing of the platforms used to collect data to form catalogs of functional elements. For ChIP datasets, comparisons of ChIP-seq to ChIP-chip have been performed, as well as analysis of detection of spiked-in standards. For RNA-seq, examination of the behavior of spike-in standards has been performed. In addition to the standard ENCODE antibody characterization efforts, a systematic analysis of histone antibodies has been performed. Summaries to these individual studies and links to the publications are below.

#### Platform characterization studies:

Comparison of sequence-specific transcription factor determinations by ChIP-seq and ChIP-qPCR.
Gertz J, Reddy TE, Pauli F, and Myers RM

The key platform characterization finding is that there is good agreement between ChIP-seq and ChIP-qPCR. For each of 12 transcription factors, the enrichment of 44 binding sites was measured by qPCR. There was a high concordance between enrichment results from qPCR and the density of reads in ChIP-seq binding sites. These results indicate that high-throughput DNA sequencing maintains a robust representation of immunoprecipitated material.

Synthetic spike-in standards for RNA-seq experiments.
Jiang L, Schlesinger F, Davis CA, Zhang Y, Li R, Salit M, Gingeras TR, Oliver B.
Genome Res. 2011 Aug 4. [Epub ahead of print]; PMID: 21816910

Key platform characterization finding is that over a wide range, there is a linear correlation between signal (read density) and RNA concentration (input) in RNA-seq experiments. Another key finding is excellent agreement between replicates. A pool of RNA standards (96 different RNAs, various lengths and GC content) spanning a million fold concentration range was used in this determination. Some bias was found with respect to GC content and fragment length; these biases were reproducible and protocol dependent.

ChIP-chip versus ChIP-seq: lessons for experimental design and data analysis.
Ho JW, Bishop E, Karchenko PV, Nègre N, White KP, Park PJ.
BMC Genomics. 2011 Feb 28;12:134. PMID: 21356108; PMCID: PMC3053263

Key platform characterization findings include ChIP-seq data are generally better than ChIP-chip data with respect to signal-to-noise ratio, number of detected peaks and resolution. While there is strong agreement between the two platforms, the peaks identified using these two platforms can be significantly different, depending on the factor antibody and the analysis pipeline. Identification of binding regions is dependent on the peak calling pipeline used, and more difficult for factors that are enriched in broad regions. In addition, input DNA libraries used for ChIP-seq can vary, and high-quality input samples sequenced with sufficient depth are important for accurate peak calling.

An assessment of histone-modification antibody quality.
Egelhofer TA, Minoda A, Klugman S, Lee K, Kolasinska-Zwierz P, Alekseyenko AA, Cheung MS, Day DS, Gadel S, Gorchakov AA et al.
Nat Struct Mol Biol. 2011 Jan;18(1):91-3. Epub 2010 Dec 5. PMID: 21131980; PMCID: PMC3017233

Histone modification antibodies were characterized for specificity and ChIP. More than 25% of the tested antibodies failed specificity tests by dot blot or western blot. More than 20% of the antibodies that passed the specificity test failed in ChIP experiments. A website was developed for posting new results (http://compbio.med.harvard.edu/antibodies/).

Systematic evaluation of variability in ChIP-chip experiments using predefined DNA targets.
Johnson DS, Li W, Gordon DB, Bhattacharjee A, Curry B, Ghosh J, Brizuela L, Carroll JS, Brown M, Flicek P et al.
Genome Res. 2008 Mar;18(3):393-403. Epub 2008 Feb 7.PMID: 18258921; PMCID: PMC2259103

A number of microarray platforms were tested using a spike-in positive control approach, and found to provide results that were consistent with each other. Variance specific to microarray platform was similar or smaller than the variance associated with laboratory, protocols and analysis pipeline. Sensitivity was good even at relatively low spike-in levels. Simple repeats and segmental duplication caused false positive errors in peak detection.

Updated October 26th, 2011