ChIP-seq Reads Research Articles

HOCOMOCO in 2024: a rebuild of the curated collection of binding models for human and mouse transcription factors.

We present a major update of the HOCOMOCO collection that provides DNA binding specificity patterns of 949 human transcription factors and 720 mouse orthologs. To make this release, we performed motif discovery in peak sets that originated from 14 183 ChIP-Seq experiments and reads from 2554 HT-SELEX experiments yielding more than 400 thousand candidate motifs. The candidate motifs were annotated according to their similarity to known motifs and the hierarchy of DNA-binding domains of the respective transcription factors. Next, the motifs underwent human expert curation to stratify distinct motif subtypes and remove non-informative patterns and common artifacts. Finally, the curated subset of 100 thousand motifs was supplied to the automated benchmarking to select the best-performing motifs for each transcription factor. The resulting HOCOMOCO v12 core collection contains 1443 verified position weight matrices, including distinct subtypes of DNA binding motifs for particular transcription factors. In addition to the core collection, HOCOMOCO v12 provides motif sets optimized for the recognition of binding sites in vivo and in vitro, and for annotation of regulatory sequence variants. HOCOMOCO is available at https://hocomoco12.autosome.org and https://hocomoco.autosome.org.

Quantifying the arms race between LINE-1 and KRAB-zinc finger genes through TECookbook.

To defend against the invasion of transposons, hundreds of KRAB-zinc finger genes (ZNFs) evolved to recognize and silence various repeat families specifically. However, most repeat elements reside in the human genome with high copy numbers, making the ChIP-seq reads of ZNFs targeting these repeats predominantly multi-mapping reads. This complicates downstream data analysis and signal quantification. To better visualize and quantify the arms race between transposons and ZNFs, the R package TECookbook has been developed to lift ChIP-seq data into reference repeat coordinates with proper normalization and extract all putative ZNF binding sites from defined loci of reference repeats for downstream analysis. In conjunction with specificity profiles derived from in vitro Spec-seq data, human ZNF10 has been found to bind to a conserved ORF2 locus of selected LINE-1 subfamilies. This provides insight into how LINE-1 evaded capture at least twice and was subsequently recaptured by ZNF10 during evolutionary history. Through similar analyses, ZNF382 and ZNF248 were shown to be broad-spectrum LINE-1 binders. Overall, this work establishes a general analysis workflow to decipher the arms race between ZNFs and transposons through nucleotide substitutions rather than structural variations, particularly in the protein-coding region of transposons.

Low cell number ChIP-seq reveals chromatin state-based regulation of gene transcription in the rice male meiocytes.

Low cell number ChIP-seq reveals chromatin state-based regulation of gene transcription in the rice male meiocytes.

Considerations regarding centromere assembly in plant whole-genome sequencing.

The assembly of centromeric regions has become one of the most intractable tasks in whole-genome sequencing due to the enrichment of highly repetitive DNA sequences in most eukaryotic centromeres. Here, we describe a method used to identify centromeric DNAs through chromatin immunoprecipitation and sequencing (ChIP-seq). By mapping ChIP-seq reads, centromeric regions can be indicated in genome assemblies. We demonstrated that the assembly quality of centromeres obtained using ChIP-seq mapping can reflect and indicate the quality of a whole-genome assembly. We discuss an expected 'high-quality' centromere assembly obtained via centromere ChIP-seq mapping.

FANCY: fast estimation of privacy risk in functional genomics data

MotivationFunctional genomics data are becoming clinically actionable, raising privacy concerns. However, quantifying privacy leakage via genotyping is difficult due to the heterogeneous nature of sequencing techniques. Thus, we present FANCY, a tool that rapidly estimates the number of leaking variants from raw RNA-Seq, ATAC-Seq and ChIP-Seq reads, without explicit genotyping. FANCY employs supervised regression using overall sequencing statistics as features and provides an estimate of the overall privacy risk before data release.ResultsFANCY can predict the cumulative number of leaking SNVs with an average 0.95 R2 for all independent test sets. We realize the importance of accurate prediction when the number of leaked variants is low. Thus, we develop a special version of the model, which can make predictions with higher accuracy when the number of leaking variants is low.Availability and implementationA python and MATLAB implementation of FANCY, as well as custom scripts to generate the features can be found at https://github.com/gersteinlab/FANCY. We also provide jupyter notebooks so that users can optimize the parameters in the regression model based on their own data. An easy-to-use webserver that takes inputs and displays results can be found at fancy.gersteinlab.org.Supplementary information Supplementary data are available at Bioinformatics online.

SMARTcleaner: identify and clean off-target signals in SMART ChIP-seq analysis

BackgroundNoises and artifacts may arise in several steps of the next-generation sequencing (NGS) process. Recently, an NGS library preparation method called SMART, or Switching Mechanism At the 5′ end of the RNA Transcript, is introduced to prepare ChIP-seq (chromatin immunoprecipitation and deep sequencing) libraries from small amount of DNA material, using the DNA SMART ChIP-seq Kit. The protocol adds Ts to the 3′ end of DNA templates, which is subsequently recognized and used by SMART poly(dA) primers for reverse transcription and then addition of PCR primers and sequencing adapters. The poly(dA) primers, however, can anneal to poly(T) sequences in a genome and amplify DNA fragments that are not enriched in the immunoprecipitated DNA templates. This off-target amplification results in false signals in the ChIP-seq data.ResultsHere, we show that the off-target ChIP-seq reads derived from false amplification of poly(T/A) genomic sequences have unique and strand-specific features. Accordingly, we develop a tool (called “SMARTcleaner”) that can exploit these features to remove SMART ChIP-seq artifacts. Application of SMARTcleaner to several SMART ChIP-seq datasets demonstrates that it can remove reads from off-target amplification effectively, leading to significantly improved ChIP-seq peaks and results.ConclusionsSMARTcleaner could identify and clean the false signals in SMART-based ChIP-seq libraries, leading to improvement in peak calling, and downstream data analysis and interpretation.

A Type 2 Diabetes-Associated Functional Regulatory Variant in a Pancreatic Islet Enhancer at the ADCY5 Locus.

Molecular mechanisms remain unknown for most type 2 diabetes genome-wide association study identified loci. Variants associated with type 2 diabetes and fasting glucose levels reside in introns of ADCY5, a gene that encodes adenylate cyclase 5. Adenylate cyclase 5 catalyzes the production of cyclic AMP, which is a second messenger molecule involved in cell signaling and pancreatic β-cell insulin secretion. We demonstrated that type 2 diabetes risk alleles are associated with decreased ADCY5 expression in human islets and examined candidate variants for regulatory function. rs11708067 overlaps a predicted enhancer region in pancreatic islets. The type 2 diabetes risk rs11708067-A allele showed fewer H3K27ac ChIP-seq reads in human islets, lower transcriptional activity in reporter assays in rodent β-cells (rat 832/13 and mouse MIN6), and increased nuclear protein binding compared with the rs11708067-G allele. Homozygous deletion of the orthologous enhancer region in 832/13 cells resulted in a 64% reduction in expression level of Adcy5, but not adjacent gene Sec22a, and a 39% reduction in insulin secretion. Together, these data suggest that rs11708067-A risk allele contributes to type 2 diabetes by disrupting an islet enhancer, which results in reduced ADCY5 expression and impaired insulin secretion.

GenoGAM: genome-wide generalized additive models for ChIP-Seq analysis.

Chromatin immunoprecipitation followed by deep sequencing (ChIP-Seq) is a widely used approach to study protein-DNA interactions. Often, the quantities of interest are the differential occupancies relative to controls, between genetic backgrounds, treatments, or combinations thereof. Current methods for differential occupancy of ChIP-Seq data rely however on binning or sliding window techniques, for which the choice of the window and bin sizes are subjective. Here, we present GenoGAM (Genome-wide Generalized Additive Model), which brings the well-established and flexible generalized additive models framework to genomic applications using a data parallelism strategy. We model ChIP-Seq read count frequencies as products of smooth functions along chromosomes. Smoothing parameters are objectively estimated from the data by cross-validation, eliminating ad hoc binning and windowing needed by current approaches. GenoGAM provides base-level and region-level significance testing for full factorial designs. Application to a ChIP-Seq dataset in yeast showed increased sensitivity over existing differential occupancy methods while controlling for type I error rate. By analyzing a set of DNA methylation data and illustrating an extension to a peak caller, we further demonstrate the potential of GenoGAM as a generic statistical modeling tool for genome-wide assays. Software is available from Bioconductor: https://www.bioconductor.org/packages/release/bioc/html/GenoGAM.html . gagneur@in.tum.de. Supplementary information is available at Bioinformatics online.

BaalChIP: Bayesian analysis of allele-specific transcription factor binding in cancer genomes

Allele-specific measurements of transcription factor binding from ChIP-seq data are key to dissecting the allelic effects of non-coding variants and their contribution to phenotypic diversity. However, most methods of detecting an allelic imbalance assume diploid genomes. This assumption severely limits their applicability to cancer samples with frequent DNA copy-number changes. Here we present a Bayesian statistical approach called BaalChIP to correct for the effect of background allele frequency on the observed ChIP-seq read counts. BaalChIP allows the joint analysis of multiple ChIP-seq samples across a single variant and outperforms competing approaches in simulations. Using 548 ENCODE ChIP-seq and six targeted FAIRE-seq samples, we show that BaalChIP effectively corrects allele-specific analysis for copy-number variation and increases the power to detect putative cis-acting regulatory variants in cancer genomes.

Ubiquitously expressed genes participate in cell-specific functions via alternative promoter usage.

How do different cell types acquire their specific identities and functions is a fundamental question of biology. Previously significant efforts have been devoted to search for cell-type-specifically expressed genes, especially transcription factors, yet how do ubiquitously expressed genes participate in the formation or maintenance of cell-type-specific features remains largely unknown. Here, we have identified 110 mouse embryonic stem cell (mESC) specifically expressed transcripts with cell-stage-specific alternative transcription start sites (SATS isoforms) from 104 ubiquitously expressed genes, majority of which have active epigenetic modification- or stem cell-related functions. These SATS isoforms are specifically expressed in mESCs, and tend to be transcriptionally regulated by key pluripotency factors through direct promoter binding. Knocking down the SATS isoforms of Nmnat2 or Usp7 leads to differentiation-related phenotype in mESCs. These results demonstrate that cell-type-specific transcription factors are capable to produce cell-type-specific transcripts with alternative transcription start sites from ubiquitously expressed genes, which confer ubiquitously expressed genes novel functions involved in the establishment or maintenance of cell-type-specific features.

Pasha: a versatile R package for piling chromatin HTS data.

We describe an R package designed for processing aligned reads from chromatin-oriented high-throughput sequencing experiments. Pasha (preprocessing of aligned sequences from HTS analyses) allows easy manipulation of aligned reads from short-read sequencing technologies (ChIP-seq, FAIRE-seq, MNase-Seq, …) and offers innovative approaches such as ChIP-seq reads elongation, nucleosome midpoint piling strategy for positioning analyses, or the ability to subset paired-end reads by groups of insert size that can contain biologically relevant information. Pasha is a multi-platform R package, available on CRAN repositories under GPL-3 license (https://cran.r-project.org/web/packages/Pasha/). rfenouil@gmail.com or jean-christophe.andrau@igmm.cnrs.fr Supplementary data are available at Bioinformatics online.

A uniform survey of allele-specific binding and expression over 1000-Genomes-Project individuals.

Large-scale sequencing in the 1000 Genomes Project has revealed multitudes of single nucleotide variants (SNVs). Here, we provide insights into the functional effect of these variants using allele-specific behaviour. This can be assessed for an individual by mapping ChIP-seq and RNA-seq reads to a personal genome, and then measuring ‘allelic imbalances' between the numbers of reads mapped to the paternal and maternal chromosomes. We annotate variants associated with allele-specific binding and expression in 382 individuals by uniformly processing 1,263 functional genomics data sets, developing approaches to reduce the heterogeneity between data sets due to overdispersion and mapping bias. Since many allelic variants are rare, aggregation across multiple individuals is necessary to identify broadly applicable ‘allelic elements'. We also found SNVs for which we can anticipate allelic imbalance from the disruption of a binding motif. Our results serve as an allele-specific annotation for the 1000 Genomes variant catalogue and are distributed as an online resource (alleledb.gersteinlab.org).

Looking beyond GWAS: allele-specific transcription factor binding drives the association of GALNT2 to HDL-C plasma levels.

BackgroundPlasma levels of high-density lipoprotein cholesterol (HDL-C) have been associated to cardiovascular disease. The high heritability of HDL-C plasma levels has been an incentive for several genome wide association studies (GWASs) which identified, among others, variants in the first intron of the GALNT2 gene strongly associated to HDL-C levels. However, the lead GWAS SNP associated to HDL-C levels in this genomic region, rs4846914, is located outside of transcription factor (TF) binding sites defined by chromatin immunoprecipitation followed by DNA sequencing (ChIP-seq) experiments in the ENCODE project and is therefore unlikely to be functional. In this study we apply a bioinformatics approach which rely on the premise that ChIP-seq reads can identify allele specific binding of a TF at cell specific regulatory elements harboring allele specific SNPs (AS-SNPs). EMSA and luciferase assays were used to validate the allele specific binding and to test the enhancer activity of the regulatory element harboring the AS-SNP rs4846913 as well as the neighboring rs2144300 which are in high LD with rs4846914.FindingsUsing luciferase assays we found that rs4846913 and the neighboring rs2144300 displayed allele specific enhancer activity. We propose that an inhibitor binds preferentially to the rs4846913-C allele with an inhibitory boost from the synergistic binding of other TFs at the neighboring SNP rs2144300. These events influence the transcription level of GALNT2.ConclusionsThe results suggest that rs4846913 and rs2144300 drive the association to HDL-C plasma levels through an inhibitory regulation of GALNT2 rather than the reported lead GWAS SNP rs4846914.

WASP: allele-specific software for robust molecular quantitative trait locus discovery.

Allele-specific sequencing reads provide a powerful signal for identifying molecular quantitative trait loci (QTLs), however they are challenging to analyze and prone to technical artefacts. Here we describe WASP, a suite of tools for unbiased allele-specific read mapping and discovery of molecular QTLs. Using simulated reads, RNA-seq reads and ChIP-seq reads, we demonstrate that WASP has a low error rate and is far more powerful than existing QTL mapping approaches.

Removing reference mapping biases using limited or no genotype data identifies allelic differences in protein binding at disease-associated loci.

BackgroundGenetic variation can alter transcriptional regulatory activity contributing to variation in complex traits and risk of disease, but identifying individual variants that affect regulatory activity has been challenging. Quantitative sequence-based experiments such as ChIP-seq and DNase-seq can detect sites of allelic imbalance where alleles contribute disproportionately to the overall signal suggesting allelic differences in regulatory activity.MethodsWe created an allelic imbalance detection pipeline, AA-ALIGNER, to remove reference mapping biases influencing allelic imbalance detection and evaluate accuracy of allelic imbalance predictions in the absence of complete genotype data. Using the sequence aligner, GSNAP, and varying amounts of genotype information to remove mapping biases we investigated the accuracy of allelic imbalance detection (binomial test) in CREB1 ChIP-seq reads from the GM12878 cell line. Additionally we thoroughly evaluated the influence of experimental and analytical parameters on imbalance detection.ResultsCompared to imbalances identified using complete genotypes, using imputed partial sample genotypes, AA-ALIGNER detected >95 % of imbalances with >90 % accuracy. AA-ALIGNER performed nearly as well using common variants when genotypes were unknown. In contrast, predicting additional heterozygous sites and imbalances using the sequence data led to >50 % false positive rates. We evaluated effects of experimental data characteristics and key analytical parameter settings on imbalance detection. Overall, total base coverage and signal dispersion across the genome most affected our ability to detect imbalances, while parameters such as imbalance significance, imputation quality thresholds, and alignment mismatches had little effect. To assess the biological relevance of imbalance predictions, we used electrophoretic mobility shift assays to functionally test for predicted allelic differences in CREB1 binding in the GM12878 lymphoblast cell line. Six of nine tested variants exhibited allelic differences in binding. Two of these variants, rs2382818 and rs713875, are located within inflammatory bowel disease-associated loci.ConclusionsAA-ALIGNER accurately detects allelic imbalance in quantitative sequence data using partial genotypes or common variants filling a critical methodological gap in these analyses, as full genotypes are rarely available. Importantly, we demonstrate how experimental and analytical features impact imbalance detection providing guidance for similar future studies.Electronic supplementary materialThe online version of this article (doi:10.1186/s12920-015-0117-x) contains supplementary material, which is available to authorized users.

ABC: a tool to identify SNVs causing allele-specific transcription factor binding from ChIP-Seq experiments.

Detection of allelic imbalances in ChIP-Seq reads is a powerful approach to identify functional non-coding single nucleotide variants (SNVs), either polymorphisms or mutations, which modulate the affinity of transcription factors for chromatin. We present ABC, a computational tool that identifies allele-specific binding of transcription factors from aligned ChIP-Seq reads at heterozygous SNVs. ABC controls for potential false positives resulting from biases introduced by the use of short sequencing reads in ChIP-Seq and can efficiently process a large number of heterozygous SNVs. ABC successfully identifies previously characterized functional SNVs, such as the rs4784227 breast cancer risk associated SNP that modulates the affinity of FOXA1 for the chromatin. The code is open-source under an Artistic-2.0 license and versioned on GitHub (https://github.com/mlupien/ABC/). ABC is written in PERL and can be run on any platform with both PERL (≥5.18.1) and R (≥3.1.1) installed. The script requires the PERL Statistics::R module. mlupien@uhnres.utoronto.ca Supplementary data are available at Bioinformatics online.

Important biological information uncovered in previously unaligned reads from chromatin immunoprecipitation experiments (ChIP-Seq).

Establishing the architecture of gene regulatory networks (GRNs) relies on chromatin immunoprecipitation followed by massively parallel sequencing (ChIP-Seq) methods that provide genome-wide transcription factor binding sites (TFBSs). ChIP-Seq furnishes millions of short reads that, after alignment, describe the genome-wide binding sites of a particular TF. However, in all organisms investigated an average of 40% of reads fail to align to the corresponding genome, with some datasets having as much as 80% of reads failing to align. We describe here the provenance of previously unaligned reads in ChIP-Seq experiments from animals and plants. We show that a substantial portion corresponds to sequences of bacterial and metazoan origin, irrespective of the ChIP-Seq chromatin source. Unforeseen was the finding that 30%–40% of unaligned reads were actually alignable. To validate these observations, we investigated the characteristics of the previously unaligned reads corresponding to TAL1, a human TF involved in lineage specification of hemopoietic cells. We show that, while unmapped ChIP-Seq read datasets contain foreign DNA sequences, additional TFBSs can be identified from the previously unaligned ChIP-Seq reads. Our results indicate that the re-evaluation of previously unaligned reads from ChIP-Seq experiments will significantly contribute to TF target identification and determination of emerging properties of GRNs.

HiChIP: a high-throughput pipeline for integrative analysis of ChIP-Seq data

BackgroundChromatin immunoprecipitation (ChIP) followed by next-generation sequencing (ChIP-Seq) has been widely used to identify genomic loci of transcription factor (TF) binding and histone modifications. ChIP-Seq data analysis involves multiple steps from read mapping and peak calling to data integration and interpretation. It remains challenging and time-consuming to process large amounts of ChIP-Seq data derived from different antibodies or experimental designs using the same approach. To address this challenge, there is a need for a comprehensive analysis pipeline with flexible settings to accelerate the utilization of this powerful technology in epigenetics research.ResultsWe have developed a highly integrative pipeline, termed HiChIP for systematic analysis of ChIP-Seq data. HiChIP incorporates several open source software packages selected based on internal assessments and published comparisons. It also includes a set of tools developed in-house. This workflow enables the analysis of both paired-end and single-end ChIP-Seq reads, with or without replicates for the characterization and annotation of both punctate and diffuse binding sites. The main functionality of HiChIP includes: (a) read quality checking; (b) read mapping and filtering; (c) peak calling and peak consistency analysis; and (d) result visualization. In addition, this pipeline contains modules for generating binding profiles over selected genomic features, de novo motif finding from transcription factor (TF) binding sites and functional annotation of peak associated genes.ConclusionsHiChIP is a comprehensive analysis pipeline that can be configured to analyze ChIP-Seq data derived from varying antibodies and experiment designs. Using public ChIP-Seq data we demonstrate that HiChIP is a fast and reliable pipeline for processing large amounts of ChIP-Seq data.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2105-15-280) contains supplementary material, which is available to authorized users.

Abstract LB-118: ChIP-seq based identification of genes regulated by a pancreatic β-cell differentiation factor, Hlxb9, that is dysregulated in β-cell tumors.

Abstract The homeobox transcription factor Hlxb9 controls pancreatic β-cell differentiation during the early and late stages of endocrine pancreas development. Recently, increased expression of Hlxb9 was observed in β-cell tumors associated with multiple endocrine neoplasia type 1 (MEN1), a tumor syndrome caused by germline mutation in the MEN1 tumor suppressor gene encoding menin. In MIN6 cells, a mouse pancreatic β-cell line, overexpression of Hlxb9 has been shown to cause apoptosis in a menin-dependent manner. Although Hlxb9 is known to act as a transcriptional repressor, its DNA-binding site at its target genes in pancreatic β-cells has not been elucidated. To identify targets of Hlxb9 and genomic binding sites in β-cells (MIN6 cells) we used chromatin immunoprecipitation and deep sequencing (ChIP-seq) followed by bioinformatics analysis of the ChIP-seq data. The Hlxb9 specific ChIP-seq libraries and corresponding input controls were sequenced on the Illumina Hi Seq 2000. The ChIP-seq reads were subjected to peak calling tools, and upon stringent filtering 2569 peaks were obtained. After assigning chromosomal positions to the peaks, 24 statistically significant (FDR-P<0.05) targets were considered as potential Hlxb9-binding regions for further analysis. The ChIP-seq targets were validated by qPCR. We identified a potential Hlxb9 consensus-binding motif ‘NCNNNNGCTCCTGN’ de novo using the Genomatix Genome Analyzer suite that appeared to be common in most of the targets (Re-value: 1.88). Further analysis revealed that this motif was similar to binding sequences of the following proteins: Tcfe2a (involved in brain development), Ascl2 (involved in nervous system), REI1 (involved in mitotic signal network), ZFp691 (zinc finger protein), myf (involved in mitosis), and Bcl6b (involved in apoptosis). Gene Ontology analysis of the identified targets showed that 18.3% were associated with the Wnt signaling pathway, 27.3% with cell proliferation, 54.5% were involved in cellular response to stimulus, 13.6% in MAPK cascade and 31.8% in negative regulation of biological processes. Our genome-wide analysis of Hlxb9 target genes demonstrates the presence of a potential Hlxb9-binding site in target genes associated with important processes relevant for cellular differentiation and neoplasia. Further studies investigating these targets for menin-dependent and menin-independent actions of Hlxb9 will provide insights into the role of menin-Hlxb9 interaction in normal physiology and in the development of pancreatic β-cell tumors. Citation Format: Shruti S. Desai, Sita D. Modali, Weiping Chen, Vaishali I. Parekh, Sunita K. Agarwal. ChIP-seq based identification of genes regulated by a pancreatic β-cell differentiation factor, Hlxb9, that is dysregulated in β-cell tumors. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr LB-118. doi:10.1158/1538-7445.AM2013-LB-118

Abstract LB-113: Genome-wide mapping of TFE3-fusion target genes.

Abstract Xp11.2 translocation renal cell carcinoma (RCC), also known as TFE3-fusion associated RCC, is a recently classified distinct subtype of RCC. It is defined by several different translocations involving chromosome Xp11.2, all resulting in gene fusions involving the Transcription Factor Binding to IGHM Enhancer 3 (TFE3) gene. However, the detailed function of TFE3-fusion proteins in TFE3-fusion associated RCC still remains unknown, and there is no effective treatment for patients with this RCC subtype. In this study we sought to investigate the detailed regulatory network of TFE3-fusion proteins by using doxycycline-inducible HA-tagged TFE3-fusion cell lines and chromatin immunoprecipitation (ChIP)-sequencing. Doxycycline-inducible cell lines that express HA-tagged TFE3-fusion (PRCC-TFE3, PSF-TFE3 and NONO-TFE3) and TFE3 wild-type proteins were established in the HK2 cell line. Genome-wide screening of the binding characteristics of TFE3-fusion proteins and TFE3 wild-type protein in doxycycline-induced cell lines was performed using a chromatin immunoprecipitation sequencing (ChIP-seq) assay. Hierarchical clustering was used to analyze the genes regulated by each type of TFE3-fusion protein compared with TFE3 wild-type protein. The total number of CHIP-seq reads in TFE3 wild-type cell line was 18552874 and enrichment of the promoter region was 1.2. In contrast, the total number of CHIP-seq reads in PRCC-TFE3, PSF-TFE3 and NONO-TFE3 cell lines were 15332969, 20426159 and 17639467, respectively. Enrichment of promoter region in each of the TFE3-fusion cell line was 1.5, 2.4 and 2.2, respectively. There were 80 motifs enriched (Z score >1.5) by expression of PRCC-TFE3 protein compared to TFE3-wild type protein, 104 motifs enriched by PSF-TFE3 protein and 39 motifs by NONO-TFE3 protein expression. Work is ongoing to identify the target genes that are co-regulated in TFE3-fusion cell lines relative to the TFE3 wild-type cell line. Our aim is to identify activated pathways in common among the different TFE3-fusion cell lines compared to the TFE3 wild-type cell line. We anticipate that this study will further our understanding of mechanisms that activate TFE3-fusion proteins facilitating the identification of therapeutic targets for effective treatment of TFE3-fusion associated RCC. Citation Format: Ying Huang, Masaya Baba, Hisashi Hasumi, Yukiko Hasumi, Laura Schmidt, Yoshi Wakabayashi, Jun Zhu, Wenjing Yang, Marston Linehan. Genome-wide mapping of TFE3-fusion target genes. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr LB-113. doi:10.1158/1538-7445.AM2013-LB-113