Optimization of transcription factor binding map accuracy by utilizing their knockout-mouse models
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ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE55317 | GEO | 2014/07/17
SECONDARY ACCESSION(S): PRJNA239314
REPOSITORIES: GEO
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Project description:Genome-wide assessment of protein-DNA interactions binding by ChIP-seq is a key technology to study transcription factor (TF) localization and regulation of gene-expression. In ChIP-seq, signal-to-noise-ratio as well as signal specificity depend on many variables including antibody quality, and efforts to improve ChIP-seq data thus far focused mostly on generating better reagents. Here we introduce KOIN (KO implemented normalization) as a novel strategy to increase signal specificity and reduce noise by using TF knockout-mice as a critical control for ChIP-seq. We tested our new peak calling strategy (KO implemented normalization = KOIN) on different ChIP-seq datasets to increase signal specificity and reduce noise.
2014-07-17 | E-GEOD-55317 | biostudies-arrayexpress
Project description:Genome-wide assessment of protein-DNA interaction by chromatin immunoprecipitation followed by massive parallel sequencing (ChIP-seq) is a key technology for studying transcription factor (TF) localization and regulation of gene expression. Signal-to-noise-ratio and signal specificity in ChIP-seq studies depend on many variables, including antibody affinity and specificity. Thus far, efforts to improve antibody reagents for ChIP-seq experiments have focused mainly on generating higher quality antibodies. Here we introduce KOIN (knockout implemented normalization) as a novel strategy to increase signal specificity and reduce noise by using TF knockout mice as a critical control for ChIP-seq data experiments. Additionally, KOIN can identify 'hyper ChIPable regions' as another source of false-positive signals. As the use of the KOIN algorithm reduces false-positive results and thereby prevents misinterpretation of ChIP-seq data, it should be considered as the gold standard for future ChIP-seq analyses, particularly when developing ChIP-assays with novel antibody reagents.
| S-EPMC4245947 | biostudies-literature
Project description:Motivation: The DNA binding specificity of a transcription factor (TF) is typically represented using a position weight matrix (PWM) model, which implicitly assumes that individual bases in a TF binding site contribute independently to the binding affinity, an assumption that does not always hold. For this reason, more complex models of binding specificity have been developed. However, these models have their own caveats: they typically have a large number of parameters, which makes them hard to learn and interpret. Results: We propose novel regression-based models of TF-DNA binding specificity, trained using high resolution in vitro data from custom protein binding microarray (PBM) experiments. Our PBMs are specifically designed to cover a large number of putative DNA binding sites for the TFs of interest (yeast TFs Cbf1 and Tye7, and human TFs c-Myc, Max, and Mad2) in their native genomic context. These high-throughput, quantitative data are well suited for training complex models that take into account not only independent contributions from individual bases, but also contributions from di- and trinucleotides at various positions within or near the binding sites. To ensure that our models remain interpretable, we use feature selection to identify a small number of sequence features that accurately predict TF-DNA binding specificity. To further illustrate the accuracy of our regression models, we show that even in the case of paralogous TF with highly similar PWMs, our new models can distinguish the specificities of individual factors. Thus, our work represents an important step towards better sequence-based models of individual TF-DNA binding specificity. Four protein binding microarray (PBM) experiments of human transcription factors were performed. Briefly, the PBMs involved binding GST-tagged transcription factors c-Myc, Max, and Mad2(Mxi1) to double-stranded 180K Agilent microarrays in order to determine their binding specificity for putative DNA binding sites in native genomic context. Briefly, we represent three categories of 36-bp sequences: 1) bound probes, 2) unbound probes (or negative controls), and 3) test probes. Bound probes corresponded to genomic regions bound in vivo by c-Myc, Max, or Mad2 (ChIP-seq P < 10^(-10) in HeLaS3 or K562 celld (ENCODE)) that contain at least two consecutive 8-mers with universal PBM E-score > 0.4 (Munteanu and Gordan, LNCS 2013). All putative binding sites occurr at the same position within the probes on the array. M-bM-^@M-^\UnboundM-bM-^@M-^] probes corresponded to genomic regions with ChIP-seq P < 10^(-10) and a maximum 8-mer E-score < 0.2. We also designed test probes that contain, within constant flanking regions, all nnCACGTGnn 10-mers and 18 nnnCACGTGnnn 12-mers (where n = A, C, G, or T). Each DNA sequence represented on the array is present in 6 replicate spots. We report the PBM signal intensity for each spot. The PBM protocol is described in Berger et al., Nature Biotechnology 2006 (PMID 16998473).
2013-05-17 | E-GEOD-47026 | biostudies-arrayexpress
Project description:A high-confidence map of the direct, functional targets of each transcription factor (TF) requires convergent evidence from independent sources. Two significant sources of evidence are TF binding locations and the transcriptional responses to direct TF perturbations. Systematic data sets of both types exist for yeast and human. Standard analysis of the genes whose regulatory DNA is bound by a TF, assayed by ChIP-chip/seq, and the genes that respond to a perturbation of that TF, shows that these two data sources rarely converge on a common set of direct, functional targets. Even taking the few genes that are both bound and responsive as direct functional targets is not safe -- when there are many non-functional binding sites and many indirect targets, non-functional sites are expected to occur in the cis-regulatory DNA of indirect targets by chance. To address this problem, we introduce Dual Threshold Optimization, a new method for setting significance thresholds on binding and response data, and show that it improves convergence. It also enables comparison of binding data to perturbation-response data that has been processed by network inference algorithms, which further improves convergence. Next, we analyze a comprehensive new data set measuring the transcriptional response shortly after inducing overexpression of a yeast TF. We also present a new yeast binding location data set obtained by transposon calling cards and compare it to recent ChIP-exo data. The combination of dual threshold optimization and network inference greatly expands the high-confidence TF network map in both yeast and human. In yeast, measuring the response shortly after inducing TF overexpression and measuring binding locations by using transposon calling cards or ChIP-exo improve the network synergistically.
2020-02-04 | GSE144657 | GEO
Project description:Here, we have collapsed multiple analysis problems into two coherent categories, signal detection and signal estimation and adapted linear-optimal solutions from signal processing theory. Our algorithms for detection (DFilter) and estimation (EFilter) extend naturally to integration of multiple datasets. In benchmarking tests, DFilter outperformed assay-specific algorithms at identifying promoters from histone ChIP-seq, binding sites from transcription factor (TF) ChIP-seq and open chromatin regions from DNase- and FAIRE-seq data. EFilter similarly outperformed an existing method for predicting mRNA levels from histone ChIP-seq data (Spearman correlation: 0.81 - 0.89). We performed H3K4me3 and H3K36me3 ChIP-seq on e11.5 mouse forebrain and used DFilter and EFilter to predict promoters and developmental gene expression, uncovering plausible gene targets for SNPs associated with neurodevelopmental disorders. Generated two histone modifiction ChiP-seq in developing embryo mouse forebrain and using them for making bioligical inferences
2013-06-10 | E-GEOD-34073 | biostudies-arrayexpress
Project description:The key myeloid transcription factor (TF) CEBPA is frequently mutated in acute myeloid leukemia (AML), but the molecular ramifications of this leukemic driver mutation remain elusive. To investigate CEBPA mutant AML, we compared gene expression changes in human CEBPA mutant AML and in the corresponding CebpaLp30 mouse model, and identified a conserved cross-species transcriptional program. ChIP-seq revealed aberrantly activated enhancers, exclusively occupied by the leukemia-associated CEBPA-p30 isoform. One leukemic-enhancer upstream of Nt5e, encoding CD73, was physically and functionally linked to this conserved AML gene, and could be activated by CEBPA. Targeting of CD73-adenosine signaling increased AML survival in transplanted mice. Our data indicate a first-in-class link between a TF cancer driver mutation and a druggable, direct transcriptional target.
2019-08-08 | PXD011359 | Pride
Project description:Abstract: Bacteria adapt to the constantly changing environments largely by transcriptional regulation through the activities of various transcription factors (TFs). However, techniques that monitor the in situ TF-promoter interactions in living bacteria are lacking. Herein, we developed a whole-cell TF-promoter binding assay based on the intermolecular Förster resonance energy transfer (FRET) between a fluorescent unnatural amino acid CouA which is genetically encoded into defined sites in TFs and the live cell fluorescent nucleic acid stain SYTO 9. We show that this new FRET pair monitors the intricate TF-promoter interactions elicited by various types of signal transduction systems with specificity and sensitivity. Furthermore, the assay is applicable to identify novel modulators of the regulatory systems of interest and monitor TF activities in bacteria colonized in C. elegans. In conclusion, we established a tractable and sensitive TF-promoter binding assay in living bacteria which not only complements currently available approaches for DNA-protein interactions but also provides novel opportunities for functional annotation of bacterial signal transduction systems and studies of the bacteria-host interface
2022-10-13 | PXD030973 | Pride
Project description:Performing ChIP-seq analyses in clinical specimens has remained largely challenging due to multiple technical limitations and low quantities of starting material, resulting in low enrichments and poor signal-to-noise ratio. Here, we refined the original protocols for transcription factor ChIP-seq analyses in breast, prostate, and endometrial tumor tissue. In addition to the standard fixative formaldehyde, a second crosslinker Disuccinimidyl glutarate (DSG) was included in the procedure.
2018-12-27 | GSE114737 | GEO
Project description:The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid cost and labour intensive TF ChIP-seq assays.It is important to develop reliable and fast computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices.TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq, using either peaks or footprints as input.In addition to open-chromatin data, also Histone-Marks (HMs) can be used in TEPIC to identify candidate TF binding sites.TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength.Using machine learning techniques, we show that incorporating low affinity binding sites improves our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites.Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance.In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq datasets.Finally, we show that these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively.
| EGAS00001002073 | EGA