Unknown,Transcriptomics,Genomics,Proteomics

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Specificity quantification of Glucocorticoid receptor binding using high-throughput sequencing

ABSTRACT: In this work, we used our recently developed method Spec-seq to characterize the binding specificity of Glucocorticoid receptor in vitro. This GR Spec-seq experiment has been run twice separately (Dec 2014 and Mar 2015) with different library compositions. The basic workflow is the same as our previous work for lac repressor published in Genetics 198.3 (2014): 1329-1343. Recombinant human GR protein was used to facilitate in vitro DNA-binding and separation experiments. Bound and Unbound DNA fragments were separated in EMSA gels, purified, barcoded for further Illumina sequencing. An initial experiment was performed using the putative consensus sequence: AGAACA GGG TGTTCT; randomized library 1: AGAACN NSN NGTTCT [diversity =512]; randomized library 2: AGAANN GGG NNNTCT [diversity = 1024]; randomized library 3: AGAACA GGG TGNNNN [diversity =256]; randomized library 4: AGAACA GGGC NNNTCT [diversity = 64]. The initial total library diversity was ~1853 with a library composition of 10% positive control sequence + randomized libraries 1-4 + 5% negative control sequence. A 5th library containing 2304 sequences based on DDNACW KKN KGTTCT, where D="not C", N="any base", W="A or T" and K="G or T" was subsequently prepared and analyzed using similar ratios of control sequences. Binding conditions for EMSA were 100ng FAM-labelled dsDNA+ 0/0.5/1/2/4uM GR DBD protein for each lane, 1X NEB buffer 4. GR DBD was prepared as previously described. The EMSA was performed using a 9% 33:1 acrylamide gel and TB buffer, and was run at 200V for 30mins @ 0 degrees C. The 2uM protein lane used for final sequencing. Bound/unbound fractions resulting from EMSA of these libraries and conditions were used to generate PWMs as described. The GR PWM that was generated through this analysis was used to define relative binding energies using the patser program (35), which can be accessed online at (http://stormo.wustl.edu/consensus/cgi-bin/Server/Interface/patser.cgi ). Derived binding affinities are proportional to the inverse of the natural log of the calculated energies.

ORGANISM(S): Homo sapiens

SUBMITTER: Gary Stormo

PROVIDER: E-GEOD-69386 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:We examine the use of high-throughput sequencing on binding sites recovered using a bacterial one-hybrid (B1H) system and find that improved models of transcription factor (TF) binding specificity can be obtained compared to standard methods of sequencing a small subset of the selected clones. We can obtain even more accurate binding models using a modified version of B1H selection method with constrained variation (CV-B1H). However, achieving these improved models using CV-B1H data required the development of a new method of analysis - GRaMS (Growth Rate Modeling of Specificity) - that estimates bacterial growth rates as a function of the quality of the recognition sequence. We benchmark these different methods of motif discovery using Zif268, a well characterized C2H2 zinc finger transcription factor on both a 28bp randomized library for the standard B1H method and on 6bp randomized library for the CV-B1H method for which forty-five different experimental conditions were tested: 5 time points and three different IPTG and 3-AT concentrations. We find that GRaMS analysis is robust to the different experimental parameters whereas other analysis methods give widely varying results depending on the conditions of the experiment. Finally, we demonstrate that the CV-B1H assay can be performed in liquid media, which produces recognition models that are similar in quality to sequences recovered from selection on solid media. All selections were performed using the bait transcription factor Zif268. 45 different B1H selections were performed on plates and the selected sites were multiplexed and sequenced in a single lane. A single selection was performed in liquid media and sequenced along with other multiplexed sets of sites in a separate lane. For the experiments performed on plates, the of the selections assays was varied (4, 8, 12, 18, 24 hours). The stringency of the selections was also varied by changing the concentration of IPTG (0, 10, 50 uM) and 3-AT (0.5, 1, 2 mM). The initial randomized library used for all selections was also sequenced in a single lane. The randomized portion of each transcription factor binding site is 6bp long and is immediately 5' of the constant sequence, GCGG, which is the consensus binding site for finger 1 of Zif268. Upstream of the randomized binding site is a 4bp randomized region referred to as the key region. The purpose of the key region is to detect possible PCR 'jackpotting.' The single liquid media experiment was run for 4 hours using 50uM IPTG and 5mM 3-AT. Four selections were performed using a prey library with a 28bp long randomized region and not fixed consensus site region. These selections were performed at either 36 or 48 hours using 10uM IPTG and 0, 2, or 5 mM 3-AT.

Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Carrie Davis mailto:davisc@cshl.edu (experimental), Alex Dobin mailto:dobin@cshl.edu (computational), Felix Schlesinger mailto:schlesin@cshl.edu (computational), Tom Gingeras mailto:gingeras@cshl.edu (primary investigator), and Roderic Guigo's group mailto:rguigo@imim.es at the CRG). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). These tracks were generate by the ENCODE Consortium. They contain information about human RNAs > 200 nucleotides in length obtained as short reads off the Illumina GAIIx platform. Data is available from biological replicates of several cell lines. In addition to profiling Poly-A+ and Poly-A- RNA from whole cells, we have also gather data from various subcellular compartments. In many cases, there are Cap Analysis of Gene Expression (CAGE, RIKEN Institute) and Small RNA-Seq (<200 nucleotides, CSHL) and Pair-End di-TAG-RNA (PET-RNA, Genome Institute of Singapore) datasets available from the same biological replicates. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf We are using the published protocol http://www.ncbi.nlm.nih.gov/pubmed/19620212. This protocol generates directional libraries and reports the transcripts strand of origin. Exogenous RNA spike-ins (Round 5, pool 14), in development at National Institutes Standards Technology were added to each endogenous RNA isolate and carried through library construction and sequencing. The Illumina PhiX control library was also spiked-in at 1% to each completed human library just prior to cluster formation. Accompanying each RNA-Seq dataset is a "Production Document". This document contains details about the RNA isolations and treatments, library construction, spike-ins as well as quality control figures for individual libraries. The spike-in sequence and the concentrations can are available for download in the supplemental directory. The libraries are sequenced on the Illumina platform to an average depth of ~200 million reads (100 million mate-pairs). The data are mapped against hg19 using Spliced Transcript Alignment and Reconstruction (STAR) written by Alex Dobin (CSHL). More information, about STAR including the parameters used for these data can be found at: http://gingeraslab.cshl.edu/STAR/. Additionally, we provide the following processed "element" data files: de novo splice junctions, de novo transcripts, and contigs. These elements are assessed for reproducibility using a nonparametric irreproducible detection (IDR) rate script. The IDR values for each element are included in the files for end-users to threshold on. An IDR value of 0.1 means that the probability of detecting that element in a third experiment equivalent in depth to the the sum of the bioreplicates is 90%. In addition, we also compute expression values for annotated genes, transcripts and exons.

Project description:To accelerate previous RNA structure probing approaches, which focus on analyzing one RNA sequence at a time, we have developed FragSeq, a high-throughput RNA structure probing method that uses high-throughput RNA sequencing to identify single-stranded RNA (ssRNA) regions from fragments generated by nuclease P1, which is specific for single-stranded nucleic acids. In the accompanying study, we show that we can accurately and simultaneously map ssRNA regions in multiple non-coding RNAs with known structure in experiments probing the entire mouse nuclear transcriptome. We carried out probing in two cell types to assess reproducibility. We also identified and experimentally validated structured regions in ncRNAs never previously probed. We examined mouse nuclear RNA from two cell types: undifferentiated embryonic stem cells (UNDIFF) and cells differentiated into neural precursors (D5NP). For each cell type, nuclear RNA was purified and deproteinized, denatured, and refolded in vitro, from which we prepared three barcoded samples: "nuclease" (RNA partially digested with P1 ssRNA-specific nuclease, yielding 5'-PO4/3'-OH end chemistry at each cleavage site), "control" (control for "nuclease" sample to idenfity endogenous 5'-PO4/3'-OH), and "PNK" (same as "control" but followed by a polynucleotide kinase treatment to convert 5'-OH/3'-cyclic-phosphate ends to clonable 5'-PO4/3'-OH ends). Resulting RNA fragments were cloned using the SOLiD Small RNA Expression Kit (SREK) protocol, which ligates linkers only to 5'-PO4/3'-OH containing RNA, enriching for clones of products resulting from P1 cleavage in "nuclease" sample and selecting against random degradation. Two cell types, three treatments each, thus resulted in six barcoded samples total (barcodes 01, 02, 04, 05, 07, 08). Four other barcoded samples were prepared for separate experiments not used in our study (barcodes 03, 06, 09, 10), so their preparation is not described here. The total run of ten barcodes was done on the ABI SOLiD3 platform and a custom algorithm (FragSeq v0.0.1) was used to compute "cutting scores" (as described in our paper) that show ssRNA regions in hundreds of ncRNAs.

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Specificity quantification of Glucocorticoid receptor binding using high-throughput sequencing

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