Universal correction of enzymatic sequence bias
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE92674 | GEO | 2016/12/21
SECONDARY ACCESSION(S): PRJNA358315
REPOSITORIES: GEO
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Project description:DNase I footprinting is an established assay for identifying transcription factor (TF)-DNA interactions with single base pair resolution. High throughput DNase-seq assays have recently been used to detect in vivo DNase footprints across the genome. A number of computational approaches have been developed to accurately identify DNase-seq footprints and these methods have been used as a predictor of TF-DNA interactions by itself or in combination with other epigenetic features. However, recent studies have pointed to a substantial cleavage bias of DNase and its impact on footprinting, casting doubts on its predictive performance. To assess the potential for using DNaseI to identify individual binding sites, we performed DNase-seq experiments on deproteinized naked genomic DNA isolated from two different cell types and determined sequence cleavage bias associated with the DNase-seq protocol. This allowed us to build cleavage bias corrected footprint models specific to individual transcription factors. The predictive performance of these DNase-seq-based binding site models demonstrated that predicted footprints corresponded to high confidence TF-DNA interactions.
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Project description:The interactions between proteins and nucleic acids have a fundamental function in many biological processes well beyond nuclear gene transcription and include RNA homeostasis, protein translation and pathogen sensing for innate immunity. While our knowledge of the ensemble of proteins binding individual mRNAs in mammalian cells has greatly been augmented by recent surveys, no systematic study on the native proteins of human cells differentially engaging various types of nucleic acids in a non sequence-specific manner has been reported. We designed an experimental approach to cover the non sequence-specific RNA and DNA binding space broadly, including methylation, and test for its ability to interact with the human proteome. We used 25 rationally designed nucleic acid probes in an affinity purification mass spectrometry and bioinformatics workflow to identify proteins from whole cell extracts of three different human cell lines. The proteins were profiled for their binding preferences to the different general types of nucleic acids. The study identified 746 high confidence direct binders, 249 of which were devoid of previous experimental evidence for binding nucleic acids. We could assign 513 specific affinities for sub-types of nucleic acid probes to 219 distinct proteins and to individual domains. The evolutionary conserved protein YB-1, previously associated with cancer and gene regulation, is shown to bind methylated cytosine preferentially conferring YB-1 a potential epigenetic function. Collectively, the dataset represents a rich resource of experimentally determined nucleic acid-specific binding proteins in humans and, indirectly, for other species.
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Project description:RNA-Seq is a powerful tool for transcriptome profiling, but is hampered by sequence-dependent bias and inaccuracy at low copy numbers intrinsic to exponential PCR amplification. We developed a simple strategy for mitigating these complications, allowing truly digital RNA-Seq. Following reverse transcription, a large set of barcode sequences is added in excess, and nearly every cDNA molecule is uniquely labeled by random attachment of barcode sequences to both ends. After PCR, we applied paired-end deep sequencing to read the two barcodes and cDNA sequences. Rather than counting the number of reads, RNA abundance is measured based on the number of unique barcode sequences observed for a given cDNA sequence. We optimized the barcodes to be unambiguously identifiable even in the presence of multiple sequencing errors. This method allows counting with single copy resolution despite sequence-dependent bias and PCR amplification noise, and is analogous to digital PCR but amendable to quantifying a whole transcriptome. We demonstrated transcriptome profiling of E. coli with more accurate and reproducible quantification than conventional RNA-Seq.
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