Multiplexed massively parallel SELEX for characterization of human transcription factor binding specificities.

By A. Jolma, Teemu Kivioja, Jarkko Toivonen, Luciano Cheng, Gong-hong Wei, M. Enge, Mikko Taipale, Juan M. Vaquerizas, Jian Yan, Mikko J. Sillanpää and 7 more. 2010

467 citations recorded by the source corpus.

The genetic code-the binding specificity of all transfer-RNAs--defines how protein primary structure is determined by DNA sequence. DNA also dictates when and where proteins are expressed, and this information is encoded in a pattern of specific sequence motifs that are recognized by transcription factors. However, the DNA-binding specificity is only known for a small fraction of the approximately 1400 human transcription factors (TFs). We describe here a high-throughput method for analyzing transcription factor binding specificity that is based on systematic evolution of ligands by exponential enrichment (SELEX) and massively parallel sequencing. The method is optimized for analysis of large numbers of TFs in parallel through the use of affinity-tagged proteins, barcoded selection oligonucleotides, and multiplexed sequencing. Data are analyzed by a new bioinformatic platform that uses the hundreds of thousands of sequencing reads obtained to control the quality of the experiments and to generate binding motifs for the TFs. The described technology allows higher throughput and identification of much longer binding profiles than current microarray-based methods. In addition, as our method is based on proteins expressed in mammalian cells, it can also be used to characterize DNA-binding preferences of full-length proteins or proteins requiring post-translational modifications. We validate the method by determining binding specificities of 14 different classes of TFs and by confirming the specificities for NFATC1 and RFX3 using ChIP-seq. Our results reveal unexpected dimeric modes of binding for several factors that were thought to preferentially bind DNA as monomers.

Subfields: Molecular Biology

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Citations in the corpus

Ensembl 2019 (2018, methodology): “We have improved our characterization of transcription factor binding specificities by importing 632 human and 85 mouse transcription factor binding motifs imputed through SELEX (24–26) and mapping them onto the respective genomes.”
Conservation of transcription factor binding specificities across 600 million years of bilateria evolution (2015, methodology): “DOI: 10.7554/eLife.04837.001”
A comparative analysis of transcription factor binding models learned from PBM, HT-SELEX and ChIP data (2014, methodology): “This was noted in (6) and in previous studies using the SELEX technology (29).”
Current approaches in SELEX: An update to aptamer selection technology. (2015, methodology): “SELEX-HTS enabled identification of different transcription factor specific binding motifs in DNA (Jolma et al., 2010) and selection of RNA sequences that bind HIV-RT (Ditzler et al., 2013).”
Deep mutational scanning: assessing protein function on a massive scale (2011, background): “The estimated non-specific carryover rate can then be used to adjust variant fitness estimates [42].”
Annotation of functional variation in personal genomes using RegulomeDB (2012, methodology): “Inaddition, we have included transcriptionfactor ChIP-seq data availablefrom the NCBI Sequence Read Archive (Hollenhorst et al. 2009; Jolma et al. 2010; Verzi et al. 2010; Wei et al. 2010; Hu et al. 2011; Lo et al. 2011; Novershtern et al. 2011; Palii et al. 2011; Yu et al. 2011).”
Activity-enhancing mutations in an E3 ubiquitin ligase identified by high-throughput mutagenesis (2013, methodology): “A nonspecific carryover correction factor was applied to the tally of each variant in the selected population (27).”
Cistrome and Epicistrome Features Shape the Regulatory DNA Landscape (2016, unknown): “The two most commonly used in vitro methods are systematic evolution of ligands by exponential enrichment (SELEX) (Jolma et al., 2010) and protein binding microarrays (PBM) (Berger and Bulyk, 2009).”