2018
DOI: 10.1038/nmeth.4632
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A toolbox of immunoprecipitation-grade monoclonal antibodies to human transcription factors

Abstract: A key component of efforts to address the reproducibility crisis in biomedical research is the development of rigorously validated and renewable protein-affinity reagents. As part of the US National Institutes of Health (NIH) Protein Capture Reagents Program (PCRP), we have generated a collection of 1,406 highly validated immunoprecipitation- and/or immunoblotting-grade mouse monoclonal antibodies (mAbs) to 737 human transcription factors, using an integrated production and validation pipeline. We used HuProt … Show more

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Cited by 61 publications
(63 citation statements)
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“…We note that although we constructed our predicted TF binding annotations using the neuralnetwork predictor Basset 19 , there exist many other effective methods for making such signed predictions 1,[13][14][15][16]18,153,154 and many other data sets on which to train them [155][156][157] . In an initial effort to assess these, we repeated our analyses of molecular traits in blood, gene expression in 48 GTEx tissues, and 46 diseases and complex traits using annotations generated via three other approaches: 382 annotations generated using the DeepSEA neural-network predictor 15 applied to the same ENCODE ChIP-seq data that we analyzed using Basset; 184 annotations generated using the Basset predictor trained on a larger but noisier set of meta-analyzed ChIP-seq data from the Gene Transcription Regulation Database 4 (GTRD) followed by our Basset QC procedures; and 276 annotations generated using position-weight matrices (PWMs) from the Homo sapiens Comprehensive Model Collection 156 (HOCOMOCO), which are based in part on data from the GTRD (see Online Methods).…”
Section: Discussionmentioning
confidence: 99%
“…We note that although we constructed our predicted TF binding annotations using the neuralnetwork predictor Basset 19 , there exist many other effective methods for making such signed predictions 1,[13][14][15][16]18,153,154 and many other data sets on which to train them [155][156][157] . In an initial effort to assess these, we repeated our analyses of molecular traits in blood, gene expression in 48 GTEx tissues, and 46 diseases and complex traits using annotations generated via three other approaches: 382 annotations generated using the DeepSEA neural-network predictor 15 applied to the same ENCODE ChIP-seq data that we analyzed using Basset; 184 annotations generated using the Basset predictor trained on a larger but noisier set of meta-analyzed ChIP-seq data from the Gene Transcription Regulation Database 4 (GTRD) followed by our Basset QC procedures; and 276 annotations generated using position-weight matrices (PWMs) from the Homo sapiens Comprehensive Model Collection 156 (HOCOMOCO), which are based in part on data from the GTRD (see Online Methods).…”
Section: Discussionmentioning
confidence: 99%
“…2f). To determine whether these failed mAbs could recognize linear epitopes, we performed mAb binding assays on HuProt arrays, each comprised of 20,240 human proteins in full-length and denatured using 9 M urea treatment 18 . Except anti-S1PR1, which was not tested because S1PR1 was not available on HuProt, the rest nine mAbs failed to recognize their intended targets as the top targets.…”
Section: Profiling Antibody Specificity On Vird-gpcr Arraymentioning
confidence: 99%
“…The generation of monoclonal antibodies in mice involves multiple steps: (i) design and synthesis of non-hydrolyzable Ubpeptide conjugates for immunization; (ii) design and synthesis of extended native iso-peptide linked Ub-peptide conjugates for screening; (iii) immunization, and generation and screening of hybridomas; (iv) clone selection and antibody validation in native context (Figure 1). In this manuscript we focus on the steps that are specific for synthesis of ubiquitin-peptide conjugates for the generation of site-specific ubiquitin antibodies (steps i and ii), as detailed excellent general protocols for antibody development (step iii) and validation (step iv) have been described elsewhere (Egelhofer et al, 2010;Yokoyama et al, 2013;Greenfield, 2014;Ossipow and Fischer, 2014;Kungulovski et al, 2015;Marcon et al, 2015;Rothbart et al, 2015;Uhlen et al, 2016;Guillemette et al, 2017;Holzlöhner and Hanack, 2017;Edfors et al, 2018;Venkataraman et al, 2018;Weller, 2018;Marx, 2019). In addition, we discuss the rationale for the design of antigens used for immunization and screening.…”
Section: Introductionmentioning
confidence: 99%