Co-SELECT reveals sequence non-specific contribution of DNA shape to transcription factor binding in vitro

Pal, Soumitra; Hoinka, Jan; Przytycka, Teresa M.

doi:10.1093/nar/gkz540

Cited by 15 publications

(7 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, because SELEX libraries are often only weakly enriched with true binding sequences (Additional file 1: Fig.S1, see also Fig. 1 in [24]), we take only a top percentile of the positive and negative scores (e.g., the top 10%) for ROC AUC value computation. Importantly, before PWM scoring, we extend the random insert sequences obtained from the sequence repository with the primer and barcode sequences that were present (and thus accessible to proteins) during the SELEX experiments.…”

Section: Protocol For Ht-selex Datamentioning

confidence: 99%

Insights gained from a comprehensive all-against-all transcription factor binding motif benchmarking study

et al. 2020

View full text Add to dashboard Cite

Background: Positional weight matrix (PWM) is a de facto standard model to describe transcription factor (TF) DNA binding specificities. PWMs inferred from in vivo or in vitro data are stored in many databases and used in a plethora of biological applications. This calls for comprehensive benchmarking of public PWM models with large experimental reference sets. Results: Here we report results from all-against-all benchmarking of PWM models for DNA binding sites of human TFs on a large compilation of in vitro (HT-SELEX, PBM) and in vivo (ChIP-seq) binding data. We observe that the best performing PWM for a given TF often belongs to another TF, usually from the same family. Occasionally, binding specificity is correlated with the structural class of the DNA binding domain, indicated by good cross-family performance measures. Benchmarking-based selection of family-representative motifs is more effective than motif clustering-based approaches. Overall, there is good agreement between in vitro and in vivo performance measures. However, for some in vivo experiments, the best performing PWM is assigned to an unrelated TF, indicating a binding mode involving protein-protein cooperativity. Conclusions: In an all-against-all setting, we compute more than 18 million performance measure values for different PWM-experiment combinations and offer these results as a public resource to the research community. The benchmarking protocols are provided via a web interface and as docker images. The methods and results from this study may help others make better use of public TF specificity models, as well as public TF binding data sets.

show abstract

Section: Protocol For Ht-selex Datamentioning

confidence: 99%

Insights gained from a comprehensive all-against-all transcription factor binding motif benchmarking study

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The identified motifs provide a set of novel human regulatory lexicons and can be used to construct a gene regulatory atlas for the human genome. Specifically, shape motifs can potentially contribute to the interpretation of indiscriminate binding behavior of human TFs (76). Co-regulated gene groups, revealed by identification of motifs may define cell-type specific regulons and thus, provide critical biological insights to the cell heterogeneity mechanisms (77).…”

Section: Discussionmentioning

confidence: 99%

Prediction of regulatory motifs from human Chip-sequencing data using a deep learning framework

Yang

Hoppe

et al. 2019

Nucleic Acids Research

View full text Add to dashboard Cite

The identification of transcription factor binding sites and cis-regulatory motifs is a frontier whereupon the rules governing protein–DNA binding are being revealed. Here, we developed a new method (DEep Sequence and Shape mOtif or DESSO) for cis-regulatory motif prediction using deep neural networks and the binomial distribution model. DESSO outperformed existing tools, including DeepBind, in predicting motifs in 690 human ENCODE ChIP-sequencing datasets. Furthermore, the deep-learning framework of DESSO expanded motif discovery beyond the state-of-the-art by allowing the identification of known and new protein–protein–DNA tethering interactions in human transcription factors (TFs). Specifically, 61 putative tethering interactions were identified among the 100 TFs expressed in the K562 cell line. In this work, the power of DESSO was further expanded by integrating the detection of DNA shape features. We found that shape information has strong predictive power for TF–DNA binding and provides new putative shape motif information for human TFs. Thus, DESSO improves in the identification and structural analysis of TF binding sites, by integrating the complexities of DNA binding into a deep-learning framework.

show abstract

“…Though for the simple example in Fig. 1 JUDI takes almost same number of lines as Snakemake, for a larger pipeline as in Pal et al (2018) JUDI requires far less scripting. For example, the file name expansion due to the masked parameter 'group' needed hard-coding in lines 11-12, Listing 1 of Köster and Rahmann (2012), imagine the effort required if there were more masked parameters and one or more parameters had a relatively large number of possible values!…”

Section: Discussionmentioning

confidence: 99%

“…We applied JUDI to the Co-SELECT tool (Pal et al, 2018) developed for the analysis of HT-SELEX data for transcription factor (TF) DNA binding. The tool analyzed 5 rounds of data from 81 TF experiments for 3 families by dividing the sequencing reads into two populations to find statistically significant shape-strings which were enriched at 5 possible thresholds in both populations.…”

Section: A Case Studymentioning

confidence: 99%

Bioinformatics pipeline using JUDI: Just Do It!

Pal

Przytycka

2019

Bioinformatics

Self Cite

View full text Add to dashboard Cite

Summary Large-scale data analysis in bioinformatics requires pipelined execution of multiple software. Generally each stage in a pipeline takes considerable computing resources and several workflow management systems (WMS), e.g. Snakemake, Nextflow, Common Workflow Language, Galaxy, etc. have been developed to ensure optimum execution of the stages across two invocations of the pipeline. However, when the pipeline needs to be executed with different settings of parameters, e.g. thresholds, underlying algorithms, etc. these WMS require significant scripting to ensure an optimal execution. We developed JUDI on top of DoIt, a Python based WMS, to systematically handle parameter settings based on the principles of database management systems. Using a novel modular approach that encapsulates a parameter database in each task and file associated with a pipeline stage, JUDI simplifies plug-and-play of the pipeline stages. For a typical pipeline with n parameters, JUDI reduces the number of lines of scripting required by a factor of O(n). With properly designed parameter databases, JUDI not only enables reproducing research under published values of parameters but also facilitates exploring newer results under novel parameter settings. Availability and implementation https://github.com/ncbi/JUDI Supplementary information Supplementary data are available at Bioinformatics online.

show abstract

Co-SELECT reveals sequence non-specific contribution of DNA shape to transcription factor binding in vitro

Cited by 15 publications

References 39 publications

Insights gained from a comprehensive all-against-all transcription factor binding motif benchmarking study

Insights gained from a comprehensive all-against-all transcription factor binding motif benchmarking study

Prediction of regulatory motifs from human Chip-sequencing data using a deep learning framework

Bioinformatics pipeline using JUDI: Just Do It!

Contact Info

Product

Resources

About