English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Transcription factors (TFs) are macromolecules that bind to\textit{cis}-regulatory specific sub-regions of DNA promoters and initiatetranscription. Finding the exact location of these binding sites (aka motifs)is important in a variety of domains such as drug design and development. Toaddress this need, several \textit{in vivo} and \textit{in vitro} techniqueshave been developed so far that try to characterize and predict the bindingspecificity of a protein to different DNA loci. The major problem with thesetechniques is that they are not accurate enough in prediction of the bindingaffinity and characterization of the corresponding motifs. As a result,downstream analysis is required to uncover the locations where proteins ofinterest bind. Here, we propose DeeperBind, a long short term recurrentconvolutional network for prediction of protein binding specificities withrespect to DNA probes. DeeperBind can model the positional dynamics of probesequences and hence reckons with the contributions made by individualsub-regions in DNA sequences, in an effective way. Moreover, it can be trainedand tested on datasets containing varying-length sequences. We apply ourpipeline to the datasets derived from protein binding microarrays (PBMs), anin-vitro high-throughput technology for quantification of protein-DNA bindingpreferences, and present promising results. To the best of our knowledge, thisis the most accurate pipeline that can predict binding specificities of DNAsequences from the data produced by high-throughput technologies throughutilization of the power of deep learning for feature generation and positionaldynamics modeling.

DeeperBind: Enhancing Prediction of Sequence Specificities of DNA Binding Proteins