Dataset Information

Identification of Predictive Cis-Regulatory Elements Using a Discriminative Objective Function and a Dynamic Search Space.

ABSTRACT: The generation of genomic binding or accessibility data from massively parallel sequencing technologies such as ChIP-seq and DNase-seq continues to accelerate. Yet state-of-the-art computational approaches for the identification of DNA binding motifs often yield motifs of weak predictive power. Here we present a novel computational algorithm called MotifSpec, designed to find predictive motifs, in contrast to over-represented sequence elements. The key distinguishing feature of this algorithm is that it uses a dynamic search space and a learned threshold to find discriminative motifs in combination with the modeling of motifs using a full PWM (position weight matrix) rather than k-mer words or regular expressions. We demonstrate that our approach finds motifs corresponding to known binding specificities in several mammalian ChIP-seq datasets, and that our PWMs classify the ChIP-seq signals with accuracy comparable to, or marginally better than motifs from the best existing algorithms. In other datasets, our algorithm identifies novel motifs where other methods fail. Finally, we apply this algorithm to detect motifs from expression datasets in C. elegans using a dynamic expression similarity metric rather than fixed expression clusters, and find novel predictive motifs.

SUBMITTER: Karnik R

PROVIDER: S-EPMC4605740 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Identification of Predictive Cis-Regulatory Elements Using a Discriminative Objective Function and a Dynamic Search Space.

Karnik Rahul R Beer Michael A MA

PloS one 20151014 10

The generation of genomic binding or accessibility data from massively parallel sequencing technologies such as ChIP-seq and DNase-seq continues to accelerate. Yet state-of-the-art computational approaches for the identification of DNA binding motifs often yield motifs of weak predictive power. Here we present a novel computational algorithm called MotifSpec, designed to find predictive motifs, in contrast to over-represented sequence elements. The key distinguishing feature of this algorithm is ...[more]

PMID: 26465884

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Project description:Post-transcriptional gene regulation controls the amount of protein produced from an individual mRNA by altering rates of decay and translation. Many sequence elements that direct post-transcriptional regulation have been found; in mammals, most such elements are located within the 3' untranslated regions (3'UTRs). Comparative genomic studies demonstrate that mammalian 3'UTRs contain extensive conserved sequence tracts, yet only a small fraction corresponds to recognized elements, implying that many additional novel elements exist. Despite a variety of computational, molecular, and biochemical approaches, identifying functional 3'UTRs elements remains difficult.We created a high-throughput cell-based screen that enables identification of functional post-transcriptional 3'UTR regulatory elements. Our system exploits integrated single-copy reporters, which are expressed and processed as endogenous genes. We screened many thousands of short random sequences for their regulatory potential. Control sequences with known effects were captured effectively using our approach, establishing that our methodology was robust. We found hundreds of functional sequences, which we validated in traditional reporter assays, including verifying their regulatory impact in native sequence contexts. Although 3'UTRs are typically considered repressive, most of the functional elements were activating, including ones that were preferentially conserved. Additionally, we adapted our screening approach to examine the effect of elements on RNA abundance, revealing that most elements act by altering mRNA stability.We developed and used a high-throughput approach to discover hundreds of post-transcriptional cis-regulatory elements. These results imply that most human 3'UTRs contain many previously unrecognized cis-regulatory elements, many of which are activating, and that the post-transcriptional fate of an mRNA is largely due to the actions of many individual cis-regulatory elements within its 3'UTR.

Dataset Information

Identification of Predictive Cis-Regulatory Elements Using a Discriminative Objective Function and a Dynamic Search Space.

Publications

Identification of Predictive Cis-Regulatory Elements Using a Discriminative Objective Function and a Dynamic Search Space.

Similar Datasets

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