Yaron Orenstein | bioRxiv Science

bioRxiv2016

Sequence biases in CLIP experimental data are incorporated in protein RNA-binding models

We report a newly-identified bias in CLIP data that results from cleaving enzyme specificity. This bias is inadvertently incorporated into standard peak calling methods [1], which identify the most likely locations where proteins bind RNA. We further show how, in downstream analysis, this bias is incorporated into models inferred by the state-of-the-art GraphProt method to predict protein RNA-binding. We call for both experimental controls to measure enzyme specificities and algorithms to identify unbiased CLIP binding sites.

Bioinformatics↗

bioRxiv2015

HTS-IBIS: fast and accurate inference of binding site motifs from HT-SELEX data

SummaryRecent technological advancements enable measuring the binding of a transcription factor to thousands of DNA sequences, in order to infer its binding preferences. High-throughput-SELEX measures protein-DNA binding by deep sequencing over several cycles of enrichment. We devised a new algorithm called HTS-IBIS for the inference task. HTS-IBIS corrects for technological biases, selects the cycle and k, and builds a motif starting from a consensus k-mer in that cycle. In large scale tests, HTS-IBIS outperformed the extant automatic algorithm for the motif finding task on both in vitro and in vivo binding prediction.\n\nAvailabilityHTS-IBIS is available on acgt.cs.tau.ac.il/HTS-IBIS.\n\nContactrshamir@tau.ac.il

Bioinformatics↗