A model for sequential evolution of ligands by exponential enrichment (SELEX) data

TL;DR

This paper introduces a novel biochemical model for SELEX data that estimates oligonucleotide affinities across all rounds and aligns sequences simultaneously, improving binding site prediction accuracy.

Contribution

The paper presents a new model that jointly aligns and estimates affinities of oligonucleotides from all SELEX rounds, enhancing analysis of ligand evolution data.

Findings

Model outperforms existing methods in predicting Bicoid binding sites

Uses all round data for more accurate affinity estimation

Successfully aligns oligonucleotides without prior alignment steps

Abstract

A Systematic Evolution of Ligands by EXponential enrichment (SELEX) experiment begins in round one with a random pool of oligonucleotides in equilibrium solution with a target. Over a few rounds, oligonucleotides having a high affinity for the target are selected. Data from a high throughput SELEX experiment consists of lists of thousands of oligonucleotides sampled after each round. Thus far, SELEX experiments have been very good at suggesting the highest affinity oligonucleotide, but modeling lower affinity recognition site variants has been difficult. Furthermore, an alignment step has always been used prior to analyzing SELEX data. We present a novel model, based on a biochemical parametrization of SELEX, which allows us to use data from all rounds to estimate the affinities of the oligonucleotides. Most notably, our model also aligns the oligonucleotides. We use our model to analyze a SELEX experiment containing double stranded DNA oligonucleotides and the transcription factor Bicoid as the target. Our SELEX model outperformed other published methods for predicting putative binding sites for Bicoid as indicated by the results of an in-vivo ChIP-chip experiment.