ReactIDR: evaluation of the statistical reproducibility of high-throughput structural analyses towards a robust RNA structure prediction

Risa Kawaguchi,Hisanori Kiryu,Junichi Iwakiri,Jun Sese

doi:10.1186/s12859-019-2645-4

Abstract

BackgroundRecently, next-generation sequencing techniques have been applied for the detection of RNA secondary structures, which is referred to as high-throughput RNA structural (HTS) analyses, and many different protocols have been used to detect comprehensive RNA structures at single-nucleotide resolution. However, the existing computational analyses heavily depend on the experimental methodology to generate data, which results in difficulties associated with statistically sound comparisons or combining the results obtained using different HTS methods.ResultsHere, we introduced a statistical framework, reactIDR, which can be applied to the experimental data obtained using multiple HTS methodologies. Using this approach, nucleotides are classified into three structural categories, loop, stem/background, and unmapped. reactIDR uses the irreproducible discovery rate (IDR) with a hidden Markov model to discriminate between the true and spurious signals obtained in the replicated HTS experiments accurately, and it is able to incorporate an expectation-maximization algorithm and supervised learning for efficient parameter optimization. The results of our analyses of the real-life HTS data showed that reactIDR had the highest accuracy in the classification of ribosomal RNA stem/loop structures when using both individual and integrated HTS datasets, and its results corresponded the best to the three-dimensional structures.ConclusionsWe have developed a novel software, reactIDR, for the prediction of stem/loop regions from the HTS analysis datasets. For the rRNA structure analyses, reactIDR was shown to have robust accuracy across different datasets by using the reproducibility criterion, suggesting its potential for increasing the value of existing HTS datasets. reactIDR is publicly available at https://github.com/carushi/reactIDR.

Highlights

Next-generation sequencing techniques have been applied for the detection of RNA secondary structures, which is referred to as high-throughput RNA structural (HTS) analyses, and many different protocols have been used to detect comprehensive RNA structures at single-nucleotide resolution
To evaluate the reliability in a way applicable to a general HTS dataset, we extend a statistical method for chromatin immunoprecipitation (ChIP)-Seq peak detection, named irreproducible discovery rate (IDR) [13]
Input – Assuming that K is the number of samples in an HTS experiment, and the sequencing reads were obtained from two K/2 samples, under two different conditions, each read can be mapped against a reference sequence, and the indicator of reactivity at each base can be measured by counting the reads that start at the subsequent (3 ) base

Summary

Introduction

Next-generation sequencing techniques have been applied for the detection of RNA secondary structures, which is referred to as high-throughput RNA structural (HTS) analyses, and many different protocols have been used to detect comprehensive RNA structures at single-nucleotide resolution. To analyze the comprehensive landscape of RNA secondary structures, novel types of high-throughput experimental methods, such as PARS [5] and icSHAPE [6], have been developed using short-read next-generation sequencers, and are referred to as highthroughput RNA structural (HTS) analyses [7,8,9] These methods involve the use of certain types of chemical reagents or enzymes that cause probing (e.g., modification or cleavage) at each RNA nucleotide with a different “reactivity” depending on the existence of base pairing. Using these approaches, RNA secondary structures are not directly predicted, instead, some structure-indicating scores which provide information about the molecular structures, such as reactivity scores, are obtained. The reactivity scores provide in silico analyses with the information necessary to guide secondary structure prediction [10], as well as to indicate the propensity of structural accessibility directly

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