Beta approximation of ratio distribution and its application to next generation sequencing read counts

Gene transcription changes in a locust model of noise-induced deafness

Locusts have auditory structures called Müller’s organs attached to tympanic membranes on either side of the abdomen. We measured the normalized abundances of 500 different mRNA transcripts in 320 Müller’s organs obtained from 160 locusts (Schistocerca gregaria) that had been subjected to a loud continuous 3-kHz tone for 24 h. Abundance ratios were then measured relative to transcripts from 360 control organs. A histogram of the number of observed transcripts versus their abundance ratios (noise exposed/control) was well fitted by a Cauchy distribution with median value near one. Transcripts below 5% and above 95% of the cumulative distribution function of the fitted Cauchy distribution were selected as putatively different from the expected values of an untreated preparation. This yielded eight transcripts with ratios increased by noise exposure (ratios 1.689–3.038) and 18 transcripts with reduced ratios (0.069–0.457). Most of the transcripts with increased abundance represented genes responsible for cuticular construction, suggesting extensive remodeling of some or all the cuticular components of the auditory structure, whereas the reduced abundance transcripts were mostly involved in lipid and protein storage and metabolism, suggesting a profound reduction in metabolic activity in response to the overstimulation.

NEW & NOTEWORTHY Locust ears have functional and genetic similarities to human ears, including loss of hearing from age or noise exposure. We measured transcript abundances in transcriptomes of noise-exposed and control locust ears. The data indicate remodeling of the ear tympanum and profound reductions in metabolism that may explain reduced sound transduction. These findings advance our understanding of this useful model and suggest further experiments to elucidate mechanisms that ears use to cope with excessive stimulation.

DFseq: Distribution-Free Method to Detect Differential Gene Expression for RNA-Sequencing Data

Many current RNA-sequencing data analysis methods compare expressions one gene at a time, taking little consideration of the correlations among genes. In this study, we propose a method to convert such an one-dimensional comparison approach into a two-dimensional evaluation of the ratio of standard deviations (SD) of two constructed random variables. This method allows the identification of differentially expressed genes while controlling a preset significance level conditional on the read count mean-variance relationship. Meanwhile, correlations among genes are naturally accommodated due to the clustering of genes with similar distribution in the proposed σ-σ plot. The proposed distribution-free method is designated as DFseq, because it does not depend on a parametric distribution to fit read count. As a result, compared with parametric methods, DFseq can effectively handle genes with a bimodal-like distribution and/or genes with excessive 0 read counts, as well as genes with outlying observations. Besides, DFseq is an ideal platform for comparing performance of different differential gene expression detection methods.

An Integrated Approach for RNA-seq Data Normalization

BACKGROUND

DNA copy number alteration is common in many cancers. Studies have shown that insertion or deletion of DNA sequences can directly alter gene expression, and significant correlation exists between DNA copy number and gene expression. Data normalization is a critical step in the analysis of gene expression generated by RNA-seq technology. Successful normalization reduces/removes unwanted nonbiological variations in the data, while keeping meaningful information intact. However, as far as we know, no attempt has been made to adjust for the variation due to DNA copy number changes in RNA-seq data normalization.

RESULTS

In this article, we propose an integrated approach for RNA-seq data normalization. Comparisons show that the proposed normalization can improve power for downstream differentially expressed gene detection and generate more biologically meaningful results in gene profiling. In addition, our findings show that due to the effects of copy number changes, some housekeeping genes are not always suitable internal controls for studying gene expression.

CONCLUSIONS

Using information from DNA copy number, integrated approach is successful in reducing noises due to both biological and nonbiological causes in RNA-seq data, thus increasing the accuracy of gene profiling.