Comparison of normalization and differential expression analyses using RNA-Seq data from 726 individual Drosophila melanogaster

Abstract

BackgroundA generally accepted approach to the analysis of RNA-Seq read count data does not yet exist. We sequenced the mRNA of 726 individuals from the Drosophila Genetic Reference Panel in order to quantify differences in gene expression among single flies. One of our experimental goals was to identify the optimal analysis approach for the detection of differential gene expression among the factors we varied in the experiment: genotype, environment, sex, and their interactions. Here we evaluate three different filtering strategies, eight normalization methods, and two statistical approaches using our data set. We assessed differential gene expression among factors and performed a statistical power analysis using the eight biological replicates per genotype, environment, and sex in our data set.ResultsWe found that the most critical considerations for the analysis of RNA-Seq read count data were the normalization method, underlying data distribution assumption, and numbers of biological replicates, an observation consistent with previous RNA-Seq and microarray analysis comparisons. Some common normalization methods, such as Total Count, Quantile, and RPKM normalization, did not align the data across samples. Furthermore, analyses using the Median, Quantile, and Trimmed Mean of M-values normalization methods were sensitive to the removal of low-expressed genes from the data set. Although it is robust in many types of analysis, the normal data distribution assumption produced results vastly different than the negative binomial distribution. In addition, at least three biological replicates per condition were required in order to have sufficient statistical power to detect expression differences among the three-way interaction of genotype, environment, and sex.ConclusionsThe best analysis approach to our data was to normalize the read counts using the DESeq method and apply a generalized linear model assuming a negative binomial distribution using either edgeR or DESeq software. Genes having very low read counts were removed after normalizing the data and fitting it to the negative binomial distribution. We describe the results of this evaluation and include recommended analysis strategies for RNA-Seq read count data.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-2353-z) contains supplementary material, which is available to authorized users.

Microenvironmental Gene Expression Plasticity Among IndividualDrosophila melanogaster

“…Replicate environments were stratified across plates. Additional details concerning our experimental approach can be found in (Lin et al 2016) and in the Gene Expression Omnibus (GEO) entry (GSE60314) (see “DGRP_Number,” “Sex,” Environment,” “Fly_Number,” and “Fly_Plate_Location” in the GSE60314_GEO_run_summary.xlsx file, GEO entry GSE60314, for the order of genotypes, sexes, and replicate environments).…”

“…We then added 96 ERCC spike-in standards (External RNA Controls Consortium, SRM2374, beta version, pools 78A/78B) to each total RNA sample before proceeding with library preparation. We prepared 300–350 bp stranded PolyA libraries for each fly following the method of Wang et al (2011), with modifications to the procedure as detailed in Lin et al (2016) in a 96-well plate format. Using equal amounts of each library, we pooled libraries in groups of 24 for sequencing in plate order (see the “Sequence_Run_ID” for the 4-letter multiplex pool identifier in the GSE60314_GEO_run_summary.xlsx file, GEO entry GSE60314).…”

“…We mapped pre-miRNAs, pseudogenes, mRNAs, ncRNAs, rRNAs, snoRNAs, snRNAs, tRNAs, genes, coding sequences, and exons. We used HTSeq (Anders et al 2015) to count the number of reads per gene using the option “–stranded=reverse -i gene_id -t exon” (Lin et al 2016). …”

RNA-Seq reveals that mitochondrial genes and long non-coding RNAs may play important roles in the bivoltine generations of the non-social Neotropical bee Tetrapedia diversipes

“…Samples were divided into three replicates, each containing the RNA of three individuals from the same developmental stage. This sampling approach was adopted to improve gene identification and differential expression analyses (Hart et al 2013;Lin et al 2016). Although the larval samples contained all developmental stages, the larvae in each replication were pooled according to their instar-one pool of larvae from the first to fourth instar; one from second to fifth; and one consisting of fifth instar larvae only.…”

DEgenes Hunter - A Flexible R Pipeline for Automated RNA-seq Studies in Organisms without Reference Genome

“…However, the normalisation method of trimmed mean of M-values included in edgeR package and the DESeq normalisation method seem to perform better than others [7][8][9][10][11][12].…”