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Freedman, A. H.

Publications and source records attributed to Freedman, A. H..

2 recordsLinked to original sources

Short paired-end reads trump long single-end reads for expression analysis

BackgroundTypical experimental design advice for expression analyses using RNA-seq generally assumes that single-end reads provide robust gene-level expression estimates in a cost-effective manner, and that the additional benefits obtained from paired-end sequencing are not worth the additional cost. However, in many cases (e.g., with Illumina NextSeq and NovaSeq instruments), shorter paired-end reads and longer single-end reads can be generated for the same cost, and it is not obvious which strategy should be preferred. Using publicly available data, we test whether short-paired end reads can achieve more robust expression estimates and differential expression results than single-end reads of approximately the same total number of sequenced bases. ResultsAt both the transcript and gene levels, 2x40 paired-end reads unequivocally provide expression estimates that are more highly correlated with 2x125 than 1x75 reads; in nearly all cases, those correlations are also greater than for 1x125, despite the greater total number of sequenced bases for the latter. Across an array of metrics, differential expression tests based upon 2x40 consistently outperform those using 1x75. ConclusionResearchers seeking a cost-effective approach for gene-level expression analysis should prefer short paired-end reads over a longer single-end strategy. Short paired-end reads will also give reasonably robust expression estimates and differential expression results at the isoform level.

bioinformatics

Error, noise and bias in de novo transcriptome assemblies

De novo transcriptome assembly is a powerful tool, widely used over the last decade for making evolutionary inferences. However, it relies on two implicit assumptions: that the assembled transcriptome is an unbiased representation of the underlying expressed transcriptome, and that expression estimates from the assembly are good, if noisy approximations of the relative abundance of expressed transcripts. Using publicly available data for model organisms, we demonstrate that, across assembly algorithms and data sets, these assumptions are consistently violated. Bias exists at the nucleotide level, with genotyping error rates ranging from 30-83%. As a result, diversity is underestimated in transcriptome assemblies, with consistent under-estimation of heterozygosity in all but the most inbred samples. Even at the gene level, expression estimates show wide deviations from map-to-reference estimates, and positive bias at lower expression levels. Standard filtering of transcriptome assemblies improves the robustness of gene expression estimates but leads to the loss of a meaningful number of protein-coding genes, including many that are highly expressed. We demonstrate a computational method, length-rescaled CPM, to partly alleviate noise and bias in expression estimates. Researchers should consider ways to minimize the impact of bias in transcriptome assemblies.

bioinformatics