Errors in RNA-Seq quantification affect genes of relevance to human disease

Robert, Christelle; Watson, Michael

doi:10.1186/s13059-015-0734-x

Cited by 150 publications

(145 citation statements)

References 40 publications

(52 reference statements)

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“…Although in general more than 95% of the reads are aligned, a high percentage of them (>20%) are mapped in more than one place. Multi-mapped reads can severely affect the expression levels of genes, and the user should be very cautious when drawing conclusions about differentially expressed genes that contain a high percentage of multi-mapped reads 35 .…”

Section: Anticipated Resultsmentioning

confidence: 99%

Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown

Pertea

Kim²,

Pertea³

et al. 2016

Nat Protoc

4,890

3,986

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High-throughput sequencing of messenger RNA (RNA-seq) has become the standard method for measuring and comparing the levels of gene expression in a wide variety of species and conditions. RNA-seq experiments generate very large, complex data sets that demand fast, accurate, and flexible software to reduce the raw read data to comprehensible results. HISAT, StringTie, and Ballgown are free, open-source software tools for comprehensive analysis of RNA-seq experiments. Together, they allow scientists to align reads to a genome, assemble transcripts including novel splice variants, compute the abundance of these transcripts in each sample, and compare experiments to identify differentially expressed genes and transcripts. This protocol describes all the steps necessary to process a large set of raw sequencing reads and create lists of gene transcripts, expression levels, and differentially expressed genes and transcripts. The protocol’s execution time depends on the computing resources, but typically takes under 45 minutes of computer time.

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Section: Anticipated Resultsmentioning

confidence: 99%

Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown

Pertea

Kim²,

Pertea³

et al. 2016

Nat Protoc

4,890

3,986

View full text Add to dashboard Cite

show abstract

“…Because some miRNAs belong to multicopy families with high sequence similarity, it was not always possible to pinpoint the precise genomic locus from which a miRNA read was derived. We therefore grouped mature miRNAs into multimap groups (MMGs; Methods), which were thereafter treated as single expression units (Robert and Watson 2015). We chose a relatively conservative MMG cutoff of 5%, meaning that if a miRNA shared >5% of its total reads with another miRNA, these two miRNAs were assigned to the same MMG.…”

Section: Resultsmentioning

confidence: 99%

“…A previous study estimated that up to two-thirds of all reads in a miRNA sequencing experiment mapped ambiguously if one mismatch was allowed . Because most tools for miRNA quantification do not offer a robust procedure for dealing with ambiguously mapped reads, we adapted the strategy of multimap groups (MMGs), which was previously developed for protein-coding genes (Robert and Watson 2015). Mature miRNAs were grouped into MMGs based on all mapped reads from a given species and a cutoff of 5%, i.e., if a given mature miRNA shared at least 5% of its reads with another miRNA, the two were assigned to the same MMG.…”

Section: Detection Of Sex-biased Mirna Expressionmentioning

confidence: 99%

Sex-biased microRNA expression in mammals and birds reveals underlying regulatory mechanisms and a role in dosage compensation

et al. 2017

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Sexual dimorphism depends on sex-biased gene expression, but the contributions of microRNAs (miRNAs) have not been globally assessed. We therefore produced an extensive small RNA sequencing data set to analyze male and female miRNA expression profiles in mouse, opossum, and chicken. Our analyses uncovered numerous cases of somatic sex-biased miRNA expression, with the largest proportion found in the mouse heart and liver. Sex-biased expression is explained by miRNA-specific regulation, including sex-biased chromatin accessibility at promoters, rather than piggybacking of intronic miRNAs on sex-biased protein-coding genes. In mouse, but not opossum and chicken, sex bias is coordinated across tissues such that autosomal testis-biased miRNAs tend to be somatically male-biased, whereas autosomal ovary-biased miRNAs are female-biased, possibly due to broad hormonal control. In chicken, which has a Z/W sex chromosome system, expression output of genes on the Z Chromosome is expected to be male-biased, since there is no global dosage compensation mechanism that restores expression in ZW females after almost all genes on the W Chromosome decayed. Nevertheless, we found that the dominant liver miRNA, miR-122-5p, is Z-linked but expressed in an unbiased manner, due to the unusual retention of a W-linked copy. Another Z-linked miRNA, the male-biased miR-2954-3p, shows conserved preference for dosage-sensitive genes on the Z Chromosome, based on computational and experimental data from chicken and zebra finch, and acts to equalize male-to-female expression ratios of its targets. Unexpectedly, our findings thus establish miRNA regulation as a novel gene-specific dosage compensation mechanism.

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“…A further argument in favor of gene-level analysis is the unidentifiability of transcript expression that can result from uneven coverage caused by underlying technical biases ( Figure 1C). Intermediate approaches, grouping together “indistinguishable” features are also conceiveable 20 , but not yet standard practice.…”

Section: Gene Abundance Estimates Are More Accurate Than Transcript Amentioning

confidence: 99%

Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences

2015

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High-throughput sequencing of cDNA (RNA-seq) is used extensively to characterize the transcriptome of cells. Many transcriptomic studies aim at comparing either abundance levels or the transcriptome composition between given conditions, and as a first step, the sequencing reads must be used as the basis for abundance quantification of transcriptomic features of interest, such as genes or transcripts. Several different quantification approaches have been proposed, ranging from simple counting of reads that overlap given genomic regions to more complex estimation of underlying transcript abundances. In this paper, we show that gene-level abundance estimates and statistical inference offer advantages over transcript-level analyses, in terms of performance and interpretability. We also illustrate that while the presence of differential isoform usage can lead to inflated false discovery rates in differential expression analyses on simple count matrices and transcript-level abundance estimates improve the performance in simulated data, the difference is relatively minor in several real data sets. Finally, we provide an R package ( tximport) to help users integrate transcript-level abundance estimates from common quantification pipelines into count-based statistical inference engines.

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Errors in RNA-Seq quantification affect genes of relevance to human disease

Cited by 150 publications

References 40 publications

Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown

Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown

Sex-biased microRNA expression in mammals and birds reveals underlying regulatory mechanisms and a role in dosage compensation

Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences

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