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Effect of 4sU labeling on quantification bias in nucleotide conversion RNA-seq data

ABSTRACT: This SuperSeries is composed of the SubSeries listed below.

ORGANISM(S): Mus musculus Homo sapiens

PROVIDER: GSE229507 | GEO | 2024/02/06

REPOSITORIES: GEO

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Effect of 4sU labeling durations on quantification bias in nucleotide conversion RNA-seq data

Project description:To investigate the effect of labeling durations on quantification bias in nucleotide conversion RNA-seq, we labeled NIH cells with 4sU. We then measured 4sU dropout in 4sU samples compared to 4sU naive samples.

2024-02-06 | GSE229506 | GEO

Effect of 4sU labeling concentrations on quantification bias in nucleotide conversion RNA-seq data

Project description:To investigate the effect of increasing 4sU labeling concentrations on quantification bias in nucleotide conversion RNA-seq, we labeled 3 different cell lines with increasing concentrations of 4sU. We then measured 4sU dropout in 4sU samples compared to 4sU naive samples.

2024-02-06 | GSE229504 | GEO

Effect of TUC-seq protocol versus SLAM-seq on quantification bias in nucleotide conversion RNA-seq data

Project description:To investigate the effect of TUC-seq versus SLAM-seq on quantification bias in nucleotide conversion RNA-seq, we performed SLAM-seq and TUC-seq on NIH cells. We then measured 4sU dropout in 4sU samples compared to 4sU naive samples.

2024-02-06 | GSE253169 | GEO

Effect of performing SLAM-seq chemistry in methanol fixed cells versus standard tube processing on quantification bias in nucleotide conversion RNA-seq data

Project description:To investigate the occuring 4sU dropout during library preparation we performed SLAM conversion either in intact, methanol fixed cells or in tubes

2024-02-06 | GSE253370 | GEO

Ultrashort and progressive 4sU-tagging reveals key characteristics of RNA processing at nucleotide resolution.

Project description:RNA synthesis and decay rates determine the steady-state levels of cellular RNAs. Metabolic tagging of newly transcribed RNA by 4-thiouridine (4sU) can reveal the relative contributions of RNA synthesis and decay rates. The kinetics of RNA processing, however, had so far remained unresolved. Here, we show that ultrashort 4sU-tagging not only provides snapshot pictures of eukaryotic gene expression but, when combined with progressive 4sU-tagging and RNA-seq, reveals global RNA processing kinetics at nucleotide resolution. Using this method, we identified classes of rapidly and slowly spliced/degraded introns. Interestingly, each class of splicing kinetics was characterized by a distinct association with intron length, gene length, and splice site strength. For a large group of introns, we also observed long lasting retention in the primary transcript, but efficient secondary splicing or degradation at later time points. Finally, we show that processing of most, but not all small nucleolar (sno)RNA-containing introns is remarkably inefficient with the majority of introns being spliced and degraded rather than processed into mature snoRNAs. In summary, our study yields unparalleled insights into the kinetics of RNA processing and provides the tools to study molecular mechanisms of RNA processing and their contribution to the regulation of gene expression.

| S-EPMC3460197 | biostudies-literature

Large-scale labeling and assessment of sex bias in publicly available expression data.

Project description:BackgroundWomen are at more than 1.5-fold higher risk for clinically relevant adverse drug events. While this higher prevalence is partially due to gender-related effects, biological sex differences likely also impact drug response. Publicly available gene expression databases provide a unique opportunity for examining drug response at a cellular level. However, missingness and heterogeneity of metadata prevent large-scale identification of drug exposure studies and limit assessments of sex bias. To address this, we trained organism-specific models to infer sample sex from gene expression data, and used entity normalization to map metadata cell line and drug mentions to existing ontologies. Using this method, we inferred sex labels for 450,371 human and 245,107 mouse microarray and RNA-seq samples from refine.bio.ResultsOverall, we find slight female bias (52.1%) in human samples and (62.5%) male bias in mouse samples; this corresponds to a majority of mixed sex studies in humans and single sex studies in mice, split between female-only and male-only (25.8% vs. 18.9% in human and 21.6% vs. 31.1% in mouse, respectively). In drug studies, we find limited evidence for sex-sampling bias overall; however, specific categories of drugs, including human cancer and mouse nervous system drugs, are enriched in female-only and male-only studies, respectively. We leverage our expression-based sex labels to further examine the complexity of cell line sex and assess the frequency of metadata sex label misannotations (2-5%).ConclusionsOur results demonstrate limited overall sex bias, while highlighting high bias in specific subfields and underscoring the importance of including sex labels to better understand the underlying biology. We make our inferred and normalized labels, along with flags for misannotated samples, publicly available to catalyze the routine use of sex as a study variable in future analyses.

| S-EPMC8011224 | biostudies-literature

Quantification of PCR bias caused by a single nucleotide polymorphism in SMN gene dosage analysis.

Project description:Approximately 94% of patients with spinal muscular atrophy lack both copies of SMN1 exon 7, and most carriers have only one copy of SMN1 exon 7. We described previously the effect of SMN1/SMN2 heteroduplex formation on SMN gene dosage analysis, which is a multiplex quantitative PCR assay to determine the copy numbers of SMN1 and SMN2 using DraI digestion to differentiate SMN2 from SMN1. We describe herein the quantification of PCR bias between SMN1 exon 7 and SMN2 exon 7, which differ by only one nucleotide that is not present in either primer binding site. Using samples from 272 individuals with various SMN genotypes, we found that the amplification efficiency of SMN2 was consistent only approximately 80% that of SMN1. Thus, even a single nucleotide polymorphism, not in primer binding sites, can cause reproducible PCR bias. The precision and accuracy of our SMN gene dosage analysis are high because our assay design and controls take advantage of the consistency of the PCR bias. As additional clinically significant single nucleotide polymorphisms (SNPs) are discovered, assessment of PCR bias, and judicious selection of standards and controls, will be increasingly important for quantitative PCR assays.

| S-EPMC3278980 | biostudies-literature

Quantification, dynamic visualization, and validation of bias in ATAC-seq data with ataqv

Project description:The assay for transposase-accessible chromatin using sequencing (ATAC-seq) has become the preferred method for mapping chromatin accessibility, due to its simplicity, speed, and low input material requirements. However, it can be difficult to evaluate data quality, particularly when processing many samples. Here, we present ataqv, a computational toolkit for efficiently measuring, visualizing, and comparing ATAC-seq quality control results. We perform controlled ATAC-seq experiments and utilize ataqv to show that the ratio of Tn5 transposase to nuclei and sequencing flowcell cluster density induce reproducible technical bias in ATAC-seq results, significantly changing the fragment length distribution and enrichment of reads in promoter and enhancer chromatin states.

2020-03-23 | GSE130450 | GEO

Evaluation of Wpa-generated amplicons for SNP typing (allele bias), locus bias, and detection of known CNVs

Project description:This SuperSeries is composed of the SubSeries listed below.

2008-09-24 | GSE12751 | GEO

Quantification, Dynamic Visualization, and Validation of Bias in ATAC-Seq Data with ataqv.

Project description:The assay for transposase-accessible chromatin using sequencing (ATAC-seq) has become the preferred method for mapping chromatin accessibility due to its time and input material efficiency. However, it can be difficult to evaluate data quality and identify sources of technical bias across samples. Here, we present ataqv, a computational toolkit for efficiently measuring, visualizing, and comparing quality control (QC) results across samples and experiments. We use ataqv to analyze 2,009 public ATAC-seq datasets; their QC metrics display a 10-fold range. Tn5 dosage experiments and statistical modeling show that technical variation in the ratio of Tn5 transposase to nuclei and sequencing flowcell density induces systematic bias in ATAC-seq data by changing the enrichment of reads across functional genomic annotations including promoters, enhancers, and transcription-factor-bound regions, with the notable exception of CTCF. ataqv can be integrated into existing computational pipelines and is freely available at https://github.com/ParkerLab/ataqv/.

| S-EPMC8245295 | biostudies-literature

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