ArticlePloS one2011
Detection and removal of biases in the analysis of next-generation sequencing reads.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed.
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- Diversity of Mycorrhizal Fungi in Temperate Orchid Species: Comparison of Culture-Dependent and Culture-Independent Methods.Journal of fungi (Basel, Switzerland) · 2024Article
- Artifacts and biases of the reverse transcription reaction in RNA sequencing.RNA (New York, N.Y.) · 2023Review
- Long-read mapping to repetitive reference sequences using Winnowmap2.Nature methods · 2022Article
- Anti-bias training for (sc)RNA-seq: experimental and computational approaches to improve precision.Briefings in bioinformatics · 2021Article
- Discovering Cellular Mitochondrial Heteroplasmy Heterogeneity with Single Cell RNA and ATAC Sequencing.Biology · 2021Review
- Massively parallel single-cell mitochondrial DNA genotyping and chromatin profiling.Nature biotechnology · 2021Article
- Confidence intervals for Markov chain transition probabilities based on next generation sequencing reads data.Quantitative biology (Beijing, China) · 2020Article
- Modeling of shotgun sequencing of DNA plasmids using experimental and theoretical approaches.BMC bioinformatics · 2020Article
- Identification and removal of sequencing artifacts produced by mispriming during reverse transcription in multiple RNA-seq technologies.RNA (New York, N.Y.) · 2018Article
- Bacterial communities associated with honeybee food stores are correlated with land use.Ecology and evolution · 2018Article
- Local sequence and sequencing depth dependent accuracy of RNA-seq reads.BMC bioinformatics · 2017Article
- Correcting nucleotide-specific biases in high-throughput sequencing data.BMC bioinformatics · 2017Article
- Global analyses of endonucleolytic cleavage in mammals reveal expanded repertoires of cleavage-inducing small RNAs and their targets.Nucleic acids research · 2016Article
- A semi-parametric statistical model for integrating gene expression profiles across different platforms.BMC bioinformatics · 2016Article
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- SiNG-PCRseq: Accurate inter-sequence quantification achieved by spiking-in a neighbor genome for competitive PCR amplicon sequencing.Scientific reports · 2015Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Since the emergence of next-generation sequencing (NGS) technologies, great effort has been put into the development of tools for analysis of the short reads. In parallel, knowledge is increasing regarding biases inherent in these technologies. Here we discuss four different biases we encountered while analyzing various Illumina datasets. These biases are due to both biological and statistical effects that in particular affect comparisons between different genomic regions. Specifically, we encountered biases pertaining to the distributions of nucleotides across sequencing cycles, to mappability, to contamination of pre-mRNA with mRNA, and to non-uniform hydrolysis of RNA. Most of these biases are not specific to one analyzed dataset, but are present across a variety of datasets and within a variety of genomic contexts. Importantly, some of these biases correlated in a highly significant manner with biological features, including transcript length, gene expression levels, conservation levels, and exon-intron architecture, misleadingly increasing the credibility of results due to them. We also demonstrate the relevance of these biases in the context of analyzing an NGS dataset mapping transcriptionally engaged RNA polymerase II (RNAPII) in the context of exon-intron architecture, and show that elimination of these biases is crucial for avoiding erroneous interpretation of the data. Collectively, our results highlight several important pitfalls, challenges and approaches in the analysis of NGS reads.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.