Biases in Illumina transcriptome sequencing caused by random hexamer priming

By Kasper D. Hansen, Steven E. Brenner, Sandrine Dudoit. Nucleic Acids Research, 2010

722 citations recorded by the source corpus.

Generation of cDNA using random hexamer priming induces biases in the nucleotide composition at the beginning of transcriptome sequencing reads from the Illumina Genome Analyzer. The bias is independent of organism and laboratory and impacts the uniformity of the reads along the transcriptome. We provide a read count reweighting scheme, based on the nucleotide frequencies of the reads, that mitigates the impact of the bias.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/95ca62fa56b4586b5bb021057b7723dd7641140c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

Modelling and simulating generic RNA-Seq experiments with the flux simulator (2012, methodology): “Randomly primed RT (7) and/or the adapter ligation process (16,17) promote sequence-selective biases which manifest as motifs at the fragment ends (7,8); promising RNA-ligation based protocols avoiding both steps have been demonstrated (17,18).”
GC-Content Normalization for RNA-Seq Data (2011, background): “[11] report large and reproducible base-specific read biases associated with random hexamer priming in Illumina’s standard library preparation protocol.”
RSeQC: quality control of RNA-seq experiments (2012, methodology): “…the RSeQC package to comprehensively evaluate different aspects of RNA-seq experiments, such as sequence quality, GC bias, polymerase chain reaction bias, nucleotide composition bias, sequencing depth, strand specificity, coverage uniformity and read distribution over the genome structure.”
A Comparative Study of Techniques for Differential Expression Analysis on RNA-Seq Data (2014, background): “biases introduced during library preparation, nucleotide-specific and read-position specific biases in sequence quality and error rate) [16,17]; 2) biases of abundance measures due to the effects of nucleotide composition and the varying length of genes or transcripts [5]; 3) undetermined effects of”
rMATS: Robust and flexible detection of differential alternative splicing from replicate RNA-Seq data (2014, positive): “It should be noted that a series of studies have revealed systematic biases in RNA-Seq data and have proposed methods to correct for the raw RNA-Seq read counts (32 – 35).”
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome (2011, methodology): “Future work will include incorporating additional biases into the model, such as sequence-specific read position preferences [38,40] and transcript-specific read distributions [41].”
Summarizing and correcting the GC content bias in high-throughput sequencing (2012, background): “Sequence-related biases in the priming (9), size selection (3), PCR (10) and probability of sequencing errors (11–13) have all been found.”
Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses (2015, positive): “In such analyses, the application of statistical modelling to remove systematic biases and reduce variability via normalization (2,3), batch correction (4,5) and subsequently prioritize changes in gene expression between treatment groups (6,7) have each been shown to play a key role in extracting me”
Sequence assembly demystified (2013, background): “The sequences to be reconstructed thus have highly non-uniform coverage, even within the same transcript, and this problem is compounded by experimental limitations, such as the non-uniform amplification of mRNA 70 .”
Developmental regulation of human cortex transcription and its clinical relevance at base resolution (2014, neutral): “RNA-seq data processing and analysis involves a number of well-documented technical biases 53–56 , but we found little evidence for the significant DERs originating from technical or computational artifacts.”
Improving RNA-Seq expression estimates by correcting for fragment bias (2011, methodology): “A comparison of our method (Cufflinks) with Genominator[8] and mseq[10].”
From RNA-seq reads to differential expression results (2010, background): “However, it has been shown that read coverage along expressed transcripts can be non-uniform because of sequence content [49] and RNA preparation methods, such as random hexamer priming [50].”
StringTie enables improved reconstruction of a transcriptome from RNA-seq reads (2015, methodology): “Limitations of simulated data include an imperfect ability to model sample preparation protocols and the distribution of reads across the genome 25 .”