Analyzing and minimizing PCR amplification bias in Illumina sequencing libraries

By Daniel Aird, M. Ross, Wei-Sheng Chen, Maxwell Danielsson, Tim Fennell, C. Russ, David B. Jaffe, Chad Nusbaum, Andreas Gnirke. Genome Biology, 2011

1,169 citations recorded by the source corpus.

Despite the ever-increasing output of Illumina sequencing data, loci with extreme base compositions are often under-represented or absent. To evaluate sources of base-composition bias, we traced genomic sequences ranging from 6% to 90% GC through the process by quantitative PCR. We identified PCR during library preparation as a principal source of bias and optimized the conditions. Our improved protocol significantly reduces amplification bias and minimizes the previously severe effects of PCR instrument and temperature ramp rate.

Subfields: Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/5fc496119d5291e20dcc8a89b49d749d360b74cf

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Citations in the corpus

Benchmarking Metagenomics Tools for Taxonomic Classification. (2019, background): “Steps such as PCR amplification tend to bias towards certain DNA library sequences, altering the original abundance compositions (Aird et al., 2011).”
UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy (2017, neutral): “overrepresented in the final library (Aird et al. 2011).”
UMI-tools: Modelling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy (2016, background): “Biases in the PCR amplification step lead to particular sequences becoming overrepresented in the final library (Aird et al. 2011).”
Next-generation transcriptome assembly (2011, background): “Recently it has shown that amplification-free protocols can reduce the bias [which type of bias?] originated from PCR amplification 32,33 .”
Assessing the performance of the Oxford Nanopore Technologies MinION (2015, background): “Extreme G + C content is known to affect the performance of DNA equencers [1].”
Denoising the Denoisers: an independent evaluation of microbiome sequence error-correction approaches (2018, background): “This non-agreement could also be due to steps 525 during the sequencing processes such as PCR amplification, which may be causing primer bias 526 (Aird et al., 2011) or the inclusion of contaminant organisms.”
Conducting a Microbiome Study (2014, methodology): “…Cell 158, July 17, 2014 ª2014 Elsevier Inc. practice to perform and pool multiple (i.e., triplicate) PCRs for each sample, to minimize PCR cycle number, and to use a standard but relatively high DNA template concentration across samples (Acinas et al., 2005; Aird et al., 2011; Sipos et al., 2007).”
Characterizing and measuring bias in sequence data (2013, methodology): “Table 2 includes human Illumina data produced using the methods described in Aird et al. [20] that are illustrative of this, showing a striking improvement at high GC when the PCR enzyme Phusion HF (data set 10) was supplemented by betaine (data set 11) or replaced by AccuPrime Taq HiFi (data set…”
Special features of RAD Sequencing data: implications for genotyping (2012, methodology): “Second, read 2 sequences have been used to attempt to remove PCR duplicates from RAD-Seq data (Baxter et al. 2011), with the aim of reducing GC bias known to be introduced by PCR (Benjamini & Speed 2012; Aird et al. 2011, Quail et al. 2008).”
HiCUP: pipeline for mapping and processing Hi-C data (2015, background): “Consequently, when considering that PCR results in certain di-tags being amplified disproportionately 14 , and that only a small number of observed fragment-fragment contacts are needed to qualify an interaction as statistically significant, retaining PCR duplicates will lead to an erroneous…”
CONTRA: copy number analysis for targeted resequencing (2012, neutral): “Received on April 5, 2011; revised on February 27, 2012; accepted on March 23, 2012”
Microbiome definition re-visited: old concepts and new challenges (2020, background): “A well-known example in DNA processing is the selective amplification induced by primer selection and PCR or the large number of unknown reads when metagenomes are analyzed [152, 157, 158].”
Adapterama I: universal stubs and primers for 384 unique dual-indexed or 147,456 combinatorially-indexed Illumina libraries (iTru & iNext) (2019, background): “Alternative commercial kits have been produced to increase efficiency, reduce GC bias ( Aird et al., 2011 ; Kozerewa et al., 2009 ), and/or increase the number of indexes, but costs remain high and the total number of commercially available indexes still generally remains”