Dataset Information

Sequencing on the SOLiD 5500xl System - in-depth characterization of the GC bias.

ABSTRACT: Different types of sequencing biases have been described and subsequently improved for a variety of sequencing systems, mostly focusing on the widely used Illumina systems. Similar studies are missing for the SOLiD 5500xl system, a sequencer which produced many data sets available to researchers today. Describing and understanding the bias is important to accurately interpret and integrate these published data in various ongoing research projects. We report a particularly strong GC bias for this sequencing system when analyzing a defined gDNA mix of 5 microbes with a wide range of different GC contents (20-72%) when comparing to the expected distribution and Illumina MiSeq data from the same DNA pool. Since we observed this bias already under PCR-free conditions, changing the PCR conditions during library preparation - a common strategy to handle bias in the Illumina system - was not relevant. Source of the bias appeared to be an uneven heat distribution during the SOLiD emulsion PCR (ePCR) - for enrichment of libraries prior loading - since ePCR in either small pouches or in 96-well plates improved the GC bias. Sequencing of chromatin immunoprecipitated DNA (ChIP-seq) is a common approach in epigenetics. ChIP-seq of the mixed source histone mark H3K9ac (acetyl Histone H3 lysine 9), typically found on promoter regions and on gene bodies, including CpG islands, performed on a SOLiD 5500xl machine, resulted in major loss of reads at GC rich loci (GC content ? 62%), not explained by low sequencing depth. This was improved with adaptations of the ePCR.

SUBMITTER: Roeh S

PROVIDER: S-EPMC5597294 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Sequencing on the SOLiD 5500xl System - in-depth characterization of the GC bias.

Roeh Simone S Weber Peter P Rex-Haffner Monika M Deussing Jan M JM Binder Elisabeth B EB Jakovcevski Mira M

Nucleus (Austin, Tex.) 20170427 4

Different types of sequencing biases have been described and subsequently improved for a variety of sequencing systems, mostly focusing on the widely used Illumina systems. Similar studies are missing for the SOLiD 5500xl system, a sequencer which produced many data sets available to researchers today. Describing and understanding the bias is important to accurately interpret and integrate these published data in various ongoing research projects. We report a particularly strong GC bias for this ...[more]

PMID: 28448740

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Project description:BACKGROUND:In spite of extensive research on the effect of mutation and selection on codon usage, a general model of codon usage bias due to mutational bias has been lacking. Because most amino acids allow synonymous GC content changing substitutions in the third codon position, the overall GC bias of a genome or genomic region is highly correlated with GC3, a measure of third position GC content. For individual amino acids as well, G/C ending codons usage generally increases with increasing GC bias and decreases with increasing AT bias. Arginine and leucine, amino acids that allow GC-changing synonymous substitutions in the first and third codon positions, have codons which may be expected to show different usage patterns. PRINCIPAL FINDINGS:In analyzing codon usage bias in hundreds of prokaryotic and plant genomes and in human genes, we find that two G-ending codons, AGG (arginine) and TTG (leucine), unlike all other G/C-ending codons, show overall usage that decreases with increasing GC bias, contrary to the usual expectation that G/C-ending codon usage should increase with increasing genomic GC bias. Moreover, the usage of some codons appears nonlinear, even nonmonotone, as a function of GC bias. To explain these observations, we propose a continuous-time Markov chain model of GC-biased synonymous substitution. This model correctly predicts the qualitative usage patterns of all codons, including nonlinear codon usage in isoleucine, arginine and leucine. The model accounts for 72%, 64% and 52% of the observed variability of codon usage in prokaryotes, plants and human respectively. When codons are grouped based on common GC content, 87%, 80% and 68% of the variation in usage is explained for prokaryotes, plants and human respectively. CONCLUSIONS:The model clarifies the sometimes-counterintuitive effects that GC mutational bias can have on codon usage, quantifies the influence of GC mutational bias and provides a natural null model relative to which other influences on codon bias may be measured.

Dataset Information

Sequencing on the SOLiD 5500xl System - in-depth characterization of the GC bias.

Publications

Sequencing on the SOLiD 5500xl System - in-depth characterization of the GC bias.

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