PCR cycles affect gibbs free energy bias
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ABSTRACT:
PROVIDER: PRJNA630479 | ENA |
REPOSITORIES: ENA
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Project description:We amplified DNA fragments randomly sheared from PER1 BAC library with four different PCR cycles (3, 6, 12, and 18 cycles). We report the effect of Gibbs free energy bias to coverage significantly increases with additional number of PCR cycles, especially for fragments with high Gibbs free energy (usually corresponding to low GC content).
2020-05-06 | GSE149914 | GEO
Project description:As transposon sequencing (TnSeq) assays have become prolific in the microbiology field, it is of interest to scrutinize their potential drawbacks. TnSeq results are determined by counting transposon insertions following the PCR-based enrichment and subsequent deep sequencing of transposon insertions. Here we explore the possibility that PCR amplification of transposon insertions in a TnSeq library skews the results by introducing bias into the detection and/or enumeration of insertions. We compared the detection and frequency of mapped insertions when altering the number of PCR cycles in the enrichment step. In addition, we devised and validated a novel, PCR-free TnSeq method where the insertions are enriched via CRISPR/Cas9-targeted transposon cleavage and subsequent Oxford Nanopore sequencing. These PCR-based and PCR-free experiments demonstrate that, overall, PCR amplification does not significantly bias the results of the TnSeq assay insofar as insertions in the majority of genes represented in our library were similarly detected regardless of PCR cycle number and whether or not PCR amplification was employed. However, the detection of a small subset of genes which had been previously described as essential is indeed sensitive to the number of PCR cycles. We conclude that PCR-based enrichment of transposon insertions in a TnSeq assay is reliable but researchers interested in profiling essential genes should carefully weigh the number of amplification cycles employed in their library preparation protocols. In addition, we present a PCR-free TnSeq alternative that is comparable to traditional PCR-based methods although the latter remain superior owing to their accessibility and high sequencing depth.
2020-12-21 | GSE162988 | GEO
Project description:Investigating PCR bias in TnSeq
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Project description:Budding yeast grown under continuous, nutrient-limited conditions exhibit robust, highly periodic cycles in the form of respiratory bursts. Microarray studies reveal that over half of the yeast genome is expressed periodically during these metabolic cycles. Genes encoding proteins having a common function exhibit similar temporal expression patterns, and genes specifying functions associated with energy and metabolism tend to be expressed with exceptionally robust periodicity. Essential cellular and metabolic events occur in synchrony with the metabolic cycle, demonstrating that key processes in a simple eukaryotic cell are compartmentalized in time. This data set contains the raw affymetrix gene expression data over three successive metabolic cycles. 12 time intervals per cycle, ~25 min per time interval.
2007-06-24 | E-GEOD-3431 | biostudies-arrayexpress
Project description:RNA-Seq is ubiquitous, but depending on the study, sub-optimal sample handling may be required, resulting in repeated freeze-thaw cycles. However, little is known about how each cycle impacts downstream analyses, due to a lack of study and known limitations in common RNA quality metrics, e.g., RIN, at quantifying RNA degradation following repeated freeze-thaws. Here we quantify the impact of repeated freeze-thaw on the reliability of downstream RNA-Seq analysis. To do so, we developed a method to estimate the relative noise between technical replicates independently of RIN. Using this approach we inferred the effect of both RIN and the number of freeze-thaw cycles on sample noise. We find that RIN is unable to fully account for the change in sample noise due to freeze-thaw cycles. Additionally, freeze-thaw is detrimental to sample quality and differential expression (DE) reproducibility, approaching zero after three cycles for poly(A)-enriched samples, wherein the inherent 3’ bias in read coverage is more exacerbated by freeze-thaw cycles, while ribosome-depleted samples are less affected by freeze-thaws. The use of poly(A)-enrichment for RNA sequencing is pervasive in library preparation of frozen tissue, and thus, it is important during experimental design and data analysis to consider the impact of repeated freeze-thaw cycles on reproducibility.
2020-05-09 | GSE150097 | GEO
Project description:Spot intensity serves as a proxy for gene expression in dual-label microarray experiments. Dye bias is defined as an intensity difference between samples labeled with different dyes attributable to the dyes instead of the gene expression in the samples. Dye bias that is not removed by array normalization can introduce bias into comparisons between samples of interest. But if the bias is consistent across the samples for the same gene, it can be corrected by proper experimental design and analysis. If the dye bias is not consistent across samples for the same gene, but is different for different samples, then removing the bias becomes more problematic, perhaps indicating a technical limitation to the ability of fluorescent signals to accurately represent gene expression. Thus, it is important to characterize dye bias to determine: (1) whether it will be removed for all genes by array normalization, (2) whether it will not be removed by normalization but can be removed by proper experimental design and analysis and (3) whether dye bias correction is more problematic than either of these and is not easily removable. Keywords: dye swap design
2006-06-06 | GSE4982 | GEO
Project description:Standard Gibbs energies of reactions are increasingly being used in metabolic modeling for applying thermodynamic constraints on reaction rates, metabolite concentrations and kinetic parameters. The increasing scope and diversity of metabolic models has led scientists to look for genome-scale solutions that can estimate the standard Gibbs energy of all the reactions in metabolism. Group contribution methods greatly increase coverage, albeit at the price of decreased precision. We present here a way to combine the estimations of group contribution with the more accurate reactant contributions by decomposing each reaction into two parts and applying one of the methods on each of them. This method gives priority to the reactant contributions over group contributions while guaranteeing that all estimations will be consistent, i.e. will not violate the first law of thermodynamics. We show that there is a significant increase in the accuracy of our estimations compared to standard group contribution. Specifically, our cross-validation results show an 80% reduction in the median absolute residual for reactions that can be derived by reactant contributions only. We provide the full framework and source code for deriving estimates of standard reaction Gibbs energy, as well as confidence intervals, and believe this will facilitate the wide use of thermodynamic data for a better understanding of metabolism.
| S-EPMC3708888 | biostudies-literature
Project description:Spot intensity serves as a proxy for gene expression in dual-label microarray experiments. Dye bias is defined as an intensity difference between samples labeled with different dyes attributable to the dyes instead of the gene expression in the samples. Dye bias that is not removed by array normalization can introduce bias into comparisons between samples of interest. But if the bias is consistent across the samples for the same gene, it can be corrected by proper experimental design and analysis. If the dye bias is not consistent across samples for the same gene, but is different for different samples, then removing the bias becomes more problematic, perhaps indicating a technical limitation to the ability of fluorescent signals to accurately represent gene expression. Thus, it is important to characterize dye bias to determine: (1) whether it will be removed for all genes by array normalization, (2) whether it will not be removed by normalization but can be removed by proper experimental design and analysis and (3) whether dye bias correction is more problematic than either of these and is not easily removable. For two dual-label experiments, one with cDNA arrays and the other with printed oligonucleotide arrays, Stratagene universal human reference RNA was used as a standard for testing with RNA from cell lines MCF10a, LNCAP, L428, SUDHL, OCILY3 and Jurkat. All arrays were dye-swapped at least twice. There were a total of 28 cDNA arrays and 30 oligonucleotide arrays.
2006-06-06 | E-GEOD-4982 | biostudies-arrayexpress
Project description:OTUD3 regulates energy metabolism
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Project description:Coupling molecular biology to high throughput sequencing has revolutionized the study of biology. Molecular genomics techniques are continually refined to provide higher resolution mapping of nucleic acid interactions and nucleic acid structure. These assays are converging on single-nucleotide resolution measurements, but the sequence preferences of molecular biology enzymes can interfere with the accurate interpretation of the data. Enzymatic sequence preferences manifest more prominently as the resolution of these assays increase. We developed seqOutBias to seek out enzymatic sequence bias from experimental data and scale individual sequence reads to correct the bias. We show that this software efficiently and successfully corrects the sequence bias resulting from DNase-seq, TACh-seq, ATAC-seq, MNase-seq, and PRO-seq data.
2016-12-21 | GSE92674 | GEO