De-biasing microbiome sequencing data: bacterial morphology-based correction of extraction bias and correlates of chimera formation
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ABSTRACT:
PROVIDER: PRJEB67827 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR12157629_1.fastq.gz | Fastqsanger.gz | |||
| ERR12157629_2.fastq.gz | Fastqsanger.gz | |||
| ERR12157630_1.fastq.gz | Fastqsanger.gz | |||
| ERR12157630_2.fastq.gz | Fastqsanger.gz | |||
| ERR12157631_1.fastq.gz | Fastqsanger.gz |
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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
Project description:The use of RNA-seq as the preferred method for the discovery and validation of small RNA biomarkers is hindered by high variability and biased sequence counts. In this paper we develop a statistical model for sequence counts that accounts for ligase bias and stochastic variation in library amplification steps and sequencing depth variation. Our analytical contributions are the description of the Linear Quadratic (LQ) relation between the mean and variance of the sequence counts in an RNA-seq experiment and the derivation of the Poisson truncated mixture as the underlying probability distribution for RNA-seq data. Using a large number of sequencing datasets, we demonstrate here how one can use this modeling framework to calculate empirical correction factors for ligase bias, while accounting for random variation in sequence counts. Bias correction may remove the majority of bias in the absence of differential expression and more than 40% of the bias in the presence of variable expression of miRNAs. Empirical bias correction factors appear to be nearly constant over at least one and up to four orders of magnitude of total RNA input and independent of sample composition.
2017-03-15 | GSE93399 | 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: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:We compared 3 small RNA library prep kits (CleanTag, NEXTflex, QIAseq) and two RNA extraction methods (miRNeasy and MagnaZol) on plasma. We report that library preparation has a significant effect upon the miRNA profile detected, with QIAseq libraries exhibiting the least sequencing bias of the three library kits. RNA extraction methods also contribute, to a lesser extent, to the miRNA profile detected, with MagnaZol RNA extraction increasing the percentage of reads mapping to miRNAs and the number of individual miRNAs detected.
2019-05-21 | GSE118125 | GEO
Project description:Background: During the analysis of ripening related gene expression in tomato fruit, we observed a bias towards certain classes of messenger RNA based upon the type of buffer used in the initial step of the extraction procedure. We postulated that there was a functional association of the separated transcripts. To test this hypothesis, we carried out extractions from the same tissue sample where only the buffer varied. Transcripts were hybridised to Affymetrix oligo arrays and the data was analysed according to the predicted cellular component of the encoded proteins. Results: The use of an extraction buffer that lacked high levels of caeotropic agents resulted in a reduction of mRNAs encoding proteins that were either secreted or were predicted to be routed through the endomembrane system and hence could have been bound to the endoplasmic reticulum in polyribosomes. Extraction of the cell debris immediately following the initial buffer based extraction and subsequent micro array analysis revealed that the expected transcripts could be recovered. This demonstrated that the buffer was separating the mRNAs based on the cellular component of the encoded proteins. Analysis of selected transcripts by northern hybridisation again supported this theory. Conclusions: Some traditional buffers used for fruit RNA extraction selectively deplete for transcripts encoding proteins that are membrane-associated or secreted. This can be explained if polyribosomes that are bound to the endoplasmic reticulum (ER) are not effectively disrupted when the extraction buffer lacks either detergent or organic solvents. These findings have important implications with respect to experimental bias, as well as opportunities for message enrichment and protein characterisation.
2011-01-21 | GSE17222 | GEO
Project description:Background Ribosome depleted RNA-seq is well suited for understanding coding- and noncoding transcript expression in clinical samples with diverse input RNA quality and mass. Current choices for RNA extraction include phase-separation (e.g., ThermoFisher TRIzol) approaches to column-based (e.g., Norgen Spin Columns) approaches. Each method, however, has been noted to have differing effects on the properties of the extracted nucleic acids, precluding the straightforward bioinformatic comparison of data generated by the two methods. Methods In this study, we present differences in data differing only in total RNA extraction approaches (phase separation vs. column) from clinical samples processed by the same tissue bank and sequencing facility personnel. We use data from seven samples with libraries generated from RNA extracted with both extraction methods to characterize the effect of ‘differentially extracted’ genes (DExGs) on transcriptomic profiles. We then use another set of 148 samples extracted with one or the other method to confirm the differences observed in the 7-sample dataset. Results The large number of samples employed in this study allows us to find significant differences in important quality control parameters as well as many DExGs between the two methods. We identify several biophysical properties which are potential drivers of these differences. Conclusions We show that partial correction of extraction method mediated differences can be achieved but full correction is not possible. These results lay the foundation for principled decision making of RNA extraction methods for use in clinical samples in a translational environment, particularly in light of recent reagent shortages during the COVID-19 pandemic.
2023-09-28 | GSE158705 | GEO
Project description:DNA extraction method targeted loci environmental
| PRJNA315949 | ENA
Project description:Understanding chromatin dynamics is a key to other related processes, including DNA replication, transcription and recombination. As a first step, recently, an increasing amount of effort has been devoted to precisely define nucleosome positioning in different organisms. The most popular method to do so is digestion by Micrococcal nuclease (MNase), nowadays followed by ultrasequencing of the generated fragments. Although the sequence bias of MNase has been known for a long time, a data-based way of correcting this bias for sequencing experiments is still missing. Here we provide such a method, based on the digestion of naked DNA and the use of the bioinformatic tool DANPOS. Using Saccharomyces cerevisiae as a model, we demonstrate that the overall quality of the data is better after the correction proposed here. The characteristics of the method make it perfectly applicable to any other organism. Moreover, we find that the correction affects more to TATA containing genes than to TATA-like genes. Importantly, the correction uncovers a new role for TFIIS in chromatin dynamics. The absence of TFIIS caused a general increase in nucleosome fuzziness, and a higher peak of the -1 nucleosome at some promoters
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