Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Measuring sequencer size bias using REcount: a novel method for highly accurate Illumina sequencing-based quantification.

ABSTRACT: Quantification of DNA sequence tags from engineered constructs such as plasmids, transposons, or other transgenes underlies many functional genomics measurements. Typically, such measurements rely on PCR followed by next-generation sequencing. However, PCR amplification can introduce significant quantitative error. We describe REcount, a novel PCR-free direct counting method. Comparing measurements of defined plasmid pools to droplet digital PCR data demonstrates that REcount is highly accurate and reproducible. We use REcount to provide new insights into clustering biases due to molecule length across different Illumina sequencers and illustrate the impacts on interpretation of next-generation sequencing data and the economics of data generation.

SUBMITTER: Gohl DM

PROVIDER: S-EPMC6489363 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Measuring sequencer size bias using REcount: a novel method for highly accurate Illumina sequencing-based quantification.

Gohl Daryl M DM Magli Alessandro A Garbe John J Becker Aaron A Johnson Darrell M DM Anderson Shea S Auch Benjamin B Billstein Bradley B Froehling Elyse E McDevitt Shana L SL Beckman Kenneth B KB

Genome biology 20190429 1

Quantification of DNA sequence tags from engineered constructs such as plasmids, transposons, or other transgenes underlies many functional genomics measurements. Typically, such measurements rely on PCR followed by next-generation sequencing. However, PCR amplification can introduce significant quantitative error. We describe REcount, a novel PCR-free direct counting method. Comparing measurements of defined plasmid pools to droplet digital PCR data demonstrates that REcount is highly accurate ...[more]

PMID: 31036053

Similar Datasets

High Coverage Highly Accurate Long-Read Sequencing of a Mouse Neuronal Cell Line Using the PacBio Revio Sequencer.

Project description:Recently, Pacific Biosciences released a new highly accurate long-read sequencer called the Revio System that is projected to generate 30× HiFi whole-genome sequencing for the human genome within one sequencing SMRT Cell. Mouse and human genomes are similar in size. In this study, we sought to test this new sequencer by characterizing the genome and epigenome of the mouse neuronal cell line Neuro-2a. We generated long-read HiFi whole-genome sequencing on three Revio SMRT Cells, achieving a total coverage of 98×, with 30×, 32×, and 36× coverage respectively for each of the three Revio SMRT Cells. We performed several tests on these data including single-nucleotide variant and small insertion detection using GPU-accelerated DeepVariant, structural variant detection with pbsv, methylation detection with pb-CpG-tools, and generating de novo assemblies with the HiCanu and hifiasm assemblers. Overall, we find consistency across SMRT Cells in coverage, detection of variation, methylation, and de novo assemblies for each of the three SMRT Cells.

| S-EPMC10274723 | biostudies-literature

Two-tailed RT-qPCR: a novel method for highly accurate miRNA quantification.

Project description:MicroRNAs are a class of small non-coding RNAs that serve as important regulators of gene expression at the posttranscriptional level. They are stable in body fluids and pose great potential to serve as biomarkers. Here, we present a highly specific, sensitive and cost-effective system to quantify miRNA expression based on two-step RT-qPCR with SYBR-green detection chemistry called Two-tailed RT-qPCR. It takes advantage of novel, target-specific primers for reverse transcription composed of two hemiprobes complementary to two different parts of the targeted miRNA, connected by a hairpin structure. The introduction of a second probe ensures high sensitivity and enables discrimination of highly homologous miRNAs irrespectively of the position of the mismatched nucleotide. Two-tailed RT-qPCR has a dynamic range of seven logs and a sensitivity sufficient to detect down to ten target miRNA molecules. It is capable to capture the full isomiR repertoire, leading to accurate representation of the complete miRNA content in a sample. The reverse transcription step can be multiplexed and the miRNA profiles measured with Two-tailed RT-qPCR show excellent correlation with the industry standard TaqMan miRNA assays (r2 = 0.985). Moreover, Two-tailed RT-qPCR allows for rapid testing with a total analysis time of less than 2.5 hours.

| S-EPMC5587787 | biostudies-literature

The new sequencer on the block: comparison of Life Technology's Proton sequencer to an Illumina HiSeq for whole-exome sequencing.

Project description:We assessed the performance of the new Life Technologies Proton sequencer by comparing whole-exome sequence data in a Centre d'Etude du Polymorphisme Humain trio (family 1463) to the Illumina HiSeq instrument. To simulate a typical user's results, we utilized the standard capture, alignment and variant calling methods specific to each platform. We restricted data analysis to include the capture region common to both methods. The Proton produced high quality data at a comparable average depth and read length, and the Ion Reporter variant caller identified 96 % of single nucleotide polymorphisms (SNPs) detected by the HiSeq and GATK pipeline. However, only 40 % of small insertion and deletion variants (indels) were identified by both methods. Usage of the trio structure and segregation of platform-specific alleles supported this result. Further comparison of the trio data with Complete Genomics sequence data and Illumina SNP microarray genotypes documented high concordance and accurate SNP genotyping of both Proton and Illumina platforms. However, our study underscored the problem of accurate detection of indels for both the Proton and HiSeq platforms.

| S-EPMC4564298 | biostudies-literature

SequencErr: measuring and suppressing sequencer errors in next-generation sequencing data.

Project description:BackgroundThere is currently no method to precisely measure the errors that occur in the sequencing instrument/sequencer, which is critical for next-generation sequencing applications aimed at discovering the genetic makeup of heterogeneous cellular populations.ResultsWe propose a novel computational method, SequencErr, to address this challenge by measuring the base correspondence between overlapping regions in forward and reverse reads. An analysis of 3777 public datasets from 75 research institutions in 18 countries revealed the sequencer error rate to be ~ 10 per million (pm) and 1.4% of sequencers and 2.7% of flow cells have error rates > 100 pm. At the flow cell level, error rates are elevated in the bottom surfaces and > 90% of HiSeq and NovaSeq flow cells have at least one outlier error-prone tile. By sequencing a common DNA library on different sequencers, we demonstrate that sequencers with high error rates have reduced overall sequencing accuracy, and removal of outlier error-prone tiles improves sequencing accuracy. We demonstrate that SequencErr can reveal novel insights relative to the popular quality control method FastQC and achieve a 10-fold lower error rate than popular error correction methods including Lighter and Musket.ConclusionsOur study reveals novel insights into the nature of DNA sequencing errors incurred on DNA sequencers. Our method can be used to assess, calibrate, and monitor sequencer accuracy, and to computationally suppress sequencer errors in existing datasets.

| S-EPMC7829059 | biostudies-literature

Highly accurate, automated quantification of 2D/3D orientation for cerebrovasculature using window optimizing method.

Project description:SignificanceDeep-imaging of cerebral vessels and accurate organizational characterization are vital to understanding the relationship between tissue structure and function.AimWe aim at large-depth imaging of the mouse brain vessels based on aggregation-induced emission luminogens (AIEgens), and we create a new algorithm to characterize the spatial orientation adaptively with superior accuracy.ApproachAssisted by AIEgens with near-infrared-II excitation, three-photon fluorescence (3PF) images of large-depth cerebral blood vessels are captured. A window optimizing (WO) method is developed for highly accurate, automated 2D/3D orientation determination. The application of this system is demonstrated by establishing the orientational architecture of mouse cerebrovasculature down to the millimeter-level depth.ResultsThe WO method is proved to have significantly higher accuracy in both 2D and 3D cases than the method with a fixed window size. Depth- and diameter-dependent orientation information is acquired based on in vivo 3PF imaging and the WO analysis of cerebral vessel images with a penetration depth of 800 μm in mice.ConclusionsWe built an imaging and analysis system for cerebrovasculature that is conducive to applications in neuroscience and clinical fields.

| S-EPMC9587757 | biostudies-literature

Immune Repertoire Sequencing Using Molecular Identifiers Enables Accurate Clonality Discovery and Clone Size Quantification.

Project description:Unique molecular identifiers (MIDs) have been demonstrated to effectively improve immune repertoire sequencing (IR-seq) accuracy, especially to identify somatic hypermutations in antibody repertoire sequencing. However, evaluating the sensitivity to detect rare T cells and the degree of clonal expansion in IR-seq has been difficult due to the lack of knowledge of T cell receptor (TCR) RNA molecule copy number and a generalized approach to estimate T cell clone size from TCR RNA molecule quantification. This limited the application of TCR repertoire sequencing (TCR-seq) in clinical settings, such as detecting minimal residual disease in lymphoid malignancies after treatment, evaluating effectiveness of vaccination and assessing degree of infection. Here, we describe using an MID Clustering-based IR-Seq (MIDCIRS) method to quantitatively study TCR RNA molecule copy number and clonality in T cells. First, we demonstrated the necessity of performing MID sub-clustering to eliminate erroneous sequences. Further, we showed that MIDCIRS enables a sensitive detection of a single cell in as many as one million naïve T cells and an accurate estimation of the degree of T cell clonal expression. The demonstrated accuracy, sensitivity, and wide dynamic range of MIDCIRS TCR-seq provide foundations for future applications in both basic research and clinical settings.

| S-EPMC5808239 | biostudies-literature

Accurate Sphingolipid Quantification Reducing Fragmentation Bias by Nonlinear Models.

Project description:Quantitative sphingolipid analysis is crucial for understanding the roles of these bioactive molecules in various physiological and pathological contexts. Molecular sphingolipid species are typically quantified using sphingoid base-derived fragments relative to a class-specific internal standard. However, the commonly employed "one standard per class" strategy fails to account for fragmentation differences presented by the structural diversity of sphingolipids. To address this limitation, we developed a novel approach for quantitative sphingolipid analysis. This approach utilizes fragmentation models to correct for structural differences and thus overcomes the limitations associated with using a limited number of standards for quantification. Importantly, our method is independent of the internal standard, instrumental setup, and collision energy. Furthermore, we integrated this method into a user-friendly KNIME workflow. The validation results illustrate the effectiveness of our approach in accurately quantifying ceramide subclasses from various biological matrices. This breakthrough opens up new avenues for exploring sphingolipid metabolism and gaining insights into its implications.

| S-EPMC10585660 | biostudies-literature

Analyzing and minimizing PCR amplification bias in Illumina sequencing libraries.

Project description: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.

| S-EPMC3188800 | biostudies-literature

RELIC: a novel dye-bias correction method for Illumina Methylation BeadChip.

Project description:BackgroundThe Illumina Infinium HumanMethylation450 BeadChip and its successor, Infinium MethylationEPIC BeadChip, have been extensively utilized in epigenome-wide association studies. Both arrays use two fluorescent dyes (Cy3-green/Cy5-red) to measure methylation level at CpG sites. However, performance difference between dyes can result in biased estimates of methylation levels.ResultsHere we describe a novel method, called REgression on Logarithm of Internal Control probes (RELIC) to correct for dye bias on whole array by utilizing the intensity values of paired internal control probes that monitor the two color channels. We evaluate the method in several datasets against other widely used dye-bias correction methods. Results on data quality improvement showed that RELIC correction statistically significantly outperforms alternative dye-bias correction methods. We incorporated the method into the R package ENmix, which is freely available from the Bioconductor website ( https://www.bioconductor.org/packages/release/bioc/html/ENmix.html ).ConclusionsRELIC is an efficient and robust method to correct for dye-bias in Illumina Methylation BeadChip data. It outperforms other alternative methods and conveniently implemented in R package ENmix to facilitate DNA methylation studies.

| S-EPMC5209853 | biostudies-literature

BCseq: accurate single cell RNA-seq quantification with bias correction.

Project description:With rapid technical advances, single cell RNA-seq (scRNA-seq) has been used to detect cell subtypes exhibiting distinct gene expression profiles and to trace cell transitions in development and disease. However, the potential of scRNA-seq for new discoveries is constrained by the robustness of subsequent data analysis. Here we propose a robust model, BCseq (bias-corrected sequencing analysis), to accurately quantify gene expression from scRNA-seq. BCseq corrects inherent bias of scRNA-seq in a data-adaptive manner and effectively removes technical noise. BCseq rescues dropouts through weighted consideration of similar cells. Cells with higher sequencing depths contribute more to the quantification nonlinearly. Furthermore, BCseq assigns a quality score for the expression of each gene in each cell, providing users an objective measure to select genes for downstream analysis. In comparison to existing scRNA-seq methods, BCseq demonstrates increased robustness in detection of differentially expressed (DE) genes and cell subtype classification.

| S-EPMC6101504 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data