Computational and Analytical Framework for Small RNA Profiling by High-Throughput Sequencing (standards)
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ABSTRACT:
ORGANISM(S): Arabidopsis thaliana
PROVIDER: GSE14695 | GEO | 2009/06/03
SECONDARY ACCESSION(S): PRJNA114413
REPOSITORIES: GEO
Similar Datasets
Project description:The purpose of this work was to describe a computational and analytical methodology for profiling small RNA by high-throughput sequencing. The datasets here were used to develop synthetic oligoribonucleotides as spike-in standards. We assessed the use of synthetic oligoribonucleotide standards as spike-in controls. These standards can be used to set an objective standard against which to compare samples. Standards were added to the total RNA (100 ug) in the following amounts: Std2 (TATATGCAAGTCCGGCCATAC) 0.01 pmol, Std3 (TAGCTAACGCATATCCGCATC) 0.1 pmol, Std6 (TGAAGCTGACATCGGTCATCC) 1.0 pmol.
2009-06-02 | E-GEOD-14695 | biostudies-arrayexpress
Project description:This SuperSeries is composed of the following subset Series: GSE14694: Computational and Analytical Framework for Small RNA Profiling by High-Throughput Sequencing (reproducibility) GSE14695: Computational and Analytical Framework for Small RNA Profiling by High-Throughput Sequencing (standards) Refer to individual Series
2009-06-02 | E-GEOD-14696 | biostudies-arrayexpress
Project description:The purpose of this work was to describe a computational and analytical methodology for profiling small RNA by high-throughput sequencing. The datasets here were used to assess the reproducibility of small RNA datasets produced using Illumina sequencing-by-synthesis technology (SBS). We analyzed the reproducibility of small RNA SBS datasets by comparing libraries generated for biological replicates (rep1 and rep2) and technical replicates (rep2 and rep3).
2009-06-02 | E-GEOD-14694 | biostudies-arrayexpress
Project description:The purpose of this work was to describe a computational and analytical methodology for profiling small RNA by high-throughput sequencing. The datasets here were used to assess the reproducibility of small RNA datasets produced using Illumina sequencing-by-synthesis technology (SBS).
2009-06-03 | GSE14694 | GEO
Project description:While the importance of random sequencing errors decreases at higher DNA or RNA sequencing depths, systematic sequencing errors (SSEs) dominate at high sequencing depths and can be difficult to distinguish from biological variants. These SSEs can cause base quality scores to underestimate the probability of error at certain genomic positions, resulting in false positive variant calls, particularly in mixtures such as samples with RNA editing, tumors, circulating tumor cells, bacteria, mitochondrial heteroplasmy, or pooled DNA. Most algorithms proposed for correction of SSEs require a training data set, which is typically either from a part of the data set being “recalibrated” (Genome Analysis ToolKit, or GATK) or from a separate data set with special characteristics (SysCall). Here, we combine the advantages of these approaches by adding synthetic RNA spike-in standards to human RNA, and use GATK to recalibrate base quality scores with reads mapped to the spike-in standards. Compared to conventional GATK recalibration that uses reads mapped to the genome, spike-ins improve the accuracy of Illumina base quality scores by a mean of 5 units, and by as much as 13 units at CpG sites. In addition, since reads mapping to the genome are not used for recalibration, our method allows run-specific recalibration even for the many species without a comprehensive and accurate SNP database. We also use GATK with the spike-in standards to demonstrate that the Illumina RNA sequencing runs overestimate quality scores for AC, CC, GC, GG, and TC dinucleotides, while SOLiD has less dinucleotide SSEs but more SSEs for certain cycles. We conclude that using these DNA and RNA spike-in standards with GATK improves base quality score recalibration.
2012-03-03 | GSE36217 | GEO
Project description:This SuperSeries is composed of the SubSeries listed below.
2009-06-03 | GSE14696 | GEO
Project description:While the importance of random sequencing errors decreases at higher DNA or RNA sequencing depths, systematic sequencing errors (SSEs) dominate at high sequencing depths and can be difficult to distinguish from biological variants. These SSEs can cause base quality scores to underestimate the probability of error at certain genomic positions, resulting in false positive variant calls, particularly in mixtures such as samples with RNA editing, tumors, circulating tumor cells, bacteria, mitochondrial heteroplasmy, or pooled DNA. Most algorithms proposed for correction of SSEs require a training data set, which is typically either from a part of the data set being M-bM-^@M-^\recalibratedM-bM-^@M-^] (Genome Analysis ToolKit, or GATK) or from a separate data set with special characteristics (SysCall). Here, we combine the advantages of these approaches by adding synthetic RNA spike-in standards to human RNA, and use GATK to recalibrate base quality scores with reads mapped to the spike-in standards. Compared to conventional GATK recalibration that uses reads mapped to the genome, spike-ins improve the accuracy of Illumina base quality scores by a mean of 5 units, and by as much as 13 units M-BM- at CpG sites. In addition, since reads mapping to the genome are not used for recalibration, our method allows run-specific recalibration even for the many species without a comprehensive and accurate SNP database. We also use GATK with the spike-in standards to demonstrate that the Illumina RNA sequencing runs overestimate quality scores for AC, CC, GC, GG, and TC dinucleotides, while SOLiD has less dinucleotide SSEs but more SSEs for certain cycles. We conclude that using these DNA and RNA spike-in standards with GATK improves base quality score recalibration. Four human RNA samples with equimolar ERCC spike-in standards were sequenced on Illumina. Two human brain/liver/muscle RNA mixtures with dynamic range of ERCC spike-in standards were sequenced on SOLiD.
2012-03-03 | E-GEOD-36217 | biostudies-arrayexpress
Project description:The advent of high-throughput sequencing (HTS) methods has enabled direct approaches to quantitatively profile small RNA populations. However, these methods have been limited by several factors, including representational artifacts and lack of established statistical methods of analysis. Furthermore, massive HTS data sets present new problems related to data processing and mapping to a reference genome. Here, we show that cluster-based sequencing-by-synthesis technology is highly reproducible as a quantitative profiling tool for several classes of small RNA from Arabidopsis thaliana. We introduce the use of synthetic RNA oligoribonucleotide standards to facilitate objective normalization between HTS data sets, and adapt microarray-type methods for statistical analysis of multiple samples. These methods were tested successfully using mutants with small RNA biogenesis (miRNA-defective dcl1 mutant and siRNA-defective dcl2 dcl3 dcl4 triple mutant) or effector protein (ago1 mutant) deficiencies. Computational methods were also developed to rapidly and accurately parse, quantify, and map small RNA data.
| S-EPMC2673065 | biostudies-literature
Project description:Computational and Analytical Framework for Small RNA Profiling by High-Throughput Sequencing (standards)
| PRJNA114413 | ENA
Project description:Single-cell RNA sequencing significantly deepened our insights into complex tissues and latest techniques are capable to analyze ten-thousands of cells simultaneously. With bigSCale, we provide an analytical framework being scalable to analyze millions of cells, addressing challenges of future large data sets. Unlike other methods, bigSCale does not constrain data to fit an a priori-defined distribution and instead uses an accurate numerical model of noise. We evaluate the performance of bigSCale using a biological model of aberrant gene expression and simulated data sets, which underlined its speed and accuracy. We further apply bigSCale to analyze 1.3 million cells from the mouse developing forebrain. The directed down-sampling strategy identified rare populations, such as Reelin positive Cajal-Retzius neurons, for which we determined a previously not recognized heterogeneity associated to distinct differentiation stages, spatial organization and cellular function. Together, bigSCale presents a perfect solution to address future challenges of large single-cell data sets.
2018-04-15 | GSE102934 | GEO