Two Sets of Synthetic Spike-in Controls for small RNA-seq
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ABSTRACT:
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR647589.fastq.gz | Fastqsanger.gz | |||
| ERR647590.fastq.gz | Fastqsanger.gz | |||
| ERR647591.fastq.gz | Fastqsanger.gz | |||
| ERR647592.fastq.gz | Fastqsanger.gz | |||
| ERR647593.fastq.gz | Fastqsanger.gz |
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Project description:A highly complex set of 264 molecular spikes, based on 11 unique spike sequences spanning different lengths (570 to 3070 nts) and GC contents (40-60%) was designed. In order to be able to precisely evaluate quantification over different expression levels, transcript lengths and GC contents, barcodes of 7 nucleotides in 2-fold abundance steps were cloned into each spike sequence (12 steps in duplicates; 24 barcodes per sequence) creating a standard curve for each spike sequence. To determine the molecular abundance of each of the 264 molecular spike-ins (i.e., the ‘ground truth’), we performed an exhaustive sequencing across the spike barcodes and spUMIs and determined the total complexity in the pool to be 76 million unique molecules
2022-02-16 | E-MTAB-11448 | biostudies-arrayexpress
Project description:Deep sequencing of synthetic RNA (complex molecular spike-in set)
| PRJEB50953 | ENA
Project description:The development and the validation of innovative approaches for biomarker selection are of paramount importance in many -omics technologies. Unfortunately, the actual testing of new methods on real data is difficult, because in real data sets, one can never be sure about the “true” biomarkers. In this paper, we present a publicly available metabolomic ultra performance liquid chromatography–mass spectrometry spike-in data set for apples. The data set consists of 10 control samples and three spiked sets of the same size (G1,G2,G3), where naturally occurring compounds are added in different concentrations. In this sense, the data set can serve as a test bed to assess the performance of new algorithms (eg. biomarker selection, peak picking, etc.) and compare them with previously published results. The dataset includes the raw CDF files for all the injections. The raw data are organized in four assays: positive ion mode, negative ion mode, injections of the standard mixes (in both positive and negative ion modes). The spiked compounds are: epicatechin (CHEBI 90), catechin (CHEBI 15600), cyanidin-3-galactoside (CHEBI 27475), quercetin-3-galactoside (CHEBI 67486), quercetin-3-glucoside (CHEBI 28299), quercetin-3-rhamnoside (CHEBI 17558), resveratrol (CHEBI 27881), phloridzin (CHEBI 8113), quercetin (CHEBI 16243). All the compounds except resveratrol and cyanidin-3-galactoside are known to be present in the apple extracts. Spiking concentrations have been defined taking as a reference the ones measured in a pooled apple extract. A detailed description of the spiking concentration in the three group is included in the spiking.pdf file.
2013-10-24 | MTBLS59 | MetaboLights
Project description:Background.The cell-free methylated DNA immunoprecipitation-sequencing (cfMeDIP-seq) method, is adapted to work with low input DNA and with circulating cell-free DNA (cfDNA). This method allowsfor epigenetic profiling from liquid biopsy samples, providing potential information about tissue of origin. Similar to classical immunoprecipitation based enrichment protocols, interpretation requires a referenceor control to draw inference against a composite experimental baseline and against designed standards allowing for cross-experiment comparisons. Methods.To meet the need for a reference control in cfMeDIP-seqexperiments, we designed spike-in controlsand integrated the use of unique molecular index (UMI) to adjust for polymerase chain reaction (PCR)bias, and immunoprecipitation bias caused by the fragment length, G+C content, and CpG density ofthe DNA fragments. This enables for absolute quantification of methylated DNA in picomoles, while retaining epigenomic information that allows for sensitive, tissue-specific detection as well as comparableresults between different experiments. We designed 54 DNA fragments with combinations of methylationstatus (methylated and unmethylated), fragment length in base pair (bp) (80 bp,160 bp,320 bp), G+C content (35%,50%,65%), and fraction of CpGs within a fragment (1/80 bp,1/40 bp,1/20 bp). We checked spike-in control DNA sequence to ensure they had no cross alignment to the human genome and minimized formation of secondary structures to avoid issues with amplification. We carried outcfMeDIP-seq on either solely spike-in DNA fragments, spike-in DNA added to sheared HCT116 genomic DNA or spike-inDNA added tocfDNAfrom acute myeloid leukemia (AML) samples to assess technical and biological biases, determine optimal amount of spike-in DNA required for an experiment and to assess batch effects,respectively. Results. We show thatcfMeDIP-seqenriches for highly methylated regions, with less than 0.01%non-specific binding and preference to high G+C content and CpG fraction DNA fragments. The use of 0.01 ngof spike-in control DNA results in sufficient sequencing reads to adjust for variance due to fragment length,G+C content and CpG fraction without negatively impacting the number of sequencing reads generatedfor each sample. With known amount of each spike-in control, we generated a generalized linear modelthat can absolutely quantify molar amount from read counts while adjusting for fragment length, G+C content, and CpG fraction. Using our spike-in controls, we show that we can greatly mitigate batch effects,reducing batch associated variance in the data to ≤5%of the total variance. Conclusions.The incorporation of spike-in controls allows for easier interpretation of data generated from cfMeDIP-seq and MeDIP-seq experiments when compared to relative read count. Through the use of a generalized linear model tailored to each experiment, molar amount for each genomic region can becalculated, greatly mitigating both biological and technical biases in the data. We have created an Rpackage, spiky, to convert read counts to DNA picomoles while adjusting for fragment length, G+C contentand CpG fraction.
2021-02-16 | GSE166259 | GEO
Project description:Synthetic spike-in controls enable sensitive and reproducible cell-free methylome interrogation
| PRJNA699882 | ENA
Project description:We designed spike-in controls for cfMeDIP-seq experiments. These spike-in controls mitigate batch effects and allow for absolute quantification of cell-free DNA.
| EGAS00001005069 | EGA
Project description:This study aims to assess the reliability of spike-in normalization for analyzing single-cell RNA sequencing data. This is done by performing mixture experiments where two different sets of spike-in RNA (ERCC and SIRV) are added separately to a single mouse cell (416B or trophoblast stem cells (TSCs)), followed by generation of sequencing libraries using a modified version of the Smart-seq2 protocol. The aim is to measure the variance of the log-ratio of the total counts between the two spike-in sets. This will quantify how precisely the spike-in RNA was added to each well. As a control, addition was also performed with a premixed solution of both spike-ins, to quantify the variability in the log-ratios due to the experimental protocol. The same data can also be used to measure the well-to-well variability in the differences in behaviour between the two spike-in sets. The data contain four batches of libraries (block), using different batches of cells that were processed and sequenced separately. Each batch contains libraries with all three types of spike-in addition (ERCC+SIRV, SIRV+ERCC or Premixed). The 416B cells also contain a CBFB-MYH11 oncogene, which is expressed in half of the cells (Induced) and silent in the other half (Control).
2017-05-31 | E-MTAB-5522 | biostudies-arrayexpress
Project description:A key challenge in quantitative ChIP-seq is the normalisation of data in the presence of genome-wide changes. Data-based methods often rely on assumptions that do not hold true. Misapplication of these methods to ChIP-seq data results in the suppression of the biological signal or erroneous measurement of differential occupancy. To develop methods that address this challenge, we generated three ChIP-seq datasets, each measuring Estrogen Receptor-alpha (ER) binding in MCF7 before and after 100 nm Fulvestrant treatment for 48 hours. The three methods were: a novel internal control using CTCF binding to normalise (SLX-14229 & SLX-14438); a spike-in control using H2av binding to D. Melanogaster chromatin (SLX-8047); and a spike-in control using the cross reactivity of ER antibody against M. Musculus chromatin (SLX-12998).
2017-08-22 | GSE102882 | GEO
Project description:Synthetic spike-in standards for high-throughput 16S rRNA gene amplicon sequencing
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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