Systematic assessment of commercially available low-input miRNA library preparation kits
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ABSTRACT:
PROVIDER: PRJNA552326 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR9627580.fastq.gz | Fastqsanger.gz | |||
| SRR9627590.fastq.gz | Fastqsanger.gz | |||
| SRR9627600.fastq.gz | Fastqsanger.gz | |||
| SRR9627610.fastq.gz | Fastqsanger.gz | |||
| SRR9627620.fastq.gz | Fastqsanger.gz |
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Project description:An extension to: Systematic assessment of commercially available low-input miRNA library preparation kits
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Project description:We compared the performance of six commercial kits (QIAseq, SMARTer, CATS, CleanTag, srLp, TailorMix) capable of handling <100ng total RNA input for miRNA detection sensitivity, reliability, titration response and ability to detect differentially expressed miRNAs at different amounts of synthetic miRNAs. We observed differences in detection sensitivity between the kits, but none were able to detect the full repertoire of expected miRNAs. The reliability within the replicates of all kits was good, while larger differences were observed between the kits, although none could accurately quantify the majority of miRNAs.
2019-09-27 | GSE133719 | GEO
Project description:We compared the performance of two commercial kits (NEBNext and NEXTflex) for miRNA detection sensitivity, reliability, titration response and ability to detect differentially expressed miRNAs at different amounts of synthetic miRNAs. We observed differences in detection sensitivity between the kits, but none were able to detect the full repertoire of expected miRNAs. The reliability within the replicates of all kits was good, while larger differences were observed between the kits, although none could accurately quantify the majority of miRNAs. In a previous publication (https://doi.org/10.1080/15476286.2019.1667741; GEO accession number GSE133719) we accessed the performance of six additional miRNA library preparation kits.
2020-05-31 | GSE141658 | GEO
Project description:We evaluated the effect of the small RNA library preparation method on 5' tRNA-halves and miRNA abundance in libraries prepared from serum RNA using three commercially available small RNA library preparation kits (TruSeq small RNA library preparation kit v2 (Illumina), TailorMix miRNA sample preparation kit v2 (Seqmatic) and the NEBNext Multiplex Small RNA library prep kit (New England Biolabs)). RNA isolated from 100 µl of serum collected from healthy mice was used as input for the preparation of a small RNA library in duplicate and libraries were single end sequenced.
2017-02-22 | GSE88914 | GEO
Project description:High-throughput sequencing is increasingly favoured to assay the presence and abundance of microRNAs (miRNAs) in biological samples, even from low RNA amounts, and a number of commercial vendors now offer kits that allow miRNA sequencing from sub-nanogram (ng) inputs. Although biases introduced during library preparation have been documented, the relative performance of current reagent kits has not been investigated in detail. Here, six commercial kits capable of handling <100ng total RNA input were used for library preparation, performed by kit manufactures, on synthetic miRNAs of known quantities and human total RNA samples. We compared the performance of miRNA detection sensitivity, reliability, titration response and the ability to detect differentially expressed miRNAs. In addition, we assessed the use of unique molecular identifiers (UMI) sequence tags in one kit. We observed differences in detection sensitivity and ability to identify differentially expressed miRNAs between the kits, but none were able to detect the full repertoire of synthetic miRNAs. The reliability within the replicates of all kits was good, while larger differences were observed between the kits, although none could accurately quantify the relative levels of the majority of miRNAs. UMI tags, at least within the input ranges tested, offered little advantage to improve data utility. In conclusion, biases in miRNA abundance are heavily influenced by the kit used for library preparation, suggesting that comparisons of datasets prepared by different procedures should be made with caution. This article is intended to assist researchers select the most appropriate kit for their experimental conditions.
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Project description:Plant small RNAs are a diverse and complex set of molecules, ranging in length from 21 to 24 nt, involved in a wide range of essential biological processes. Nowadays, high-throughput sequencing is the most commonly used method for the discovery and quantification of small RNAs. However, it is known that several biases can occur during the preparation of small RNA libraries, especially using low input RNA. We used two types of plant biological samples to evaluate the performance of seven commercially available methods for small RNA library construction, using different RNA input amounts. We show that when working with plant material, library construction methods have differing capabilities to capture small RNAs, and that different library construction methods provide better results when applied to the detection of microRNAs, phased small RNAs, or tRNA-derived fragments.
2021-08-01 | GSE156973 | GEO
Project description:Library preparation is a key step in gene expression quantification. There are considerable advantages to both strand specific sequencing and the ability to sequence samples with very small amounts of starting material. Until recently there was no kit available that allowed both simultaneously. The standard Illumina-TruSeq stranded mRNA Sample Preparation kit requires abundant starting quantity while the Takara Bio-SMART-Seq® v4 Ultra® Low Input RNA kit allows for ultra low starting quantities but sacrifices strand specificity. Recently a kit that can do both, SMARTer® Stranded Total RNA-Seq Kit v2 - Pico Input Mammalian by Takara Bio, has become available. Evaluating the performance and effects of these sample preparation kits is a critical determinant for selecting the appropriate sequencing protocol, but a comprehensive comparison is currently missing. To address this we performed a detailed comparative analysis of sequencing libraries prepared with the three kits. We prepared a set of samples representing two experimental conditions with each kit, allowing for comparison of the kits in a standard realistic differential expression analysis. We find substantial differences in the levels of alignment and differential gene expression. Using differential expression analysis we show that using Pico results in identifying 55% less differentially expressed genes than TruSeq. Nevertheless, using gene pathway enrichment analysis we find similar results for all three kits, indicating that ultimately comparable functional results can be reached.
2019-09-17 | GSE124167 | GEO
Project description:BackgroundEvolving interest in comprehensively profiling the full range of small RNAs present in small tissue biopsies and in circulating biofluids, and how the profile differs with disease, has launched small RNA sequencing (RNASeq) into more frequent use. However, known biases associated with small RNASeq, compounded by low RNA inputs, have been both a significant concern and a hurdle to widespread adoption. As RNASeq is becoming a viable choice for the discovery of small RNAs in low input samples and more labs are employing it, there should be benchmark datasets to test and evaluate the performance of new sequencing protocols and operators. In a recent publication from the National Institute of Standards and Technology, Pine et al., 2018, the investigators used a commercially available set of three tissues and tested performance across labs and platforms.ResultsIn this paper, we further tested the performance of low RNA input in three commonly used and commercially available RNASeq library preparation kits; NEB Next, NEXTFlex, and TruSeq small RNA library preparation. We evaluated the performance of the kits at two different sites, using three different tissues (brain, liver, and placenta) with high (1 μg) and low RNA (10 ng) input from tissue samples, or 5.0, 3.0, 2.0, 1.0, 0.5, and 0.2 ml starting volumes of plasma. As there has been a lack of robust validation platforms for differentially expressed miRNAs, we also compared low input RNASeq data with their expression profiles on three different platforms (Abcam Fireplex, HTG EdgeSeq, and Qiagen miRNome).ConclusionsThe concordance of RNASeq results on these three platforms was dependent on the RNA expression level; the higher the expression, the better the reproducibility. The results provide an extensive analysis of small RNASeq kit performance using low RNA input, and replication of these data on three downstream technologies.
| S-EPMC5936030 | biostudies-literature
Project description:High-throughput sequencing has emerged as the favoured method to study microRNA (miRNA) expression, but biases introduced during library preparation have been reported. We recently compared the performance (sensitivity, reliability, titration response and differential expression) of six commercially-available kits on synthetic miRNAs and human RNA, where library preparation was performed by the vendors. We hereby supplement this study with data from two further commonly used kits (NEBNext, NEXTflex) whose manufacturers initially declined to participate. NEXTflex demonstrated the highest sensitivity, which may reflect its use of partially-randomized adapter sequences, but overall performance was lower than the QIAseq and TailorMix kits. NEBNext showed intermediate performance. We reaffirm that biases are kit specific, complicating the comparison of miRNA datasets generated using different kits.
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Project description:Fungal community from commercially available Reishi products Metagenome
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