Systematic evaluation of RNA-Seq preparation protocol performance (RNASeq: SMARTer)
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ABSTRACT:
PROVIDER: PRJNA543483 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR9080300_1.fastq.gz | Fastqsanger.gz | |||
| SRR9080300_2.fastq.gz | Fastqsanger.gz | |||
| SRR9080301_1.fastq.gz | Fastqsanger.gz | |||
| SRR9080301_2.fastq.gz | Fastqsanger.gz | |||
| SRR9080302_1.fastq.gz | Fastqsanger.gz |
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Project description:In this study, we present a comprehensive evaluation of four RNA-Seq library preparation methods. We used three standard input protocols, the Illumina TruSeq Stranded Total RNA and TruSeq Stranded mRNA kits, and a modified NuGEN Ovation v2 kit; and an ultra-low-input RNA protocol, the TaKaRa SMARTer Ultra Low RNA Kit v3. Our evaluation of these kits included quality control measures such as overall reproducibility, 5’ and 3’ end-bias, and the identification of DEGs, lncRNAs, and alternatively spliced transcripts. Overall, we found that the two Illumina kits were most similar in terms of recovering DEGs, and the Illumina, modified NuGEN, and TaKaRa kits allowed identification of a similar set of DEGs. However, we also discovered that the Illumina, NuGEN and TaKaRa kits each enriched for different sets of genes.
2019-06-27 | GSE131396 | GEO
Project description:Systematic evaluation of RNA-Seq preparation protocol performance
| PRJNA543480 | ENA
Project description:In this study, we present a comprehensive evaluation of four RNA-Seq library preparation methods. We used three standard input protocols, the Illumina TruSeq Stranded Total RNA and TruSeq Stranded mRNA kits, and a modified NuGEN Ovation v2 kit; and an ultra-low-input RNA protocol, the TaKaRa SMARTer Ultra Low RNA Kit v3. Our evaluation of these kits included quality control measures such as overall reproducibility, 5’ and 3’ end-bias, and the identification of DEGs, lncRNAs, and alternatively spliced transcripts. Overall, we found that the two Illumina kits were most similar in terms of recovering DEGs, and the Illumina, modified NuGEN, and TaKaRa kits allowed identification of a similar set of DEGs. However, we also discovered that the Illumina, NuGEN and TaKaRa kits each enriched for different sets of genes.
2019-06-27 | GSE131397 | GEO
Project description:Systematic evaluation of RNA-Seq preparation protocol performance (RNASeq: TruSeq)
| PRJNA543482 | ENA
Project description:This SuperSeries is composed of the SubSeries listed below.
2019-06-27 | GSE131398 | 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:BackgroundRNA-Seq is currently the most widely used tool to analyze whole-transcriptome profiles. There are numerous commercial kits available to facilitate preparing RNA-Seq libraries; however, it is still not clear how some of these kits perform in terms of: 1) ribosomal RNA removal; 2) read coverage or recovery of exonic vs. intronic sequences; 3) identification of differentially expressed genes (DEGs); and 4) detection of long non-coding RNA (lncRNA). In RNA-Seq analysis, understanding the strengths and limitations of commonly used RNA-Seq library preparation protocols is important, as this technology remains costly and time-consuming.ResultsIn this study, we present a comprehensive evaluation of four RNA-Seq kits. We used three standard input protocols: Illumina TruSeq Stranded Total RNA and mRNA kits, a modified NuGEN Ovation v2 kit, and the TaKaRa SMARTer Ultra Low RNA Kit v3. Our evaluation of these kits included quality control measures such as overall reproducibility, 5' and 3' end-bias, and the identification of DEGs, lncRNAs, and alternatively spliced transcripts. Overall, we found that the two Illumina kits were most similar in terms of recovering DEGs, and the Illumina, modified NuGEN, and TaKaRa kits allowed identification of a similar set of DEGs. However, we also discovered that the Illumina, NuGEN and TaKaRa kits each enriched for different sets of genes.ConclusionsAt the manufacturers' recommended input RNA levels, all the RNA-Seq library preparation protocols evaluated were suitable for distinguishing between experimental groups, and the TruSeq Stranded mRNA kit was universally applicable to studies focusing on protein-coding gene profiles. The TruSeq protocols tended to capture genes with higher expression and GC content, whereas the modified NuGEN protocol tended to capture longer genes. The SMARTer Ultra Low RNA Kit may be a good choice at the low RNA input level, although it was inferior to the TruSeq mRNA kit at standard input level in terms of rRNA removal, exonic mapping rates and recovered DEGs. Therefore, the choice of RNA-Seq library preparation kit can profoundly affect data outcomes. Consequently, it is a pivotal parameter to consider when designing an RNA-Seq experiment.
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Project description:The continuing ‘desire’ in obtaining more quantitative and detailed information from cellular proteomics experiments in regards to proteome coverage and protein modifications asks for a systematic investigation of the protein extraction and digestion protocols, in particular when working with unicellular organisms with a strong cell wall such as the model yeast S. cerevisiae. The selection of a suitable sample preparation workflow is crucial to obtain in-depth proteome coverage, as the use of certain sample preparation protocols may bias the protein identification or induce (unexpected) peptide modifications. Here, we made an extensive comparison of preparation workflows commonly applied to S. cerevisiae. Workflows were compared on the basis of identified MS/MS spectra, peptide sequences and number and type of modifications using both restricted (Peaks) and unrestricted (TagGraph) database search approaches. The proteome coverage was mainly affected by the sample collection approach, while it was maximized using a FASP. Extensive reagent specific peptide modifications were detected when using formic acid, but also when using acetone. Such artefacts split the analyte mass signals and generate additional chemical noise that may also elute differently compared to the native peptide. The use of both an unrestricted and restricted database search increased identification rates significantly and resulted in the identification of approximately 70% of the MS2 spectra for the best protocol. The unidentified spectra were assessed by their de novo sequencing score. This confirmed that those spectra consisted by a majority of very low quality spectra, insufficient to match to database sequences. However, a small fraction of the unidentified spectra showed high quality, which presumably derive from unknown sequence variants not present in the database. This study demonstrates the high importance of the sample preparation workflow and the obtained results will guide researchers in the field to optimize sample preparation procedures.
2022-03-31 | PXD026806 | Pride
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