Ontology highlight
ABSTRACT:
PROVIDER: PRJNA660476 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR12664000_1.fastq.gz | Fastqsanger.gz | |||
| SRR12664000_2.fastq.gz | Fastqsanger.gz | |||
| SRR12664001_1.fastq.gz | Fastqsanger.gz | |||
| SRR12664001_2.fastq.gz | Fastqsanger.gz | |||
| SRR12664002_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 152 |
Similar Datasets
Project description:Formalin-fixed, paraffin-embedded (FFPE) tissues have many advantages for identification of risk biomarkers, including wide availability and potential for extended follow-up endpoints. However, RNA derived from archival FFPE samples has limited quality. Here we identified parameters that determine which FFPE samples have the potential for successful RNA extraction, library preparation, and generation of usable RNAseq data. We optimized library preparation protocols designed for use with FFPE samples using seven FFPE and Fresh Frozen replicate pairs, and tested optimized protocols using a study set of 130 FFPE biopsies from women with benign breast disease. Metrics from RNA extraction and preparation procedures were collected and compared with bioinformatics sequencing summary statistics. Finally, a decision tree model was built to learn the relationship between pre-sequencing lab metrics and qc pass/fail status as determined by bioinformatics metrics.. Samples that failed bioinformatics qc tended to have low median sample-wise correlation within the cohort (Spearman correlation < 0.75), low number of reads mapped to gene regions (< 25 million), or low number of detectable genes (11,400 # of detected genes with TPM > 4). The median RNA concentration and pre-capture library Qubit values for qc failed samples were 18.9 ng/ul and 2.08 ng/ul respectively, which were significantly lower than those of qc pass samples (40.8 ng/ul and 5.82 ng/ul). We built a decision tree model based on input RNA concentration, input library qubit values, and achieved an F score of 0.848 in predicting QC status (pass/fail) of FFPE samples. We provide a bioinformatics quality control recommendation for FFPE samples from breast tissue by evaluating bioinformatic and sample metrics. Our results suggest a minimum concentration of 25 ng/ul FFPE-extracted RNA for library preparation and 1.7 ng/ul pre-capture library output to achieve adequate RNA-seq data for downstream bioinformatics analysis.
2023-09-29 | GSE244325 | GEO
Project description:We deployed CapTS-seq for sequencing synthetic miRNAs, human total brain RNA, and liver FFPE RNA, and demonstrated that chemical capping in conjunction with template switching consistently reduces sequencing bias and improves library quality in comparison with commercially available RNA-seq kits. Finally, we showed the simultaneous detection of miRNAs and mRNAs in FFPE derived samples, underscoring the potential of this workflow for dissecting regulatory networks between miRNAs and their target gene transcripts.
2021-09-13 | GSE171049 | GEO
Project description:Formalin-fixed, paraffin-embedded (FFPE) tissues are an underused resource for molecular analyses. This proof of concept study aimed to compare RNAseq results from FFPE biopsies with the corresponding RNAlater® (Qiagen, Germany) stored samples from clear cell renal cell carcinoma (ccRCC) patients, to enable the decision of whether or not it is feasible to apply RNAseq technology to archival tissue. Core biopsies were obtained with a 16g needle from 16 patients undergoing partial or full nephrectomy. The RNA sequencing libraries were generated applying the new Illumina TruSeq® Access library preparation protocol.
2016-01-08 | GSE76207 | GEO
Project description:Advantages of RNA-Seq over array based platforms are quantitative gene expression and discovery of expressed single nucleotide variants (eSNVs) and fusion transcripts from a single platform, but the sensitivity for each of these characteristics is unknown. We measured gene expression in a set of manually degraded RNAs, nine pairs of matched fresh-frozen, and FFPE RNA isolated from breast tumor with the hybridization based, NanoString nCounter, (226 gene panel) and with whole transcriptome RNA-Seq using RiboZeroGold ScriptSeq V2 library preparation kits. We performed correlation analyses of gene expression between samples and across platforms. We then specifically assessed whole transcriptome expression of lincRNA and discovery of eSNVs and fusion transcripts in the FFPE RNA-Seq data. For gene expression in the manually degraded samples, we observed Pearson correlation of >0.94 and >0.80 with NanoString and ScriptSeq protocols respectively. Gene expression data for matched fresh-frozen and FFPE samples yielded mean Pearson correlations of 0.874 and 0.783 for NanoString (226 genes) and ScriptSeq whole transcriptome protocols respectively. Specifically for lincRNAs, we observed superb Pearson correlation (0.988) between matched fresh-frozen and FFPE pairs. FFPE samples across NanoString and RNA-Seq platforms gave a mean Pearson correlation of 0.838. In FFPE libraries, we detected 53.4% of high confidence SNVs and 24% of high confidence fusion transcripts. Sensitivity of fusion transcript detection was not overcome by an increase in depth of sequencing up to 3-fold (increase from ~56 to ~159 million reads). Both NanoString and ScriptSeq RNA-Seq technologies yield reliable gene expression data for degraded and FFPE material. The high degree of correlation between NanoString and RNA-Seq platforms suggests discovery based whole transciptome studies from FFPE material will produce reliable expression data. The RiboZeroGold ScriptSeq protocol performed particularly well for lincRNA expression from FFPE libraries but detection of eSNV and fusion transcripts was less sensitive. We performed RNASeq on RNA from nine matched pairs of fresh-frozen and FFPE tissues from breast cancer patients. The goal was to test the RiboZeroGold ScriptSeq complete low input library preparation kit for degraded RNA samples.
2013-12-06 | E-GEOD-51124 | biostudies-arrayexpress
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: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:RNA-Seq technique was applied to investigate the effects of four cDNA amplification kits and two RNA-Seq library preparation kits to the deep sequencing results at different perspectives.
2015-05-09 | GSE57503 | GEO
Project description:We evaluated the performance of 5 library prep protocols by using total mRNA and IP RNA of mouse liver,we found all the 5 library preparation kits detect more enrichment effects than depletion effect. The profiles being generated by SMARTer kit is different than all other kits.
2017-10-30 | GSE104213 | GEO
Project description:Transcriptome analysis via RNA sequencing (RNA-seq) has become a standard technique employed across a variety of biological fields of study. This rapid adoption of the RNA-seq approach has been mediated, in part, by the development of various commercial RNA-seq library preparation kits compatible with common next-generation sequencing (NGS) platforms. Generally, the essential steps of library preparation such as rRNA depletion and first-strand cDNA synthesis are tailored to a certain group of organisms (e.g. eukaryotes versus prokaryotes) or genomic GC content. Therefore, selection of appropriate commercial products is of crucial importance to capture the transcriptome of interest as closely to the native state as possible without introduction of technical bias. However, researchers rarely have the resources and time to test various commercial RNA-seq kits for their samples. In this work, we report a side-by-side comparison of RNA-seq data from the same Clostridium autoethanogenum input samples obtained using three commercial rRNA removal and strand-specific library construction products by NuGEN Technologies, Qiagen, and Zymo Research, and compare their performance with published data. While all three vendors advertise their products as suitable for prokaryotes, we found significant differences in their performance in terms of rRNA removal, strand-specificity, and, most importantly, transcript abundance distribution profiles. Notably, RNA-seq data obtained with Qiagen products were most similar to published data and delivered the best results in terms of library strandedness and transcript abundance distribution range. Our results highlight the importance of finding appropriate organism-specific workflows and library preparation products for RNA-seq studies.
2022-07-20 | GSE200959 | GEO