Ontology highlight
ABSTRACT:
PROVIDER: PRJNA791831 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR17322700.fastq.gz | Fastqsanger.gz | |||
| SRR17322800.fastq.gz | Fastqsanger.gz | |||
| SRR17322900.fastq.gz | Fastqsanger.gz | |||
| SRR17323000.fastq.gz | Fastqsanger.gz | |||
| SRR17323100.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 1240 |
Similar Datasets
Project description:Formalin-fixed, paraffin-embedded (FFPE) tissues are an invaluable resource for retrospective studies but protein extraction and subsequent sample processing steps have shown to be challenging for mass spectrometry (MS) analysis. Streamlined high-throughput sample preparation workflows are essential for efficient peptide extraction from complex clinical specimens such as fresh frozen tissues or FFPE. Overall, proteome analysis has gained significant improvements in the instrumentation, acquisition methods, sample preparation workflows and analysis pipelines yet even the most recent FFPE workflows remain complex and are not readily scalable. Here, we present an optimized workflow for Automated Sonication-free Acid-assisted Proteome (ASAP) extraction from FFPE sections. ASAP enables efficient protein extraction from FFPE specimens achieving similar proteome coverage as established methods using time in equipment-heavy sonication-based methods at reduced sample processing time. The broad applicability of ASAP on archived pediatric tumor FFPE specimens resulted in high-quality data with increased proteome coverage and quantitative reproducibility. Our study demonstrates the practicality and superiority of the ASAP workflow as a streamlined, time and cost-effective pipeline for high-throughput FFPE proteomics of clinical specimens.
2024-05-21 | PXD036910 | Pride
Project description:Current methods for R-loop mapping need to perform DNA:RNA immunoprecipitation for each sample individually, with consequent limitations in throughput. Here, we develop and validate mDRIP-seq, a multi-sample barcoding and pooling method for R-loop mapping. We show mDRIP-seq performs equivalently as conventional methods, but with the merits of high throughput and cost-efficiency. We also show the simplicity of mDRIP-seq for relative and absolute quantitation of genomic R-loop fractions for multiple samples. Together, mDRIP-seq is a high-throughput and cost-efficient method for R-loop mapping and quantitative assessment and can be widely applied to large-scale dynamic profiles of these important structures for diverse organisms.
2024-06-11 | GSE219068 | GEO
Project description:Current methods for R-loop mapping need to perform DNA:RNA immunoprecipitation for each sample individually, with consequent limitations in throughput. Here, we develop and validate mDRIP-seq, a multi-sample barcoding and pooling method for R-loop mapping. We show mDRIP-seq performs equivalently as conventional methods, but with the merits of high throughput and cost-efficiency. We also show the simplicity of mDRIP-seq for relative and absolute quantitation of genomic R-loop fractions for multiple samples. Together, mDRIP-seq is a high-throughput and cost-efficient method for R-loop mapping and quantitative assessment and can be widely applied to large-scale dynamic profiles of these important structures for diverse organisms.
2024-06-12 | GSE219069 | GEO
Project description:Current methods for R-loop profiling need to perform experiments for each sample individually, with consequent limitations in throughput. Here, based on the barcoding strategy, we develop mDRIP-seq, a high-throughput method showing equivalent performance as conventional methods, but with merits of 7-fold less cost and 6-fold less hand-on time per sample. We also show the simplicity and effectiveness of mDRIP-seq for relative and absolute quantitation of genomic R-loop fractions for multiple samples. Together, mDRIP-seq is a high-throughput and cost-efficient method for R-loop mapping and quantitative assessment that can be widely applied to large-scale dynamic profiles of these important structures for diverse organisms.
2024-06-11 | GSE219071 | GEO
Project description:Current methods for R-loop mapping need to perform DNA:RNA immunoprecipitation for each sample individually, with consequent limitations in throughput. Here, we develop and validate mDRIP-seq, a multi-sample barcoding and pooling method for R-loop mapping. We show mDRIP-seq performs equivalently as conventional methods, but with the merits of high throughput and cost-efficiency. We also show the simplicity of mDRIP-seq for relative and absolute quantitation of genomic R-loop fractions for multiple samples. Together, mDRIP-seq is a high-throughput and cost-efficient method for R-loop mapping and quantitative assessment and can be widely applied to large-scale dynamic profiles of these important structures for diverse organisms.
2024-06-12 | GSE219070 | GEO
Project description:Current methods for R-loop mapping need to perform DNA:RNA immunoprecipitation for each sample individually, with consequent limitations in throughput. Here, we develop and validate mDRIP-seq, a multi-sample barcoding and pooling method for R-loop mapping. We show mDRIP-seq performs equivalently as conventional methods, but with the merits of high throughput and cost-efficiency. We also show the simplicity of mDRIP-seq for relative and absolute quantitation of genomic R-loop fractions for multiple samples. Together, mDRIP-seq is a high-throughput and cost-efficient method for R-loop mapping and quantitative assessment and can be widely applied to large-scale dynamic profiles of these important structures for diverse organisms.
2024-06-12 | GSE246423 | GEO
Project description:Affinity capture of DNA methylation combined with high-throughput sequencing strikes a good balance between the high cost of whole genome bisulfite sequencing and the low coverage of methylation arrays. We present BayMeth, an empirical Bayes approach that uses a fully methylated control sample to transform observed read counts into regional methylation levels. In our model, inefficient capture can readily be distinguished from low methylation levels. BayMeth improves on existing methods, allows explicit modeling of copy number variation, and offers computationally-efficient analytical mean and variance estimators. BayMeth is available in the Repitools Bioconductor package.
2014-01-25 | GSE54375 | GEO
Project description:Affinity capture of DNA methylation combined with high-throughput sequencing strikes a good balance between the high cost of whole genome bisulfite sequencing and the low coverage of methylation arrays. We present BayMeth, an empirical Bayes approach that uses a fully methylated control sample to transform observed read counts into regional methylation levels. In our model, inefficient capture can readily be distinguished from low methylation levels. BayMeth improves on existing methods, allows explicit modeling of copy number variation, and offers computationally-efficient analytical mean and variance estimators. BayMeth is available in the Repitools Bioconductor package. Benchmarking samples to compare MBD- and MeDIP-seq [GSE38679, GSE24546; PMID 21045081] datasets against 450k measurements
2014-01-25 | E-GEOD-54375 | biostudies-arrayexpress
Project description:Proteomic methods typically involve lengthy, multi-step sample preparation protocols (14-16 hrs), especially for the lysis and digestion of solid tissue samples or cells. We developed a streamlined proteomic sample preparation protocol termed Accelerated Barocycler Lysis and Extraction (ABLE), that substantially reduces the time and cost of tissue sample processing. ABLE is based on pressure cycling technology (PCT) for rapid tissue solubilisation and reliable, controlled proteolytic digestion. Here, the previously reported PCT protocol was optimised using 1-4 mg biopsy punches from rat kidney. The tissue denaturant urea was substituted with a combination of sodium deoxycholate (SDC) and N-propanol. ABLE produced comparable numbers of protein identifications in half the sample preparation time and with reduced cost, being ready for MS injection in 3 hrs (vs the conventional urea PCT method of 6 hrs). To validate the method, it was applied across a diverse range of rat tissues (kidney, lung, muscle, brain, testis), human HEK 293 cells and ovarian tumours by coupling PCT with SWATH-mass spectrometry (SWATH-MS). There were similar numbers of quantified proteins between ABLE-SWATH and PCT-SWATH methods, with greater than 70% overlap for all sample types, except muscle with 58% overlap. The ABLE tissue processing protocol offers a standardised, high-throughput, efficient and reproducible proteomic preparation method, accelerating sample throughput for any type of MS analysis. Coupled with SWATH-MS, ABLE has the potential to accelerate proteomics analysis towards a clinically relevant turn-around-time.
2019-01-21 | PXD010688 | Pride