Generating high-quality chromatogram libraries for DIA-MS with empirically corrected peptide predictions.
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Orbitrap Fusion, Q Exactive
ORGANISM(S): Plasmodium Falciparum (ncbitaxon:5833) Saccharomyces Cerevisiae (ncbitaxon:4932)
SUBMITTER: 
Brian C. Searle
PROVIDER: MSV000084000 | MassIVE | Wed Jun 19 18:20:00 BST 2019
SECONDARY ACCESSION(S): PXD017705
REPOSITORIES: MassIVE
Similar Datasets
Project description: Not available
| S-EPMC7096433 | biostudies-literature
Project description:Targeted proteomics by selected/multiple reaction monitoring or, on a larger scale, by SWATH MS relies on spectral reference libraries for peptide identification. Quality and coverage of these libraries are therefore of critical importance. Here we present a detailed protocol that has been successfully used to build high-quality, extensive reference libraries supporting targeted proteomics by SWATH MS. We describe each step of the process, including data acquisition by discovery proteomics, assertion of peptide-spectrum matches, generation of consensus spectra and compilation of mass spectrometric coordinates that uniquely define each targeted peptide. Crucial steps of this process such as FDR control, retention time normalization and handling of post-translationally modified peptides are discussed in detail. Finally we show how to use the library to extract SWATH data with the open-source software Skyline. The protocol takes 2-3 days to complete, depending on the extent of the library and the computational resources available.
2015-01-26 | PXD001126 | Pride
Project description:Raw DDA and DIA data to support Searle et al. 2018. Comprehensive peptide quantification for data independent acquisition mass spectrometry using chromatogram libraries. bioRxiv doi:10.1101/277822
2018-08-08 | MSV000082805 | MassIVE
Project description:For the complete description see the manuscript. Data dependent acquisition (DDA) is the method of choice for mass spectrometry based proteomics discovery experiments, data-independent acquisition (DIA) is steadily becoming more important. One of the most important requirement to perform a DIA analysis is the availability of spectral libraries for the peptide identification and quantification. Several researches were already conducted regarding the creation of spectral libraries from DDA analyses and obtaining identifications with these in DIA measurements. But so far only few experiments were conducted, to estimate the effect of these libraries on the quantitative level. In this work we created a spike-in gold standard dataset with known contents and ratios of proteins in a complex sample matrix. With this dataset, we first created spectral libraries using different sample preparation approaches with and without sample prefractionation on peptide and protein level. Two different search engines were used for protein identification. In total, five different spike-in states were compared with DIA analyses, comparing eight different spectral libraries generated by varying approaches and one library free method, as well as one default DDA analysis. Not only the number of identifications on peptide and protein level in the spectral libraries and the corresponding analyses was inspected, but also the number of expected and identified significant quantifications and their ratios were thoroughly examined.
2019-11-08 | PXD012987 | Pride
Project description:Data-independent mass spectrometry is the method of choice for deep, consistent and accurate single-shot profiling in bottom-up proteomics. While classic workflows required auxiliary DDA-MS analysis of subject samples to derive prior knowledge spectral libraries for targeted quantification from DIA-MS maps, library-free approaches based on in silico predicted libraries promise deep DIA-MS profiling with reduced experimental effort and cost. Coverage and sensitivity in such analyses, however, is limited, in part, by large library size and persistent deviations from experimental data. We present MSLibrarian, a workflow and tool to obtain optimized predicted spectral libraries by the integrated usage of spectrum-centric DIA data interpretation via the DIA-Umpire approach to inform and calibrate the in silico predicted library approach. Predicted-vs-observed comparisons enable optimization of intensity prediction parameters, calibration of retention time prediction for deviating chromatographic setups and optimization of library scope and sample representativeness. Benchmarking via a dedicated ground-truth-embedded species mixture experiment and quantitative ratio-validation confirms gains of up to 9 % on precursor and 7 % protein level at equivalent FDR control and validation criteria. MSLibrarian has been implemented as open-source R software package and, with step-by-step usage instructions, is availabe at https://github.com/MarcIsak/MSLibrarian.
2022-01-17 | PXD028901 | Pride
Project description:Next-generation RNA-sequencing (RNA-seq) is rapidly emerging as the technology of choice for whole-transcriptome studies. However, RNA-seq is not a bias free technique. It requires large amounts of RNA and library preparation can introduce multiple artifacts, compounded by problems from later stages in the process. Nevertheless, RNA-seq is increasingly used in multiple studies, including the characterization of tissue-specific transcriptomes from invertebrate models of human disease. The generation of samples in this context is complex, involving the establishment of mutant strains and the delicate contamination prone process of dissecting the target tissue. Moreover, in order achieve the required amount of RNA, multiple samples need to be pooled. Such datasets pose extra challenges due to the large variability that may occur between similar pools, mostly due to the presence of cells from surrounding tissues. Therefore, in addition to standard quality control of RNA-seq data, analytical procedures for control of 'biological quality’ are critical for successful comparison of gene expression profiles. In this study, the transcriptome of the central nervous system of a Drosophila transgenic strain with neuronal-specific RNAi of a ubiquitous gene was profiled using RNA-seq. While conducting our investigation, we observed the existence of an unusual variance in the libraries and showed that the expression profile of a small panel of genes was sufficient to identify libraries with low levels of contamination from neighboring tissues, enabling the selection of a robust dataset for differential expression analysis. We additionally analyzed the potential of profiling a complex tissue to identify cell-type specific changes in response to target gene down-regulation. Finally, we demonstrated that trimming 5’ ends of reads decreases nucleotide frequency biases, increasing the coverage of protein coding genes with a potential positive impact in the incurrence of sampling errors.
2014-04-01 | GSE54724 | GEO
Project description:Targeted proteomics by selected/multiple reaction monitoring or, on a larger scale, by SWATH MS relies on spectral reference libraries for peptide identification. Quality and coverage of these libraries are therefore of critical importance. Here we present a detailed protocol that has been successfully used to build high-quality, extensive reference libraries supporting targeted proteomics by SWATH MS. We describe each step of the process, including data acquisition by discovery proteomics, assertion of peptide-spectrum matches, generation of consensus spectra and compilation of mass spectrometric coordinates that uniquely define each targeted peptide. Crucial steps of this process such as FDR control, retention time normalization and handling of post-translationally modified peptides are discussed in detail. Finally we show how to use the library to extract SWATH data with the open-source software Skyline. The protocol takes 2-3 days to complete, depending on the extent of the library and the computational resources available.
2018-06-26 | MSV000082506 | MassIVE
Project description:We have developed two methods for efficiently consructing RNA-seq libraries using transposition. Each method constructs high quality RNA-seq libraries when compared to standard approaches. One of the methods (Directional Tn-RNA-seq) maintains strand-of-origin information and exhibits strand specificity comparable to current approaches.
2011-10-10 | GSE32307 | GEO
Project description:Next-generation RNA-sequencing (RNA-seq) is rapidly emerging as the technology of choice for whole-transcriptome studies. However, RNA-seq is not a bias free technique. It requires large amounts of RNA and library preparation can introduce multiple artifacts, compounded by problems from later stages in the process. Nevertheless, RNA-seq is increasingly used in multiple studies, including the characterization of tissue-specific transcriptomes from invertebrate models of human disease. The generation of samples in this context is complex, involving the establishment of mutant strains and the delicate contamination prone process of dissecting the target tissue. Moreover, in order achieve the required amount of RNA, multiple samples need to be pooled. Such datasets pose extra challenges due to the large variability that may occur between similar pools, mostly due to the presence of cells from surrounding tissues. Therefore, in addition to standard quality control of RNA-seq data, analytical procedures for control of 'biological quality’ are critical for successful comparison of gene expression profiles. In this study, the transcriptome of the central nervous system of a Drosophila transgenic strain with neuronal-specific RNAi of a ubiquitous gene was profiled using RNA-seq. While conducting our investigation, we observed the existence of an unusual variance in the libraries and showed that the expression profile of a small panel of genes was sufficient to identify libraries with low levels of contamination from neighboring tissues, enabling the selection of a robust dataset for differential expression analysis. We additionally analyzed the potential of profiling a complex tissue to identify cell-type specific changes in response to target gene down-regulation. Finally, we demonstrated that trimming 5’ ends of reads decreases nucleotide frequency biases, increasing the coverage of protein coding genes with a potential positive impact in the incurrence of sampling errors. Central Nervous System profiles of third instar larvae WT (N=6), C24 (N=7), and X7/C24 D. melanogaster were generated by sequencing using the Illumina Hiseq2000.
2014-04-01 | E-GEOD-54724 | biostudies-arrayexpress