Comparative evaluation of label-free quantification strategies
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo Sapiens Escherichia Coli
SUBMITTER: 
Bangce Ye
PROVIDER: PXD019408 | iProX | Wed May 27 00:00:00 GMT+01:00 2020
REPOSITORIES: iProX
Similar Datasets
Project description:Performance evaluation of Arabidopsis scRNA-seq sample processing strategies
| PRJNA1279991 | ENA
Project description:MS1-based label-free quantification can compare precursor ion peaks across runs, allowing reproducible protein measurements. Among bioinformatic platforms enabling MS1-based quantification, MaxQuant (MQ) is one of the most used, while Proteome Discoverer (PD) has recently introduced the Minora tool. Here, we present a comparative evaluation of six MS1-based quantification methods available in MQ and PD. Intensity (MQ and PD) and area (PD only) of the precursor ion peaks were measured and then subjected or not to normalization. The six methods were applied to datasets simulating various differential proteomics scenarios and covering a wide range of protein abundance ratios and concentrations. PD outperformed MQ in terms of quantification yield, dynamic range, and reproducibility, although neither platform reached a fully satisfactory quality of measurements at low-concentration ranges. PD methods including normalization were the most accurate in estimating the abundance ratio between groups and the most sensitive when comparing groups with a narrow abundance ratio; on the contrary, MQ methods generally reached slightly higher specificity, accuracy, and precision values. Moreover, we found that applying an optimized log ratio-based threshold can maximize specificity, accuracy, and precision. Taken together, these results can help researchers choose the most appropriate MS1-based protein quantification strategy for their studies.
2021-05-31 | PXD022169 | Pride
Project description:The DDA and DIA data for accuracy evaluation of absolute quantification algorithms.
2018-05-08 | PXD009719 |
Project description:Mycobacterium tuberculosis employs several strategies to combat and adapt to adverse conditions encountered inside the host. The non-replicative dormant state of the bacterium is linked to drug resistance and slower response to anti-tubercular therapy. It is known that alterations in lipid content allow dormant bacteria to acclimatize to cellular stress. Employing comparative lipidomic analysis we profiled the changes in lipid metabolism in M. tuberculosis using a modified Wayne's model of hypoxia-induced dormancy. Further we subjected the dormant bacteria to resuscitation, and analyzed their lipidomes until the lipid profile was similar to that of normoxially grown bacteria. An enhanced degradation of cell wall-associated and cytoplasmic lipids during dormancy, and their gradual restoration during reactivation, were clearly evident. This study throws light on distinct lipid metabolic patterns that M. tuberculosis undergoes to maintain its cellular energetics during dormancy and reactivation.
2018-10-22 | MTBLS304 | MetaboLights
Project description:Ribonucleotides incorporated in the genome are a source of endogenous DNA damage, and also serve as signals for repair. Although recent advances of ribonucleotide detection by sequencing, the balance between incorporation and repair of ribonucleotides has not been elucidated. Here, we describe a competitive sequencing method, Ribonucleotide Scanning Quantification sequencing (RiSQ-seq), which enables absolute quantification of misincorporated ribonucleotides throughout the genome by background normalization and standard adjustment within a single sample. RiSQ-seq analysis of cells harboring wild-type DNA polymerases revealed that ribonucleotides were incorporated non-uniformly in the genome with a 3’-shifted distribution and preference for GC sequences. Although ribonucleotide profiles in wild-type and repair-deficient mutant strains showed a similar pattern, direct comparison of distinct ribonucleotide levels in the strains by RiSQ-seq enabled evaluation of ribonucleotide excision repair activity at base resolution and revealed the strand bias of repair. The distinct preferences of ribonucleotide incorporation and repair create vulnerable regions associated with indel hotspots, suggesting that repair at sites of ribonucleotide misincorporation serves to maintain genome integrity and that RiSQ-seq can provide an estimate of indel risk.
2021-05-21 | GSE85130 | GEO
Project description:Primary outcome(s): Comparative evaluation of the existing cfDNA quantification method based on clinical samples and this method
Study Design: Observational Study Model : Cohort, Time Perspective : Prospective, Enrollment : 20, Biospecimen Retention : Collect & Archive- Sample without DNA, Biospecimen Description : serum
| 2513434 | ecrin-mdr-crc
Project description:Label-free quantification based on data-independent acquisition (DIA) workflows is increasingly popular. Several software tools have been recently published or are commercially available. The present study focuses on the critical evaluation of three different software packages (Progenesis, synapter and ISOQuant) supporting ion-mobility enhanced DIA data. In order to benchmark the label-free quantification performance of the different tools, we generated two hybrid proteome samples of defined quantitative composition containing tryptically digested proteomes of three different species (mouse, yeast, E.coli). This model data set simulates complex biological samples containing large numbers of both unregulated (background) proteins as well as up- and down-regulated proteins with exactly known ratios between samples. We determined the number and dynamic range of quantifiable proteins and analyzed the influence of applied algorithms (retention time alignment, clustering, normalization, etc.) on the variation of reported protein quantities between technical replicates.
2017-04-06 | PXD001240 | Pride
Project description:<p>Liquid chromatography coupled to mass spectrometry (LC-MS) has become a standard technology in metabolomics. In particular, label-free quantification based on LC-MS is easily amenable to large-scale studies and thus well suited to clinical metabolomics. Large-scale studies, however, require automated processing of the large and complex LC-MS datasets. We present a novel algorithm for the detection of mass traces and their aggregation into features (i.e. all signals caused by the same analyte species) that is computationally efficient and sensitive and that leads to reproducible quantification results. The algorithm is based on a sensitive detection of mass traces, which are then assembled into features based on mass-to-charge spacing, co-elution information, and a support vector machine–based classifier able to identify potential metabolite isotope patterns. The algorithm is not limited to metabolites but is applicable to a wide range of small molecules (e.g. lipidomics, peptidomics), as well as to other separation technologies. We assessed the algorithm's robustness with regard to varying noise levels on synthetic data and then validated the approach on experimental data investigating human plasma samples. We obtained excellent results in a fully automated data-processing pipeline with respect to both accuracy and reproducibility. Relative to state-of-the art algorithms, ours demonstrated increased precision and recall of the method. The algorithm is available as part of the open-source software package OpenMS and runs on all major operating systems.</p><p><br></p><p><strong>Simulated data</strong> is reported in the current study <a href='https://www.ebi.ac.uk/metabolights/MTBLS235' rel='noopener noreferrer' target='_blank'><strong>MTBLS235</strong></a>.</p><p><strong>Plasma data</strong> is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS234' rel='noopener noreferrer' target='_blank'><strong>MTBLS234</strong></a>.</p>
2015-12-16 | MTBLS235 | MetaboLights
Project description:<p>Liquid chromatography coupled to mass spectrometry (LC-MS) has become a standard technology in metabolomics. In particular, label-free quantification based on LC-MS is easily amenable to large-scale studies and thus well suited to clinical metabolomics. Large-scale studies, however, require automated processing of the large and complex LC-MS datasets. We present a novel algorithm for the detection of mass traces and their aggregation into features (i.e. all signals caused by the same analyte species) that is computationally efficient and sensitive and that leads to reproducible quantification results. The algorithm is based on a sensitive detection of mass traces, which are then assembled into features based on mass-to-charge spacing, co-elution information, and a support vector machine–based classifier able to identify potential metabolite isotope patterns. The algorithm is not limited to metabolites but is applicable to a wide range of small molecules (e.g. lipidomics, peptidomics), as well as to other separation technologies. We assessed the algorithm's robustness with regard to varying noise levels on synthetic data and then validated the approach on experimental data investigating human plasma samples. We obtained excellent results in a fully automated data-processing pipeline with respect to both accuracy and reproducibility. Relative to state-of-the art algorithms, ours demonstrated increased precision and recall of the method. The algorithm is available as part of the open-source software package OpenMS and runs on all major operating systems. </p><p><br></p><p><strong>Plasma data</strong> is reported in the current study <a href='https://www.ebi.ac.uk/metabolights/MTBLS234' rel='noopener noreferrer' target='_blank'><strong>MTBLS234</strong></a>.</p><p><strong>Simulated data</strong> is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS235' rel='noopener noreferrer' target='_blank'><strong>MTBLS235</strong></a>.</p>
2015-12-16 | MTBLS234 | MetaboLights
Project description:Heart Failure Network - Renal Optimization Strategies Evaluation in Acute Heart Failure and Reliable Evaluation of Dyspnea (HFN ROSE-BioLINCC)
| PRJNA1089319 | ENA