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New mixture models for decoy-free false discovery rate estimation in mass-spectrometry proteomics

ABSTRACT: We introduce a new decoy-free framework for false discovery rate (FDR) estimation that generalizes present decoy-free approaches (DFAs) while exploiting more search data in a manner similar to target-decoy approaches (TDAs).

INSTRUMENT(S): Orbitrap Fusion Lumos

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Hela Cell

SUBMITTER: Michal Gregus

LAB HEAD: Alexander R. Ivanov

PROVIDER: PXD020322 | Pride | 2020-01-10

REPOSITORIES: Pride

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	01ngHeLa_01.raw	Raw
	01ngHeLa_02.raw	Raw
	01ngHeLa_03.raw	Raw
	100ngHeLa_01.raw	Raw
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Publications

New mixture models for decoy-free false discovery rate estimation in mass spectrometry proteomics.

Peng Yisu Y Jain Shantanu S Li Yong Fuga YF Greguš Michal M Ivanov Alexander R AR Vitek Olga O Radivojac Predrag P

Bioinformatics (Oxford, England) 20201201 Suppl_2

<h4>Motivation</h4>Accurate estimation of false discovery rate (FDR) of spectral identification is a central problem in mass spectrometry-based proteomics. Over the past two decades, target-decoy approaches (TDAs) and decoy-free approaches (DFAs) have been widely used to estimate FDR. TDAs use a database of decoy species to faithfully model score distributions of incorrect peptide-spectrum matches (PSMs). DFAs, on the other hand, fit two-component mixture models to learn the parameters of correc ...[more]

PMID: 33381824

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New mixture models for decoy-free false discovery rate estimation in mass spectrometry proteomics.

Project description:MotivationAccurate estimation of false discovery rate (FDR) of spectral identification is a central problem in mass spectrometry-based proteomics. Over the past two decades, target-decoy approaches (TDAs) and decoy-free approaches (DFAs) have been widely used to estimate FDR. TDAs use a database of decoy species to faithfully model score distributions of incorrect peptide-spectrum matches (PSMs). DFAs, on the other hand, fit two-component mixture models to learn the parameters of correct and incorrect PSM score distributions. While conceptually straightforward, both approaches lead to problems in practice, particularly in experiments that push instrumentation to the limit and generate low fragmentation-efficiency and low signal-to-noise-ratio spectra.ResultsWe introduce a new decoy-free framework for FDR estimation that generalizes present DFAs while exploiting more search data in a manner similar to TDAs. Our approach relies on multi-component mixtures, in which score distributions corresponding to the correct PSMs, best incorrect PSMs and second-best incorrect PSMs are modeled by the skew normal family. We derive EM algorithms to estimate parameters of these distributions from the scores of best and second-best PSMs associated with each experimental spectrum. We evaluate our models on multiple proteomics datasets and a HeLa cell digest case study consisting of more than a million spectra in total. We provide evidence of improved performance over existing DFAs and improved stability and speed over TDAs without any performance degradation. We propose that the new strategy has the potential to extend beyond peptide identification and reduce the need for TDA on all analytical platforms.Availabilityand implementationhttps://github.com/shawn-peng/FDR-estimation.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC7773488 | biostudies-literature

Cell cycle motif decoy oligos

Project description:Single-stranded oligos listed below (and the corresponding reverse compliment) were synthesized and annealed: E2F: CTAGATTTCCCGCGGATC (decoy); CTAGACTCTGCTCGGATC (scrambled) KTGGYRSGAA: CTAGATTCCCGCCAAGGATC (decoy); CTAGACAGCTACTCCGGATC (scrambled) TGCGCANK: CTAGACATGCGCAGGATC (decoy); CTAGATCACAGGCGGATC (scrambled) CCAATNNSNNNGCG: CTAGACGCCCTCCGATTGGGGATC (decoy); CTAGATGCACGCTCGGTCCGGATC (scrambled) ACTWSNACTNY: CTAGAGGAGTTGTAGTGGATC (decoy); CTAGAGATAGTGTGTGGGATC (scrambled) HeLa cells were propagated in DMEM (Invitrogen) plus 10% FBS and transfected with 0.5 uM of double-stranded DNA. Total RNA was extracted with TRIzol (Invitrogen) from HeLa cells 2 d after transfection of the indicated decoy oligo or scrambled oligo in duplicate. RNA was amplified using the Ambion Amino Allyl MessageAmp II aRNA kit. For each motif, decoy oligo transfected samples (labeled with Cy5) and the corresponding scrambled oligo transfected samples (labeled with Cy3) were competitively hybridized to HEEBO microarrays as described (http://www.microarray.org/sfgf/heebo.do). genetic_modification_design

2012-08-23 | E-GEOD-39572 | biostudies-arrayexpress

Target-small decoy search strategy for false discovery rate estimation.

Project description:BackgroundOne of the most important steps in peptide identification is to estimate the false discovery rate (FDR). The most commonly used method for estimating FDR is the target-decoy search strategy (TDS). While this method is simple and effective, it is time/space-inefficient because it searches a database that is twice as large as the original protein database. This inefficiency problem becomes more evident as protein databases get bigger and bigger. We propose a target-small decoy search strategy and present a rigorous verification that it reduces the database size and search time while retaining the accuracy of target-decoy search strategy (TDS).ResultsWe show that peptide spectrum matches (PSMs) obtained at 1% FDR in TDS overlap ~ 99% with those in our method. (Considering that 1% FDR is used, 99% overlap means our method is very accurate.) Moreover, our method is more time/space-efficient than TDS. The search time of our method is reduced to only 1/4 of that of TDS when UniProt and its 1/8 decoy database are used.ConclusionsWe demonstrate that our method is almost as accurate as TDS and more time/space-efficient than TDS. Since the efficiency of our method is more evident as the database size increases, our method is expected to be useful for identifying peptides in proteogenomics databases constructed from inflated databases using genomic data.

| S-EPMC6708216 | biostudies-literature

Cell cycle motif decoy oligos

2012-08-23 | GSE39572 | GEO

Target-Decoy-Based False Discovery Rate Estimation for Large-Scale Metabolite Identification.

Project description:Metabolite identification is a crucial step in mass spectrometry (MS)-based metabolomics. However, it is still challenging to assess the confidence of assigned metabolites. We report a novel method for estimating the false discovery rate (FDR) of metabolite assignment with a target-decoy strategy, in which the decoys are generated through violating the octet rule of chemistry by adding small odd numbers of hydrogen atoms. The target-decoy strategy was integrated into JUMPm, an automated metabolite identification pipeline for large-scale MS analysis and was also evaluated with two other metabolomics tools, mzMatch and MZmine 2. The reliability of FDR calculation was examined by false data sets, which were simulated by altering MS1 or MS2 spectra. Finally, we used the JUMPm pipeline coupled to the target-decoy strategy to process unlabeled and stable-isotope-labeled metabolomic data sets. The results demonstrate that the target-decoy strategy is a simple and effective method for evaluating the confidence of high-throughput metabolite identification.

| S-EPMC6252074 | biostudies-literature

On the estimation of false positives in peptide identifications using decoy search strategy.

Project description:False positive control/estimate in peptide identifications by MS is of critical importance for reliable inference at the protein level and downstream bioinformatics analysis. Approaches based on search against decoy databases have become popular for its conceptual simplicity and easy implementation. Although various decoy search strategies have been proposed, few studies have investigated their difference in performance. With datasets collected on a mixture of model proteins, we demonstrate that a single search against the target database coupled with its reversed version offers a good balance between performance and simplicity. In particular, both the accuracy of the estimate of the number of false positives and sensitivity is at least comparable to other procedures examined in this study. It is also shown that scrambling while preserving frequency of amino acid words can potentially improve the accuracy of false positive estimate, though more studies are needed to investigate the optimal scrambling procedure for specific condition and the variation of the estimate across repeated scrambling.

| S-EPMC3076744 | biostudies-literature

Expression data from U87-2M1 glioma cells transduced with baculoviral control decoy vector or baculoviral miR-10b decoy vector

Project description:MicroRNA-10b may target numerous genes in gliomagenesis. The target genes of miR-10b may differ according to the cellular context. We used microarray analyses to determine the phenotypic effects and gene targets of miR-10b by silencing miR-10b in invasive U87-2M1 glioma cells. Early passage U87-2M1 cells treated with the baculoviral control decoy vector or miR-10b decoy vector were selected for RNA extraction and hybridization on microarray

2012-12-31 | E-GEOD-35170 | biostudies-arrayexpress

A method for independent estimation of false localisation rate for phosphoproteomics

Project description:Phosphoproteomics methods are commonly employed in labs to identify and quantify the sites of phosphorylation on proteins. In recent years, various software tools have been developed, incorporating scores or statistics related to whether a given phosphosite has been correctly identified, or to estimate the global false localisation rate (FLR) within a given data set for all sites reported. These scores have generally been calibrated using synthetic data sets, and their statistical reliability on real datasets is largely unknown. As a result, there is considerable problem in the field of reporting incorrectly localised phosphosites, due to inadequate statistical control. In this work, we develop the concept of using scoring and ranking modifications on a decoy amino acid, i.e. one that cannot be modified, to allow for independent estimation of global FLR. We test a variety of different amino acids to act as the decoy, on both synthetic and real data sets, demonstrating that the amino acid selection can make a substantial difference to the estimated global FLR. We conclude that while several different amino acids might be appropriate, the most reliable FLR results were achieved using alanine and leucine as decoys, although we have a preference for alanine due to the risk of potential confusion between leucine and isoleucine amino acids. We propose that the phosphoproteomics field should adopt the use of a decoy amino acid, so that there is better control of false reporting in the literature, and in public databases that re-distribute the data.

2022-06-09 | PXD028840 | Pride

LuciPHOr: algorithm for phosphorylation site localization with false localization rate estimation using modified target-decoy approach.

Project description:The localization of phosphorylation sites in peptide sequences is a challenging problem in large-scale phosphoproteomics analysis. The intense neutral loss peaks and the coexistence of multiple serine/threonine and/or tyrosine residues are limiting factors for objectively scoring site patterns across thousands of peptides. Various computational approaches for phosphorylation site localization have been proposed, including Ascore, Mascot Delta score, and ProteinProspector, yet few address direct estimation of the false localization rate (FLR) in each experiment. Here we propose LuciPHOr, a modified target-decoy-based approach that uses mass accuracy and peak intensities for site localization scoring and FLR estimation. Accurate estimation of the FLR is a difficult task at the individual-site level because the degree of uncertainty in localization varies significantly across different peptides. LuciPHOr carries out simultaneous localization on all candidate sites in each peptide and estimates the FLR based on the target-decoy framework, where decoy phosphopeptides generated by placing artificial phosphorylation(s) on non-candidate residues compete with the non-decoy phosphopeptides. LuciPHOr also reports approximate site-level confidence scores for all candidate sites as a means to localize additional sites from multiphosphorylated peptides in which localization can be partially achieved. Unlike the existing tools, LuciPHOr is compatible with any search engine output processed through the Trans-Proteomic Pipeline. We evaluated the performance of LuciPHOr in terms of the sensitivity and accuracy of FLR estimates using two synthetic phosphopeptide libraries and a phosphoproteomic dataset generated from complex mouse brain samples.

| S-EPMC3820951 | biostudies-literature

Specific inhibition of splicing factor activity by decoy RNA oligonucleotides

Project description:Alternative splicing, a fundamental step in gene expression, is deregulated in many diseases. Splicing factors (SFs), which regulate this process, are up- or down regulated or mutated in several diseases including cancer. To date, there are no inhibitors that directly inhibit the activity of SFs. We designed decoy oligonucleotides, composed of several repeats of a RNA motif, which is recognized by a single SF. Here we show that decoy oligonucleotides targeting splicing factors RBFOX1/2, SRSF1 and PTBP1, can specifically bind to their respective SFs and inhibit their splicing and biological activities both in vitro and in vivo. These decoy oligonucleotides present a novel approach to specifically downregulate SF activity and have the potential to treat diseases where SFs are up-regulated, such as cancer.

2019-02-14 | GSE126503 | GEO

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