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Josephine M Pemberton - One of the best experts on this subject based on the ideXlab platform.
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a retrospective assessment of the accuracy of the paternity inference program cervus
Molecular Ecology, 2000Co-Authors: Jon Slate, T C Marshall, Josephine M PembertonAbstract:cervus is a Windows-based software package written to infer paternity in natural populations. It offers advantages over exclusionary-based methods of paternity inference in that multiple nonexcluded males can be Statistically distinguished, laboratory typing error is considered and Statistical Confidence is determined for assigned paternities through simulation. In this study we use a panel of 84 microsatellite markers to retrospectively determine the accuracy of Statistical Confidence when cervus was used to infer paternity in a population of red deer (Cervus elaphus). The actual Confidence of cervus-assigned paternities was not significantly different from that predicted by simulation.
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Statistical Confidence for likelihood based paternity inference in natural populations
Molecular Ecology, 1998Co-Authors: T C Marshall, Jon Slate, Loeske E B Kruuk, Josephine M PembertonAbstract:Paternity inference using highly polymorphic codominant markers is becoming common in the study of natural populations. However, multiple males are often found to be genetically compatible with each offspring tested, even when the probability of excluding an unrelated male is high. While various methods exist for evaluating the likelihood of paternity of each nonexcluded male, interpreting these likelihoods has hitherto been difficult, and no method takes account of the incomplete sampling and error-prone genetic data typical of large-scale studies of natural systems. We derive likelihood ratios for paternity inference with codominant markers taking account of typing error, and define a statistic Δ for resolving paternity. Using allele frequencies from the study population in question, a simulation program generates criteria for Δ that permit assignment of paternity to the most likely male with a known level of Statistical Confidence. The simulation takes account of the number of candidate males, the proportion of males that are sampled and gaps and errors in genetic data. We explore the potentially confounding effect of relatives and show that the method is robust to their presence under commonly encountered conditions. The method is demonstrated using genetic data from the intensively studied red deer (Cervus elaphus) population on the island of Rum, Scotland. The Windows-based computer program, CERVUS, described in this study is available from the authors. CERVUS can be used to calculate allele frequencies, run simulations and perform parentage analysis using data from all types of codominant markers.
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Statistical Confidence for likelihood based paternity inference in natural populations
Molecular Ecology, 1998Co-Authors: T C Marshall, Jon Slate, Loeske E B Kruuk, Josephine M PembertonAbstract:Paternity inference using highly polymorphic codominant markers is becoming common in the study of natural populations. However, multiple males are often found to be genetically compatible with each offspring tested, even when the probability of excluding an unrelated male is high. While various methods exist for evaluating the likelihood of paternity of each nonexcluded male, interpreting these likelihoods has hitherto been difficult, and no method takes account of the incomplete sampling and error-prone genetic data typical of large-scale studies of natural systems. We derive likelihood ratios for paternity inference with codominant markers taking account of typing error, and define a statistic delta for resolving paternity. Using allele frequencies from the study population in question, a simulation program generates criteria for delta that permit assignment of paternity to the most likely male with a known level of Statistical Confidence. The simulation takes account of the number of candidate males, the proportion of males that are sampled and gaps and errors in genetic data. We explore the potentially confounding effect of relatives and show that the method is robust to their presence under commonly encountered conditions. The method is demonstrated using genetic data from the intensively studied red deer (Cervus elaphus) population on the island of Rum, Scotland. The Windows-based computer program, CERVUS, described in this study is available from the authors. CERVUS can be used to calculate allele frequencies, run simulations and perform parentage analysis using data from all types of codominant markers.
T C Marshall - One of the best experts on this subject based on the ideXlab platform.
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a retrospective assessment of the accuracy of the paternity inference program cervus
Molecular Ecology, 2000Co-Authors: Jon Slate, T C Marshall, Josephine M PembertonAbstract:cervus is a Windows-based software package written to infer paternity in natural populations. It offers advantages over exclusionary-based methods of paternity inference in that multiple nonexcluded males can be Statistically distinguished, laboratory typing error is considered and Statistical Confidence is determined for assigned paternities through simulation. In this study we use a panel of 84 microsatellite markers to retrospectively determine the accuracy of Statistical Confidence when cervus was used to infer paternity in a population of red deer (Cervus elaphus). The actual Confidence of cervus-assigned paternities was not significantly different from that predicted by simulation.
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Statistical Confidence for likelihood based paternity inference in natural populations
Molecular Ecology, 1998Co-Authors: T C Marshall, Jon Slate, Loeske E B Kruuk, Josephine M PembertonAbstract:Paternity inference using highly polymorphic codominant markers is becoming common in the study of natural populations. However, multiple males are often found to be genetically compatible with each offspring tested, even when the probability of excluding an unrelated male is high. While various methods exist for evaluating the likelihood of paternity of each nonexcluded male, interpreting these likelihoods has hitherto been difficult, and no method takes account of the incomplete sampling and error-prone genetic data typical of large-scale studies of natural systems. We derive likelihood ratios for paternity inference with codominant markers taking account of typing error, and define a statistic Δ for resolving paternity. Using allele frequencies from the study population in question, a simulation program generates criteria for Δ that permit assignment of paternity to the most likely male with a known level of Statistical Confidence. The simulation takes account of the number of candidate males, the proportion of males that are sampled and gaps and errors in genetic data. We explore the potentially confounding effect of relatives and show that the method is robust to their presence under commonly encountered conditions. The method is demonstrated using genetic data from the intensively studied red deer (Cervus elaphus) population on the island of Rum, Scotland. The Windows-based computer program, CERVUS, described in this study is available from the authors. CERVUS can be used to calculate allele frequencies, run simulations and perform parentage analysis using data from all types of codominant markers.
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Statistical Confidence for likelihood based paternity inference in natural populations
Molecular Ecology, 1998Co-Authors: T C Marshall, Jon Slate, Loeske E B Kruuk, Josephine M PembertonAbstract:Paternity inference using highly polymorphic codominant markers is becoming common in the study of natural populations. However, multiple males are often found to be genetically compatible with each offspring tested, even when the probability of excluding an unrelated male is high. While various methods exist for evaluating the likelihood of paternity of each nonexcluded male, interpreting these likelihoods has hitherto been difficult, and no method takes account of the incomplete sampling and error-prone genetic data typical of large-scale studies of natural systems. We derive likelihood ratios for paternity inference with codominant markers taking account of typing error, and define a statistic delta for resolving paternity. Using allele frequencies from the study population in question, a simulation program generates criteria for delta that permit assignment of paternity to the most likely male with a known level of Statistical Confidence. The simulation takes account of the number of candidate males, the proportion of males that are sampled and gaps and errors in genetic data. We explore the potentially confounding effect of relatives and show that the method is robust to their presence under commonly encountered conditions. The method is demonstrated using genetic data from the intensively studied red deer (Cervus elaphus) population on the island of Rum, Scotland. The Windows-based computer program, CERVUS, described in this study is available from the authors. CERVUS can be used to calculate allele frequencies, run simulations and perform parentage analysis using data from all types of codominant markers.
Jon Slate - One of the best experts on this subject based on the ideXlab platform.
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a retrospective assessment of the accuracy of the paternity inference program cervus
Molecular Ecology, 2000Co-Authors: Jon Slate, T C Marshall, Josephine M PembertonAbstract:cervus is a Windows-based software package written to infer paternity in natural populations. It offers advantages over exclusionary-based methods of paternity inference in that multiple nonexcluded males can be Statistically distinguished, laboratory typing error is considered and Statistical Confidence is determined for assigned paternities through simulation. In this study we use a panel of 84 microsatellite markers to retrospectively determine the accuracy of Statistical Confidence when cervus was used to infer paternity in a population of red deer (Cervus elaphus). The actual Confidence of cervus-assigned paternities was not significantly different from that predicted by simulation.
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Statistical Confidence for likelihood based paternity inference in natural populations
Molecular Ecology, 1998Co-Authors: T C Marshall, Jon Slate, Loeske E B Kruuk, Josephine M PembertonAbstract:Paternity inference using highly polymorphic codominant markers is becoming common in the study of natural populations. However, multiple males are often found to be genetically compatible with each offspring tested, even when the probability of excluding an unrelated male is high. While various methods exist for evaluating the likelihood of paternity of each nonexcluded male, interpreting these likelihoods has hitherto been difficult, and no method takes account of the incomplete sampling and error-prone genetic data typical of large-scale studies of natural systems. We derive likelihood ratios for paternity inference with codominant markers taking account of typing error, and define a statistic Δ for resolving paternity. Using allele frequencies from the study population in question, a simulation program generates criteria for Δ that permit assignment of paternity to the most likely male with a known level of Statistical Confidence. The simulation takes account of the number of candidate males, the proportion of males that are sampled and gaps and errors in genetic data. We explore the potentially confounding effect of relatives and show that the method is robust to their presence under commonly encountered conditions. The method is demonstrated using genetic data from the intensively studied red deer (Cervus elaphus) population on the island of Rum, Scotland. The Windows-based computer program, CERVUS, described in this study is available from the authors. CERVUS can be used to calculate allele frequencies, run simulations and perform parentage analysis using data from all types of codominant markers.
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Statistical Confidence for likelihood based paternity inference in natural populations
Molecular Ecology, 1998Co-Authors: T C Marshall, Jon Slate, Loeske E B Kruuk, Josephine M PembertonAbstract:Paternity inference using highly polymorphic codominant markers is becoming common in the study of natural populations. However, multiple males are often found to be genetically compatible with each offspring tested, even when the probability of excluding an unrelated male is high. While various methods exist for evaluating the likelihood of paternity of each nonexcluded male, interpreting these likelihoods has hitherto been difficult, and no method takes account of the incomplete sampling and error-prone genetic data typical of large-scale studies of natural systems. We derive likelihood ratios for paternity inference with codominant markers taking account of typing error, and define a statistic delta for resolving paternity. Using allele frequencies from the study population in question, a simulation program generates criteria for delta that permit assignment of paternity to the most likely male with a known level of Statistical Confidence. The simulation takes account of the number of candidate males, the proportion of males that are sampled and gaps and errors in genetic data. We explore the potentially confounding effect of relatives and show that the method is robust to their presence under commonly encountered conditions. The method is demonstrated using genetic data from the intensively studied red deer (Cervus elaphus) population on the island of Rum, Scotland. The Windows-based computer program, CERVUS, described in this study is available from the authors. CERVUS can be used to calculate allele frequencies, run simulations and perform parentage analysis using data from all types of codominant markers.
William Stafford Noble - One of the best experts on this subject based on the ideXlab platform.
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progressive calibration and averaging for tandem mass spectrometry Statistical Confidence estimation why settle for a single decoy
Research in Computational Molecular Biology, 2017Co-Authors: Uri Keich, William Stafford NobleAbstract:Estimating the false discovery rate (FDR) among a list of tandem mass spectrum identifications is mostly done through target-decoy competition (TDC). Here we offer two new methods that can use an arbitrarily small number of additional randomly drawn decoy databases to improve TDC. Specifically, “Partial Calibration” utilizes a new meta-scoring scheme that allows us to gradually benefit from the increase in the number of identifications calibration yields and “Averaged TDC” (a-TDC) reduces the liberal bias of TDC for small FDR values and its variability throughout. Combining a-TDC with “Progressive Calibration” (PC), which attempts to find the “right” number of decoys required for calibration we see substantial impact in real datasets: when analyzing the Plasmodium falciparum data it typically yields almost the entire 17% increase in discoveries that “full calibration” yields (at FDR level 0.05) using 60 times fewer decoys. Our methods are further validated using a novel realistic simulation scheme and importantly, they apply more generally to the problem of controlling the FDR among discoveries from searching an incomplete database.
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tandem mass spectrum identification via cascaded search
Journal of Proteome Research, 2015Co-Authors: Attila Kerteszfarkas, Uri Keich, William Stafford NobleAbstract:Accurate assignment of peptide sequences to observed fragmentation spectra is hindered by the large number of hypotheses that must be considered for each observed spectrum. A high score assigned to a particular peptide–spectrum match (PSM) may not end up being Statistically significant after multiple testing correction. Researchers can mitigate this problem by controlling the hypothesis space in various ways: considering only peptides resulting from enzymatic cleavages, ignoring possible post-translational modifications or single nucleotide variants, etc. However, these strategies sacrifice identifications of spectra generated by rarer types of peptides. In this work, we introduce a Statistical testing framework, cascade search, that directly addresses this problem. The method requires that the user specify a priori a Statistical Confidence threshold as well as a series of peptide databases. For instance, such a cascade of databases could include fully tryptic, semitryptic, and nonenzymatic peptides or pe...
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Statistical Confidence estimation for hi c data reveals regulatory chromatin contacts
Genome Research, 2014Co-Authors: Timothy L Bailey, William Stafford NobleAbstract:Our current understanding of how DNA is packed in the nucleus is most accurate at the fine scale of individual nucleosomes and at the large scale of chromosome territories. However, accurate modeling of DNA architecture at the intermediate scale of ∼50 kb-10 Mb is crucial for identifying functional interactions among regulatory elements and their target promoters. We describe a method, Fit-Hi-C, that assigns Statistical Confidence estimates to mid-range intra-chromosomal contacts by jointly modeling the random polymer looping effect and previously observed technical biases in Hi-C data sets. We demonstrate that our proposed approach computes accurate empirical null models of contact probability without any distribution assumption, corrects for binning artifacts, and provides improved Statistical power relative to a previously described method. High-Confidence contacts identified by Fit-Hi-C preferentially link expressed gene promoters to active enhancers identified by chromatin signatures in human embryonic stem cells (ESCs), capture 77% of RNA polymerase II-mediated enhancer-promoter interactions identified using ChIA-PET in mouse ESCs, and confirm previously validated, cell line-specific interactions in mouse cortex cells. We observe that insulators and heterochromatin regions are hubs for high-Confidence contacts, while promoters and strong enhancers are involved in fewer contacts. We also observe that binding peaks of master pluripotency factors such as NANOG and POU5F1 are highly enriched in high-Confidence contacts for human ESCs. Furthermore, we show that pairs of loci linked by high-Confidence contacts exhibit similar replication timing in human and mouse ESCs and preferentially lie within the boundaries of topological domains for human and mouse cell lines.
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learning score function parameters for improved spectrum identification in tandem mass spectrometry experiments
Journal of Proteome Research, 2012Co-Authors: Marina Spivak, Michael S Bereman, Michael J Maccoss, William Stafford NobleAbstract:The identification of proteins from spectra derived from a tandem mass spectrometry experiment involves several challenges: matching each observed spectrum to a peptide sequence, ranking the resulting collection of peptide-spectrum matches, assigning Statistical Confidence estimates to the matches, and identifying the proteins. The present work addresses algorithms to rank peptide–spectrum matches. Many of these algorithms, such as PeptideProphet, IDPicker, or Q-ranker, follow a similar methodology that includes representing peptide-spectrum matches as feature vectors and using optimization techniques to rank them. We propose a richer and more flexible feature set representation that is based on the parametrization of the SEQUEST XCorr score and that can be used by all of these algorithms. This extended feature set allows a more effective ranking of the peptide-spectrum matches based on the target-decoy strategy, in comparison to a baseline feature set devoid of these XCorr-based features. Ranking using t...
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qvality non parametric estimation of q values and posterior error probabilities
Bioinformatics, 2009Co-Authors: Lukas Kall, John D Storey, William Stafford NobleAbstract:Summary: Qvality is a C++ program for estimating two types of standard Statistical Confidence measures: the q-value, which is an analog of the p-value that incorporates multiple testing correction, and the posterior error probability (PEP, also known as the local false discovery rate), which corresponds to the probability that a given observation is drawn from the null distribution. In computing q-values, qvality employs a standard bootstrap procedure to estimate the prior probability of a score being from the null distribution; for PEP estimation, qvality relies upon non-parametric logistic regression. Relative to other tools for estimating Statistical Confidence measures, qvality is unique in its ability to estimate both types of scores directly from a null distribution, without requiring the user to calculate p-values. Availability: A web server, C++ source code and binaries are available under MIT license at http://noble.gs.washington.edu/proj/ qvality Contact: lukas.kall@cbr.su.se Supplementary information: Supplementary data are available at Bioinformatics online.
John E Burke - One of the best experts on this subject based on the ideXlab platform.
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escherichia coli and sf9 contaminant databases to increase efficiency of tandem mass spectrometry peptide identification in structural mass spectrometry experiments
Journal of the American Society for Mass Spectrometry, 2020Co-Authors: Joseph M Dobbs, Meredith L Jenkins, John E BurkeAbstract:Abstract: Filtering of non-specifically binding contaminant proteins from affinity purification mass spectrometry (AP-MS) data is a well-established strategy to improve Statistical Confidence in id...
