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TL;DR: A nonlinear curve fitting method is described for calculating the local FDR, which estimates the chance that an individual protein (or peptide) is incorrect, and a simple tool is presented that implements this analysis.
Abstract: False discovery rate (FDR) analyses of protein and peptide identification results using decoy database searching conventionally report aggregate or global FDRs for a whole set of identifications, which are often not very informative about the error rates of individual members in the set. We describe a nonlinear curve fitting method for calculating the local FDR, which estimates the chance that an individual protein (or peptide) is incorrect, and present a simple tool that implements this analysis. The goal of this method is to offer a simple extension to the now commonplace decoy database searching, providing additional valuable information.
324 citations
TL;DR: The history, progress, potential applications and future developments of single-cell transcriptome analysis are reviewed to decipher the full gene expression network underlying physiological functions of individual cells in embryos and adults.
Abstract: Dissecting the relationship between genotype and phenotype is one of the central goals in developmental biology and medicine. Transcriptome analysis is a powerful strategy to connect genotype to phenotype of a cell. Here we review the history, progress, potential applications and future developments of single-cell transcriptome analysis. In combination with live cell imaging and lineage tracing, it will be possible to decipher the full gene expression network underlying physiological functions of individual cells in embryos and adults, and to study diseases.
324 citations
McGill University1, Institute for Systems Biology2, University of British Columbia3, National Institutes of Health4, Invitrogen5, Allergan6, Northeastern University7, Ruhr University Bochum8, Massachusetts Institute of Technology9, Discovery Institute10, Fred Hutchinson Cancer Research Center11, Georgetown University12, University of Gothenburg13, Harvard University14, Thermo Fisher Scientific15, Laval University16, Walter and Eliza Hall Institute of Medical Research17, University of Toronto18, Scripps Research Institute19, University of Alberta20, University of California, Los Angeles21, University College Dublin22, University of Michigan23, University of Pittsburgh24, University of Victoria25, University of Western Ontario26, Wistar Institute27, Yamaguchi University28, Yonsei University29, Agilent Technologies30, Applied Biosystems31, Waters Corporation32
TL;DR: Central analysis determined missed identifications, environmental contamination, database matching and curation of protein identifications as sources of problems in liquid chromatography–mass spectrometry–based proteomics.
Abstract: We performed a test sample study to try to identify errors leading to irreproducibility, including incompleteness of peptide sampling, in liquid chromatography-mass spectrometry-based proteomics. We distributed an equimolar test sample, comprising 20 highly purified recombinant human proteins, to 27 laboratories. Each protein contained one or more unique tryptic peptides of 1,250 Da to test for ion selection and sampling in the mass spectrometer. Of the 27 labs, members of only 7 labs initially reported all 20 proteins correctly, and members of only 1 lab reported all tryptic peptides of 1,250 Da. Centralized analysis of the raw data, however, revealed that all 20 proteins and most of the 1,250 Da peptides had been detected in all 27 labs. Our centralized analysis determined missed identifications (false negatives), environmental contamination, database matching and curation of protein identifications as sources of problems. Improved search engines and databases are needed for mass spectrometry-based proteomics.
324 citations
TL;DR: The efficiency compensation control facilitates identification of patient samples that are so contaminated with PCR inhibitory compounds that different amplification reactions are affected to a different extent and accurate quantitation of residual disease in these samples is therefore impossible with the current quantitative real-time PCR protocols.
321 citations
TL;DR: A second-generation linkage map that incorporates sequence-based positional information and a regression-based smoothed map is provided that facilitates interpolation of positions of unmapped markers on this map.
Abstract: We have completed a second-generation linkage map that incorporates sequence-based positional information. This new map, the Rutgers Map v.2, includes 28,121 polymorphic markers with physical positions corroborated by recombination-based data. Sex-averaged and sex-specific linkage map distances, along with confidence intervals, have been estimated for all map intervals. In addition, a regression-based smoothed map is provided that facilitates interpolation of positions of unmapped markers on this map. With nearly twice as many markers as our first-generation map, the Rutgers Map continues to be a unique and comprehensive resource for obtaining genetic map information for large sets of polymorphic markers.
319 citations
Authors
Showing all 1521 results
| Name | H-index | Papers | Citations |
|---|---|---|---|
| Richard A. Gibbs | 172 | 889 | 249708 |
| Friedrich C. Luft | 113 | 1095 | 47619 |
| Alexander N. Glazer | 71 | 208 | 21068 |
| Vineet Bafna | 68 | 236 | 42574 |
| Kevin R. Coombes | 63 | 308 | 23592 |
| Darryl J. Pappin | 61 | 170 | 29409 |
| Mark D. Johnson | 60 | 289 | 16103 |
| György Marko-Varga | 56 | 409 | 12600 |
| Paul Thomas | 56 | 128 | 44810 |
| Gerald Zon | 55 | 256 | 11126 |
| Michael W. Hunkapiller | 51 | 130 | 29756 |
| Bjarni V. Halldorsson | 51 | 145 | 13180 |
| David H. Hawke | 50 | 157 | 9824 |
| Ellson Y. Chen | 50 | 71 | 28836 |
| Sridhar Hannenhalli | 49 | 162 | 21959 |