scholarly journals DIAlignR provides precise retention time alignment across distant runs in DIA and targeted proteomics

2018 ◽  
Author(s):  
Shubham Gupta ◽  
Sara Ahadi ◽  
Wenyu Zhou ◽  
Hannes Röst

AbstractSWATH-MS has been widely used for proteomics analysis given its high-throughput and reproducibility but ensuring consistent quantification of analytes across large-scale studies of heterogeneous samples such as human-plasma remains challenging. Heterogeneity in large-scale studies can be caused by large time intervals between data-acquisition, acquisition by different operators or instruments, intermittent repair or replacement of parts, such as the liquid chromatography column, all of which affect retention time (RT) reproducibility and successively performance of SWATH-MS data analysis. Here, we present a novel algorithm for retention time alignment of SWATH-MS data based on direct alignment of raw MS2 chromatograms using a hybrid dynamic programming approach. The algorithm does not impose a chronological order of elution and allows for alignment of elution-order swapped peaks. Furthermore, allowing RT-mapping in a certain window around coarse global fit makes it robust against noise. On a manually validated dataset, this strategy outperforms the current state-of-the-art approaches. In addition, on a real-world clinical data, our approach outperforms global alignment methods by mapping 98% of peaks compared to 67% cumulatively and DIAlignR can reduce alignment error up to 30-fold for extremely distant runs. The robustness of technical parameters used in this pairwise alignment strategy has also been demonstrated. The source code is released under the BSD license at https://github.com/Roestlab/DIAlignR.Abbreviations:AUCArea Under the CurveDIAData-independent acquisitionLCLiquid chromatographyLOESSLocal weighted regressionRSEResidual Standard ErrorRTRetention timeXICExtracted ion chromatogramsData Availability:Raw chromatograms and features extracted by OpenSWATH are available on PeptideAtlas.Servername: ftp.peptideatlas.orgUsername: PASS01280Password: KQ2592b

2019 ◽  
Author(s):  
Ying Sheng ◽  
Chiung-Yu Huang ◽  
Siarhei Lobach ◽  
Lydia Zablotska ◽  
Iryna Lobach ◽  
...  

ABSTRACTLarge-scale genome-wide analyses scans provide massive volumes of genetic variants on large number of cases and controls that can be used to estimate the genetic effects. Yet, the sets of non-genetic variables available in publicly available databases are often brief. It is known that omitting a continuous variable from a logistic regression model can result in biased estimates of odds ratios (OR) (e.g., Gail et al (1984), Neuhaus et al (1993), Hauck et al (1991), Zeger et al (1988)). We are interested to assess what information is needed to recover the bias in the OR estimate of genotype due to omitting a continuous variable in settings when the actual values of the omitted variable are not available. We derive two estimating procedures that can recover the degree of bias based on a conditional density of the omitted variable or knowing the distribution of the omitted variable. Importantly, our derivations show that omitting a continuous variable can result in either under- or over-estimation of the genetic effects. We performed extensive simulation studies to examine bias, variability, false positive rate, and power in the model that omits a continuous variable. We show the application to two genome-wide studies of Alzheimer’s disease.Data Availability StatementThe data that support the findings of this study are openly available in the Database of Genotypes and Phenotypes at [https://www.ncbi.nlm.nih.gov/projects/gap/cgibin/study.cgi?study_id=phs000372.v1.p1], reference number [phs000372.v1.p1] and at the Alzheimer’s Disease Neuroimaging Initiative http://adni.loni.usc.edu/.


2007 ◽  
Vol 18 (4) ◽  
Author(s):  
Marc Kirchner ◽  
Benjamin Saussen ◽  
Hanno Steen ◽  
Judith A. J. Steen ◽  
Fred A. Hamprecht

2009 ◽  
Vol 25 (6) ◽  
pp. 758-764 ◽  
Author(s):  
Katharina Podwojski ◽  
Arno Fritsch ◽  
Daniel C. Chamrad ◽  
Wolfgang Paul ◽  
Barbara Sitek ◽  
...  

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