scholarly journals RobNorm: model-based robust normalization method for labeled quantitative mass spectrometry proteomics data

2020 ◽  
Author(s):  
Meng Wang ◽  
Lihua Jiang ◽  
Ruiqi Jian ◽  
Joanne Y Chan ◽  
Qing Liu ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Protein Expression ◽  
Real Data ◽  
Tissue Expression ◽  
Supplementary Information ◽  
Systematic Bias ◽  
Proteomics Data ◽  
Robust Fitting ◽  
Fitting Method ◽  
The One

Abstract Motivation Data normalization is an important step in processing proteomics data generated in mass spectrometry experiments, which aims to reduce sample-level variation and facilitate comparisons of samples. Previously published methods for normalization primarily depend on the assumption that the distribution of protein expression is similar across all samples. However, this assumption fails when the protein expression data is generated from heterogenous samples, such as from various tissue types. This led us to develop a novel data-driven method for improved normalization to correct the systematic bias meanwhile maintaining underlying biological heterogeneity. Results To robustly correct the systematic bias, we used the density-power-weight method to down-weigh outliers and extended the one-dimensional robust fitting method described in the previous work to our structured data. We then constructed a robustness criterion and developed a new normalization algorithm, called RobNorm. In simulation studies and analysis of real data from the genotype-tissue expression project, we compared and evaluated the performance of RobNorm against other normalization methods. We found that the RobNorm approach exhibits the greatest reduction in systematic bias while maintaining across-tissue variation, especially for datasets from highly heterogeneous samples. Availabilityand implementation https://github.com/mwgrassgreen/RobNorm. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals RobNorm: Model-Based Robust Normalization Method for Labeled Quantitative Mass Spectrometry Proteomics Data

2019 ◽  
Author(s):  
Meng Wang ◽  
Lihua Jiang ◽  
Ruiqi Jian ◽  
Joanne Y. Chan ◽  
Qing Liu ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Protein Expression ◽  
Real Data ◽  
Tissue Expression ◽  
Systematic Bias ◽  
Proteomics Data ◽  
Robust Fitting ◽  
Fitting Method ◽  
Weight Method ◽  
The One

AbstractMotivationData normalization is an important step in processing proteomics data generated in mass spectrometry (MS) experiments, which aims to reduce sample-level variation and facilitate comparisons of samples. Previously published methods for normalization primarily depend on the assumption that the distribution of protein expression is similar across all samples. However, this assumption fails when the protein expression data is generated from heterogenous samples, such as from various tissue types. This led us to develop a novel data-driven method for improved normalization to correct the systematic bias meanwhile maintaining underlying biological heterogeneity.MethodsTo robustly correct the systematic bias, we used the density-power-weight method to down-weigh outliers and extended the one-dimensional robust fitting method described in the previous work of (Windham, 1995, Fujisawa and Eguchi, 2008) to our structured data. We then constructed a robustness criterion and developed a new normalization algorithm, called RobNorm.ResultsIn simulation studies and analysis of real data from the genotype-tissue expression (GTEx) project, we compared and evaluated the performance of RobNorm against other normalization methods. We found that the RobNorm approach exhibits the greatest reduction in systematic bias while maintaining across-tissue variation, especially for datasets from highly heterogeneous samples.Availabilityhttps://github.com/mwgrassgreen/[email protected] and [email protected]

scholarly journals ProteomicsBrowser: MS/proteomics data visualization and investigation

2018 ◽  
Vol 35 (13) ◽  
pp. 2313-2314 ◽  
Author(s):  
Gang Peng ◽  
Rashaun Wilson ◽  
Yishuo Tang ◽  
TuKiet T Lam ◽  
Angus C Nairn ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Data Visualization ◽  
Large Scale ◽  
Supplementary Information ◽  
Proteomics Data ◽  
Sequence Coverage ◽  
Protein Database ◽  
Post Translational Modifications ◽  
Parent Protein ◽  
Focus Analysis

Abstract Summary Large-scale, quantitative proteomics data are being generated at ever increasing rates by high-throughput, mass spectrometry technologies. However, due to the complexity of these large datasets as well as the increasing numbers of post-translational modifications (PTMs) that are being identified, developing effective methods for proteomic visualization has been challenging. ProteomicsBrowser was designed to meet this need for comprehensive data visualization. Using peptide information files exported from mass spectrometry search engines or quantitative tools as input, the peptide sequences are aligned to an internal protein database such as UniProtKB. Each identified peptide ion including those with PTMs is then visualized along the parent protein in the Browser. A unique property of ProteomicsBrowser is the ability to combine overlapping peptides in different ways to focus analysis of sequence coverage, charge state or PTMs. ProteomicsBrowser includes other useful functions, such as a data filtering tool and basic statistical analyses to qualify quantitative data. Availability and implementation ProteomicsBrowser is implemented in Java8 and is available at https://medicine.yale.edu/keck/nida/proteomicsbrowser.aspx and https://github.com/peng-gang/ProteomicsBrowser. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals (2S,3R,6R)-2-[(R)-1-Hydroxyallyl]-4,4-dimethoxy-6-methyltetrahydro-2H-pyran-3-ol

Molbank ◽  
10.3390/m1140 ◽  
2020 ◽  
Vol 2020 (2) ◽  
pp. M1140
Author(s):  
Jack Bennett ◽  
Paul Murphy
Keyword(s):  
Mass Spectrometry ◽  
Nmr Spectroscopy ◽  
Ir Spectroscopy ◽  
13C Nmr ◽  
Conjugate Addition ◽  
13C Nmr Spectroscopy ◽  
One Pot ◽  
1H And 13C Nmr ◽  
Esi Mass Spectrometry ◽  
The One

(2S,3R,6R)-2-[(R)-1-Hydroxyallyl]-4,4-dimethoxy-6-methyltetrahydro-2H-pyran-3-ol was isolated in 18% after treating the glucose derived (5R,6S,7R)-5,6,7-tris[(triethylsilyl)oxy]nona-1,8-dien-4-one with (1S)-(+)-10-camphorsulfonic acid (CSA). The one-pot formation of the title compound involved triethylsilyl (TES) removal, alkene isomerization, intramolecular conjugate addition and ketal formation. The compound was characterized by 1H and 13C NMR spectroscopy, ESI mass spectrometry and IR spectroscopy. NMR spectroscopy was used to establish the product structure, including the conformation of its tetrahydropyran ring.

scholarly journals sepal: identifying transcript profiles with spatial patterns by diffusion-based modeling

2021 ◽  
Author(s):  
Alma Andersson ◽  
Joakim Lundeberg
Keyword(s):  
Spatial Patterns ◽  
Expression Profiles ◽  
Synthetic Data ◽  
Real Data ◽  
Cell Types ◽  
Statistical Hypothesis ◽  
Supplementary Information ◽  
Statistical Hypothesis Testing ◽  
Transcriptomics Data ◽  
Transcript Profiles

Abstract Motivation Collection of spatial signals in large numbers has become a routine task in multiple omics-fields, but parsing of these rich datasets still pose certain challenges. In whole or near-full transcriptome spatial techniques, spurious expression profiles are intermixed with those exhibiting an organized structure. To distinguish profiles with spatial patterns from the background noise, a metric that enables quantification of spatial structure is desirable. Current methods designed for similar purposes tend to be built around a framework of statistical hypothesis testing, hence we were compelled to explore a fundamentally different strategy. Results We propose an unexplored approach to analyze spatial transcriptomics data, simulating diffusion of individual transcripts to extract genes with spatial patterns. The method performed as expected when presented with synthetic data. When applied to real data, it identified genes with distinct spatial profiles, involved in key biological processes or characteristic for certain cell types. Compared to existing methods, ours seemed to be less informed by the genes’ expression levels and showed better time performance when run with multiple cores. Availabilityand implementation Open-source Python package with a command line interface (CLI), freely available at https://github.com/almaan/sepal under an MIT licence. A mirror of the GitHub repository can be found at Zenodo, doi: 10.5281/zenodo.4573237. Supplementary information Supplementary data are available at Bioinformatics online.

Intuitionistic fuzzy two-factor variance analysis of movie ticket sales

2021 ◽  
pp. 1-11
Author(s):  
Velichka Traneva ◽  
Stoyan Tranev
Keyword(s):  
Comparative Analysis ◽  
Real Data ◽  
Intuitionistic Fuzzy Sets ◽  
Real Numbers ◽  
Data Set ◽  
Important Method ◽  
Ticket Sales ◽  
Intuitionistic Fuzzy ◽  
The One ◽  
First Time

Analysis of variance (ANOVA) is an important method in data analysis, which was developed by Fisher. There are situations when there is impreciseness in data In order to analyze such data, the aim of this paper is to introduce for the first time an intuitionistic fuzzy two-factor ANOVA (2-D IFANOVA) without replication as an extension of the classical ANOVA and the one-way IFANOVA for a case where the data are intuitionistic fuzzy rather than real numbers. The proposed approach employs the apparatus of intuitionistic fuzzy sets (IFSs) and index matrices (IMs). The paper also analyzes a unique set of data on daily ticket sales for a year in a multiplex of Cinema City Bulgaria, part of Cineworld PLC Group, applying the two-factor ANOVA and the proposed 2-D IFANOVA to study the influence of “ season ” and “ ticket price ” factors. A comparative analysis of the results, obtained after the application of ANOVA and 2-D IFANOVA over the real data set, is also presented.

scholarly journals Constructing Large-Scale Genetic Maps Using an Evolutionary Strategy Algorithm

Genetics ◽  
2003 ◽  
Vol 165 (4) ◽  
pp. 2269-2282
Author(s):  
D Mester ◽  
Y Ronin ◽  
D Minkov ◽  
E Nevo ◽  
A Korol
Keyword(s):  
Discrete Optimization ◽  
High Performance ◽  
Large Scale ◽  
Simulated Data ◽  
Real Data ◽  
Genetic Maps ◽  
Chromosome 1 ◽  
Evolutionary Strategy ◽  
Group A ◽  
The One

Abstract This article is devoted to the problem of ordering in linkage groups with many dozens or even hundreds of markers. The ordering problem belongs to the field of discrete optimization on a set of all possible orders, amounting to n!/2 for n loci; hence it is considered an NP-hard problem. Several authors attempted to employ the methods developed in the well-known traveling salesman problem (TSP) for multilocus ordering, using the assumption that for a set of linked loci the true order will be the one that minimizes the total length of the linkage group. A novel, fast, and reliable algorithm developed for the TSP and based on evolution-strategy discrete optimization was applied in this study for multilocus ordering on the basis of pairwise recombination frequencies. The quality of derived maps under various complications (dominant vs. codominant markers, marker misclassification, negative and positive interference, and missing data) was analyzed using simulated data with ∼50-400 markers. High performance of the employed algorithm allows systematic treatment of the problem of verification of the obtained multilocus orders on the basis of computing-intensive bootstrap and/or jackknife approaches for detecting and removing questionable marker scores, thereby stabilizing the resulting maps. Parallel calculation technology can easily be adopted for further acceleration of the proposed algorithm. Real data analysis (on maize chromosome 1 with 230 markers) is provided to illustrate the proposed methodology.

scholarly journals CNV-BAC: Copy number Variation Detection in Bacterial Circular Genome

2020 ◽  
Vol 36 (12) ◽  
pp. 3890-3891
Author(s):  
Linjie Wu ◽  
Han Wang ◽  
Yuchao Xia ◽  
Ruibin Xi
Keyword(s):  
Copy Number Variation ◽  
Copy Number ◽  
Genome Structure ◽  
Real Data ◽  
Read Depth ◽  
Supplementary Information ◽  
Circular Genome ◽  
Number Variation ◽  
Copy Number Variation Detection ◽  
Cnv Detection

Abstract Motivation Whole-genome sequencing (WGS) is widely used for copy number variation (CNV) detection. However, for most bacteria, their circular genome structure and high replication rate make reads more enriched near the replication origin. CNV detection based on read depth could be seriously influenced by such replication bias. Results We show that the replication bias is widespread using ∼200 bacterial WGS data. We develop CNV-BAC (CNV-Bacteria) that can properly normalize the replication bias and other known biases in bacterial WGS data and can accurately detect CNVs. Simulation and real data analysis show that CNV-BAC achieves the best performance in CNV detection compared with available algorithms. Availability and implementation CNV-BAC is available at https://github.com/XiDsLab/CNV-BAC. Supplementary information Supplementary data are available at Bioinformatics online.

Detection of differentially methylated CpG sites between tumor samples with uneven tumor purities

2019 ◽  
Vol 36 (7) ◽  
pp. 2017-2024
Author(s):  
Weiwei Zhang ◽  
Ziyi Li ◽  
Nana Wei ◽  
Hua-Jun Wu ◽  
Xiaoqi Zheng
Keyword(s):  
Real Data ◽  
R Package ◽  
Differential Methylation ◽  
Least Square ◽  
Epigenetic Mechanism ◽  
Supplementary Information ◽  
Cpg Sites ◽  
Tumor Purity ◽  
Different Sources ◽  
Normal Controls

Abstract Motivation Inference of differentially methylated (DM) CpG sites between two groups of tumor samples with different geno- or pheno-types is a critical step to uncover the epigenetic mechanism of tumorigenesis, and identify biomarkers for cancer subtyping. However, as a major source of confounding factor, uneven distributions of tumor purity between two groups of tumor samples will lead to biased discovery of DM sites if not properly accounted for. Results We here propose InfiniumDM, a generalized least square model to adjust tumor purity effect for differential methylation analysis. Our method is applicable to a variety of experimental designs including with or without normal controls, different sources of normal tissue contaminations. We compared our method with conventional methods including minfi, limma and limma corrected by tumor purity using simulated datasets. Our method shows significantly better performance at different levels of differential methylation thresholds, sample sizes, mean purity deviations and so on. We also applied the proposed method to breast cancer samples from TCGA database to further evaluate its performance. Overall, both simulation and real data analyses demonstrate favorable performance over existing methods serving similar purpose. Availability and implementation InfiniumDM is a part of R package InfiniumPurify, which is freely available from GitHub (https://github.com/Xiaoqizheng/InfiniumPurify). Supplementary information Supplementary data are available at Bioinformatics online.

Sign in / Sign up

Export Citation Format

Share Document