scholarly journals Detection of Discordant Peptide Quantities in Shotgun Proteomics Data by Peptide Correlation Analysis (PeCorA)

2020 ◽  
Author(s):  
Jesse G. Meyer
Keyword(s):  
Correlation Analysis ◽  
Linear Models ◽  
Shotgun Proteomics ◽  
Indirect Detection ◽  
Proteomics Data ◽  
Treatment Groups ◽  
Proteomic Data ◽  
Protein Quantitation ◽  
Primary Mouse ◽  
Direct And Indirect Detection

ABSTRACTShotgun proteomics techniques infer the presence and quantity of proteins using peptide proxies, which are produced by cleavage of all isolated protein by a protease. Most protein quantitation strategies assume that multiple peptides derived from a protein will behave quantitatively similar across treatment groups, but this assumption may be false for biological or technical reasons. Here, I describe a strategy called peptide correlation analysis (PeCorA) that detects quantitative disagreements between peptides mapped to the same protein. Simple linear models are used to assess whether the slope of a peptide’s change across treatment groups differs from the slope of all other peptides assigned to the same protein. Reanalysis of proteomic data from primary mouse microglia with PeCorA revealed that about 15% of proteins contain one discordant peptide. Inspection of the discordant peptides shows utility of PeCorA for direct and indirect detection of regulated PTMs, and also for discovery of poorly quantified peptides that should be excluded. PeCorA can be applied to an arbitrary list of quantified peptides, and is freely available as a script written in R.

scholarly journals Revealing missing isoforms encoded in the human genome by integrating genomic, transcriptomic and proteomic data

2014 ◽  
Author(s):  
Zhiqiang Hu ◽  
Hamish S. Scott ◽  
Guangrong Qin ◽  
Guangyong Zheng ◽  
Xixia Chu ◽  
...  
Keyword(s):  
Alternative Splicing ◽  
Shotgun Proteomics ◽  
Proteomics Data ◽  
Protein Coding ◽  
Novel Proteins ◽  
Proteomic Data ◽  
Human Genes ◽  
Human Proteins ◽  
Novel Transcripts ◽  
Coding Potential

Biological and biomedical research relies on comprehensive understanding of protein-coding transcripts. However, the total number of human proteins is still unknown due to the prevalence of alternative splicing and is much larger than the number of human genes. In this paper, we detected 31,566 novel transcripts with coding potential by filtering our ab initio predictions with 50 RNA-seq datasets from diverse tissues/cell lines. PCR followed by MiSeq sequencing showed that at least 84.1% of these predicted novel splice sites could be validated. In contrast to known transcripts, the expression of these novel transcripts were highly tissue-specific. Based on these novel transcripts, at least 36 novel proteins were detected from shotgun proteomics data of 41 breast samples. We also showed L1 retrotransposons have a more significant impact on the origin of new transcripts/genes than previously thought. Furthermore, we found that alternative splicing is extraordinarily widespread for genes involved in specific biological functions like protein binding, nucleoside binding, neuron projection, membrane organization and cell adhesion. In the end, the total number of human transcripts with protein-coding potential was estimated to be at least 204,950.

scholarly journals PeptideMind – applying machine learning algorithms to assess replicate quality in shotgun proteomic data

2020 ◽  
Author(s):  
D.C.L. Handler ◽  
P.A. Haynes
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Shotgun Proteomics ◽  
Machine Learning Algorithms ◽  
Proteomics Data ◽  
Proteomic Data ◽  
Proteomics Experiment ◽  
Significance Measures ◽  
Proteomics Data Analysis

AbstractAssessment of replicate quality is an important process for any shotgun proteomics experiment. One fundamental question in proteomics data analysis is whether any specific replicates in a set of analyses are biasing the downstream comparative quantitation. In this paper, we present an experimental method to address such a concern. PeptideMind uses a series of clustering Machine Learning algorithms to assess outliers when comparing proteomics data from two states with six replicates each. The program is a JVM native application written in the Kotlin language with Python sub-process calls to scikit-learn. By permuting the six data replicates provided into four hundred triplet non redundant pairwise comparisons, PeptideMind determines if any one replicate is biasing the downstream quantitation of the states. In addition, PeptideMind generates useful visual representations of the spread of the significance measures, allowing researchers a rapid, effective way to monitor the quality of those identified proteins found to be differentially expressed between sample states.

Inheritance and Correlation Analysis of Main Agronomic Traits in Mungbean (Vigna radiata L.) by Mixed Linear Models

2013 ◽  
Vol 38 (4) ◽  
pp. 624-631
Author(s):  
Chang-You LIU ◽  
Bao-Jie FAN ◽  
Zhi-Min CAO ◽  
Yan WANG ◽  
Zhi-Xiao ZHANG ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Vigna Radiata ◽  
Linear Models ◽  
Agronomic Traits ◽  
Mixed Linear Models

Chlamydia trachomatis in Andrologic Patients – Direct and Indirect Detection

Infection ◽  
2001 ◽  
Vol 29 (3) ◽  
pp. 113-118 ◽  
Author(s):  
R. Bollmann ◽  
S. Engel ◽  
R. Petzoldt ◽  
U.B. Göbel
Keyword(s):  
Chlamydia Trachomatis ◽  
Indirect Detection ◽  
Direct And Indirect Detection

scholarly journals Dark matter candidates in a type-II radiative neutrino mass model

2021 ◽  
Vol 2021 (6) ◽  
Author(s):  
Roberto A. Lineros ◽  
Mathias Pierre
Keyword(s):  
Dark Matter ◽  
Neutral Particle ◽  
Matter Density ◽  
Indirect Detection ◽  
Dark Matter Candidate ◽  
Neutrino Masses ◽  
Type Ii ◽  
Mass Model ◽  
Direct And Indirect Detection

Abstract We explore the connection between Dark Matter and neutrinos in a model inspired by radiative Type-II seessaw and scotogenic scenarios. In our model, we introduce new electroweakly charged states (scalars and a vector-like fermion) and impose a discrete ℤ2 symmetry. Neutrino masses are generated at the loop level and the lightest ℤ2-odd neutral particle is stable and it can play the role of a Dark Matter candidate. We perform a numerical analysis of the model showing that neutrino masses and flavour structure can be reproduced in addition to the correct dark matter density, with viable DM masses from 700 GeV to 30 TeV. We explore direct and indirect detection signatures and show interesting detection prospects by CTA, Darwin and KM3Net and highlight the complementarity between these observables.

scholarly journals Vector dark matter from split SU(2) gauge bosons

2021 ◽  
Vol 2021 (7) ◽  
Author(s):  
Zexi Hu ◽  
Chengfeng Cai ◽  
Yi-Lei Tang ◽  
Zhao-Huan Yu ◽  
Hong-Hao Zhang
Keyword(s):  
Dark Matter ◽  
Gauge Group ◽  
Measurement Data ◽  
Indirect Detection ◽  
Gauge Bosons ◽  
Dark Matter Relic Density ◽  
Matter Model ◽  
Direct And Indirect Detection ◽  
Numerous Parameter ◽  
Dark Matter Model

Abstract We propose a vector dark matter model with an exotic dark SU(2) gauge group. Two Higgs triplets are introduced to spontaneously break the symmetry. All of the dark gauge bosons become massive, and the lightest one is a viable vector DM candidate. Its stability is guaranteed by a remaining Z2 symmetry. We study the parameter space constrained by the Higgs measurement data, the dark matter relic density, and direct and indirect detection experiments. We find numerous parameter points satisfying all the constraints, and they could be further tested in future experiments. Similar methodology can be used to construct vector dark matter models from an arbitrary SO(N) gauge group.

scholarly journals Stool and ruminal microbiome components associated with methane emission and feed efficiency in Nelore beef cattle.

2021 ◽  
Author(s):  
Bruno G.N. Andrade ◽  
Haithem Afli ◽  
Flavia A. Bressani ◽  
Rafael R. C. Cuadrat ◽  
Priscila S. N. de Oliveira ◽  
...  
Keyword(s):  
Methane Emission ◽  
Feed Efficiency ◽  
Linear Models ◽  
Anthropogenic Activities ◽  
Mixed Linear Models ◽  
Human Welfare ◽  
Feed Composition ◽  
Fecal Microbiome ◽  
Treatment Groups ◽  
The Impact

Abstract Background: The impact of extreme changes in weather patterns in the economy and human welfare are some of the biggest challenges that our civilization is facing. From the anthropogenic activities that contribute to climate change, reducing the impact of farming activities is a priority, since it is responsible for up to 18% of greenhouse gases linked to such activities. To this end, we tested if the ruminal and fecal microbiome components of 52 Brazilian Nelore bulls, belonging to two treatment groups based on the feed intervention, conventional and by-products based diet, could be used in the future as biomarkers for methane emission and feed efficiency in bovine.Results: We identified a total of 5,693 Amplicon Sequence Variants (ASVs) in the Nelore bulls microbiomes. Differential abundance (DA) analysis with the ANCOM approach identified 30 bacterial and 15 archaea ASVs as DA among treatment groups. Association analysis using Maaslin2 and Mixed Linear Models indicated that bacterial ASVs are linked to the residual methane emission (RCH4) and Residual Feed Intake (RFI) phenotypes, contributing to the host’s phenotypic variation, suggesting their potential as targets for interventions and/or biomarkers.Conclusion: Feed composition induced significant differences in abundance and richness of ruminal and fecal microbial populations. The diet based on industrial byproducts applied to our treatment groups influenced the microbiome diversity of bacteria and archaea, but not of protozoa. Different ASVs were associated with RCH4 emission and RFI in both ruminal and fecal microbiomes. While ruminal ASVs are expected to directly influence RCH4 emission and RFI, the relation of fecal taxa, such as Alistipes and Rikenellaceae (gut group RC9), with these traits might also be associated with host health due to their link to anti-inflammatory compounds, and these have the potential to be used as accessible biomarkers for these complex phenotypes.

scholarly journals Deep Semi-Supervised Learning Improves Universal Peptide Identification of Shotgun Proteomics Data

2020 ◽  
Author(s):  
John T. Halloran ◽  
Gregor Urban ◽  
David Rocke ◽  
Pierre Baldi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Peptide Identification ◽  
Shotgun Proteomics ◽  
Database Search ◽  
Supervised Machine Learning ◽  
Superior Performance ◽  
Support Vector ◽  
Proteomics Data ◽  
Learning Classifier

AbstractSemi-supervised machine learning post-processors critically improve peptide identification of shot-gun proteomics data. Such post-processors accept the peptide-spectrum matches (PSMs) and feature vectors resulting from a database search, train a machine learning classifier, and recalibrate PSMs using the trained parameters, often yielding significantly more identified peptides across q-value thresholds. However, current state-of-the-art post-processors rely on shallow machine learning methods, such as support vector machines. In contrast, the powerful training capabilities of deep learning models have displayed superior performance to shallow models in an ever-growing number of other fields. In this work, we show that deep models significantly improve the recalibration of PSMs compared to the most accurate and widely-used post-processors, such as Percolator and PeptideProphet. Furthermore, we show that deep learning is able to adaptively analyze complex datasets and features for more accurate universal post-processing, leading to both improved Prosit analysis and markedly better recalibration of recently developed database-search functions.

scholarly journals Two natural scenarios for dark matter particles coexisting with supersymmetry

2019 ◽  
Vol 34 (02) ◽  
pp. 1930001 ◽  
Author(s):  
Maxwell Throm ◽  
Reagan Thornberry ◽  
John Killough ◽  
Brian Sun ◽  
Gentill Abdulla ◽  
...  
Keyword(s):  
Dark Matter ◽  
Cross Sections ◽  
Higgs Sector ◽  
Weakly Interacting Massive Particles ◽  
Indirect Detection ◽  
Experimental Facilities ◽  
Direct And Indirect Detection ◽  
Extended Higgs Sector ◽  
Matter Component ◽  
Multicomponent Model

We describe two natural scenarios in which both dark matter, weakly interacting massive particles (WIMPs) and a variety of supersymmetric partners should be discovered in the foreseeable future. In the first scenario, the WIMPs are neutralinos, but they are only one component of the dark matter, which is dominantly composed of other relic particles such as axions. (This is the multicomponent model of Baer, Barger, Sengupta and Tata.) In the second scenario, the WIMPs result from an extended Higgs sector and may be the only dark matter component. In either scenario, both the dark matter WIMP and a plethora of other neutral and charged particles await discovery at many experimental facilities. The new particles in the second scenario have far weaker cross-sections for direct and indirect detection via their gauge interactions, which are either momentum-dependent or second-order. However, as we point out here, they should have much stronger interactions via the Higgs. We estimate that their interactions with fermions will then be comparable to (although not equal to) those of neutralinos with a corresponding Higgs interaction. It follows that these newly proposed dark matter particles should be within the reach of emerging and proposed facilities for direct, indirect and collider-based detection.

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