17 research outputs found
CXCL-8-dependent and -independent neutrophil activation in COPD:experiences from a pilot study of the CXCR2 antagonist danirixin
Building and Maintaining Contexts in Interactive Networked Writing: An Examination of Deixis and Intertextuality in Instant Messaging
Paracrine and Autocrine Signals Induce and Maintain Mesenchymal and Stem Cell States in the Breast
GlyQ-IQ: Glycomics Quintavariate-Informed Quantification with High-Performance Computing and GlycoGrid 4D Visualization
Glycomics quintavariate-informed
quantification (GlyQ-IQ) is a
biologically guided glycomics analysis tool for identifying N-glycans
in liquid chromatographyâmass spectrometry (LCâMS) data.
Glycomics LCâMS data sets have convoluted extracted ion chromatograms
that are challenging to deconvolve with existing software tools. LC
deconvolution into constituent pieces is critical in glycomics data
sets because chromatographic peaks correspond to different intact
glycan structural isomers. The biological targeted analysis approach
offers several key advantages to traditional LCâMS data processing. <i>A priori</i> glycan information about the individual targetâs
elemental composition allows for improved sensitivity by utilizing
the exact isotope profile information to focus chromatogram generation
and LC peak fitting on the isotopic species having the highest intensity.
Glycan target annotation utilizes glycan family relationships and
in source fragmentation in addition to high specificity feature LCâMS
detection to improve the specificity of the analysis. The GlyQ-IQ
software was developed in this work and evaluated in the context of
profiling the N-glycan compositions from human serum LCâMS
data sets. A case study is presented to demonstrate how GlyQ-IQ identifies
and removes confounding chromatographic peaks from high mannose glycan
isomers from human blood serum. In addition, GlyQ-IQ was used to generate
a broad human serum N-glycan profile from a high resolution nanoelectrospray-liquid
chromatographyâtandem mass spectrometry (nESI-LCâMS/MS)
data set. A total of 156 glycan compositions and 640 glycan isomers
were detected from a single sample. Over 99% of the GlyQ-IQ glycan-feature
assignments passed manual validation and are backed with high-resolution
mass spectra
GlyQ-IQ: Glycomics Quintavariate-Informed Quantification with High-Performance Computing and GlycoGrid 4D Visualization
Glycomics quintavariate-informed
quantification (GlyQ-IQ) is a
biologically guided glycomics analysis tool for identifying N-glycans
in liquid chromatographyâmass spectrometry (LCâMS) data.
Glycomics LCâMS data sets have convoluted extracted ion chromatograms
that are challenging to deconvolve with existing software tools. LC
deconvolution into constituent pieces is critical in glycomics data
sets because chromatographic peaks correspond to different intact
glycan structural isomers. The biological targeted analysis approach
offers several key advantages to traditional LCâMS data processing. <i>A priori</i> glycan information about the individual targetâs
elemental composition allows for improved sensitivity by utilizing
the exact isotope profile information to focus chromatogram generation
and LC peak fitting on the isotopic species having the highest intensity.
Glycan target annotation utilizes glycan family relationships and
in source fragmentation in addition to high specificity feature LCâMS
detection to improve the specificity of the analysis. The GlyQ-IQ
software was developed in this work and evaluated in the context of
profiling the N-glycan compositions from human serum LCâMS
data sets. A case study is presented to demonstrate how GlyQ-IQ identifies
and removes confounding chromatographic peaks from high mannose glycan
isomers from human blood serum. In addition, GlyQ-IQ was used to generate
a broad human serum N-glycan profile from a high resolution nanoelectrospray-liquid
chromatographyâtandem mass spectrometry (nESI-LCâMS/MS)
data set. A total of 156 glycan compositions and 640 glycan isomers
were detected from a single sample. Over 99% of the GlyQ-IQ glycan-feature
assignments passed manual validation and are backed with high-resolution
mass spectra
Core academic language skills: An expanded operational construct and a novel instrument to chart school-relevant language proficiency in preadolescent and adolescent learners
GlyQ-IQ: Glycomics Quintavariate-Informed Quantification with High-Performance Computing and GlycoGrid 4D Visualization
Glycomics quintavariate-informed
quantification (GlyQ-IQ) is a
biologically guided glycomics analysis tool for identifying N-glycans
in liquid chromatographyâmass spectrometry (LCâMS) data.
Glycomics LCâMS data sets have convoluted extracted ion chromatograms
that are challenging to deconvolve with existing software tools. LC
deconvolution into constituent pieces is critical in glycomics data
sets because chromatographic peaks correspond to different intact
glycan structural isomers. The biological targeted analysis approach
offers several key advantages to traditional LCâMS data processing. <i>A priori</i> glycan information about the individual targetâs
elemental composition allows for improved sensitivity by utilizing
the exact isotope profile information to focus chromatogram generation
and LC peak fitting on the isotopic species having the highest intensity.
Glycan target annotation utilizes glycan family relationships and
in source fragmentation in addition to high specificity feature LCâMS
detection to improve the specificity of the analysis. The GlyQ-IQ
software was developed in this work and evaluated in the context of
profiling the N-glycan compositions from human serum LCâMS
data sets. A case study is presented to demonstrate how GlyQ-IQ identifies
and removes confounding chromatographic peaks from high mannose glycan
isomers from human blood serum. In addition, GlyQ-IQ was used to generate
a broad human serum N-glycan profile from a high resolution nanoelectrospray-liquid
chromatographyâtandem mass spectrometry (nESI-LCâMS/MS)
data set. A total of 156 glycan compositions and 640 glycan isomers
were detected from a single sample. Over 99% of the GlyQ-IQ glycan-feature
assignments passed manual validation and are backed with high-resolution
mass spectra
GlyQ-IQ: Glycomics Quintavariate-Informed Quantification with High-Performance Computing and GlycoGrid 4D Visualization
Glycomics quintavariate-informed
quantification (GlyQ-IQ) is a
biologically guided glycomics analysis tool for identifying N-glycans
in liquid chromatographyâmass spectrometry (LCâMS) data.
Glycomics LCâMS data sets have convoluted extracted ion chromatograms
that are challenging to deconvolve with existing software tools. LC
deconvolution into constituent pieces is critical in glycomics data
sets because chromatographic peaks correspond to different intact
glycan structural isomers. The biological targeted analysis approach
offers several key advantages to traditional LCâMS data processing. <i>A priori</i> glycan information about the individual targetâs
elemental composition allows for improved sensitivity by utilizing
the exact isotope profile information to focus chromatogram generation
and LC peak fitting on the isotopic species having the highest intensity.
Glycan target annotation utilizes glycan family relationships and
in source fragmentation in addition to high specificity feature LCâMS
detection to improve the specificity of the analysis. The GlyQ-IQ
software was developed in this work and evaluated in the context of
profiling the N-glycan compositions from human serum LCâMS
data sets. A case study is presented to demonstrate how GlyQ-IQ identifies
and removes confounding chromatographic peaks from high mannose glycan
isomers from human blood serum. In addition, GlyQ-IQ was used to generate
a broad human serum N-glycan profile from a high resolution nanoelectrospray-liquid
chromatographyâtandem mass spectrometry (nESI-LCâMS/MS)
data set. A total of 156 glycan compositions and 640 glycan isomers
were detected from a single sample. Over 99% of the GlyQ-IQ glycan-feature
assignments passed manual validation and are backed with high-resolution
mass spectra