scholarly journals Tools for generating and analyzing glycan microarray data

2020 ◽  
Vol 16 ◽  
pp. 2260-2271
Author(s):  
Akul Y Mehta ◽  
Jamie Heimburg-Molinaro ◽  
Richard D Cummings
Keyword(s):  
Microarray Data ◽  
Automated Analysis ◽  
Experimental Methods ◽  
Vital Role ◽  
Data Repositories ◽  
Glycan Microarray ◽  
Manual Analysis ◽  
Analysis Tools ◽  
Biological Polymers ◽  
Informatics Tools

Glycans are one of the major biological polymers found in the mammalian body. They play a vital role in a number of physiologic and pathologic conditions. Glycan microarrays allow a plethora of information to be obtained on protein–glycan binding interactions. In this review, we describe the intricacies of the generation of glycan microarray data and the experimental methods for studying binding. We highlight the importance of this knowledge before moving on to the data analysis. We then highlight a number of tools for the analysis of glycan microarray data such as data repositories, data visualization and manual analysis tools, automated analysis tools and structural informatics tools.

scholarly journals Automated analysis of 3D-echocardiography using spatially registered patient-specific CMR meshes

2021 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
D Zhao ◽  
E Ferdian ◽  
GD Maso Talou ◽  
GM Quill ◽  
K Gilbert ◽  
...  
Keyword(s):  
New Zealand ◽  
Interobserver Variability ◽  
Ground Truth ◽  
Automated Analysis ◽  
3D Echocardiography ◽  
Training Data ◽  
Patient Specific ◽  
Manual Analysis ◽  
Lv Mass ◽  
3D Echo

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Heart Foundation (NHF) of New Zealand Health Research Council (HRC) of New Zealand Artificial intelligence shows considerable promise for automated analysis and interpretation of medical images, particularly in the domain of cardiovascular imaging. While application to cardiac magnetic resonance (CMR) has demonstrated excellent results, automated analysis of 3D echocardiography (3D-echo) remains challenging, due to the lower signal-to-noise ratio (SNR), signal dropout, and greater interobserver variability in manual annotations. As 3D-echo is becoming increasingly widespread, robust analysis methods will substantially benefit patient evaluation.  We sought to leverage the high SNR of CMR to provide training data for a convolutional neural network (CNN) capable of analysing 3D-echo. We imaged 73 participants (53 healthy volunteers, 20 patients with non-ischaemic cardiac disease) under both CMR and 3D-echo (<1 hour between scans). 3D models of the left ventricle (LV) were independently constructed from CMR and 3D-echo, and used to spatially align the image volumes using least squares fitting to a cardiac template. The resultant transformation was used to map the CMR mesh to the 3D-echo image. Alignment of mesh and image was verified through volume slicing and visual inspection (Fig. 1) for 120 paired datasets (including 47 rescans) each at end-diastole and end-systole. 100 datasets (80 for training, 20 for validation) were used to train a shallow CNN for mesh extraction from 3D-echo, optimised with a composite loss function consisting of normalised Euclidian distance (for 290 mesh points) and volume. Data augmentation was applied in the form of rotations and tilts (<15 degrees) about the long axis. The network was tested on the remaining 20 datasets (different participants) of varying image quality (Tab. I). For comparison, corresponding LV measurements from conventional manual analysis of 3D-echo and associated interobserver variability (for two observers) were also estimated. Initial results indicate that the use of embedded CMR meshes as training data for 3D-echo analysis is a promising alternative to manual analysis, with improved accuracy and precision compared with conventional methods. Further optimisations and a larger dataset are expected to improve network performance. (n = 20) LV EDV (ml) LV ESV (ml) LV EF (%) LV mass (g) Ground truth CMR 150.5 ± 29.5 57.9 ± 12.7 61.5 ± 3.4 128.1 ± 29.8 Algorithm error -13.3 ± 15.7 -1.4 ± 7.6 -2.8 ± 5.5 0.1 ± 20.9 Manual error -30.1 ± 21.0 -15.1 ± 12.4 3.0 ± 5.0 Not available Interobserver error 19.1 ± 14.3 14.4 ± 7.6 -6.4 ± 4.8 Not available Tab. 1. LV mass and volume differences (means ± standard deviations) for 20 test cases. Algorithm: CNN – CMR (as ground truth). Abstract Figure. Fig 1. CMR mesh registered to 3D-echo.

scholarly journals A Mini-review of the Computational Methods Used in Identifying RNA 5- Methylcytosine Sites

2020 ◽  
Vol 21 (1) ◽  
pp. 3-10 ◽  
Author(s):  
Jianwei Li ◽  
Yan Huang ◽  
Yuan Zhou
Keyword(s):  
Computational Model ◽  
Computational Methods ◽  
Experimental Methods ◽  
Rna Metabolism ◽  
Vital Role ◽  
Accurate Localization ◽  
Growing Body ◽  
Tissue Cells

RNA 5-methylcytosine (m5C) is one of the pillars of post-transcriptional modification (PTCM). A growing body of evidence suggests that m5C plays a vital role in RNA metabolism. Accurate localization of RNA m5C sites in tissue cells is the premise and basis for the in-depth understanding of the functions of m5C. However, the main experimental methods of detecting m5C sites are limited to varying degrees. Establishing a computational model to predict modification sites is an excellent complement to wet experiments for identifying m5C sites. In this review, we summarized some available m5C predictors and discussed the characteristics of these methods.

scholarly journals Ian Read Gibbons. 30 October 1931—30 January 2018

2019 ◽  
Vol 66 ◽  
pp. 201-223
Author(s):  
Wendy E. Gibbons ◽  
Ronald D. Vale ◽  
Winfield S. Sale
Keyword(s):  
Molecular Biology ◽  
Mitotic Spindle ◽  
Molecular Motor ◽  
Experimental Methods ◽  
Vital Role ◽  
Biological Processes ◽  
Scientific Career ◽  
Innovative Solutions ◽  
Cilia And Flagella ◽  
The Right

Ian Read Gibbons is best known for discovering dynein, a molecular motor that powers the motion of cilia and flagella, is involved in assembling the mitotic spindle and moves chromosomes as well as other cargoes inside cells. Gibbons devoted his career in the lab of more than 50 years to understanding the mechanism of how dynein works in driving motility. In doing so, he built a life-long reputation as a creative, collaborative and tenacious researcher with an unusual gift for crafting innovative solutions to tricky experimental problems. His experimental methods spanned electron as well as light microscopy, biochemistry, molecular biology, crystallography and molecular modelling. He perceived the right set of experimental moves that would allow him to dissect and observe the physical underpinnings of minute biological processes such as the delicate movements of dynein. Gibbons traced his success to the ability to envision how pieces of a scientific puzzle might fit together to make a coherent story: ‘My own approach to research has always emphasized intuition over logic. Perhaps for that reason, I get attracted to questions with aesthetic appeal.’ Even though dynein holds much promise for helping to engineer ways to fight disease, Gibbons' focus was always on the intrinsic grace of understanding nature's workings. Gibbons’ marriage to his biochemist wife Barbara played a particularly vital role in his life. Barbara and Ian worked frequently together as she built her own scientific career through their partnership in the study of dynein. Ian's many scientific accomplishments as well as his dedication to his friends and family represent enduring gifts to us all.

scholarly journals Computational, Integrative, and Comparative Methods for the Elucidation of Genetic Coexpression Networks

2005 ◽  
Vol 2005 (2) ◽  
pp. 172-180 ◽  
Author(s):  
Nicole E. Baldwin ◽  
Elissa J. Chesler ◽  
Stefan Kirov ◽  
Michael A. Langston ◽  
Jay R. Snoddy ◽  
...  
Keyword(s):  
Microarray Data ◽  
Allelic Variation ◽  
Regulatory Element ◽  
Gene Expression Microarray ◽  
Data Repositories ◽  
Quantitative Genetic ◽  
Gene Expression Microarray Data ◽  
Trait Locus ◽  
Locus Mapping ◽  
Coexpression Networks

Gene expression microarray data can be used for the assembly of genetic coexpression network graphs. Using mRNA samples obtained from recombinant inbredMus musculusstrains, it is possible to integrate allelic variation with molecular and higher-order phenotypes. The depth of quantitative genetic analysis of microarray data can be vastly enhanced utilizing this mouse resource in combination with powerful computational algorithms, platforms, and data repositories. The resulting network graphs transect many levels of biological scale. This approach is illustrated with the extraction of cliques of putatively co-regulated genes and their annotation using gene ontology analysis andcis-regulatory element discovery. The causal basis for co-regulation is detected through the use of quantitative trait locus mapping.

scholarly journals GP3: GenePix post-processing program for automated analysis of raw microarray data

2002 ◽  
Vol 18 (5) ◽  
pp. 771-773 ◽  
Author(s):  
M. R. Fielden ◽  
R. G. Halgren ◽  
E. Dere ◽  
T. R. Zacharewski
Keyword(s):  
Microarray Data ◽  
Automated Analysis ◽  
Post Processing ◽  
Processing Program

scholarly journals Interlaboratory Diagnostic Validation of Conformation-Sensitive Capillary Electrophoresis for Mutation Scanning

2010 ◽  
Vol 56 (4) ◽  
pp. 593-602 ◽  
Author(s):  
Christopher J Mattocks ◽  
Gemma Watkins ◽  
Daniel Ward ◽  
Tom Janssens ◽  
Ermanno AJ Bosgoed ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Capillary Electrophoresis ◽  
Early Onset ◽  
High Standard ◽  
Automated Analysis ◽  
Nucleotide Substitutions ◽  
Mutation Scanning ◽  
Manual Analysis ◽  
Highly Sensitive ◽  
Bionumerics Software

Abstract Background: Indirect alternatives to sequencing as a method for mutation scanning are of interest to diagnostic laboratories because they have the potential for considerable savings in both time and costs. Ideally, such methods should be simple, rapid, and highly sensitive, and they should be validated formally to a very high standard. Currently, most reported methods lack one or more of these characteristics. We describe the optimization and validation of conformation-sensitive capillary electrophoresis (CSCE) for diagnostic mutation scanning. Methods: We initially optimized the performance of CSCE with a systematic panel of plasmid-based controls. We then compared manual analysis by visual inspection with automated analysis by BioNumerics software (Applied Maths) in a blinded interlaboratory validation with 402 BRCA1 (breast cancer 1, early onset) and BRCA2 (breast cancer 1, early onset) variants previously characterized by Sanger sequencing. Results: With automated analysis, we demonstrated a sensitivity of >99% (95% CI), which is indistinguishable from the sensitivity for conventional sequencing by capillary electrophoresis. The 95% CI for specificity was 90%–93%; thus, CSCE greatly reduces the number of fragments that need to be sequenced to fully characterize variants. By manual analysis, the 95% CIs for sensitivity and specificity were 98.3%–99.4% and 93.1%–95.5%, respectively. Conclusions: CSCE is amenable to a high degree of automation, and analyses can be multiplexed to increase both capacity and throughput. We conclude that once it is optimized, CSCE combined with analysis with BioNumerics software is a highly sensitive and cost-effective mutation-scanning technique suitable for routine genetic diagnostic analysis of heterozygous nucleotide substitutions, small insertions, and deletions.

Sign in / Sign up

Export Citation Format

Share Document