scholarly journals High-Throughput Single-Molecule Analysis via Divisive Segmentation and Clustering

2019 ◽  
Author(s):  
David S. White ◽  
Marcel P. Goldschen-Ohm ◽  
Randall H. Goldsmith ◽  
Baron Chanda
Keyword(s):  
High Throughput ◽  
Single Molecule ◽  
Data Sets ◽  
Binding Domains ◽  
High Throughput Experiments ◽  
Single Molecule Analysis ◽  
Analysis Platform ◽  
Improved Accuracy ◽  
Insight Into ◽  
Divisive Clustering

ABSTRACTSingle-molecule approaches provide insight into the dynamics of biomolecules, yet analysis methods have not scaled with the growing size of data sets acquired in high-throughput experiments. We present a new analysis platform (DISC) that uses divisive clustering to accelerate unsupervised analysis of single-molecule trajectories by up to three orders of magnitude with improved accuracy. Using DISC, we reveal an inherent lack of cooperativity between cyclic nucleotide binding domains from HCN pacemaker ion channels embedded in nanophotonic zero-mode waveguides.

scholarly journals Top-down machine learning approach for high-throughput single-molecule analysis

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
David S White ◽  
Marcel P Goldschen-Ohm ◽  
Randall H Goldsmith ◽  
Baron Chanda
Keyword(s):  
High Throughput ◽  
Single Molecule ◽  
Viterbi Algorithm ◽  
Binding Domains ◽  
Model Free ◽  
Pacemaker Channel ◽  
Multiple Binding ◽  
Single Molecule Analysis ◽  
Analysis Platform ◽  
Improved Accuracy

Single-molecule approaches provide enormous insight into the dynamics of biomolecules, but adequately sampling distributions of states and events often requires extensive sampling. Although emerging experimental techniques can generate such large datasets, existing analysis tools are not suitable to process the large volume of data obtained in high-throughput paradigms. Here, we present a new analysis platform (DISC) that accelerates unsupervised analysis of single-molecule trajectories. By merging model-free statistical learning with the Viterbi algorithm, DISC idealizes single-molecule trajectories up to three orders of magnitude faster with improved accuracy compared to other commonly used algorithms. Further, we demonstrate the utility of DISC algorithm to probe cooperativity between multiple binding events in the cyclic nucleotide binding domains of HCN pacemaker channel. Given the flexible and efficient nature of DISC, we anticipate it will be a powerful tool for unsupervised processing of high-throughput data across a range of single-molecule experiments.

scholarly journals COPAR: A ChIP-Seq Optimal Peak Analyzer

2017 ◽  
Vol 2017 ◽  
pp. 1-4
Author(s):  
Binhua Tang ◽  
Xihan Wang ◽  
Victor X. Jin
Keyword(s):  
High Throughput ◽  
Genomic Feature ◽  
Data Sets ◽  
Sequencing Data ◽  
Genomic Features ◽  
Peak Alignment ◽  
Chip Sequencing ◽  
Quality Check ◽  
User Friendly ◽  
High Throughput Experiments

Sequencing data quality and peak alignment efficiency of ChIP-sequencing profiles are directly related to the reliability and reproducibility of NGS experiments. Till now, there is no tool specifically designed for optimal peak alignment estimation and quality-related genomic feature extraction for ChIP-sequencing profiles. We developed open-sourced COPAR, a user-friendly package, to statistically investigate, quantify, and visualize the optimal peak alignment and inherent genomic features using ChIP-seq data from NGS experiments. It provides a versatile perspective for biologists to perform quality-check for high-throughput experiments and optimize their experiment design. The package COPAR can process mapped ChIP-seq read file in BED format and output statistically sound results for multiple high-throughput experiments. Together with three public ChIP-seq data sets verified with the developed package, we have deposited COPAR on GitHub under a GNU GPL license.

scholarly journals Single-molecule analysis reveals two separate DNA-binding domains in the Escherichia coli UvrA dimer

2009 ◽  
Vol 37 (6) ◽  
pp. 1962-1972 ◽  
Author(s):  
Koen Wagner ◽  
Geri Moolenaar ◽  
John van Noort ◽  
Nora Goosen
Keyword(s):  
Escherichia Coli ◽  
Dna Binding ◽  
Single Molecule ◽  
Dna Binding Domains ◽  
Binding Domains ◽  
Single Molecule Analysis

scholarly journals High-Throughput Single-Molecule Analysis of Nucleic Acid Enzyme Activity by Tethered Particle Motion

2012 ◽  
Vol 102 (3) ◽  
pp. 383a
Author(s):  
Thomas Plenat ◽  
Catherine Tardin ◽  
Philippe Rousseau ◽  
Laurence Salome
Keyword(s):  
Enzyme Activity ◽  
Nucleic Acid ◽  
High Throughput ◽  
Single Molecule ◽  
Particle Motion ◽  
Tethered Particle Motion ◽  
Single Molecule Analysis

scholarly journals Glycosylation in Indolent, Significant and Aggressive Prostate Cancer by Automated High-Throughput N-Glycan Profiling

2020 ◽  
Vol 21 (23) ◽  
pp. 9233
Author(s):  
Sarah Gilgunn ◽  
Keefe Murphy ◽  
Henning Stöckmann ◽  
Paul J. Conroy ◽  
T. Brendan Murphy ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Throughput ◽  
Expression Patterns ◽  
Ultra Performance Liquid Chromatography ◽  
High Throughput Analysis ◽  
Major Health ◽  
Glycan Profiling ◽  
Cellular Modifications ◽  
Analysis Platform ◽  
Insight Into

The diagnosis and treatment of prostate cancer (PCa) is a major health-care concern worldwide. This cancer can manifest itself in many distinct forms and the transition from clinically indolent PCa to the more invasive aggressive form remains poorly understood. It is now universally accepted that glycan expression patterns change with the cellular modifications that accompany the onset of tumorigenesis. The aim of this study was to investigate if differential glycosylation patterns could distinguish between indolent, significant, and aggressive PCa. Whole serum N-glycan profiling was carried out on 117 prostate cancer patients’ serum using our automated, high-throughput analysis platform for glycan-profiling which utilizes ultra-performance liquid chromatography (UPLC) to obtain high resolution separation of N-linked glycans released from the serum glycoproteins. We observed increases in hybrid, oligomannose, and biantennary digalactosylated monosialylated glycans (M5A1G1S1, M8, and A2G2S1), bisecting glycans (A2B, A2(6)BG1) and monoantennary glycans (A1), and decreases in triantennary trigalactosylated trisialylated glycans with and without core fucose (A3G3S3 and FA3G3S3) with PCa progression from indolent through significant and aggressive disease. These changes give us an insight into the disease pathogenesis and identify potential biomarkers for monitoring the PCa progression, however these need further confirmation studies.

scholarly journals PlantGSAD: a comprehensive gene set annotation database for plant species

2021 ◽  
Author(s):  
Xuelian Ma ◽  
Hengyu Yan ◽  
Jiaotong Yang ◽  
Yue Liu ◽  
Zhongqiu Li ◽  
...  
Keyword(s):  
Plant Species ◽  
Gene Annotation ◽  
Data Sets ◽  
Annotation Database ◽  
Gene Set ◽  
Gene Sets ◽  
Brassicaceae Species ◽  
High Throughput Experiments ◽  
Analysis Platform ◽  
Parent Child Relationships

Abstract With the accumulation of massive data sets from high-throughput experiments and the rapid emergence of new types of omics data, gene sets have become more diverse and essential for the refinement of gene annotation at multidimensional levels. Accordingly, we collected and defined 236 007 gene sets across different categories for 44 plant species in the Plant Gene Set Annotation Database (PlantGSAD). These gene sets were divided into nine main categories covering many functional subcategories, such as trait ontology, co-expression modules, chromatin states, and liquid-liquid phase separation. The annotations from the collected gene sets covered all of the genes in the Brassicaceae species Arabidopsis and Poaceae species Oryza sativa. Several GSEA tools are implemented in PlantGSAD to improve the efficiency of the analysis, including custom SEA for a flexible strategy based on customized annotations, SEACOMPARE for the cross-comparison of SEA results, and integrated visualization features for ontological analysis that intuitively reflects their parent-child relationships. In summary, PlantGSAD provides numerous gene sets for multiple plant species and highly efficient analysis tools. We believe that PlantGSAD will become a multifunctional analysis platform that can be used to predict and elucidate the functions and mechanisms of genes of interest. PlantGSAD is publicly available at http://systemsbiology.cau.edu.cn/PlantGSEAv2/.

scholarly journals Structural insight into substrate and inhibitor discrimination by human P-glycoprotein

Science ◽  
2019 ◽  
Vol 363 (6428) ◽  
pp. 753-756 ◽  
Author(s):  
Amer Alam ◽  
Julia Kowal ◽  
Eugenia Broude ◽  
Igor Roninson ◽  
Kaspar P. Locher
Keyword(s):  
Single Molecule ◽  
Conformational Changes ◽  
Binding Pocket ◽  
Drug Binding ◽  
Therapeutic Drug ◽  
Binding Domains ◽  
Drug Binding Site ◽  
Cryo Electron Microscopy ◽  
P Glycoprotein ◽  
Insight Into

ABCB1, also known as P-glycoprotein, actively extrudes xenobiotic compounds across the plasma membrane of diverse cells, which contributes to cellular drug resistance and interferes with therapeutic drug delivery. We determined the 3.5-angstrom cryo–electron microscopy structure of substrate-bound human ABCB1 reconstituted in lipidic nanodiscs, revealing a single molecule of the chemotherapeutic compound paclitaxel (Taxol) bound in a central, occluded pocket. A second structure of inhibited, human-mouse chimeric ABCB1 revealed two molecules of zosuquidar occupying the same drug-binding pocket. Minor structural differences between substrate- and inhibitor-bound ABCB1 sites are amplified toward the nucleotide-binding domains (NBDs), revealing how the plasticity of the drug-binding site controls the dynamics of the adenosine triphosphate–hydrolyzing NBDs. Ordered cholesterol and phospholipid molecules suggest how the membrane modulates the conformational changes associated with drug binding and transport.

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