Nanotubes of poly(phenylene vinylene) derivative at the air/water interfaceElectronic supplementary information (ESI) available: Detailed experimental procedures and experimental conditions. See http://www.rsc.org/suppdata/cc/b4/b403870c/

2004 ◽  
pp. 1664 ◽  
Author(s):  
Lin Guo ◽  
Zhongkui Wu ◽  
Yingqiu Liang
Keyword(s):  
Supplementary Information ◽  
Phenylene Vinylene ◽  
Experimental Conditions

scholarly journals Ribo-ODDR: Oligo design pipeline for experiment-specific rRNA depletion in ribo-seq

2021 ◽  
Author(s):  
Ferhat Alkan ◽  
Joana Silva ◽  
Eric Pintó Barberà ◽  
William J Faller
Keyword(s):  
Ribosome Profiling ◽  
Supplementary Information ◽  
Experimental Conditions ◽  
Computational Framework ◽  
Rna Translation ◽  
Rrna Depletion ◽  
Selection For ◽  
Nucleotide Resolution ◽  
User Friendly ◽  
Oligo Design

Abstract Motivation Ribosome Profiling (Ribo-seq) has revolutionized the study of RNA translation by providing information on ribosome positions across all translated RNAs with nucleotide-resolution. Yet several technical limitations restrict the sequencing depth of such experiments, the most common of which is the overabundance of rRNA fragments. Various strategies can be employed to tackle this issue, including the use of commercial rRNA depletion kits. However, as they are designed for more standardized RNAseq experiments, they may perform suboptimally in Ribo-seq. In order to overcome this, it is possible to use custom biotinylated oligos complementary to the most abundant rRNA fragments, however currently no computational framework exists to aid the design of optimal oligos. Results Here, we first show that a major confounding issue is that the rRNA fragments generated via Ribo-seq vary significantly with differing experimental conditions, suggesting that a “one-size-fits-all” approach may be inefficient. Therefore we developed Ribo-ODDR, an oligo design pipeline integrated with a user-friendly interface that assists in oligo selection for efficient experiment-specific rRNA depletion. Ribo-ODDR uses preliminary data to identify the most abundant rRNA fragments, and calculates the rRNA depletion efficiency of potential oligos. We experimentally show that Ribo-ODDR designed oligos outperform commercially available kits and lead to a significant increase in rRNA depletion in Ribo-seq. Availability Ribo-ODDR is freely accessible at https://github.com/fallerlab/Ribo-ODDR Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Metric Learning on Expression Data for Gene Function Prediction

2019 ◽  
Author(s):  
Stavros Makrodimitris ◽  
Marcel J.T. Reinders ◽  
Roeland C.H.J. van Ham
Keyword(s):  
Pearson Correlation ◽  
Metric Learning ◽  
Specific Weight ◽  
Supplementary Information ◽  
Expression Data ◽  
Rna Seq ◽  
Experimental Conditions ◽  
Expression Of Genes ◽  
Guilt By Association ◽  
Python Package

AbstractMotivationCo-expression of two genes across different conditions is indicative of their involvement in the same biological process. However, using RNA-Seq datasets with many experimental conditions from diverse sources introduces batch effects and other artefacts that might obscure the real co-expression signal. Moreover, only a subset of experimental conditions is expected to be relevant for finding genes related to a particular Gene Ontology (GO) term. Therefore, we hypothesize that when the purpose is to find similar functioning genes that the co-expression of genes should not be determined on all samples but only on those samples informative for the GO term of interest.ResultsTo address both types of effects, we developed MLC (Metric Learning for Co-expression), a fast algorithm that assigns a GO-term-specific weight to each expression sample. The goal is to obtain a weighted co-expression measure that is more suitable than the unweighted Pearson correlation for applying Guilt-By-Association-based function predictions. More specifically, if two genes are annotated with a given GO term, MLC tries to maximize their weighted co-expression, and, in addition, if one of them is not annotated with that term, the weighted co-expression is minimized. Our experiments on publicly available Arabidopsis thaliana RNA-Seq data demonstrate that MLC outperforms standard Pearson correlation in term-centric performance.AvailabilityMLC is available as a Python package at www.github.com/stamakro/[email protected] informationSupplementary data are available online.

Acid Catalyzed Polymerization of Phenyl Ethane 1,2-diol for One-Pot Synthesis of Organosoluble Poly(phenylene Vinylene)

2018 ◽  
Author(s):  
Anil Tipirneni ◽  
Hasan Zerze ◽  
Ece Tukenmez ◽  
Niyazi Bicak ◽  
Anthony J. McHugh
Keyword(s):  
Chemical Shifts ◽  
Methanesulfonic Acid ◽  
Coupling Reaction ◽  
Phenylene Vinylene ◽  
Visible Range ◽  
Experimental Conditions ◽  
One Pot ◽  
Molecular Weights ◽  
Acid Catalyzed ◽  
Catalyzed Polymerization

<div> <div> <div> <p>Acid catalyzed polymerization of phenyl ethane 1,2-diol is realized for the first time via a novel one-pot coupling reaction to produce organosoluble poly(phenylene vinylene). A variety of experimental conditions were investigated. PPV structure was confirmed by 13C-NMR and 1H-NMR chemical shifts. Fluorescence spectra of the polymer showed emission in the visible range, as to be expected. Average PPV molecular weights ranged from 670 to 6200 Da. The methanesulfonic acid (MesOH) catalyzed syntheses were shown to produce higher molecular weight and purity PPV than the sulfuric acid (H2SO4) counterparts, although both are able to produce organosoluble products. </p> </div> </div> </div>

A Novel Material for Chemical Sensor Applications: Oxidized MEH-PPV

2015 ◽  
Vol 644 ◽  
pp. 12-15
Author(s):  
Pejman Shabani ◽  
Farhad Akbari Boroumand ◽  
Faramarz Hossein-Babaei
Keyword(s):  
Electrical Conduction ◽  
Chemical Sensor ◽  
Oxidation Mechanism ◽  
Phenylene Vinylene ◽  
Experimental Conditions ◽  
Air Exposure ◽  
Sensor Applications ◽  
Hermetic Sealing ◽  
Novel Material ◽  
The Stability

Poly [2-methoxy, 5-(2¢-ethyl-hexyloxy)-p-phenylene-vinylene] (MEH-PPV) is a well known hole-conducting semiconductor utilized in the fabrication of optoelectronic devices because of its interesting electroluminescence. However, both electroluminescence and electrical conduction in this material sharply deteriorate upon exposure to oxygen, necessitating fabrication and hermetic sealing of the MEH-PPV-based devices in oxygen-free environments. Same shortcoming has excluded the material from applications requiring air exposure. We have recently presented a model for the oxidation mechanism of an MEH-PPV layer and have shown that such layers, after oxidation at certain conditions, can support air-stable electrical conduction. Here, we describe the experimental conditions required for the preparation of an oxidized MEHPPV layer, and provide experimental data on the stability of such layers at different conditions. It is shown that the fabricated air-stable oxidized MEH-PPV layers are excellent for a number of chemical sensor applications.

scholarly journals Condition-specific series of metabolic sub-networks and its application for gene set enrichment analysis

2018 ◽  
Vol 35 (13) ◽  
pp. 2258-2266 ◽  
Author(s):  
Van Du T Tran ◽  
Sébastien Moretti ◽  
Alix T Coste ◽  
Sara Amorim-Vaz ◽  
Dominique Sanglard ◽  
...  
Keyword(s):  
Fitness Function ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Supplementary Information ◽  
Optimal Order ◽  
Gene Set Enrichment ◽  
Experimental Conditions ◽  
Gene Set ◽  
Transcriptomic Data ◽  
Public Data

Abstract Motivation Genome-scale metabolic networks and transcriptomic data represent complementary sources of knowledge about an organism’s metabolism, yet their integration to achieve biological insight remains challenging. Results We investigate here condition-specific series of metabolic sub-networks constructed by successively removing genes from a comprehensive network. The optimal order of gene removal is deduced from transcriptomic data. The sub-networks are evaluated via a fitness function, which estimates their degree of alteration. We then consider how a gene set, i.e. a group of genes contributing to a common biological function, is depleted in different series of sub-networks to detect the difference between experimental conditions. The method, named metaboGSE, is validated on public data for Yarrowia lipolytica and mouse. It is shown to produce GO terms of higher specificity compared to popular gene set enrichment methods like GSEA or topGO. Availability and implementation The metaboGSE R package is available at https://CRAN.R-project.org/package=metaboGSE. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals ProCaff: protein–carbohydrate complex binding affinity database

2020 ◽  
Vol 36 (11) ◽  
pp. 3615-3617
Author(s):  
N R Siva Shanmugam ◽  
J Jino Blessy ◽  
K Veluraja ◽  
M Michael Gromiha
Keyword(s):  
Binding Affinity ◽  
Search Display ◽  
Supplementary Information ◽  
Gibbs Free Energy Change ◽  
Literature Information ◽  
Sequence Structure ◽  
Experimental Conditions ◽  
Carbohydrate Complex ◽  
And Function ◽  
Dissociation Constant Kd

Abstract Motivation Protein–carbohydrate interactions perform several cellular and biological functions and their structure and function are mainly dictated by their binding affinity. Although plenty of experimental data on binding affinity are available, there is no reliable and comprehensive database in the literature. Results We have developed a database on binding affinity of protein–carbohydrate complexes, ProCaff, which contains 3122 entries on dissociation constant (Kd), Gibbs free energy change (ΔG), experimental conditions, sequence, structure and literature information. Additional features include the options to search, display, visualization, download and upload the data. Availability and implementation The database is freely available at http://web.iitm.ac.in/bioinfo2/procaff/. The website is implemented using HTML and PHP and supports recent versions of major browsers such as Chrome, Firefox, IE10 and Opera. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals RobusTAD: A Tool for Robust Annotation of Topologically Associating Domain Boundaries

2018 ◽  
Author(s):  
Rola Dali ◽  
Guillaume Bourque ◽  
Mathieu Blanchette
Keyword(s):  
Comparative Analysis ◽  
Noise Level ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Desktop Computer ◽  
Experimental Conditions ◽  
Heterogeneous Samples ◽  
Topologically Associating Domains ◽  
Topologically Associating Domain ◽  
Domain Boundaries

AbstractMotivationTopologically Associating Domains (TADs) are chromatin structures that can be identified by analysis of Hi-C data. Tools currently available for TAD identification are sensitive to experimental conditions such as coverage, resolution and noise level.ResultsHere, we present RobusTAD, a tool to score TAD boundaries in a manner that is robust to these parameters. In doing so, RobusTAD eases comparative analysis of TAD structures across multiple heterogeneous samples.AvailabilityRobusTAD is implemented in R and released under a GPL license. RobusTAD can be downloaded from https://github.com/rdali/RobusTAD and runs on any standard desktop [email protected], [email protected] informationSupplementary data are available at Bioinformatics online.

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