C–C bond formation through oxidatively induced elimination of platinum complexes—A novel approach towards conjugated macrocyclesElectronic supplementary information (ESI) available: experimental section. See http://www.rsc.org/suppdata/cc/b3/b300542a/

2003 ◽  
pp. 948-949 ◽  
Author(s):  
Gerda Fuhrmann ◽  
Tony Debaerdemaeker ◽  
Peter Bäuerle
Keyword(s):  
Bond Formation ◽  
Platinum Complexes ◽  
Supplementary Information ◽  
Experimental Section ◽  
Novel Approach

ChemInform Abstract: Copper-Catalyzed C-N Bond Formation Through C-H/N-H Activation: A Novel Approach to the Synthesis of Multisubstituted Ureas.

ChemInform ◽  
2013 ◽  
Vol 44 (18) ◽  
pp. no-no
Author(s):  
Honglai Jiang ◽  
Aijun Lin ◽  
Chengjian Zhu ◽  
Yixiang Cheng
Keyword(s):  
Bond Formation ◽  
Novel Approach

scholarly journals PathScore: a web tool for identifying altered pathways in cancer data

2016 ◽  
Author(s):  
Stephen G. Gaffney ◽  
Jeffrey P. Townsend
Keyword(s):  
Web Application ◽  
Somatic Mutations ◽  
Supplementary Information ◽  
Web Tool ◽  
Cancer Data ◽  
Link Type ◽  
Novel Approach ◽  
Supplementary Material ◽  
User Friendly ◽  
Pathway Effect

ABSTRACTSummaryPathScore quantifies the level of enrichment of somatic mutations within curated pathways, applying a novel approach that identifies pathways enriched across patients. The application provides several user-friendly, interactive graphic interfaces for data exploration, including tools for comparing pathway effect sizes, significance, gene-set overlap and enrichment differences between projects.Availability and ImplementationWeb application available at pathscore.publichealth.yale.edu. Site implemented in Python and MySQL, with all major browsers supported. Source code available at github.com/sggaffney/pathscore with a GPLv3 [email protected] InformationAdditional documentation can be found at http://pathscore.publichealth.yale.edu/faq.

scholarly journals A neuronal signature for monogamous reunion

2019 ◽  
Author(s):  
Jennifer L. Scribner ◽  
Eric Vance ◽  
David S.W. Protter ◽  
William M. Sheeran ◽  
Elliott Saslow ◽  
...  
Keyword(s):  
Nucleus Accumbens ◽  
Bond Strength ◽  
Bond Formation ◽  
List Type ◽  
Prairie Voles ◽  
Neuronal Dynamics ◽  
Neuronal Ensembles ◽  
Novel Approach ◽  
The Stranger

AbstractPair bond formation depends vitally on neuromodulatory signaling within the nucleus accumbens, but the neuronal dynamics underlying this behavior remain unclear. Using in vivo Ca2+ imaging in monogamous prairie voles, we found that pair bonding does not elicit differences in overall nucleus accumbens Ca2+ activity. Instead, we identified distinct neuronal ensembles in this region recruited during approach to either a partner or novel vole. The partner-approach neuronal ensemble increased in size following bond formation and differences in the size of approach ensembles for partner and novel voles predicts bond strength. In contrast, neurons comprising departure ensembles do not change over time and are not correlated with bond strength indicating that ensemble plasticity is specific to partner approach. Further, the neurons comprising partner and novel approach ensembles are non-overlapping while departure ensembles are more overlapping than chance, which may reflect another key feature of approach ensembles. We posit that the features of the partner approach ensemble and its expansion upon bond formation make it a potential key substrate underlying bond formation and maturation.HighlightsWe performed in vivo Ca2+ in the nucleus accumbens of pair bonded prairie volesOverall nucleus accumbens activity did not differ during partner versus stranger interactionDistinct approach neurons exist for the partner and for the strangerPartner-approach ensemble increases as partner preference emergesWe identify a putative neuronal substrate underlying bond formation and maturation

scholarly journals Studying 3D genome evolution using genomic sequence

2019 ◽  
Author(s):  
Raphaël Mourad
Keyword(s):  
Genome Evolution ◽  
High Throughput Sequencing ◽  
Genomic Sequence ◽  
Regulation Of Gene Expression ◽  
Replication Timing ◽  
Three Dimensions ◽  
Supplementary Information ◽  
3D Genome ◽  
Chromatin Looping ◽  
Novel Approach

Abstract Motivation The three dimensions (3D) genome is essential to numerous key processes such as the regulation of gene expression and the replication-timing program. In vertebrates, chromatin looping is often mediated by CTCF, and marked by CTCF motif pairs in convergent orientation. Comparative high-throughput sequencing technique (Hi-C) recently revealed that chromatin looping evolves across species. However, Hi-C experiments are complex and costly, which currently limits their use for evolutionary studies over a large number of species. Results Here, we propose a novel approach to study the 3D genome evolution in vertebrates using the genomic sequence only, e.g. without the need for Hi-C data. The approach is simple and relies on comparing the distances between convergent and divergent CTCF motifs by computing a ratio we named the 3D ratio or ‘3DR’. We show that 3DR is a powerful statistic to detect CTCF looping encoded in the human genome sequence, thus reflecting strong evolutionary constraints encoded in DNA and associated with the 3D genome. When comparing vertebrate genomes, our results reveal that 3DR which underlies CTCF looping and topologically associating domain organization evolves over time and suggest that ancestral character reconstruction can be used to infer 3DR in ancestral genomes. Availability and implementation The R code is available at https://github.com/morphos30/PhyloCTCFLooping. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals PHANOTATE: a novel approach to gene identification in phage genomes

2019 ◽  
Vol 35 (22) ◽  
pp. 4537-4542 ◽  
Author(s):  
Katelyn McNair ◽  
Carol Zhou ◽  
Elizabeth A Dinsdale ◽  
Brian Souza ◽  
Robert A Edwards
Keyword(s):  
Gene Prediction ◽  
Optimal Path ◽  
Genome Structure ◽  
Weighted Graph ◽  
Open Reading Frames ◽  
Supplementary Information ◽  
Functional Protein ◽  
Protein Database ◽  
Protein Coding ◽  
Novel Approach

Abstract Motivation Currently there are no tools specifically designed for annotating genes in phages. Several tools are available that have been adapted to run on phage genomes, but due to their underlying design, they are unable to capture the full complexity of phage genomes. Phages have adapted their genomes to be extremely compact, having adjacent genes that overlap and genes completely inside of other longer genes. This non-delineated genome structure makes it difficult for gene prediction using the currently available gene annotators. Here we present PHANOTATE, a novel method for gene calling specifically designed for phage genomes. Although the compact nature of genes in phages is a problem for current gene annotators, we exploit this property by treating a phage genome as a network of paths: where open reading frames are favorable, and overlaps and gaps are less favorable, but still possible. We represent this network of connections as a weighted graph, and use dynamic programing to find the optimal path. Results We compare PHANOTATE to other gene callers by annotating a set of 2133 complete phage genomes from GenBank, using PHANOTATE and the three most popular gene callers. We found that the four programs agree on 82% of the total predicted genes, with PHANOTATE predicting more genes than the other three. We searched for these extra genes in both GenBank’s non-redundant protein database and all of the metagenomes in the sequence read archive, and found that they are present at levels that suggest that these are functional protein-coding genes. Availability and implementation https://github.com/deprekate/PHANOTATE Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Deep-learning approach to identifying cancer subtypes using high-dimensional genomic data

2019 ◽  
Author(s):  
Runpu Chen ◽  
Le Yang ◽  
Steve Goodison ◽  
Yijun Sun
Keyword(s):  
Deep Learning ◽  
Cluster Structure ◽  
Data Representation ◽  
Supplementary Information ◽  
High Dimensional ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Cancer Subtypes ◽  
Novel Approach ◽  
Open Source Software Package

Abstract Motivation Cancer subtype classification has the potential to significantly improve disease prognosis and develop individualized patient management. Existing methods are limited by their ability to handle extremely high-dimensional data and by the influence of misleading, irrelevant factors, resulting in ambiguous and overlapping subtypes. Results To address the above issues, we proposed a novel approach to disentangling and eliminating irrelevant factors by leveraging the power of deep learning. Specifically, we designed a deep-learning framework, referred to as DeepType, that performs joint supervised classification, unsupervised clustering and dimensionality reduction to learn cancer-relevant data representation with cluster structure. We applied DeepType to the METABRIC breast cancer dataset and compared its performance to state-of-the-art methods. DeepType significantly outperformed the existing methods, identifying more robust subtypes while using fewer genes. The new approach provides a framework for the derivation of more accurate and robust molecular cancer subtypes by using increasingly complex, multi-source data. Availability and implementation An open-source software package for the proposed method is freely available at http://www.acsu.buffalo.edu/~yijunsun/lab/DeepType.html. Supplementary information Supplementary data are available at Bioinformatics online.

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