scholarly journals Selfish: discovery of differential chromatin interactions via a self-similarity measure

2019 ◽  
Vol 35 (14) ◽  
pp. i145-i153 ◽  
Author(s):  
Abbas Roayaei Ardakany ◽  
Ferhat Ay ◽  
Stefano Lonardi
Keyword(s):  
Comparative Analysis ◽  
Similarity Measure ◽  
Fundamental Problem ◽  
Three Dimensional ◽  
Biological Significance ◽  
Real Data ◽  
Dimensional Structure ◽  
Self Similarity ◽  
Contact Maps ◽  
Chromatin Interactions

AbstractMotivationHigh-throughput conformation capture experiments, such as Hi-C provide genome-wide maps of chromatin interactions, enabling life scientists to investigate the role of the three-dimensional structure of genomes in gene regulation and other essential cellular functions. A fundamental problem in the analysis of Hi-C data is how to compare two contact maps derived from Hi-C experiments. Detecting similarities and differences between contact maps are critical in evaluating the reproducibility of replicate experiments and for identifying differential genomic regions with biological significance. Due to the complexity of chromatin conformations and the presence of technology-driven and sequence-specific biases, the comparative analysis of Hi-C data is analytically and computationally challenging.ResultsWe present a novel method called Selfish for the comparative analysis of Hi-C data that takes advantage of the structural self-similarity in contact maps. We define a novel self-similarity measure to design algorithms for (i) measuring reproducibility for Hi-C replicate experiments and (ii) finding differential chromatin interactions between two contact maps. Extensive experimental results on simulated and real data show that Selfish is more accurate and robust than state-of-the-art methods.Availability and implementationhttps://github.com/ucrbioinfo/Selfish

scholarly journals Selfish: Discovery of Differential Chromatin Interactions via a Self-Similarity Measure

2019 ◽  
Author(s):  
Abbas Roayaei Ardakany ◽  
Ferhat Ay ◽  
Stefano Lonardi
Keyword(s):  
Comparative Analysis ◽  
Similarity Measure ◽  
Fundamental Problem ◽  
Three Dimensional ◽  
Biological Significance ◽  
Real Data ◽  
Dimensional Structure ◽  
Self Similarity ◽  
Contact Maps ◽  
Chromatin Interactions

AbstractMotivationHigh-throughput conformation capture experiments such as Hi-C provide genome-wide maps of chromatin interactions, enabling life scientists to investigate the role of the three-dimensional structure of genomes in gene regulation and other essential cellular functions. A fundamental problem in the analysis of Hi-C data is how to compare two contact maps derived from Hi-C experiments. Detecting similarities and differences between contact maps is critical in evaluating the reproducibility of replicate experiments and identifying differential genomic regions with biological significance. Due to the complexity of chromatin conformations and the presence of technology-driven and sequence-specific biases, the comparative analysis of Hi-C data is analytically and computationally challenging.ResultsWe present a novel method called Selfish for the comparative analysis of Hi-C data that takes advantage of the structural self-similarity in contact maps. We define a novel self-similarity measure to design algorithms for (i) measuring reproducibility for Hi-C replicate experiments and (ii) finding differential chromatin interactions between two contact maps. Extensive experimental results on simulated and real data show that Selfish is more accurate and robust than state-of-the-art methods.Availabilityhttps://github.com/ucrbioinfo/[email protected] and [email protected]

scholarly journals multiHiCcompare: joint normalization and comparative analysis of complex Hi-C experiments

2019 ◽  
Vol 35 (17) ◽  
pp. 2916-2923 ◽  
Author(s):  
John C Stansfield ◽  
Kellen G Cresswell ◽  
Mikhail G Dozmorov
Keyword(s):  
Comparative Analysis ◽  
A Priori ◽  
Three Dimensional ◽  
R Package ◽  
Supplementary Information ◽  
Chromatin Interaction ◽  
Model Framework ◽  
Chromatin Interactions ◽  
Loess Regression ◽  
Sequencing Studies

Abstract Motivation With the development of chromatin conformation capture technology and its high-throughput derivative Hi-C sequencing, studies of the three-dimensional interactome of the genome that involve multiple Hi-C datasets are becoming available. To account for the technology-driven biases unique to each dataset, there is a distinct need for methods to jointly normalize multiple Hi-C datasets. Previous attempts at removing biases from Hi-C data have made use of techniques which normalize individual Hi-C datasets, or, at best, jointly normalize two datasets. Results Here, we present multiHiCcompare, a cyclic loess regression-based joint normalization technique for removing biases across multiple Hi-C datasets. In contrast to other normalization techniques, it properly handles the Hi-C-specific decay of chromatin interaction frequencies with the increasing distance between interacting regions. multiHiCcompare uses the general linear model framework for comparative analysis of multiple Hi-C datasets, adapted for the Hi-C-specific decay of chromatin interaction frequencies. multiHiCcompare outperforms other methods when detecting a priori known chromatin interaction differences from jointly normalized datasets. Applied to the analysis of auxin-treated versus untreated experiments, and CTCF depletion experiments, multiHiCcompare was able to recover the expected epigenetic and gene expression signatures of loss of chromatin interactions and reveal novel insights. Availability and implementation multiHiCcompare is freely available on GitHub and as a Bioconductor R package https://bioconductor.org/packages/multiHiCcompare. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A forty-year journey in plant research: original contributions to flavonoid biochemistry

2005 ◽  
Vol 83 (5) ◽  
pp. 433-450 ◽  
Author(s):  
Ragai K Ibrahim
Keyword(s):  
Three Dimensional ◽  
Biological Significance ◽  
Flavonoid Biosynthesis ◽  
Dimensional Structure ◽  
Enzymatic Reactions ◽  
Research Career ◽  
Substrate Preferences ◽  
Genes Encoding ◽  
The Common ◽  
New Compounds

This review highlights original contributions by the author to the field of flavonoid biochemistry during his research career of more than four decades. These include elucidation of novel aspects of some of the common enzymatic reactions involved in the later steps of flavonoid biosynthesis, with emphasis on methyltransferases, glucosyltransferases, sulfotransferases, and an oxoglutarate-dependent dioxygenase, as well as cloning, and inferences about phylogenetic relationships, of the genes encoding some of these enzymes. The three-dimensional structure of a flavonol O-methyltransferase was studied through homology-based modeling, using a caffeic acid O-methyltransferase as a template, to explain their strict substrate preferences. In addition, the biological significance of enzymatic prenylation of isoflavones, as well as their role as phytoanticipins and inducers of nodulation genes, are emphasized. Finally, the potential application of knowledge about the genes encoding these enzyme reactions is discussed in terms of improving plant productivity and survival, modification of flavonoid profiles, and the search for new compounds with pharmaceutical and (or) nutraceutical value.Key words: flavonoid enzymology, metabolite localization, gene cloning, 3-D structure, phylogeny.

scholarly journals FT-COMAR: fault tolerant three-dimensional structure reconstruction from protein contact maps

2008 ◽  
Vol 24 (10) ◽  
pp. 1313-1315 ◽  
Author(s):  
Marco Vassura ◽  
Luciano Margara ◽  
Pietro Di Lena ◽  
Filippo Medri ◽  
Piero Fariselli ◽  
...  
Keyword(s):  
Fault Tolerant ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Three Dimensional Structure ◽  
Contact Maps ◽  
Structure Reconstruction ◽  
Protein Contact Maps

scholarly journals A Transferable Model For Chromosome Architecture

2016 ◽  
Author(s):  
Michele Di Pierro ◽  
Bin Zhang ◽  
Erez Lieberman Aiden ◽  
Peter G. Wolynes ◽  
José N. Onuchic
Keyword(s):  
Three Dimensional ◽  
Maximum Entropy Principle ◽  
Dimensional Structure ◽  
Open Chromatin ◽  
3D Structures ◽  
Chromosome Architecture ◽  
Contact Maps ◽  
Entropy Principle ◽  
Dynamics Simulations

AbstractIn vivo, the human genome folds into a characteristic ensemble of three-dimensional structures. The mechanism driving the folding process remains unknown. We report a theoretical model for chromatin (Minimal Chromatin Model) that explains the folding of interphase chromosomes and generates chromosome conformations consistent with experimental data. The energy landscape of the model was derived by using the maximum entropy principle and relies on two experimentally derived inputs: a classification of loci into chromatin types and a catalog of the positions of chromatin loops. First, we trained our energy function using the Hi-C contact map of chromosome 10 from human GM12878 lymphoblastoid cells. Then we used the model to perform molecular dynamics simulations producing an ensemble of 3D structures for all GM12878 autosomes. Finally, we used these 3D structures to generate contact maps. We found that simulated contact maps closely agree with experimental results for all GM12878 autosomes.The ensemble of structures resulting from these simulations exhibited unknotted chromosomes, phase separation of chromatin types, and a tendency for open chromatin to lie at the periphery of chromosome territories.One Sentence Summary:We report a model for chromatin that explains and accurately reproduces the three-dimensional structure of chromosomes in interphase.

Comparative analysis of the three-dimensional structure of Periplaneta fuliginosa densovirus

2003 ◽  
Vol 48 (3) ◽  
pp. 277-281
Author(s):  
Li Li ◽  
Donghua Chen ◽  
Zhenghong Zhou ◽  
Jiamin Zhang ◽  
Yuanyang Hu
Keyword(s):  
Comparative Analysis ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Three Dimensional Structure

Inversion of pole‐pole data for 3-D resistivity structure beneath arrays of electrodes

Geophysics ◽  
1991 ◽  
Vol 56 (7) ◽  
pp. 951-960 ◽  
Author(s):  
S. K. Park ◽  
G. P. Van
Keyword(s):  
Three Dimensional ◽  
Synthetic Data ◽  
Real Data ◽  
Dimensional Structure ◽  
Resistivity Structure ◽  
Three Dimensional Structure ◽  
Resistivity Data ◽  
Practical Algorithm ◽  
Schlumberger Sounding ◽  
Well Data

We have developed a practical algorithm for inverting gridded resistivity data for three‐dimensional structure and applied it to data from an experiment designed to detect leaks from ponds. This method yields relatively accurate reconstructions of structure when applied to synthetic data, but lateral contrasts in resistivity are mapped much more accurately than are vertical contrasts. The best results are obtained when transmitting electrodes are located directly above the suspected leak. Application to real data yields results which are consistent with well data and an adjacent Schlumberger sounding.

scholarly journals Deep Homology-Based Protein Contact-Map Prediction

2020 ◽  
Author(s):  
Omer Ronen ◽  
Or Zuk
Keyword(s):  
Fundamental Problem ◽  
Homology Modelling ◽  
Three Dimensional ◽  
Amino Acid Sequences ◽  
Physical Contact ◽  
Dimensional Structure ◽  
Evolutionary Information ◽  
Contact Map ◽  
Sequence Alignments ◽  
Three Dimensional Structure

AbstractPrediction of Proteins’ three dimensional structure and their contact maps from their amino-acid sequences is a fundamental problem in structural computational biology. The structure and contacts shed light on protein function, enhance our basic understanding of their molecular biology and may potentially aid in drug design. In recent years we have seen significant progress in protein contact map prediction from Multiple Sequence Alignments (MSA) of the target protein and its homologous, using signals of co-evolution and applying deep learning methods.Homology modelling is a popular and successful approach, where the structure of a protein is determined using information from known template structures of similar proteins, and has been shown to improve prediction even in cases of low sequence identity. Motivated by these observations, we developed Periscope, a method for homology-assisted contact map prediction using a deep convolutional network. Our method automatically integrates the co-evolutionary information from the MSA, and the physical contact information from the template structures.We apply our method to families of CAMEO and membrane proteins, and show improved prediction accuracy compared to the MSA-only based method RaptorX. Finally, we use our method to improve the subsequent task of predicting the proteins’ three dimensional structure based on the (improved) predicted contact map, and show initial promising results in this task too - our overall accuracy is comparable to the template-based Modeller software, yet the two methods are complementary and succeed on different targets.

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