scholarly journals HiC-DC+ enables systematic 3D interaction calls and differential analysis for Hi-C and HiChIP

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Merve Sahin ◽  
Wilfred Wong ◽  
Yingqian Zhan ◽  
Kinsey Van Deynze ◽  
Richard Koche ◽  
...  
Keyword(s):  
Software Tool ◽  
Data Sets ◽  
Analysis Tool ◽  
Differential Analysis ◽  
3D Interaction ◽  
Chromosome Conformation ◽  
Statistical Framework ◽  
Genome Wide ◽  
Statistical Analysis Tool ◽  
And Function

AbstractRecent genome-wide chromosome conformation capture assays such as Hi-C and HiChIP have vastly expanded the resolution and throughput with which we can study 3D genomic architecture and function. Here, we present HiC-DC+, a software tool for Hi-C/HiChIP interaction calling and differential analysis using an efficient implementation of the HiC-DC statistical framework. HiC-DC+ integrates with popular preprocessing and visualization tools and includes topologically associating domain (TAD) and A/B compartment callers. We found that HiC-DC+ can more accurately identify enhancer-promoter interactions in H3K27ac HiChIP, as validated by CRISPRi-FlowFISH experiments, compared to existing methods. Differential HiC-DC+ analyses of published HiChIP and Hi-C data sets in settings of cellular differentiation and cohesin perturbation systematically and quantitatively recovers biological findings, including enhancer hubs, TAD aggregation, and the relationship between promoter-enhancer loop dynamics and gene expression changes. HiC-DC+ therefore provides a principled statistical analysis tool to empower genome-wide studies of 3D chromatin architecture and function.

scholarly journals HiC-DC+: systematic 3D interaction calls and differential analysis for Hi-C and HiChIP

2020 ◽  
Author(s):  
Merve Sahin ◽  
Wilfred Wong ◽  
Yingqian Zhan ◽  
Kinsey Van Deynze ◽  
Richard Koche ◽  
...  
Keyword(s):  
Software Tool ◽  
Efficient Implementation ◽  
Differential Analysis ◽  
3D Interaction ◽  
Statistical Framework ◽  
Loop Dynamics ◽  
Visualization Tools ◽  
Global Principles

AbstractWe present HiC-DC+, a software tool for Hi-C/Hi-ChIP interaction calling and differential analysis using an efficient implementation of the HiC-DC statistical framework. HiC-DC+ integrates with popular preprocessing and visualization tools, includes TAD and A/B compartment callers, and outperformed existing tools in H3K27ac HiChIP benchmarking as validated by CRISPRi-FlowFISH. Differential HiC-DC+ analysis recovered global principles of 3D organization during cohesin perturbation and differentiation, including TAD aggregation, enhancer hubs, and promoter-enhancer loop dynamics.

scholarly journals Mumame: a software tool for quantifying gene-specific point-mutations in shotgun metagenomic data

2019 ◽  
Vol 3 ◽  
Author(s):  
Shruthi Magesh ◽  
Viktor Jonsson ◽  
Johan Bengtsson-Palme
Keyword(s):  
Microbial Communities ◽  
Point Mutations ◽  
Software Tool ◽  
Metagenomic Data ◽  
Data Sets ◽  
Resistance Mutations ◽  
Shotgun Metagenomics ◽  
Key Factor ◽  
Detection Of Mutations ◽  
And Function

Metagenomics has emerged as a central technique for studying the structure and function of microbial communities. Often the functional analysis is restricted to classification into broad functional categories. However, important phenotypic differences, such as resistance to antibiotics, are often the result of just one or a few point mutations in otherwise identical sequences. Bioinformatic methods for metagenomic analysis have generally been poor at accounting for this fact, resulting in a somewhat limited picture of important aspects of microbial communities. Here, we address this problem by providing a software tool called Mumame, which can distinguish between wildtype and mutated sequences in shotgun metagenomic data and quantify their relative abundances. We demonstrate the utility of the tool by quantifying antibiotic resistance mutations in several publicly available metagenomic data sets. We also identified that sequencing depth is a key factor to detect rare mutations. Therefore, much larger numbers of sequences may be required for reliable detection of mutations than for most other applications of shotgun metagenomics. Mumame is freely available online (http://microbiology.se/software/mumame).

scholarly journals Functional organization of the human 4D Nucleome

2015 ◽  
Vol 112 (26) ◽  
pp. 8002-8007 ◽  
Author(s):  
Haiming Chen ◽  
Jie Chen ◽  
Lindsey A. Muir ◽  
Scott Ronquist ◽  
Walter Meixner ◽  
...  
Keyword(s):  
3D Imaging ◽  
Functional Organization ◽  
Genome Structure ◽  
Structural Features ◽  
Topological Stability ◽  
Chromosome Conformation ◽  
Dynamical Interaction ◽  
Gene Pairs ◽  
Genome Wide ◽  
And Function

The 4D organization of the interphase nucleus, or the 4D Nucleome (4DN), reflects a dynamical interaction between 3D genome structure and function and its relationship to phenotype. We present initial analyses of the human 4DN, capturing genome-wide structure using chromosome conformation capture and 3D imaging, and function using RNA-sequencing. We introduce a quantitative index that measures underlying topological stability of a genomic region. Our results show that structural features of genomic regions correlate with function with surprising persistence over time. Furthermore, constructing genome-wide gene-level contact maps aided in identifying gene pairs with high potential for coregulation and colocalization in a manner consistent with expression via transcription factories. We additionally use 2D phase planes to visualize patterns in 4DN data. Finally, we evaluated gene pairs within a circadian gene module using 3D imaging, and found periodicity in the movement of clock circadian regulator and period circadian clock 2 relative to each other that followed a circadian rhythm and entrained with their expression.

scholarly journals Integrative analysis of public ChIP-seq experiments reveals a complex multi-cell regulatory landscape

2014 ◽  
Vol 43 (4) ◽  
pp. e27-e27 ◽  
Author(s):  
Aurélien Griffon ◽  
Quentin Barbier ◽  
Jordi Dalino ◽  
Jacques van Helden ◽  
Salvatore Spicuglia ◽  
...  
Keyword(s):  
Human Genome ◽  
Enrichment Analysis ◽  
Search Space ◽  
Regulatory Elements ◽  
Data Sets ◽  
Analysis Tool ◽  
Coding Regions ◽  
Genome Wide ◽  
Public Data ◽  
Cancer Genomes

Abstract The large collections of ChIP-seq data rapidly accumulating in public data warehouses provide genome-wide binding site maps for hundreds of transcription factors (TFs). However, the extent of the regulatory occupancy space in the human genome has not yet been fully apprehended by integrating public ChIP-seq data sets and combining it with ENCODE TFs map. To enable genome-wide identification of regulatory elements we have collected, analysed and retained 395 available ChIP-seq data sets merged with ENCODE peaks covering a total of 237 TFs. This enhanced repertoire complements and refines current genome-wide occupancy maps by increasing the human genome regulatory search space by 14% compared to ENCODE alone, and also increases the complexity of the regulatory dictionary. As a direct application we used this unified binding repertoire to annotate variant enhancer loci (VELs) from H3K4me1 mark in two cancer cell lines (MCF-7, CRC) and observed enrichments of specific TFs involved in biological key functions to cancer development and proliferation. Those enrichments of TFs within VELs provide a direct annotation of non-coding regions detected in cancer genomes. Finally, full access to this catalogue is available online together with the TFs enrichment analysis tool (http://tagc.univ-mrs.fr/remap/).

scholarly journals HiCORE: Hi-C analysis for identification of core chromatin loops with higher resolution and reliability

2020 ◽  
Author(s):  
Hongwoo Lee ◽  
Pil Joon Seo
Keyword(s):  
High Resolution ◽  
High Throughput Sequencing ◽  
Stochastic Noise ◽  
3D Interaction ◽  
Chromosome Conformation ◽  
Genome Coverage ◽  
Chromatin Loops ◽  
Chromatin Interactions ◽  
Genome Wide ◽  
Physical Interactions

AbstractGenome-wide chromosome conformation capture (3C)-based high-throughput sequencing (Hi-C) has enabled identification of genome-wide chromatin loops. Because the Hi-C map with restriction fragment resolution is intrinsically associated with sparsity and stochastic noise, Hi-C data are usually binned at particular intervals; however, the binning method has limited reliability, especially at high resolution. Here, we describe a new method called HiCORE, which provides simple pipelines and algorithms to overcome the limitations of single-layered binning and predict core chromatin regions with 3D physical interactions. In this approach, multiple layers of binning with slightly shifted genome coverage are generated, and interacting bins at each layer are integrated to infer narrower regions of chromatin interactions. HiCORE predicts chromatin looping regions with higher resolution and contributes to the identification of the precise positions of potential genomic elements.Author SummaryThe Hi-C analysis has enabled to obtain information on 3D interaction of genomes. While various approaches have been developed for the identification of reliable chromatin loops, binning methods have been limitedly improved. We here developed HiCORE algorithm that generates multiple layers of bin-array and specifies core chromatin regions with 3D interactions. We validated our algorithm and provided advantages over conventional binning method. Overall, HiCORE facilitates to predict chromatin loops with higher resolution and reliability, which is particularly relevant in analysis of small genomes.

scholarly journals Design and Implementation of Reliability Analysis Tool using Real Time Data

2019 ◽  
Vol 8 (12) ◽  
pp. 3210-3213
Keyword(s):  
Real Time ◽  
Software Reliability ◽  
Web Application ◽  
Research Work ◽  
Software Tool ◽  
Security Level ◽  
Data Sets ◽  
Analysis Tool ◽  
Time Data ◽  
Design And Testing

To estimate the reliability of software numerous statistical methods are in practice. To accomplish the software reliability prediction in more accurate way there is a huge demand for data sets. The data sets that can be acquired as a result of testing the software can be used for predicting the reliability. The research work focuses on creating a layer of software design and testing method namely web software testing. The main purpose is to collect the erroneous data from real time. The reliability of software can be measured in different aspects like traffic handling capability when there are a greater number of users, the security level for cracking the passwords and the possibility of different combinations of errors that occurs when inputting the data. This proposed software tool will read the software description, and will generate test patterns according to the input types and collects testing results, predicting the software reliability in real time and suggesting the possible ways to improve the software. For designing purpose PHP for web application will be used to give the testing results.

scholarly journals A Comprehensive Study on the Importance of the Elbow and the Silhouette Metrics in Cluster Count Prediction for Partition Cluster Models

2021 ◽  
Vol 11 (4) ◽  
pp. 3792-3806
Author(s):  
A.A. Abdulnassar ◽  
Latha R. Nair
Keyword(s):  
Clustering Algorithms ◽  
Large Data ◽  
Cluster Models ◽  
Data Repository ◽  
Data Sets ◽  
Analysis Tool ◽  
Clustering Methods ◽  
K Value ◽  
Statistical Analysis Tool ◽  
Partition Clustering

Proper selection of cluster count gives better clustering results in partition models. Partition clustering methods are very simple as well as efficient. Kmeans and its modified versions are very efficient cluster models and the results are very sensitive to the chosen K value. The partition clustering algorithms are more suitable in applications where the data are arranged in a uniform manner. This work aims to evaluate the importance of assigning cluster count value in order to improve the efficiency of partition clustering algorithms using two well known statistical methods, the Elbow method and the Silhouette method. The performance of the Silhouette method and Elbow method are compared with different data sets from the UCI data repository. The values obtained using these methods are compared with the results of cluster performance obtained using the statistical analysis tool Weka on the selected data sets. Performance was evaluated on cluster efficiency for small and large data sets by varying the cluster count values. Similar results obtained from the three methods, the Elbow method, the Silhouette method and the clustering by Weka. It was also observed that the fast reduction in clustering efficiency for small changes in cluster count when the cluster count is small.

scholarly journals Identifying regulatory and spatial genomic architectural elements using cell type independent machine and deep learning models

2020 ◽  
Author(s):  
Laura D. Martens ◽  
Oisín Faust ◽  
Liviu Pirvan ◽  
Dóra Bihary ◽  
Shamith A. Samarajiwa
Keyword(s):  
Deep Learning ◽  
Cell Types ◽  
Data Sets ◽  
Chromosome Conformation ◽  
Architectural Elements ◽  
Chromatin Interactions ◽  
Genome Wide ◽  
Chromatin Organisation ◽  
Different Cell Types

AbstractChromosome conformation capture methods such as Hi-C enables mapping of genome-wide chromatin interactions and is a promising technology to understand the role of spatial chromatin organisation in gene regulation. However, the generation and analysis of these data sets at high resolutions remain technically challenging and costly. We developed a machine and deep learning approach to predict functionally important, highly interacting chromatin regions (HICR) and topologically associated domain (TAD) boundaries independent of Hi-C data in both normal physiological states and pathological conditions such as cancer. This approach utilises gradient boosted trees and convolutional neural networks trained on both Hi-C and histone modification epigenomic data from three different cell types. Given only epigenomic modification data these models are able to predict chromatin interactions and TAD boundaries with high accuracy. We demonstrate that our models are transferable across cell types, indicating that combinatorial histone mark signatures may be universal predictors for highly interacting chromatin regions and spatial chromatin architecture elements.

scholarly journals Quantifying point-mutations in shotgun metagenomic data

2018 ◽  
Author(s):  
Shruthi Magesh ◽  
Viktor Jonsson ◽  
Johan Bengtsson-Palme
Keyword(s):  
Microbial Communities ◽  
Point Mutations ◽  
Software Tool ◽  
Metagenomic Data ◽  
Data Sets ◽  
Resistance Mutations ◽  
Shotgun Metagenomics ◽  
Key Factor ◽  
Detection Of Mutations ◽  
And Function

AbstractMetagenomics has emerged as a central technique for studying the structure and function of microbial communities. Often the functional analysis is restricted to classification into broad functional categories. However, important phenotypic differences, such as resistance to antibiotics, are often the result of just one or a few point mutations in otherwise identical sequences. Bioinformatic methods for metagenomic analysis have generally been poor at accounting for this fact, resulting in a somewhat limited picture of important aspects of microbial communities. Here, we address this problem by providing a software tool called Mumame, which can distinguish between wildtype and mutated sequences in shotgun metagenomic data and quantify their relative abundances. We demonstrate the utility of the tool by quantifying antibiotic resistance mutations in several publicly available metagenomic data sets. We also identified that sequencing depth is a key factor to detect rare mutations. Therefore, much larger numbers of sequences may be required for reliable detection of mutations than for most other applications of shotgun metagenomics. Mumame is freely available from http://microbiology.se/software/mumame

scholarly journals NOCICEPTRA: Gene and microRNA signatures and their trajectories characterizing human iPSC-derived nociceptor maturation

2021 ◽  
Author(s):  
Maximilian Zeidler ◽  
Kai K. Kummer ◽  
Clemens L. Schoepf ◽  
Theodora Kalpachidou ◽  
Georg Kern ◽  
...  
Keyword(s):  
Human Model ◽  
Marker Genes ◽  
Data Sets ◽  
Analysis Tool ◽  
Peripheral Neurons ◽  
Gene Sets ◽  
Channel Expression ◽  
Exploratory Data ◽  
And Function ◽  
Human Ipsc

Nociceptors are primary afferent neurons serving the reception of acute pain but also the transit into maladaptive pain disorders. Since native human nociceptors are hardly available for mechanistic functional research, and rodent models do not necessarily mirror human pathologies in all aspects, human iPSC-derived nociceptors (iDN) offer superior advantages as a human model system. Unbiased mRNA::microRNA co-sequencing, immunofluorescence staining and qPCR validations, revealed expression trajectories as well as miRNA target spaces throughout the transition of pluripotent cells into iDNs. mRNA and miRNA candidates emerged as regulatory hubs for neurite outgrowth, synapse development and ion channel expression. The exploratory data analysis tool NOCICEPTRA is provided as a containerized platform to retrieve experimentally determined expression trajectories, and to query custom gene sets for pathway and disease enrichments. Querying NOCICEPTRA for marker genes of cortical neurogenesis revealed distinct similarities and differences for cortical and peripheral neurons. The platform provides a public domain neuroresource to exploit the entire data sets and explore miRNA and mRNA as hubs regulating human nociceptor differentiation and function.

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