HiCeekR: A Novel Shiny App for Hi-C Data Analysis

Filippo, Lucio Di; Righelli, Dario; Gagliardi, Miriam; Matarazzo, Maria R.; Angelini, Claudia

doi:10.3389/fgene.2019.01079

Cited by 16 publications

(10 citation statements)

References 48 publications

(67 reference statements)

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“…Many biologists and bioinformaticians have been developing 3D structure of chromosome, using 3 C-based methods -particularly Hi-C method. Based on these methods, they develop the image statistical analysis by applying the machine learning tools such as regression or classification techniques [25,7]. Despite the improvement in 3-D structure modeling approaches, the lack of a real structure with which to contrast these models remains a challenge [20].…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Pseudo-Location: A novel predictor for predicting pseudo-temporal gene expression patterns using spatial functional regression

Ahn

Fujiwara

2020

Preprint

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Background: It is generally known that analyses of gene expression at single-cell resolution and spatial location of genes will reveal insights into the regulation of gene expression. Although transcriptome-wide single-cell gene expression analyses with genetic spatial information are not yet feasible, many bioinformaticians and biologists are trying to understand and predict gene expression levels using genetic spatial information of genes by implementing statistical regression models.Methods: We implement a trajectory inference analysis in order to identify the pseudotemporal gene expression patterns (PTGEPs) for single cell RNA-sequencing (scRNA-seq) data. In here, each PTGEP is obtained as a functional form in which we treat as functional response in spatial functional regression model. For a predictor, we propose a new concept of genetic spatial information, pseudo-location by incorporating the chromosome number and molecular starting position of genes. Then we applied and compared several types of functional response regression models to evaluate the prediction performance, including standard function-on-scalar regression model, penalized flexible functional regression model, and Bayes function-on-scalar regression model. We calculate and compared the squared correlation, R 2 i , for each gene under each model to evaluate the prediction performance.Results: We applied these spatial functional regression model and other function-onscalar regression models to scRNA-seq data sets including Trapnell2014, Kumar2014, and Shekhar2016. To see the robustness of the prediction performance, we also increase the total number of genes for regression analysis from 1,000 to 3,000, and 10,000. Almost 30% of genes can be predicted for each model and for each data set . By comparing the predictive genes (R 2 i > 0.1) and higher predictive genes (R 2 i > 0.3), we found that spatial functional regression model using pseudo-location is able to provide the better prediction performance compared to other standard functional regression models.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Pseudo-Location: A novel predictor for predicting pseudo-temporal gene expression patterns using spatial functional regression

Ahn

Fujiwara

2020

Preprint

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“…In the last decade, GUIs are becoming very popular in bioinformatics because they simplify computational analysis allowing non-expert users to choose among several computational procedures, algorithms, and parameter settings, see for example [13,14,[16][17][18]. In particular, the shiny Nevertheless, computational studies obtained from GUIs might lack reproducibility since tracking all user choices is still challenging.…”

Section: Implementing Automatically Tracing Functions and Their Usagementioning

confidence: 99%

Easyreporting simplifies the implementation of Reproducible Research Layers in R software

Righelli

Angelini

2020

Preprint

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During last years “irreproducibility” became a general problem in omics data analysis due to the use of sophisticated and poorly described computational procedures. For avoiding misleading results, it is necessary to inspect and reproduce the entire data analysis as a unified product. Reproducible Research (RR) provides general guidelines for public access to the analytic data and related analysis code combined with natural language documentation, allowing third-parties to reproduce the findings. We developed easyreporting, a novel R/Bioconductor package, to facilitate the implementation of an RR layer inside reports/tools without requiring any knowledge of the R Markdown language. We describe the main functionalities and illustrate how to create an analysis report using a typical case study concerning the analysis of RNA-seq data. Then, we also show how to trace R functions automatically. Thanks to this latter feature, easyreporting results beneficial for developers to implement procedures that automatically keep track of the analysis steps within Graphical User Interfaces (GUIs). Easyreporting can be useful in supporting the reproducibility of any data analysis project and the implementation of GUIs. It turns out to be very helpful in bioinformatics, where the complexity of the analyses makes it extremely difficult to trace all the steps and parameters used in the study.

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“…In addition, it is natively compatible and inter-convertible with the widespread Cooler [38] and Juicer [37] Hi-C file formats and can import a large variety of different text-based matrix inputs, such as those generated by HiC-Pro [42] Table 1 Feature comparison of different Hi-C analysis tools. Tools included in the comparison are Cooler [38]/HiGlass [39], Juicer [37]/Juicebox [40], HOMER [41], HiC-Pro [42], HiC-bench [43], TADbit [44], HiFive [45], HicDat [46], HiCInspector [47], HiCUP, HiCExplorer [48,49], and HiCeekR [50]. 1: Only for interactive plotting; 2: Support for Juicer and Cooler multi-resolution files, but no native support; 3: Cooler ecosystem includes pairtools, cooler, cooltools, HiGlass, and distiller; 4: In conjunction with Juicebox; 5: Provides instructions for mapping, but no dedicated command; 6: Visualisation through Treeview; 7: With export for Fit-Hi-C; 8: Through compatibility with HiCPlotter; 9: Via HiCNorm; 10: Fit-Hi-C, C-loops, and targeted virtual 5C (in-house); 11: Only pre-processing Table 1 Feature comparison of different Hi-C analysis tools.…”

mentioning

confidence: 99%

“…1: Only for interactive plotting; 2: Support for Juicer and Cooler multi-resolution files, but no native support; 3: Cooler ecosystem includes pairtools, cooler, cooltools, HiGlass, and distiller; 4: In conjunction with Juicebox; 5: Provides instructions for mapping, but no dedicated command; 6: Visualisation through Treeview; 7: With export for Fit-Hi-C; 8: Through compatibility with HiCPlotter; 9: Via HiCNorm; 10: Fit-Hi-C, C-loops, and targeted virtual 5C (in-house); 11: Only pre-processing Table 1 Feature comparison of different Hi-C analysis tools. Tools included in the comparison are Cooler [38]/HiGlass [39], Juicer [37]/Juicebox [40], HOMER [41], HiC-Pro [42], HiC-bench [43], TADbit [44], HiFive [45], HicDat [46], HiCInspector [47], HiCUP, HiCExplorer [48,49], and HiCeekR [50]. 1: Only for interactive plotting; 2: Support for Juicer and…”

mentioning

confidence: 99%

“…Cooler multi-resolution files, but no native support; 3: Cooler ecosystem includes pairtools, cooler, cooltools, HiGlass, and distiller; 4: In conjunction with Juicebox; 5: Provides instructions for mapping, but no dedicated command; 6: Visualisation through Treeview; 7: With export for Fit-Hi-C; 8: Through compatibility with HiCPlotter; 9: Via HiCNorm; 10: Fit-Hi-C, C-loops, and targeted virtual 5C (in-house); 11: Only pre-processing [39], Juicer [37]/Juicebox [40], HOMER [41], HiC-Pro [42], HiC-bench [43], TADbit [44], HiFive [45], HicDat [46], HiCInspector [47], HiCUP, HiCExplorer [48,49], and HiCeekR [50] [39], Juicer [37]/Juicebox [40], HOMER [41], HiC-Pro [42], HiC-bench [43], TADbit [44], HiFive [45], HicDat [46], HiCInspector [47], HiCUP, HiCExplorer [48,49], and HiCeekR [50] and the 4D Nucleome project [51]. FAN-C includes a fully automated FASTQ-tomatrix pipeline, which can be adapted to accommodate the complexities and individual requirements of each specific Hi-C analysis, such as different species or analysis parameters.…”

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confidence: 99%

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FAN-C: a feature-rich framework for the analysis and visualisation of chromosome conformation capture data

2020

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Chromosome conformation capture data, particularly from high-throughput approaches such as Hi-C, are typically very complex to analyse. Existing analysis tools are often single-purpose, or limited in compatibility to a small number of data formats, frequently making Hi-C analyses tedious and time-consuming. Here, we present FAN-C, an easy-to-use command-line tool and powerful Python API with a broad feature set covering matrix generation, analysis, and visualisation for C-like data (https://github.com/vaquerizaslab/fanc). Due to its compatibility with the most prevalent Hi-C storage formats, FAN-C can be used in combination with a large number of existing analysis tools, thus greatly simplifying Hi-C matrix analysis.

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HiCeekR: A Novel Shiny App for Hi-C Data Analysis

Cited by 16 publications

References 48 publications

Pseudo-Location: A novel predictor for predicting pseudo-temporal gene expression patterns using spatial functional regression

Pseudo-Location: A novel predictor for predicting pseudo-temporal gene expression patterns using spatial functional regression

Easyreporting simplifies the implementation of Reproducible Research Layers in R software

FAN-C: a feature-rich framework for the analysis and visualisation of chromosome conformation capture data

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