Scool: a new data storage format for single-cell Hi-C data

Abstract Motivation Single-cell Hi-C research currently lacks an efficient, easy to use and shareable data storage format. Recent studies have used a variety of sub-optimal solutions: publishing raw data only, text-based interaction matrices, or reusing established Hi-C storage formats for single interaction matrices. These approaches are storage and pre-processing intensive, require long labour time and are often error-prone. Results The single-cell cooler file format (scool) provides an efficient, user-friendly and storage-saving approach for single-cell Hi-C data. It is a flavour of the established cooler format and guarantees stable API support. Availability and implementation The single-cell cooler format is part of the cooler file format as of API version 0.8.9. It is available via pip, conda and github: https://github.com/mirnylab/cooler. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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dittoSeq: universal user-friendly single-cell and bulk RNA sequencing visualization toolkit

Bioinformatics ◽

10.1093/bioinformatics/btaa1011 ◽

2020 ◽

Author(s):

Daniel G Bunis ◽

Jared Andrews ◽

Gabriela K Fragiadakis ◽

Trevor D Burt ◽

Marina Sirota

Keyword(s):

Single Cell ◽

R Package ◽

Color Blindness ◽

Ease Of Use ◽

Supplementary Information ◽

Supplementary Data ◽

Rnaseq Data ◽

Visualization Toolkit ◽

User Friendly ◽

Publication Quality

Abstract Summary A visualization suite for major forms of bulk and single-cell RNAseq data in R. dittoSeq is color blindness-friendly by default, robustly documented to power ease-of-use and allows highly customizable generation of both daily-use and publication-quality figures. Availability and implementation dittoSeq is an R package available through Bioconductor via an open source MIT license. Supplementary information Supplementary data are available at Bioinformatics online.

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G4Hunter web application: a web server for G-quadruplex prediction

Bioinformatics ◽

10.1093/bioinformatics/btz087 ◽

2019 ◽

Vol 35 (18) ◽

pp. 3493-3495 ◽

Cited By ~ 22

Author(s):

Václav Brázda ◽

Jan Kolomazník ◽

Jiří Lýsek ◽

Martin Bartas ◽

Miroslav Fojta ◽

...

Keyword(s):

Data Storage ◽

Web Application ◽

Data Representation ◽

Complete Characterization ◽

Supplementary Information ◽

Web Based ◽

Rna Molecules ◽

Dna And Rna ◽

Sorting Data ◽

User Friendly

Abstract Motivation Expanding research highlights the importance of guanine quadruplex structures. Therefore, easy-accessible tools for quadruplex analyses in DNA and RNA molecules are important for the scientific community. Results We developed a web version of the G4Hunter application. This new web-based server is a platform-independent and user-friendly application for quadruplex analyses. It allows retrieval of gene/nucleotide sequence entries from NCBI databases and provides complete characterization of localization and quadruplex propensity of quadruplex-forming sequences. The G4Hunter web application includes an interactive graphical data representation with many useful options including visualization, sorting, data storage and export. Availability and implementation G4Hunter web application can be accessed at: http://bioinformatics.ibp.cz. Supplementary information Supplementary data are available at Bioinformatics online.

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Scool: a new data storage format for single-cell Hi-C data

Bioinformatics ◽

10.1093/bioinformatics/btab243 ◽

2021 ◽

Author(s):

Joachim Wolff ◽

Nezar Abdennur ◽

Rolf Backofen ◽

Björn Grüning

Keyword(s):

Single Cell ◽

Data Storage ◽

Storage Format

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Asc-Seurat – Analytical single-cell Seurat-based web application

10.1101/2021.03.19.436196 ◽

2021 ◽

Author(s):

WJ Pereira ◽

FM Almeida ◽

KM Balmant ◽

DC Rodriguez ◽

PM Triozzi ◽

...

Keyword(s):

Single Cell ◽

Web Application ◽

Iterative Process ◽

Developmental Trajectories ◽

Cell Types ◽

Biological Information ◽

Supplementary Information ◽

Popular Approach ◽

Single Cell Rna Sequencing ◽

User Friendly

AbstractSummarySingle-cell RNA sequencing (scRNA-seq) has become a popular approach for studying the transcriptome, providing a powerful tool for discovering and characterizing cell types and their developmental trajectories. However, scRNA-seq analysis is complex, requiring a continuous, iterative process to refine the data processing and uncover relevant biological information. We present Asc-Seurat, a feature rich workbench, providing a user-friendly and easy-to-install web application encapsulating the necessary tools for an all-encompassing and fluid scRNA-seq data analysis.Availability and implementationAsc-Seurat is available at https://github.com/KirstLab/asc_seurat/ and released under GNU 3 [email protected] informationSupplementary data are available at Bioinformatics online.

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rCASC: reproducible Classification Analysis of Single Cell sequencing data

10.1101/430967 ◽

2018 ◽

Cited By ~ 1

Author(s):

Luca Alessandrì ◽

Marco Beccuti ◽

Maddalena Arigoni ◽

Martina Olivero ◽

Greta Romano ◽

...

Keyword(s):

Single Cell ◽

Single Cells ◽

R Package ◽

Cellular Heterogeneity ◽

Supplementary Information ◽

Sequencing Data ◽

Single Cell Sequencing ◽

Analysis Workflow ◽

User Friendly ◽

Bioinformatics Workflows

AbstractSummarySingle-cell RNA sequencing has emerged as an essential tool to investigate cellular heterogeneity, and highlighting cell sub-population specific signatures. Nowadays, dedicated and user-friendly bioinformatics workflows are required to exploit the deconvolution of single-cells transcriptome. Furthermore, there is a growing need of bioinformatics workflows granting both functional, i.e. saving information about data and analysis parameters, and computation reproducibility, i.e. storing the real image of the computation environment. Here, we present rCASC a modular RNAseq analysis workflow allowing data analysis from counts generation to cell sub-population signatures identification, granting both functional and computation reproducibility.Availability and ImplementationrCASC is part of the reproducible bioinfomatics project. rCASC is a docker based application controlled by a R package available at https://github.com/kendomaniac/rCASC.Supplementary informationSupplementary data are available at rCASC github

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ShinySOM: graphical SOM-based analysis of single-cell cytometry data

Bioinformatics ◽

10.1093/bioinformatics/btaa091 ◽

2020 ◽

Vol 36 (10) ◽

pp. 3288-3289

Author(s):

Miroslav Kratochvíl ◽

David Bednárek ◽

Tomáš Sieger ◽

Karel Fišer ◽

Jiří Vondrášek

Keyword(s):

Single Cell ◽

High Throughput ◽

Statistical Information ◽

Supplementary Information ◽

High Dimensional ◽

Supplementary Data ◽

Mass Cytometry ◽

High Throughput Analysis ◽

Self Organizing Maps ◽

User Friendly

Abstract Summary ShinySOM offers a user-friendly interface for reproducible, high-throughput analysis of high-dimensional flow and mass cytometry data guided by self-organizing maps. The software implements a FlowSOM-style workflow, with improvements in performance, visualizations and data dissection possibilities. The outputs of the analysis include precise statistical information about the dissected samples, and R-compatible metadata useful for the batch processing of large sample volumes. Availability and implementation ShinySOM is free and open-source, available online at gitlab.com/exaexa/ShinySOM. Supplementary information Supplementary data are available at Bioinformatics online.

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Information technology. Digitally recorded media for information interchange and storage. Test method for the estimation of lifetime of optical disks for long-term data storage

10.3403/30341531u ◽

2017 ◽

Keyword(s):

Information Technology ◽

Data Storage ◽

Test Method ◽

Storage Test ◽

Optical Disks ◽

Information Interchange ◽

And Storage ◽

Term Data

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Information technology. Digitally recorded media for information interchange and storage. Test method for the estimation of lifetime of optical disks for long-term data storage

10.3403/30341531 ◽

2017 ◽

Keyword(s):

Information Technology ◽

Data Storage ◽

Test Method ◽

Storage Test ◽

Optical Disks ◽

Information Interchange ◽

And Storage ◽

Term Data

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Pedigree and Pedigree Import Wizard

HortScience ◽

10.21273/hortsci.33.3.552g ◽

1998 ◽

Vol 33 (3) ◽

pp. 552g-553

Author(s):

Shahrokh Khandizadeh

Keyword(s):

Additional Data ◽

File Format ◽

Fruit Crops ◽

Operating Environment ◽

Agronomic Characteristics ◽

Link Type ◽

Plant Characteristics ◽

User Friendly

Pedigree for Windows is a user-friendly program that allows the user to trace agronomic characteristics, draw pedigrees, and view images of several fruit crops, including more than 1400 apple, 800 strawberry, 800 almond, 100 blackberry, 80 blueberry, 790 pear, 200 raspberry examples. Pedigree Import Wizard®© for Windows is an add-on software for users who are interested in importing their research or breeding data records of fruit, flower, and plant characteristics and any related images into Pedigree for Windows. Pedigree for Windows and Pedigree Import Wizard have been designed so that a user familiar with the Windows operating environment should have little need to refer to the documentation provided with the program. Pedigree Import Wizard uses a comma-separated value (csv) file format under the MS Excel environment. This option allows the user to add or import additional data to the existing database that are already stored in other software such as Lotus, Excel, Access, QuattroPro, WordPerfect, and MS Word tables, etc., as long as they work under the Windows environment. A free demo version of Pedigree and Pedigree Import Wizard for Windows is available from http://www.pgris.com.

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A System Architecture for Data Storage in the Cloud

International Journal of Distributed and Cloud Computing ◽

10.21863/ijdcc/2015.3.2.008 ◽

2015 ◽

Vol 3 (2) ◽

Author(s):

Maryam Hammami ◽

Hatem Bellaaj

Keyword(s):

Data Storage ◽

System Architecture ◽

Cloud Storage ◽

The Other ◽

Important Data ◽

Work In Progress ◽

Flexible System ◽

Other Hand ◽

Data Manager ◽

And Storage

The Cloud storage is the most important issue today. This is due to a rapidly changing needs and a huge mass of varied and important data to back up. In this paper, we describe a work in progress and propose a flexible system architecture for data storage in the Cloud. This system is centered on the Data Manager module. This module provides various functions such as the dispersion of data in fragments, encryption and storage of fragments... etc. This architecture proves to be very relevant. It ensures consistency between different components. On the other hand, it ensures the security and availability of data.

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