scholarly journals LabxDB: versatile databases for genomic sequencing and lab management

2020 ◽  
Vol 36 (16) ◽  
pp. 4530-4531 ◽  
Author(s):  
Charles E Vejnar ◽  
Antonio J Giraldez
Keyword(s):  
High Throughput Sequencing ◽  
Data Access ◽  
Supplementary Information ◽  
Genomic Sequencing ◽  
Multiple User ◽  
Experimental Laboratory ◽  
User Access ◽  
Flexible Framework ◽  
Rest Api ◽  
User Friendly

Abstract Summary Experimental laboratory management and data-driven science require centralized software for sharing information, such as lab collections or genomic sequencing datasets. Although database servers such as PostgreSQL can store such information with multiple-user access, they lack user-friendly graphical and programmatic interfaces for easy data access and inputting. We developed LabxDB, a versatile open-source solution for organizing and sharing structured data. We provide several out-of-the-box databases for deployment in the cloud including simple mutant or plasmid collections and purchase-tracking databases. We also developed a high-throughput sequencing (HTS) database, LabxDB seq, dedicated to storage of hierarchical sample annotations. Scientists can import their own or publicly available HTS data into LabxDB seq to manage them from production to publication. Using LabxDB’s programmatic access (REST API), annotations can be easily integrated into bioinformatics pipelines. LabxDB is modular, offering a flexible framework that scientists can leverage to build new database interfaces adapted to their needs. Availability and implementation LabxDB is available at https://gitlab.com/vejnar/labxdb and https://labxdb.vejnar.org for documentation. LabxDB is licensed under the terms of the Mozilla Public License 2.0. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals HiCBricks: building blocks for efficient handling of large Hi-C datasets

2019 ◽  
Author(s):  
Koustav Pal ◽  
Ilario Tagliaferri ◽  
Carmen Maria Livi ◽  
Francesco Ferrari
Keyword(s):  
High Throughput Sequencing ◽  
Building Blocks ◽  
Efficient Solutions ◽  
Quality Data ◽  
Supplementary Information ◽  
Complex Data ◽  
High Quality Data ◽  
Chromosome Conformation ◽  
Genome Wide ◽  
User Friendly

Abstract Summary Genome-wide chromosome conformation capture based on high-throughput sequencing (Hi-C) has been widely adopted to study chromatin architecture by generating datasets of ever-increasing complexity and size. HiCBricks offers user-friendly and efficient solutions for handling large high-resolution Hi-C datasets. The package provides an R/Bioconductor framework with the bricks to build more complex data analysis pipelines and algorithms. HiCBricks already incorporates functions for calling domain boundaries and functions for high-quality data visualization. Availability and implementation http://bioconductor.org/packages/devel/bioc/html/HiCBricks.html. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals TCGA2STAT: simple TCGA data access for integrated statistical analysis in R

2015 ◽  
Vol 32 (6) ◽  
pp. 952-954 ◽  
Author(s):  
Ying-Wooi Wan ◽  
Genevera I. Allen ◽  
Zhandong Liu
Keyword(s):  
Statistical Analysis ◽  
Domain Knowledge ◽  
Data Access ◽  
The Cancer Genome Atlas ◽  
Supplementary Information ◽  
Biological Knowledge ◽  
Specific Domain ◽  
Open Source Software Package ◽  
Cancer Genome Atlas ◽  
User Friendly

Abstract Motivation: Massive amounts of high-throughput genomics data profiled from tumor samples were made publicly available by the Cancer Genome Atlas (TCGA). Results: We have developed an open source software package, TCGA2STAT, to obtain the TCGA data, wrangle it, and pre-process it into a format ready for multivariate and integrated statistical analysis in the R environment. In a user-friendly format with one single function call, our package downloads and fully processes the desired TCGA data to be seamlessly integrated into a computational analysis pipeline. No further technical or biological knowledge is needed to utilize our software, thus making TCGA data easily accessible to data scientists without specific domain knowledge. Availability and implementation: TCGA2STAT is available from the https://cran.r-project.org/web/packages/TCGA2STAT/index.html. Supplementary information: Supplementary data are available at Bioinformatics online. Contact: [email protected]

scholarly journals FuncTree2: an interactive radial tree for functional hierarchies and omics data visualization

2019 ◽  
Vol 35 (21) ◽  
pp. 4519-4521 ◽  
Author(s):  
Youssef Darzi ◽  
Yuta Yamate ◽  
Takuji Yamada
Keyword(s):  
Web Application ◽  
Source Code ◽  
Hierarchical Classification ◽  
Supplementary Information ◽  
Biological Knowledge ◽  
Omics Data ◽  
Functional Annotations ◽  
Rest Api ◽  
User Friendly ◽  
Multiple Samples

Abstract Summary Functional annotations and their hierarchical classification are widely used in omics workflows to build novel insight upon existing biological knowledge. Currently, a plethora of tools is available to explore omics datasets at the level of functional annotations, but there is a lack of feature rich and user-friendly tools that help scientists take advantage of their hierarchical classification for additional and often invaluable insights. Here, we present FuncTree2, a user-friendly web application that turns hierarchical classifications into interactive and highly customizable radial trees, and enables researchers to visualize their data simultaneously on all its levels. FuncTree2 features mapping of data from multiple samples and several navigation features like zooming, panning, re-rooting and collapsing of nodes or levels. Availability and implementation FuncTree2 is freely available at https://bioviz.tokyo/functree2/ as a web application and a REST API. Source code is available on GitHub https://github.com/yamada-lab/functree-ng. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Advantages of using graph databases to explore chromatin conformation capture experiments

2021 ◽  
Vol 22 (S2) ◽  
Author(s):  
Daniele D’Agostino ◽  
Pietro Liò ◽  
Marco Aldinucci ◽  
Ivan Merelli
Keyword(s):  
Web Application ◽  
High Throughput Sequencing ◽  
Cell Types ◽  
Graph Database ◽  
Graph Databases ◽  
Sources Of Information ◽  
Chromosome Conformation ◽  
Wide Scale ◽  
User Friendly ◽  
Different Cell Types

Abstract Background High-throughput sequencing Chromosome Conformation Capture (Hi-C) allows the study of DNA interactions and 3D chromosome folding at the genome-wide scale. Usually, these data are represented as matrices describing the binary contacts among the different chromosome regions. On the other hand, a graph-based representation can be advantageous to describe the complex topology achieved by the DNA in the nucleus of eukaryotic cells. Methods Here we discuss the use of a graph database for storing and analysing data achieved by performing Hi-C experiments. The main issue is the size of the produced data and, working with a graph-based representation, the consequent necessity of adequately managing a large number of edges (contacts) connecting nodes (genes), which represents the sources of information. For this, currently available graph visualisation tools and libraries fall short with Hi-C data. The use of graph databases, instead, supports both the analysis and the visualisation of the spatial pattern present in Hi-C data, in particular for comparing different experiments or for re-mapping omics data in a space-aware context efficiently. In particular, the possibility of describing graphs through statistical indicators and, even more, the capability of correlating them through statistical distributions allows highlighting similarities and differences among different Hi-C experiments, in different cell conditions or different cell types. Results These concepts have been implemented in NeoHiC, an open-source and user-friendly web application for the progressive visualisation and analysis of Hi-C networks based on the use of the Neo4j graph database (version 3.5). Conclusion With the accumulation of more experiments, the tool will provide invaluable support to compare neighbours of genes across experiments and conditions, helping in highlighting changes in functional domains and identifying new co-organised genomic compartments.

scholarly journals Ribo-ODDR: Oligo design pipeline for experiment-specific rRNA depletion in ribo-seq

2021 ◽  
Author(s):  
Ferhat Alkan ◽  
Joana Silva ◽  
Eric Pintó Barberà ◽  
William J Faller
Keyword(s):  
Ribosome Profiling ◽  
Supplementary Information ◽  
Experimental Conditions ◽  
Computational Framework ◽  
Rna Translation ◽  
Rrna Depletion ◽  
Selection For ◽  
Nucleotide Resolution ◽  
User Friendly ◽  
Oligo Design

Abstract Motivation Ribosome Profiling (Ribo-seq) has revolutionized the study of RNA translation by providing information on ribosome positions across all translated RNAs with nucleotide-resolution. Yet several technical limitations restrict the sequencing depth of such experiments, the most common of which is the overabundance of rRNA fragments. Various strategies can be employed to tackle this issue, including the use of commercial rRNA depletion kits. However, as they are designed for more standardized RNAseq experiments, they may perform suboptimally in Ribo-seq. In order to overcome this, it is possible to use custom biotinylated oligos complementary to the most abundant rRNA fragments, however currently no computational framework exists to aid the design of optimal oligos. Results Here, we first show that a major confounding issue is that the rRNA fragments generated via Ribo-seq vary significantly with differing experimental conditions, suggesting that a “one-size-fits-all” approach may be inefficient. Therefore we developed Ribo-ODDR, an oligo design pipeline integrated with a user-friendly interface that assists in oligo selection for efficient experiment-specific rRNA depletion. Ribo-ODDR uses preliminary data to identify the most abundant rRNA fragments, and calculates the rRNA depletion efficiency of potential oligos. We experimentally show that Ribo-ODDR designed oligos outperform commercially available kits and lead to a significant increase in rRNA depletion in Ribo-seq. Availability Ribo-ODDR is freely accessible at https://github.com/fallerlab/Ribo-ODDR Supplementary information Supplementary data are available at Bioinformatics online.

CPVA: a web-based metabolomic tool for chromatographic peak visualization and annotation

2020 ◽  
Vol 36 (12) ◽  
pp. 3913-3915
Author(s):  
Hemi Luan ◽  
Xingen Jiang ◽  
Fenfen Ji ◽  
Zhangzhang Lan ◽  
Zongwei Cai ◽  
...  
Keyword(s):  
False Positive ◽  
Supplementary Information ◽  
Liquid Chromatography Mass Spectrometry ◽  
Targeted Metabolomics ◽  
Metabolomics Data ◽  
Web Based ◽  
Tremendous Amount ◽  
Chromatographic Peaks ◽  
User Friendly

Abstract Motivation Liquid chromatography–mass spectrometry-based non-targeted metabolomics is routinely performed to qualitatively and quantitatively analyze a tremendous amount of metabolite signals in complex biological samples. However, false-positive peaks in the datasets are commonly detected as metabolite signals by using many popular software, resulting in non-reliable measurement. Results To reduce false-positive calling, we developed an interactive web tool, termed CPVA, for visualization and accurate annotation of the detected peaks in non-targeted metabolomics data. We used a chromatogram-centric strategy to unfold the characteristics of chromatographic peaks through visualization of peak morphology metrics, with additional functions to annotate adducts, isotopes and contaminants. CPVA is a free, user-friendly tool to help users to identify peak background noises and contaminants, resulting in decrease of false-positive or redundant peak calling, thereby improving the data quality of non-targeted metabolomics studies. Availability and implementation The CPVA is freely available at http://cpva.eastus.cloudapp.azure.com. Source code and installation instructions are available on GitHub: https://github.com/13479776/cpva. Supplementary information Supplementary data are available at Bioinformatics online.

MetaADEDB 2.0: a comprehensive database on adverse drug events

2020 ◽  
Author(s):  
Zhuohang Yu ◽  
Zengrui Wu ◽  
Weihua Li ◽  
Guixia Liu ◽  
Yun Tang
Keyword(s):  
Safety Assessment ◽  
Adverse Drug Events ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Supplementary Information ◽  
Online Database ◽  
Web Interface ◽  
Drug Discovery And Development ◽  
Comprehensive Information ◽  
User Friendly

Abstract Summary MetaADEDB is an online database we developed to integrate comprehensive information on adverse drug events (ADEs). The first version of MetaADEDB was released in 2013 and has been widely used by researchers. However, it has not been updated for more than seven years. Here, we reported its second version by collecting more and newer data from the U.S. FDA Adverse Event Reporting System (FAERS) and Canada Vigilance Adverse Reaction Online Database, in addition to the original three sources. The new version consists of 744 709 drug–ADE associations between 8498 drugs and 13 193 ADEs, which has an over 40% increase in drug–ADE associations compared to the previous version. Meanwhile, we developed a new and user-friendly web interface for data search and analysis. We hope that MetaADEDB 2.0 could provide a useful tool for drug safety assessment and related studies in drug discovery and development. Availability and implementation The database is freely available at: http://lmmd.ecust.edu.cn/metaadedb/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals circtools—a one-stop software solution for circular RNA research

2018 ◽  
Vol 35 (13) ◽  
pp. 2326-2328 ◽  
Author(s):  
Tobias Jakobi ◽  
Alexey Uvarovskii ◽  
Christoph Dieterich
Keyword(s):  
High Throughput Sequencing ◽  
Circular Rna ◽  
Statistical Testing ◽  
Supplementary Information ◽  
Circular Rnas ◽  
Sequencing Data ◽  
High Throughput Sequencing Data ◽  
Multi Stage ◽  
Sequence Reconstruction ◽  
One Stop

Abstract Motivation Circular RNAs (circRNAs) originate through back-splicing events from linear primary transcripts, are resistant to exonucleases, are not polyadenylated and have been shown to be highly specific for cell type and developmental stage. CircRNA detection starts from high-throughput sequencing data and is a multi-stage bioinformatics process yielding sets of potential circRNA candidates that require further analyses. While a number of tools for the prediction process already exist, publicly available analysis tools for further characterization are rare. Our work provides researchers with a harmonized workflow that covers different stages of in silico circRNA analyses, from prediction to first functional insights. Results Here, we present circtools, a modular, Python-based framework for computational circRNA analyses. The software includes modules for circRNA detection, internal sequence reconstruction, quality checking, statistical testing, screening for enrichment of RBP binding sites, differential exon RNase R resistance and circRNA-specific primer design. circtools supports researchers with visualization options and data export into commonly used formats. Availability and implementation circtools is available via https://github.com/dieterich-lab/circtools and http://circ.tools under GPLv3.0. Supplementary information Supplementary data are available at Bioinformatics online.

The Intelligent Design of the Gear Reducer

2012 ◽  
Vol 522 ◽  
pp. 823-827
Author(s):  
Jian Jiang Fang ◽  
Wen Jun Qi
Keyword(s):  
Intelligent Design ◽  
Object Oriented ◽  
Data Access ◽  
Mechanical Transmission ◽  
Design Efficiency ◽  
Integrated Technology ◽  
Gear Drive ◽  
Object Oriented Technology ◽  
Wide Range ◽  
User Friendly

The gear drive is the wide range of applications and is particularly important as a form of mechanical transmission, but the design process requires large amounts of data access and computation. In the paper, computer integrated technology and object-oriented technology is used to research and develop the intelligent design of Straight gear reducer system with user-friendly interactive platform, easy to use, high design efficiency and reliable data.

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