scholarly journals miR2Trait: an integrated resource for investigating miRNA-disease associations

2021 ◽  
Author(s):  
Poornima Babu ◽  
Ashok Palaniappan
Keyword(s):  
Open Source ◽  
Human Health ◽  
Regulatory Networks ◽  
Command Line ◽  
Command Line Interface ◽  
Cascading Effects ◽  
Commercial Use ◽  
Link Type ◽  
Disease Associations

ABSTRACTMicroRNAs are key components of cellular regulatory networks, and breakdown in miRNA function could lead to cascading effects culminating in pathophenotypes. A better understanding of the role of miRNAs in diseases would aid human health. Here, we have devised a method for comprehensively mapping the associations between miRNAs and diseases by merging on a common key between two curated omics databases. The resulting bidirectional resource, miR2Trait, is more detailed than earlier catalogues, uncovers new relationships, and includes analytical utilities to interrogate and extract knowledge from these datasets. The resource could aid in identifying the disease enrichment of a user-given set of miRNAs and analyzing the miRNA profile of a specified diseasome. miR2Trait is available as both a web-server (https://sas.sastra.edu/pymir18) and an open-source command-line interface (https://github.com/miR2Trait) under MIT license for both commercial and non-commercial use. The datasets are available for download at: https://doi.org/10.6084/m9.figshare.8288825.

scholarly journals BioKEEN: A library for learning and evaluating biological knowledge graph embeddings

2018 ◽  
Author(s):  
Mehdi Ali ◽  
Charles Tapley Hoyt ◽  
Daniel Domingo-Fernández ◽  
Jens Lehmann ◽  
Hajira Jabeen
Keyword(s):  
Knowledge Graph ◽  
Biological Knowledge ◽  
Command Line ◽  
Graph Embeddings ◽  
Command Line Interface ◽  
Software Ecosystem ◽  
Link Type ◽  
Mapping Resource ◽  
Significant Attention

AbstractKnowledge graph embeddings (KGEs) have received significant attention in other domains due to their ability to predict links and create dense representations for graphs’ nodes and edges. However, the software ecosystem for their application to bioinformatics remains limited and inaccessible for users without expertise in programming and machine learning. Therefore, we developed BioKEEN (Biological KnowlEdge EmbeddiNgs) and PyKEEN (Python KnowlEdge EmbeddiNgs) to facilitate their easy use through an interactive command line interface. Finally, we present a case study in which we used a novel biological pathway mapping resource to predict links that represent pathway crosstalks and hierarchies.AvailabilityBioKEEN and PyKEEN are open source Python packages publicly available under the MIT License at https://github.com/SmartDataAnalytics/BioKEEN and https://github.com/SmartDataAnalytics/PyKEEN as well as through PyPI.

scholarly journals SpacePHARER: Sensitive identification of phages from CRISPR spacers in prokaryotic hosts

2020 ◽  
Author(s):  
R. Zhang ◽  
M. Mirdita ◽  
E. Levy Karin ◽  
C. Norroy ◽  
C. Galiez ◽  
...  
Keyword(s):  
Open Source ◽  
Protein Level ◽  
De Novo ◽  
False Positives ◽  
Metagenomic Data ◽  
Command Line ◽  
Link Type ◽  
Host Relationships ◽  
User Friendly

SummarySpacePHARER (CRISPR Spacer Phage-Host Pair Finder) is a sensitive and fast tool for de novo prediction of phage-host relationships via identifying phage genomes that match CRISPR spacers in genomic or metagenomic data. SpacePHARER gains sensitivity by comparing spacers and phages at the protein-level, optimizing its scores for matching very short sequences, and combining evidences from multiple matches, while controlling for false positives. We demonstrate SpacePHARER by searching a comprehensive spacer list against all complete phage genomes.Availability and implementationSpacePHARER is available as an open-source (GPLv3), user-friendly command-line software for Linux and macOS at spacepharer.soedinglab.org.

scholarly journals AGEpy: a Python package for computational biology

2018 ◽  
Author(s):  
Franziska Metge ◽  
Robert Sehlke ◽  
Jorge Boucas
Keyword(s):  
Computational Biology ◽  
Open Source ◽  
High Throughput ◽  
Biological Data ◽  
Command Line ◽  
High Throughput Analysis ◽  
Throughput Analysis ◽  
Link Type ◽  
Biological Meaning ◽  
Python Package

AbstractSummary:AGEpy is a Python package focused on the transformation of interpretable data into biological meaning. It is designed to support high-throughput analysis of pre-processed biological data using either local Python based processing or Python based API calls to local or remote servers. In this application note we describe its different Python modules as well as its command line accessible toolsaDiff,abed,blasto,david, andobo2tsv.Availability:The open source AGEpy Python package is freely available at:https://github.com/mpg-age-bioinformatics/AGEpy.Contact:[email protected]

scholarly journals pdb-tools: a swiss army knife for molecular structures

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1961 ◽  
Author(s):  
João P. G. L. M. Rodrigues ◽  
João M.C. Teixeira ◽  
Mikaël Trellet ◽  
Alexandre M. J. J. Bonvin
Keyword(s):  
Molecular Structure ◽  
Open Source ◽  
Protein Data Bank ◽  
Structure Data ◽  
Data Bank ◽  
Molecular Structures ◽  
Command Line ◽  
Efficient Manner ◽  
Link Type

The pdb-tools are a collection of Python scripts for working with molecular structure data in the Protein Data Bank (PDB) format. They allow users to edit, convert, and validate PDB files, from the command-line, in a simple but efficient manner. The pdb-tools are implemented in Python, without any external dependencies, and are freely available under the open-source Apache License at https://github.com/haddocking/pdb-tools/ and on PyPI.

scholarly journals CoV-Seq: SARS-CoV-2 Genome Analysis and Visualization

2020 ◽  
Author(s):  
Boxiang Liu ◽  
Kaibo Liu ◽  
He Zhang ◽  
Liang Zhang ◽  
Yuchen Bian ◽  
...  
Keyword(s):  
Ad Hoc ◽  
Rapid Analysis ◽  
Supplementary Information ◽  
Command Line ◽  
Command Line Interface ◽  
Link Type ◽  
Global Pandemic ◽  
Fast Pace ◽  
Public Repositories ◽  
Programming Knowledge

AbstractSummaryCOVID-19 has become a global pandemic not long after its inception in late 2019. SARS-CoV-2 genomes are being sequenced and shared on public repositories at a fast pace. To keep up with these updates, scientists need to frequently refresh and reclean datasets, which is ad hoc and labor-intensive. Further, scientists with limited bioinformatics or programming knowledge may find it difficult to analyze SARS-CoV-2 genomes. In order to address these challenges, we developed CoV-Seq, a webserver to enable simple and rapid analysis of SARS-CoV-2 genomes. Given a new sequence, CoV-Seq automatically predicts gene boundaries and identifies genetic variants, which are presented in an interactive genome visualizer and are downloadable for further analysis. A command-line interface is also available for high-throughput processing.Availability and ImplementationCoV-Seq is implemented in Python and Javascript. The webserver is available at http://covseq.baidu.com/ and the source code is available from https://github.com/boxiangliu/[email protected] informationSupplementary information are available at bioRxiv online.

scholarly journals TaxonKit: a cross-platform and efficient NCBI taxonomy toolkit

2019 ◽  
Author(s):  
Wei Shen ◽  
Jie Xiong
Keyword(s):  
Open Source ◽  
Programming Language ◽  
Operating Systems ◽  
Execution Time ◽  
Command Line ◽  
Ncbi Taxonomy ◽  
Competitive Performance ◽  
Local Database ◽  
Link Type ◽  
Cross Platform

AbstractSummaryTaxonKit is a command-line toolkit for rapid manipulation of NCBI taxonomy data. It provides executable binary files for major operating systems including Windows, Linux, and Mac OS X, and can be directly used without any dependencies nor local database buiding. TaxonKit demonstrates competitive performance in execution time compared to similar tools. The efficiency, scalability, and usability of TaxonKit enable researchers to rapidly investigate taxonomy data.AvailabilityTaxonkit is implemented in Go programming language. It is open-source and freely available for download and use from https://github.com/shenwei356/taxonkit.

Role of Nutraceuticals in Health Promotion and Disease Prevention: A Review in the Indian Context

2020 ◽  
Vol 19 (2) ◽  
pp. 139-145
Author(s):  
Sheena Chhabra ◽  
Apurva Bakshi ◽  
Ravineet Kaur
Keyword(s):  
Health Promotion ◽  
Disease Prevention ◽  
Human Health ◽  
Health Issues ◽  
Scholarly Literature ◽  
Indian Context ◽  
The Impact ◽  
Immense Potential ◽  
Specific Health

Nutraceuticals have been around for quite some time. As the nomenclature suggests, they are placed somewhere between food (nutra-) and medicine (-ceuticals) in terms of their impact on human health. Researches have focused on the impact of various types of nutraceuticals on health, their efficacy in health promotion and disease prevention, and often on suitable uses of certain categories of nutraceuticals for specific health issues. However, we are still far from utilizing the immense potential of nutraceuticals for benefiting human health in a substantial manner. We review the available scholarly literature regarding the role of nutraceuticals in health promotion, their efficacy in disease prevention and the perception of nutraceuticals' health benefits by consumers. Thereafter we analyze the need for regulation of nutraceuticals and various provisions regarding the same.

EHAI: Enhanced Human Microbe-Disease Association Identification

2020 ◽  
Vol 21 (11) ◽  
pp. 1078-1084
Author(s):  
Ruizhi Fan ◽  
Chenhua Dong ◽  
Hu Song ◽  
Yixin Xu ◽  
Linsen Shi ◽  
...  
Keyword(s):  
Microbial Community ◽  
Human Health ◽  
Complex Diseases ◽  
Disease Association ◽  
Computational Results ◽  
Computational Approaches ◽  
Disease Diagnostics ◽  
Disease Associations ◽  
Association Discovery ◽  
Biological Tool

: Recently, an increasing number of biological and clinical reports have demonstrated that imbalance of microbial community has the ability to play important roles among several complex diseases concerning human health. Having a good knowledge of discovering potential of microbe-disease relationships, which provides the ability to having a better understanding of some issues, including disease pathology, further boosts disease diagnostics and prognostics, has been taken into account. Nevertheless, a few computational approaches can meet the need of huge scale of microbe-disease association discovery. In this work, we proposed the EHAI model, which is Enhanced Human microbe- disease Association Identification. EHAI employed the microbe-disease associations, and then Gaussian interaction profile kernel similarity has been utilized to enhance the basic microbe-disease association. Actually, some known microbe-disease associations and a large amount of associations are still unavailable among the datasets. The ‘super-microbe’ and ‘super-disease’ were employed to enhance the model. Computational results demonstrated that such super-classes have the ability to be helpful to the performance of EHAI. Therefore, it is anticipated that EHAI can be treated as an important biological tool in this field.

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