MILES: a Java tool to extract node-specific enriched subgraphs in biomolecular networks

Abstract Summary The growing availability of biomolecular networks has led to a need for analysis methods that are able to extract biologically meaningful information from these complex data structures. Here we present MILES (MIning Labeled Enriched Subgraphs), a Java-based subgraph mining tool for discovering motifs that are associated to a given set of nodes of interest, such as a list of genes or proteins, in biomolecular networks. It provides a unique extension to the widely used enrichment analysis methodologies by integrating network structure and functional annotations in order to discern novel biological subgraphs which are enriched in the targets of interest. The tool can handle various types of input data, including (un)directed, (un)connected and multi-label networks, and is thus compatible with most types of biomolecular networks. Availability and implementation MILES is available as a platform-independent Java application at https://github.com/pmoris/miles-subgraph-miner alongside a user manual, example datasets and the source code. Supplementary information Supplementary data are available at Bioinformatics online.

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PyIOmica: longitudinal omics analysis and trend identification

Bioinformatics ◽

10.1093/bioinformatics/btz896 ◽

2019 ◽

Vol 36 (7) ◽

pp. 2306-2307 ◽

Cited By ~ 3

Author(s):

Sergii Domanskyi ◽

Carlo Piermarocchi ◽

George I Mias

Keyword(s):

Source Code ◽

Temporal Trends ◽

Enrichment Analysis ◽

Supplementary Information ◽

Data Normalization ◽

Supplementary Data ◽

Visibility Graphs ◽

Trend Identification ◽

Microsoft Windows ◽

Python Package

Abstract Summary PyIOmica is an open-source Python package focusing on integrating longitudinal multiple omics datasets, characterizing and categorizing temporal trends. The package includes multiple bioinformatics tools including data normalization, annotation, categorization, visualization and enrichment analysis for gene ontology terms and pathways. Additionally, the package includes an implementation of visibility graphs to visualize time series as networks. Availability and implementation PyIOmica is implemented as a Python package (pyiomica), available for download and installation through the Python Package Index (https://pypi.python.org/pypi/pyiomica), and can be deployed using the Python import function following installation. PyIOmica has been tested on Mac OS X, Unix/Linux and Microsoft Windows. The application is distributed under an MIT license. Source code for each release is also available for download on Zenodo (https://doi.org/10.5281/zenodo.3548040). Supplementary information Supplementary data are available at Bioinformatics

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SPIM workflow manager for HPC

Bioinformatics ◽

10.1093/bioinformatics/btz140 ◽

2019 ◽

Vol 35 (19) ◽

pp. 3875-3876 ◽

Cited By ~ 1

Author(s):

Jan Kožusznik ◽

Petr Bainar ◽

Jana Klímová ◽

Michal Krumnikl ◽

Pavel Moravec ◽

...

Keyword(s):

Graphical User Interface ◽

Data Transfer ◽

Source Code ◽

Image Data ◽

Turnaround Time ◽

Supplementary Information ◽

Complex Data ◽

Data Intensive ◽

Microscopy Image ◽

Workflow Manager

Abstract Summary Here we introduce a Fiji plugin utilizing the HPC-as-a-Service concept, significantly mitigating the challenges life scientists face when delegating complex data-intensive processing workflows to HPC clusters. We demonstrate on a common Selective Plane Illumination Microscopy image processing task that execution of a Fiji workflow on a remote supercomputer leads to improved turnaround time despite the data transfer overhead. The plugin allows the end users to conveniently transfer image data to remote HPC resources, manage pipeline jobs and visualize processed results directly from the Fiji graphical user interface. Availability and implementation The code is distributed free and open source under the MIT license. Source code: https://github.com/fiji-hpc/hpc-workflow-manager/, documentation: https://imagej.net/SPIM_Workflow_Manager_For_HPC. Supplementary information Supplementary data are available at Bioinformatics online.

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BiasAway: command-line and web server to generate nucleotide composition-matched DNA background sequences

Bioinformatics ◽

10.1093/bioinformatics/btaa928 ◽

2020 ◽

Author(s):

Aziz Khan ◽

Rafael Riudavets Puig ◽

Paul Boddie ◽

Anthony Mathelier

Keyword(s):

Dna Sequences ◽

Source Code ◽

Web Server ◽

Enrichment Analysis ◽

Nucleotide Composition ◽

Supplementary Information ◽

Command Line ◽

Sequence Composition ◽

Command Line Tool ◽

Gc Bias

Abstract Motivation Accurate motif enrichment analyses depend on the choice of background DNA sequences used, which should ideally match the sequence composition of the foreground sequences. It is important to avoid false positive enrichment due to sequence biases in the genome, such as GC-bias. Therefore, relying on an appropriate set of background sequences is crucial for enrichment analysis. Results We developed BiasAway, a command line tool and its dedicated easy-to-use web server to generate synthetic sequences matching any k-mer nucleotide composition or select genomic DNA sequences matching the mononucleotide composition of the foreground sequences through four different models. For genomic sequences, we provide precomputed partitions of genomes from nine species with five different bin sizes to generate appropriate genomic background sequences. Availability and implementation BiasAway source code is freely available from Bitbucket (https://bitbucket.org/CBGR/biasaway) and can be easily installed using bioconda or pip. The web server is available at https://biasaway.uio.no and a detailed documentation is available at https://biasaway.readthedocs.io. Supplementary information Supplementary data are available at Bioinformatics online.

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DiNGO: standalone application for Gene Ontology and Human Phenotype Ontology term enrichment analysis

Bioinformatics ◽

10.1093/bioinformatics/btz836 ◽

2019 ◽

Author(s):

Radoslav Davidović ◽

Vladimir Perovic ◽

Branislava Gemovic ◽

Nevena Veljkovic

Keyword(s):

Gene Ontology ◽

Source Code ◽

Enrichment Analysis ◽

Human Phenotype Ontology ◽

Supplementary Information ◽

Phenotype Ontology ◽

Ontology Term ◽

Human Phenotype ◽

Term Enrichment Analysis ◽

Term Enrichment

Abstract Summary Although various tools for Gene Ontology (GO) term enrichment analysis are available, there is still room for improvement. Hence, we present DiNGO, a standalone application based on an open source code from BiNGO, a widely-used application to assess the overrepresentation of GO categories. Besides facilitating GO term enrichment analyses, DiNGO has been developed to allow for convenient Human Phenotype Ontology (HPO) term overrepresentation investigation. This is an important contribution considering the increasing interest in HPO in scientific research and its potential in clinical settings. DiNGO supports gene/protein identifier conversion and an automatic updating of GO and HPO annotation resources. Finally, DiNGO can rapidly process a large amount of data due to its multithread design. Availability and Implementation DiNGO is implemented in the JAVA language, and its source code, example datasets and instructions are available on GitHub: https://github.com/radoslav180/DiNGO. A pre-compiled jar file is available at: https://www.vin.bg.ac.rs/180/tools/DiNGO.php Supplementary information Supplementary data are available at Bioinformatics online.

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

Bioinformatics ◽

10.1093/bioinformatics/btz245 ◽

2019 ◽

Vol 35 (21) ◽

pp. 4519-4521 ◽

Cited By ~ 1

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.

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KEC: unique sequence search by K-mer exclusion

Bioinformatics ◽

10.1093/bioinformatics/btab196 ◽

2021 ◽

Author(s):

Pavel Beran ◽

Dagmar Stehlíková ◽

Stephen P Cohen ◽

Vladislav Čurn

Keyword(s):

Amino Acid ◽

Nucleic Acid ◽

Source Code ◽

Unique Sequence ◽

Supplementary Information ◽

Supplementary Data ◽

Laptop Computers ◽

Sequence Search ◽

Target Sequences ◽

Cross Reference

Abstract Summary Searching for amino acid or nucleic acid sequences unique to one organism may be challenging depending on size of the available datasets. K-mer elimination by cross-reference (KEC) allows users to quickly and easily find unique sequences by providing target and non-target sequences. Due to its speed, it can be used for datasets of genomic size and can be run on desktop or laptop computers with modest specifications. Availability and implementation KEC is freely available for non-commercial purposes. Source code and executable binary files compiled for Linux, Mac and Windows can be downloaded from https://github.com/berybox/KEC. Supplementary information Supplementary data are available at Bioinformatics online.

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BioCommons: a robust java library for RNA structural bioinformatics

Bioinformatics ◽

10.1093/bioinformatics/btab069 ◽

2021 ◽

Author(s):

Tomasz Zok

Keyword(s):

Source Code ◽

Structural Bioinformatics ◽

Supplementary Information ◽

Supplementary Data ◽

Bioinformatic Tools ◽

Data Formats ◽

Central Repository ◽

Diverse Data ◽

2D And 3D ◽

Java Library

Abstract Motivation Biomolecular structures come in multiple representations and diverse data formats. Their incompatibility with the requirements of data analysis programs significantly hinders the analytics and the creation of new structure-oriented bioinformatic tools. Therefore, the need for robust libraries of data processing functions is still growing. Results BioCommons is an open-source, Java library for structural bioinformatics. It contains many functions working with the 2D and 3D structures of biomolecules, with a particular emphasis on RNA. Availability and implementation The library is available in Maven Central Repository and its source code is hosted on GitHub: https://github.com/tzok/BioCommons Supplementary information Supplementary data are available at Bioinformatics online.

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Identification of Key Genes and miRNAs in Osteosarcoma Patients with Chemoresistance by Bioinformatics Analysis

BioMed Research International ◽

10.1155/2018/4761064 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 9

Author(s):

Binbin Xie ◽

Yiran Li ◽

Rongjie Zhao ◽

Yuzi Xu ◽

Yuhui Wu ◽

...

Keyword(s):

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Regulation Of Transcription ◽

Functional Annotations ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Search Tool ◽

Poor Outcomes

Chemoresistance is a significant factor associated with poor outcomes of osteosarcoma patients. The present study aims to identify Chemoresistance-regulated gene signatures and microRNAs (miRNAs) in Gene Expression Omnibus (GEO) database. The results of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) included positive regulation of transcription, DNA-templated, tryptophan metabolism, and the like. Then differentially expressed genes (DEGs) were uploaded to Search Tool for the Retrieval of Interacting Genes (STRING) to construct protein-protein interaction (PPI) networks, and 9 hub genes were screened, such as fucosyltransferase 3 (Lewis blood group) (FUT3) whose expression in chemoresistant samples was high, but with a better prognosis in osteosarcoma patients. Furthermore, the connection between DEGs and differentially expressed miRNAs (DEMs) was explored. GEO2R was utilized to screen out DEGs and DEMs. A total of 668 DEGs and 5 DEMs were extracted from GSE7437 and GSE30934 differentiating samples of poor and good chemotherapy reaction patients. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used to perform GO and KEGG pathway enrichment analysis to identify potential pathways and functional annotations linked with osteosarcoma chemoresistance. The present study may provide a deeper understanding about regulatory genes of osteosarcoma chemoresistance and identify potential therapeutic targets for osteosarcoma.

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TFEA.ChIP: a tool kit for transcription factor binding site enrichment analysis capitalizing on ChIP-seq datasets

Bioinformatics ◽

10.1093/bioinformatics/btz573 ◽

2019 ◽

Vol 35 (24) ◽

pp. 5339-5340 ◽

Cited By ~ 8

Author(s):

Laura Puente-Santamaria ◽

Wyeth W Wasserman ◽

Luis del Peso

Keyword(s):

Genomic Analysis ◽

Enrichment Analysis ◽

R Package ◽

Supplementary Information ◽

Web Based ◽

Factor Binding Site ◽

Gene Sets ◽

Transcription Regulators ◽

Computational Identification ◽

On Chip

Abstract Summary The computational identification of the transcription factors (TFs) [more generally, transcription regulators, (TR)] responsible for the co-regulation of a specific set of genes is a common problem found in genomic analysis. Herein, we describe TFEA.ChIP, a tool that makes use of ChIP-seq datasets to estimate and visualize TR enrichment in gene lists representing transcriptional profiles. We validated TFEA.ChIP using a wide variety of gene sets representing signatures of genetic and chemical perturbations as input and found that the relevant TR was correctly identified in 126 of a total of 174 analyzed. Comparison with other TR enrichment tools demonstrates that TFEA.ChIP is an highly customizable package with an outstanding performance. Availability and implementation TFEA.ChIP is implemented as an R package available at Bioconductor https://www.bioconductor.org/packages/devel/bioc/html/TFEA.ChIP.html and github https://github.com/LauraPS1/TFEA.ChIP_downloads. A web-based GUI to the package is also available at https://www.iib.uam.es/TFEA.ChIP/ Supplementary information Supplementary data are available at Bioinformatics online.

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SVIM-asm: Structural variant detection from haploid and diploid genome assemblies

10.1101/2020.10.27.356907 ◽

2020 ◽

Author(s):

David Heller ◽

Martin Vingron

Keyword(s):

Genetic Information ◽

Source Code ◽

Supplementary Information ◽

Supplementary Data ◽

Diploid Genome ◽

Insertions And Deletions ◽

Structural Variant ◽

Sequencing Technologies ◽

Variant Detection ◽

Genome Assemblies

AbstractMotivationWith the availability of new sequencing technologies, the generation of haplotype-resolved genome assemblies up to chromosome scale has become feasible. These assemblies capture the complete genetic information of both parental haplotypes, increase structural variant (SV) calling sensitivity and enable direct genotyping and phasing of SVs. Yet, existing SV callers are designed for haploid genome assemblies only, do not support genotyping or detect only a limited set of SV classes.ResultsWe introduce our method SVIM-asm for the detection and genotyping of six common classes of SVs from haploid and diploid genome assemblies. Compared against the only other existing SV caller for diploid assemblies, DipCall, SVIM-asm detects more SV classes and reached higher F1 scores for the detection of insertions and deletions on two recently published assemblies of the HG002 individual.Availability and ImplementationSVIM-asm has been implemented in Python and can be easily installed via bioconda. Its source code is available at github.com/eldariont/[email protected] informationSupplementary data are available online.

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