We will demonstrate the workflows and applications that we developed using LiDAR data.

Research team concerned with Connectome-based Predictive Modeling (CPM).

Non-profit Korean Community of Bioinformatics

PiFlow is an open source, ready-to-use workflow management systems. It has won 2023 TOP 100 Open Source Achievement Award by BenchCouncil, and its latest version was released in October 2024. PiFlow is dedicated to helping both data scientists and non-technical users to solve complex tasks by providing out-of-the-box solutions.

French Biodiversity e-infrastructure

Our lab, affiliated with Genoscope and the Institut de Biologie François Jacob of CEA, is part of a leading research institution with over 20,000 employees across nine centers in France, focusing on defense, low carbon energies, technological, and fundamental research.Genoscope, founded in 1996, shifted to environmental genomics in 2006, collaborating with the national scientific community on diverse projects. Affiliated with Paris-Saclay University, Genoscope specializes in biodiversity exploitation ...

Our group is engaged in the developing of novel bioinformatics approaches for assisting study of RNA modification and post-translational modifications(PTMs).

The Workflow and Ecosystem Services (WES) group at Oak Ridge Leadership Computing Facility (OLCF) is committed to enhancing automated workflows to enable efficient and reliable execution of research applications across distributed computing environments. Our primary focus is designing and implementing technologies that seamlessly integrate diverse computational, storage, and visualization resources, while leveraging advanced network capabilities to facilitate data movement. We aim to empower ...

Disease Ontology (DO) enrichment analysis is an effective means to discover the associations between genes and diseases. However, most current DO-based enrichment methods were unable to solve the over enriched problem caused by the “true-path” rule. To address this problem, we presents EnrichDO, a double weighted iterative model, which is based on the latest annotations of the human genome with DO terms and integrates the DO graph topology on a global scale. On one hand, to reinforce the saliency ...

Download all genome from https://www.ncbi.nlm.nih.gov/labs/virus/vssi/#/virus?SeqType_s=Nucleotide with filter host:viridiplantae and Refseq on.

This document provides a detailed explanation of all the workflows, including their functionalities, problems they address, advantages, disadvantages, implementation requirements, and open points for future versions.

In the age of high-throughput data, computational workflows have made data processing tasks flexible, manageable, and automated. To administer different computational activities in a workflow, different workflow management systems (WMS) are used that necessitate a sophisticated level of standardisation. Standardisation and reproducibility can be achieved by using standard formats for specifying workflows, such as Common Workflow Language (CWL), and provenance gathering with the standard W3C PROV ...

Poster and presentation video by Ambarish Kumar

This workflow is part of the EJP RD case study on CAKUT published here: Bayjanov, J.R., Doornbos, C., Ozisik, O. et al. Integrative analysis of multi-omics data reveals importance of collagen and the PI3K AKT signalling pathway in CAKUT. Sci Rep 14, 20731 (2024). https://doi.org/10.1038/s41598-024-71721-8

Protein domains can be viewed as building blocks, essential for understanding structure-function relationships in proteins. However, each domain database classifies protein domains using its own methodology. Thus, in many cases, boundaries between different domains or families differ from one domain database to the other, raising the question of domain definition and enumeration. The answer to this question cannot be found in a single database. Rather, expert integration and curation of various ...

The workflow starts with selecting EH38E2924876 as the search term. Genomic position of provided unique regulatory element identifier was retrieved from CFDE Linked Data Hub[1]. A list of variants in the region of the regulatory element was retrieved from CFDE Linked Data Hub[1]. Variant/variant set associated allele specific epigenomic signatures were retrieved from CFDE LDH[5] based on Roadmap and ENTEx data[6], [4]. GTEx eQTL and sQTL evidence for the given variant(s) were retrieved from CFDE ...

A file containing GEO Aging Signatures was first uploaded. The file containing GEO Aging Signatures was loaded as a gene signature. A file containing GTEx Aging Signatures was first uploaded. The file containing GTEx Aging Signatures was loaded as a gene signature. Significant genes were extracted from the GEO Aging Signatures. Significant genes were extracted from the GTEx Aging Signatures. Reversers and mimickers from over 1 million signatures were identified using SigCom LINCS[1]. Resolved ...

A file was first uploaded. The file was parsed as a gene count matrix. Significantly over-expressed genes when compared to tissue expression in GTEx[1] were identified. RNA-seq-like LINCS L1000 Signatures[3] which mimick or reverse the the expression of IMP3 were visualized. Drugs which down-regulate the expression of IMP3 were identified from the RNA-seq-like LINCS L1000 Chemical Perturbagens[3]. Genes which down-regulate the expression of IMP3 were identified from the RNA-seq-like LINCS L1000 ...

The workflow starts with selecting RPE as the search term. For the given gene ID (SYMBOL), StringDB PPI was extracted using their API[1]. For the Given StringDB PPI, the list of nodes (Gene Set) is generated. For the Given StringDB PPI, the list of nodes (GeneSet) is generated. Reversers and mimickers from over 1 million signatures were identified using SigCom LINCS[2]. The gene set was submitted to Enrichr[4]. The gene set was then searched in the Metabolomics Workbench[5] to identify relevant ...

The workflow starts with a gene set created from Example gene set. CTD is applied which diffuses through all nodes in STRING[1] to identify nodes that are "guilty by association" and highly connected to the initial gene set of interest[2][3]. A list of Highly Connected Genes was obtained from the CTD output. A list of Guilty By Association Genes was obtained from the CTD output.

1. Szklarczyk, D. et al. STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic ...

The workflow starts with selecting chr2:g.39417578C>G as the search term. The closest gene to the variant was found using MyVariant.info[1]. Gene expression in tumors for CDKL4 were queried from the Open Pediatric Cancer Atlas API[3]. Median expression of CDKL4 was obtained from the GTEx Portal[4] using the portal's API. To visualize the level of expression across tumor gene expression, a bar plot was created Fig..

1. Lelong, S. et al. BioThings SDK: a toolkit for building high-performance ...

The workflow starts with selecting KLF6 as the search term. RNA-seq-like LINCS L1000 Signatures[1] which mimick or reverse the the expression of KLF6 were visualized. Median expression of KLF6 was obtained from the GTEx Portal[6] using the portal's API. To visualize the scored tissues, a vertical bar plot was created Fig..

1. Evangelista, J. E. et al. SigCom LINCS: data and metadata search engine for a million gene expression signatures. Nucleic Acids Research vol. 50 W697–W709 (2022). ...

The workflow starts with selecting chr10:g.3823823G>A as the search term. The closest gene to the variant was found using MyVariant.info[1]. RNA-seq-like LINCS L1000 Signatures[3] which mimick or reverse the the expression of KLF6 were visualized. Median expression of KLF6 was obtained from the GTEx Portal[8] using the portal's API. To visualize the scored tissues, a vertical bar plot was created Fig..

1. Lelong, S. et al. BioThings SDK: a toolkit for building high-performance data APIs in ...

The workflow starts with selecting Autophagy as the search term. Gene sets with set labels containing Autophagy were queried from Enrichr[1]. Identified matching terms from the MGI Mammalian Phenotype Level 4 2019[2] library were assembled into a collection of gene sets. A GMT was extracted from the Enrichr results for MGI_Mammalian_Phenotype_Level_4_2019. All the identified gene sets were combined using the union set operation. Reversers and mimickers from over 1 million signatures were identified ...

The workflow starts with selecting KLF4 as the search term. Gene sets with set labels containing KLF4 were queried from Enrichr[1]. Identified matching terms from the ENCODE TF ChIP-seq 2015[2] library were assembled into a collection of gene sets. A GMT was extracted from the Enrichr results for ENCODE_TF_ChIP-seq_2015. Identified matching terms from the ChEA 2022[4] library were assembled into a collection of gene sets. A GMT was extracted from the Enrichr results for ChEA_2022. Identified ...

The workflow starts with selecting atrial fibrillation as the search term. The workflow starts with selecting Ibrutinib as the search term. Gene sets with set labels containing atrial fibrillation were queried from Enrichr[1]. Identified matching terms from the MGI Mammalian Phenotype Level 4 2021[2] library were assembled into a collection of gene sets. A GMT was extracted from the Enrichr results for MGI_Mammalian_Phenotype_Level_4_2021. A consensus gene set was created by only retaining genes ...

The workflow starts with selecting Inflammation as the search term. The workflow starts with selecting Penicillin as the search term. The workflow starts with selecting Cortisol as the search term. Gene sets with set labels containing Inflammation were queried from Enrichr[1]. Identified matching terms from the GWAS Catalog 2019[2] library were assembled into a collection of gene sets. A GMT was extracted from the Enrichr results for GWAS_Catalog_2019. All the identified gene sets were combined ...

Installation

Other than cloning this repository, you need to have bash installed (which is most likely the case if you use Linux, *BSD or even MacOS). For the Python code, the arguably easiest and cleanest way is to set up a Python virtual environment and install the dependencies there:

$ python3 -m venv ./hcp-suite-venv # Setup the virtual environment 
$ source ./hcp-suite-venv/bin/activate # Activate the virtual environment 
$ pip install pandas pingouin networkx nilearn nibabel ray
...

Workflow (hybrid) metagenomic assembly and binning

• Workflow Illumina Quality: https://workflowhub.eu/workflows/336?version=1
• FastQC (control)
• fastp (quality trimming)
• kraken2 (taxonomy)
• bbmap contamination filter
• Workflow Longread Quality:
• NanoPlot (control)
• filtlong (quality trimming)
• kraken2 (taxonomy)
• minimap2 contamination filter
• Kraken2 taxonomic classification of FASTQ reads
• SPAdes/Flye (Assembly)
• Pilon/Medaka/PyPolCA (Assembly polishing)
• QUAST (Assembly ...

gimp-image-annotator

gimp-image-annotator or GIÀ, a lightweight GIMP plug-in to alllow for computer vision-assisted image annotation using the powerful GIMP selection toolbox.

Installation

Follow the guide here: https://en.wikibooks.org/wiki/GIMP/Installing_Plugins to find how to install GIMP plug-ins on your system, save the file image-annotator.py in GIMP's plug-in folder.

In GIMP v2.x, the plug-in system relies on deprecated python2. On Windows, a version of python2 is included in ...

Article abstract

Permeability is an important molecular property in drug discovery, as it co-determines pharmacokinetics whenever a drug crosses the phospholipid bilayer, e.g., into the cell, in the gastrointestinal tract or across the blood-brain barrier. Many methods for the determination of permeability have been developed, including cell line assays, cell-free model systems like PAMPA mimicking, e.g., gastrointestinal epithelia or the skin, as well as the Black lipid membrane (BLM) and ...

M6Allele Pipeline & M6Allele algorithm

Introduction

We have developed an algorithm called M6Allele for identifying allele-specific m6A modifications. To facilitate its usage by researchers, we have also encapsulated our analysis process into a pipeline. You can learn more about the pipeline and the algorithm's usage from the following two modules:

• Pipeline
• M6Allele algorithm

M6Allele Pipeline

PARAMETER INTRODUCTION

• -g/--gtf : ...

EnrichDO

EnrichDO is a double weighted iterative model by integrating the DO graph topology on a global scale. It was based on the latest annotations of the human genome with DO terms, and double weighted the annotated protein-coding genes. On one hand, to reinforce the saliency of direct gene-DO annotations, different initial weights were assigned to directly annotated genes and indirectly annotated genes, respectively. On the other hand, to detect locally most significant node between ...

GALOP - Genome Assembly using Long reads Pipeline

This repository contains an exact copy of the standard Genoscope long reads assembly pipeline.

At the moment, this is not intended for users to download as it uses grid submission commands that will only work at Genoscope. As time goes on, we intend to make this pipeline available to a broader audience. However, genome assembly and polishing commands are accessible in the lib/assembly.py and lib/polishing.py files.

galop.py -h 
Mandatory
...