Workflows

Summary

PredPrIn is a scientific workflow to predict Protein-Protein Interactions (PPIs) using machine learning to combine multiple PPI detection methods of proteins according to three categories: structural, based on primary aminoacid sequence and functional annotations.

PredPrIn contains three main steps: (i) acquirement and treatment of protein information, (ii) feature generation, and (iii) classification and analysis.

(i) The first step builds a knowledge base with the available annotations ...

Summary

HPPIDiscovery is a scientific workflow to augment, predict and perform an insilico curation of host-pathogen Protein-Protein Interactions (PPIs) using graph theory to build new candidate ppis and machine learning to predict and evaluate them by combining multiple PPI detection methods of proteins according to three categories: structural, based on primary aminoacid sequence and functional annotations.

HPPIDiscovery contains three main steps: (i) acquirement of pathogen and host proteins ...

This Galaxy workflow takes a list of tumor/normal sample pair variants in VCF format and

1. annotates them using the ENSEMBL Variant Effect Predictor and custom annotation data
2. turns the annotated VCF into a MAF file for import into cBioPortal
3. generates human-readable variant- and gene-centric reports

The input VCF is expected to encode somatic status, somatic p-value and germline p-value of each variant in varscan somatic format, i.e., via SS, SPV and GPV INFO keys, respectively.

The ultimate-level complexity workflow is one among a collection of workflows designed to address tasks up to CTF estimation. In addition to the functionalities provided by layer 0 and 1 workflows, this workflow aims to enhance the quality of both acquisition images and processing.

Quality control protocols

…

Combination of methods

• CTF consensus
• New methods to compare ctf estimations
• CTF xmipp criteria (richer parameters i.e. ice detection)

Advantages: 

• Control of ...

PAIRED-END workflow. Align reads on fasta reference/assembly using bwa mem, get a consensus, variants, mutation explanations.

IMPORTANT:

• For "bcftools call" consensus step, the --ploidy file is in "Données partagées" (Shared Data) and must be imported in your history to use the worflow by providing this file (tells bcftools to consider haploid variant calling).
• SELECT THE MOST ADAPTED VADR MODEL for annotation (see vadr parameters).

SINGLE-END workflow. Align reads on fasta reference/assembly using bwa mem, get a consensus, variants, mutation explanations.

IMPORTANT:

• For "bcftools call" consensus step, the --ploidy file is in "Données partagées" (Shared Data) and must be imported in your history to use the worflow by providing this file (tells bcftools to consider haploid variant calling).
• SELECT the mot ADAPTED VADR MODEL for annotation (see vadr parameters).

This repository contains the python code to reproduce the experiments in Dłotko, Gurnari "Euler Characteristic Curves and Profiles: a stable shape invariant for big data problems"

This workflow represents the Default ML Pipeline for AutoML feature from MLme. Machine Learning Made Easy (MLme) is a novel tool that simplifies machine learning (ML) for researchers. By integrating four essential functionalities, namely data exploration, AutoML, CustomML, and visualization, MLme fulfills the diverse requirements of researchers while eliminating the need for extensive coding efforts. MLme serves as a valuable resource that empowers researchers of all technical levels to leverage ...

We present an R script that describes the workflow for analysing honey bee (Apis mellifera) wing shape. It is based on a dataset of wing images and landmark coordinates available at Zenodo: https://doi.org/10.5281/zenodo.8128010. The dataset can be used as a reference for the identification of local bees from southern Kazakhstan, which most probably belong to the subspecies Apis mellifera pomonella. It was compared with data from Nawrocka et al. (2018), available at Zenodo: ...

This workflow is designed to analyze to a multi-omics data set that comprises genome-wide DNA methylation profiles, targeted metabolomics, and behavioral data of two cohorts that participated in the ACTION Biomarker Study (ACTION, Aggression in Children: Unraveling gene-environment interplay to inform Treatment and InterventiON strategies. (Boomsma 2015, Bartels 2018, Hagenbeek 2020, van Dongen 2021, Hagenbeek 2022). The ACTION-NTR cohort consists of twins that are either longitudinally concordant ...