Workflows
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A Retrieval-Augmented Knowledge Mining Method with Deep Thinking LLMs for Biomedical Research and Clinical Support
Introduction
Knowledge graphs and large language models (LLMs) serve as key tools for biomedical knowledge integration and reasoning, facilitating the structured organization of literature and the discovery of deep semantic relationships. However, existing methods still face challenges in knowledge mining and cross-document reasoning: knowledge graph construction is constrained ...
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).
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).
DeepAnnotation can be used to perform genomic selection (GS), which is a promising breeding strategy for agricultural breeding. DeepAnnotation predicts phenotypes from comprehensive multi-omics functional annotations with interpretable deep learning framework. The effectiveness of DeepAnnotation has been demonstrated in predicting three pork production traits (lean meat percentage at 100 kg [LMP], loin muscle depth at 100 kg [LMD], back fat thickness at 100 kg [BF]) on a population of 1940 Duroc ...
Type: Python
Creators: Wenlong Ma, Weigang Zheng, Shenghua Qin, Chao Wang, Bowen Lei, Yuwen Liu
Submitter: Ma Wenlong
High-throughput phenotyping is addressing the current bottleneck in phenotyping within breeding programs. Imaging tools are becoming the primary resource for improving the efficiency of phenotyping processes and providing large datasets for genomic selection approaches. The advent of AI brings new advantages by enhancing phenotyping methods using imaging, making them more accessible to breeding programs. In this context, we have developed an open Python workflow for analyzing morphology, colour ...
Code for the high risk autism phenotype paper
This repository implements a fully reproducible pipeline for the autism signature project. It uses invoke tasks and a Docker container for consistent, cross-platform execution.
The entire workflow—data fetching, processing, and figure generation—can be reproduced in a few commands. Much of the code in this repo originated from [ASD ...
Introduction
samba-norovirus is an adaptation of the samba workflow for the specific needs in metabarcoding analyses of norovirus. It is a FAIR scalable workflow integrating, into a unique tool, state-of-the-art bioinformatics and statistical methods to conduct reproducible metabarcoding and eDNA analyses using Nextflow (Di Tommaso et al., 2017). SAMBA performs complete metabarcoding analysis by:
...
Type: Nextflow
Creators: Cyril Noel, Antoine Veron, Françoise Vincent-Hubert, Julien Schaeffer, Marion Desdouits, Soizick Le Guyader
Submitter: Cyril Noel
SynProtX
An official implementation of our research paper "SynProtX: A Large-Scale Proteomics-Based Deep Learning Model for Predicting Synergistic Anticancer Drug Combinations".
SynProtX is a deep learning model that integrates large-scale proteomics data, molecular graphs, and chemical fingerprints to predict synergistic effects of anticancer drug combinations. It provides robust ...
Type: Python
Creators: Bundit Boonyarit, Matin Kositchutima, Tisorn Na Phattalung, Nattawin Yamprasert, Chanitra Thuwajit, Thanyada Rungrotmongkol, Sarana Nutanong
Submitter: Bundit Boonyarit
SAMBA is a FAIR scalable workflow integrating, into a unique tool, state-of-the-art bioinformatics and statistical methods to conduct reproducible eDNA analyses using Nextflow. SAMBA starts processing by verifying integrity of raw reads and metadata. Then all bioinformatics processing is done using commonly used procedure (QIIME 2 and DADA2) but adds new steps relying on dbOTU3 and microDecon to build high quality ASV count tables. Extended statistical analyses are also performed. Finally, SAMBA ...
Type: Nextflow
Creators: Cyril Noel, Alexandre Cormier, Laura Leroi, Patrick Durand, Laure Quintric
Submitter: Cyril Noel
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