Nextflow Modules
Showing module(s) with keyword "deep learning"
| Module | Keywords | Description |
|---|---|---|
| nf-core/caalm/caalm | cazyme annotation protein language model deep learning classification | Annotates carbohydrate-active enzyme (CAZyme) families from protein sequences using protein language model (ESM) embeddings and FAISS-based nearest-neighbour search. Performs three-level hierarchical classification: binary CAZyme detection (Level 0), CAZy class assignment (Level 1), and CAZy family assignment (Level 2). |
| nf-core/caalm/downloadmodels | cazyme model download huggingface deep learning | Downloads the CAALM model weights from HuggingFace Hub (lczong/CAALM) into a local models/ directory. The downloaded directory is used as input to the CAALM_CAALM annotation module for CAZyme prediction. |
| nf-core/deeparg/downloaddata | download database deeparg antimicrobial resistance genes deep learning prediction | A deep learning based approach to predict Antibiotic Resistance Genes (ARGs) from metagenomes |
| nf-core/deeparg/predict | deeparg antimicrobial resistance antimicrobial resistance genes arg deep learning prediction contigs metagenomes | A deep learning based approach to predict Antibiotic Resistance Genes (ARGs) from metagenomes |
| nf-core/deepbgc/download | database download BGC biosynthetic gene cluster deep learning neural network random forest genomes bacteria fungi | Database download module for DeepBGC which detects BGCs in bacterial and fungal genomes using deep learning. |
| nf-core/deepbgc/pipeline | BGC biosynthetic gene cluster deep learning neural network random forest genomes bacteria fungi | DeepBGC detects BGCs in bacterial and fungal genomes using deep learning. |
| nf-core/deepmased/features | metagenomics assembly quality control error detection deep learning features | DeepMAsED features subcommand: extracts alignment-based features from BAM and assembly FASTA for each contig, producing feature tables used as input for DeepMAsED predict. |
| nf-core/deepmased/predict | metagenomics assembly quality control error detection deep learning prediction | DeepMAsED predict subcommand: runs the pre-trained deep learning model on feature tables produced by DeepMAsED features to predict per-contig assembly error scores. |
| nf-core/diann/insilicolibrarygeneration | diann spectral library proteomics deep learning dia | Generate in silico predicted spectral library using DIA-NN deep learning predictor. This module uses DIA-NN software for data-independent acquisition (DIA) proteomics data processing. Output materials should include attribution: "Generated using DIA-NN". |
| nf-core/fungtion/downloadmodels | fungal effector protein prediction language model deep learning model download | Downloads the pretrained ESM-1b weights (esm1b_t33_650M_UR50S.pt) that fungtion uses for fungal effector prediction into a local models/ directory via `fungtion setup-models`. The downloaded directory is used as input to the FUNGTION_FUNGTION prediction module. |
| nf-core/fungtion/fungtion | fungal effector protein prediction language model deep learning svm classification | Predicts fungal effector proteins from protein FASTA sequences using ESM-1b protein language model embeddings and R-based SVM models. Optionally produces similarity-network and relationship-tree visualizations and an HTML report for predicted effectors. Requires the pretrained ESM-1b weights from FUNGTION_DOWNLOADMODELS as input. |
| nf-core/ribodetector | RNA RNAseq rRNA ribosomal RNA rRNA depletion rRNA removal rRNA filtering deep learning Riboseq genomics | Accurate and rapid RiboRNA sequences Detector based on deep learning |