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748 Workflows visible to you, out of a total of 809
COMPSs GPU K-Means
Stable

Name: K-Means GPU Cache OFF Contact Person: cristian.tatu@bsc.es Access Level: public License Agreement: Apache2 Platform: COMPSs Machine: Minotauro-MN4

K-Means running on GPUs. Launched using 32 GPUs (16 nodes). Parameters used: K=40 and 32 blocks of size (1_000_000, 1200). It creates a block for each GPU. Total dataset shape is (32_000_000, 1200). Version dislib-0.9

Average task execution time: 194 seconds

Type: COMPSs

Creators: Cristian Tatu, The Workflows and Distributed Computing Team (https://www.bsc.es/discover-bsc/organisation/scientific-structure/workflows-and-distributed-computing/)

Submitter: Cristian Tatu

DOI: 10.48546/workflowhub.workflow.799.1

COMPSs GPU Cache K-Means
Stable

Name: K-Means GPU Cache ON Contact Person: cristian.tatu@bsc.es Access Level: public License Agreement: Apache2 Platform: COMPSs Machine: Minotauro-MN4

K-Means running on the GPU leveraging COMPSs GPU Cache for deserialization speedup. Launched using 32 GPUs (16 nodes). Parameters used: K=40 and 32 blocks of size (1_000_000, 1200). It creates a block for each GPU. Total dataset shape is (32_000_000, 1200). Version dislib-0.9

Average task execution time: 16 seconds

Type: COMPSs

Creators: Cristian Tatu, The Workflows and Distributed Computing Team (https://www.bsc.es/discover-bsc/organisation/scientific-structure/workflows-and-distributed-computing/)

Submitter: Cristian Tatu

DOI: 10.48546/workflowhub.workflow.800.1

COMPSs GPU Cache DNN Distributed Training
Stable

Name: Dislib Distributed Training - Cache ON Contact Person: cristian.tatu@bsc.es Access Level: public License Agreement: Apache2 Platform: COMPSs Machine: Minotauro-MN4

PyTorch distributed training of CNN on GPU and leveraging COMPSs GPU Cache for deserialization speedup. Launched using 32 GPUs (16 nodes). Dataset: Imagenet Version dislib-0.9 Version PyTorch 1.7.1+cu101

Average task execution time: 36 seconds

Type: COMPSs

Creators: Cristian Tatu, The Workflows and Distributed Computing Team (https://www.bsc.es/discover-bsc/organisation/scientific-structure/workflows-and-distributed-computing/)

Submitter: Cristian Tatu

DOI: 10.48546/workflowhub.workflow.802.1

COMPSs GPU DNN Distributed Training
Stable

Name: Dislib Distributed Training - Cache OFF Contact Person: cristian.tatu@bsc.es Access Level: public License Agreement: Apache2 Platform: COMPSs Machine: Minotauro-MN4

PyTorch distributed training of CNN on GPU. Launched using 32 GPUs (16 nodes). Dataset: Imagenet Version dislib-0.9 Version PyTorch 1.7.1+cu101

Average task execution time: 84 seconds

Type: COMPSs

Creators: Cristian Tatu, The Workflows and Distributed Computing Team (https://www.bsc.es/discover-bsc/organisation/scientific-structure/workflows-and-distributed-computing/)

Submitter: Cristian Tatu

DOI: 10.48546/workflowhub.workflow.801.1

HiC scaffolding pipeline
Stable

HiC scaffolding pipeline

Snakemake pipeline for scaffolding of a genome using HiC reads using yahs.

Prerequisites

This pipeine has been tested using Snakemake v7.32.4 and requires conda for installation of required tools. To run the pipline use the command:

snakemake --use-conda --cores N

where N is number of cores to use. There are provided a set of configuration and running scripts for exectution on a slurm queueing system. After configuring the cluster.json file run:

./run_cluster ...

Type: Snakemake

Creator: Tom Brown

Submitter: Tom Brown

DOI: 10.48546/workflowhub.workflow.796.2

Purge retained haplotypes using Purge-Dups

Purge dups

This snakemake pipeline is designed to be run using as input a contig-level genome and pacbio reads. This pipeline has been tested with snakemake v7.32.4. Raw long-read sequencing files and the input contig genome assembly must be given in the config.yaml file. To execute the workflow run:

snakemake --use-conda --cores N

Or configure the cluster.json and run using the ./run_cluster command

Type: Snakemake

Creator: Tom Brown

Submitter: Tom Brown

DOI: 10.48546/workflowhub.workflow.506.2

HiC contact map generation
Stable

HiC contact map generation

Snakemake pipeline for the generation of .pretext and .mcool files for visualisation of HiC contact maps with the softwares PretextView and HiGlass, respectively.

Prerequisites

This pipeine has been tested using Snakemake v7.32.4 and requires conda for installation of required tools. To run the pipline use the command:

snakemake --use-conda

There are provided a set of configuration and running scripts for exectution on a slurm queueing system. After configuring ...

Type: Snakemake

Creator: Tom Brown

Submitter: Tom Brown

DOI: 10.48546/workflowhub.workflow.795.2

Genome-assessment-post-assembly

Post-genome assembly quality control workflow using Quast, BUSCO, Meryl, Merqury and Fasta Statistics. Updates November 2023. Inputs: reads as fastqsanger.gz (not fastq.gz), and assembly.fasta. New default settings for BUSCO: lineage = eukaryota; for Quast: lineage = eukaryotes, genome = large. Reports assembly stats into a table called metrics.tsv, including selected metrics from Fasta Stats, and read coverage; reports BUSCO versions and dependencies; and displays these tables in the workflow ...

Type: Galaxy

Creators: Gareth Price, Anna Syme

Submitter: Johan Gustafsson

gene2phylo
Stable

gene2phylo

gene2phylo is a snakemake pipeline for batch phylogenetic analysis of a given set of input genes.

Contents

Setup

The pipeline is written in Snakemake and uses conda to install the necessary tools.

It is strongly recommended to install conda using Mambaforge. See details here ...

Type: Unrecognized workflow type

Creators: None

Submitter: Oliver White

skim2rrna
Stable

skim2rrna

skim2rrna is a snakemake pipeline for the batch assembly, annotation, and phylogenetic analysis of ribosomal genes from low coverage genome skims. The pipeline was designed to work with sequence data from museum collections. However, it should also work with genome skims from recently collected samples.

Contents

Type: Snakemake

Creators: None

Submitter: Oliver White

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