Workflows

What is a Workflow?
1321 Workflows visible to you, out of a total of 1406

Parabricks-Genomics-nf is a GPU-enabled pipeline for alignment and germline short variant calling for short read sequencing data. The pipeline utilises NVIDIA's Clara Parabricks toolkit to dramatically speed up the execution of best practice bioinformatics tools. Currently, this pipeline is configured specifically for NCI's Gadi HPC.

NVIDIA's Clara Parabricks can deliver a significant ...

Type: Nextflow

Creator: Georgina Samaha

Submitter: Georgina Samaha

DOI: 10.48546/workflowhub.workflow.836.1

Work-in-progress

Perl CI DOI

Library curation BOLD

alt text

This repository contains scripts and synonymy data for pipelining the automated curation of BOLD data dumps in BCDM TSV ...

Type: Snakemake

Creators: Rutger Vos, Fabian Deister, Ben Price, Special thanks to Sujeevan Ratnasingham and the team at CBG for the creation of the BCDM data exchange format that this pipeline operates on

Submitter: Rutger Vos

DOI: 10.48546/workflowhub.workflow.833.1

Work-in-progress

The input to this workflow is a data matrix of gene expression that was collected from a pediatric patient tumor patient from the KidsFirst Common Fund program [1]. The RNA-seq samples are the columns of the matrix, and the rows are the raw expression gene count for all human coding genes (Table 1). This data matrix is fed into TargetRanger [2] to screen for targets which are highly expressed in the tumor but lowly expressed across most healthy human tissues based on gene expression data collected ...

Type: Common Workflow Language

Creators: None

Submitter: Daniel Clarke

Stable

This workflow provides a calculaiton of the power spectrum of Stochastic Gravitational Wave Backgorund (SGWB) from a first-order cosmological phase transition based on the parameterisations of Roper Pol et al. (2023). The power spectrum includes two components: from the sound waves excited by collisions of bubbles of the new phase and from the turbulence that is induced by these collisions.

The cosmological epoch of the phase transition is described by the temperature, T_star and by the number(s) ...

Type: Galaxy

Creators: Andrii Neronov, Théo Boyer

Submitter: Andrii Neronov

DOI: 10.48546/workflowhub.workflow.831.1

Stable

Name: Incrementation and Fibonacci Access Level: public License Agreement: Apache2 Platform: COMPSs

Description

Brief Overview: Demonstrates COMPSs task parallelism with increment and Fibonacci computations. Helps to understand COMPSs.

Detailed Description:

  1. Performs multiple increments of input values in parallel using COMPSs.
  2. Concurrently calculates Fibonacci numbers using recursive COMPSs tasks.
  3. Demonstrates task synchronization via compss_wait_on.

Execution

...

Type: COMPSs

Creators: Ashish Bhawel, Ashish Bhawel, Uploading this Workflow under the guidance of Raül Sirvent.

Submitter: Ashish Bhawel

Stable

The tool provides a calculation of the power spectrum of Stochastic Gravitational Wave Backgorund (SGWB) from a first-order cosmological phase transition based on the parameterisations of Roper Pol et al. (2023). The power spectrum includes two components: from the sound waves excited by collisions of bubbles of the new phase and from the turbulence that is induced by these collisions.

The cosmological epoch of the phase transition is described by the temperature, T_star and by the number(s) of ...

Stable

Cite with Zenodo Nextflow run with conda run with docker ...

Type: Nextflow

Creators: Damon-Lee Pointon, William Eagles, Ying Sims

Submitter: Damon-Lee Pointon

No description specified

Type: Galaxy

Creators: None

Submitter: Markus Konkol

Stable

Calculates the Fibonacci series up to a specified length.

Type: COMPSs

Creator: Uploading this Workflow under the guidance of Raül Sirvent.

Submitter: Ashish Bhawel

Stable

Name: Matmul GPU Case 1 Cache-ON Contact Person: cristian.tatu@bsc.es Access Level: public License Agreement: Apache2 Platform: COMPSs Machine: Minotauro-MN4

Matmul running on the GPU leveraging COMPSs GPU Cache for deserialization speedup. Launched using 32 GPUs (16 nodes). Performs C = A @ B Where A: shape (320, 56_900_000) block_size (10, 11_380_000)             B: shape (56_900_000, 10)   block_size (11_380_000, 10)             C: shape (320, 10)                block_size ...

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.798.1

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