MACLEAPS 1.0.2 – Machine Learning Analysis Pipeline for Genome-Wide Accociation Study SNP data

MACLEAPS 1.0.2

:: DESCRIPTION

MACLEAPS is an automated pipeline that uses state-of-the-art machine learning algorithms to create a disease risk model based on a given GWAS SNP dataset and assesses its predictive performance for unseen datasets. The pipeline can either use a first dataset for training the model and a second for validation, or perform a nested k-fold cross-validation on a single dataset.

::DEVELOPER

the Interfaculty Institute for Biomedical Informatics (IBMI) (Zentrum für Bioinformatik Tübingen, ZBIT).

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ WIndows/MacOsX
  • PLINK
  • Java

:: DOWNLOAD

  MACLEAPS

:: MORE INFORMATION

Citation

Hum Mutat. 2012 Dec;33(12):1708-18. doi: 10.1002/humu.22161. Epub 2012 Aug 3.
Use of support vector machines for disease risk prediction in genome-wide association studies: concerns and opportunities.
Mittag F et al.

Grape 1.1.0 – Pipeline for Processing and Analyzing RNA-Seq data

Grape 1.1.0

:: DESCRIPTION

The Grape RNAseq Analysis Pipeline Environment implements a set of workflows that allow for easy exploration of RNA-Seq data.

::DEVELOPER

Guigo Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 Grape

:: MORE INFORMATION

Citation

Bioinformatics. 2013 Mar 1;29(5):614-21. doi: 10.1093/bioinformatics/btt016. Epub 2013 Jan 17.
Grape RNA-Seq analysis pipeline environment.
Knowles DG, Röder M, Merkel A, Guigó R.

nextNEOpi v1.2 – NeoEpitope predictions Nextflow Pipeline

nextNEOpi v1.2

:: DESCRIPTION

nextNEOpi is a comprehensive pipeline for computational neoantigen prediction.

::DEVELOPER

the Institute of Bioinformatics, Innsbruck Medical University

:: SCREENSHOTS

n/a

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

nextNEOpi

:: MORE INFORMATION

Citation

Rieder D, Fotakis G, Ausserhofer M, René G, Paster W, Trajanoski Z, Finotello F.
nextNEOpi: a comprehensive pipeline for computational neoantigen prediction.
Bioinformatics. 2021 Nov 12:btab759. doi: 10.1093/bioinformatics/btab759. Epub ahead of print. PMID: 34788790.

metaSNV v2.0.1 – Metagenomic SNV Calling Pipeline

metaSNV v2.0.1

:: DESCRIPTION

MetaSNV is a pipeline for calling metagenomic single nucleotide variants (SNVs). It was designed to scale well with the exponentially increasing amount of available metagenomic datasets and is capable of handling large multi-species references.

::DEVELOPER

metaSNV team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX
  • Python

:: DOWNLOAD

metaSNV

:: MORE INFORMATION

Citation:

Van Rossum T, Costea PI, Paoli L, Alves R, Thielemann R, Sunagawa S, Bork P.
metaSNV v2: detection of SNVs and subspecies in prokaryotic metagenomes.
Bioinformatics. 2021 Nov 17:btab789. doi: 10.1093/bioinformatics/btab789. Epub ahead of print. PMID: 34791031.

Cluster Flow 0.5 – A Simple and Flexible Bioinformatics Pipeline tool

Cluster Flow 0.5

:: DESCRIPTION

Cluster Flow is a pipelining tool to automate and standardise bioinformatics analyses on cluster environments.

::DEVELOPER

Phil Ewels @ the Science for Life Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 Cluster Flow

:: MORE INFORMATION

Citation

Ewels P, Krueger F, Käller M, Andrews S.
Cluster Flow: A user-friendly bioinformatics workflow tool.
F1000Res. 2016 Dec 6;5:2824. doi: 10.12688/f1000research.10335.2. PMID: 28299179; PMCID: PMC5310375.

maplet 1.1.1 – Metabolomics Analysis PipeLinE Toolbox

maplet 1.1.1

:: DESCRIPTION

maplet is an R package for statistical data analysis with a special focus on metabolomics datasets. It allows users to create self-contained analytical pipelines.

::DEVELOPER

Jan Krumsiek lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows
  • R

:: DOWNLOAD

maplet

:: MORE INFORMATION

Citation

Chetnik K, Benedetti E, Gomari DP, Schweickart A, Batra R, Buyukozkan M, Wang Z, Arnold M, Zierer J, Suhre K, Krumsiek J.
maplet: An extensible R toolbox for modular and reproducible metabolomics pipelines.
Bioinformatics. 2021 Oct 25:btab741. doi: 10.1093/bioinformatics/btab741. Epub ahead of print. PMID: 34694386.

MSA v1.1 – Mutational Signature Attribution pipeline

MSA v1.1

:: DESCRIPTION

MSA is a reproducible pipeline designed to assign signatures of different mutation types on a single-sample basis, using Non-Negative Least Squares method with optimisation based on configurable simulations.

::DEVELOPER

Sergey Senkin

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

MSA

:: MORE INFORMATION

Citation

Senkin S.
MSA: reproducible mutational signature attribution with confidence based on simulations.
BMC Bioinformatics. 2021 Nov 4;22(1):540. doi: 10.1186/s12859-021-04450-8. PMID: 34736398; PMCID: PMC8567580.

RESCRIPt 2021.8.0 – REference Sequence annotation and CuRatIon Pipeline

RESCRIPt 2021.8.0

:: DESCRIPTION

RESCRIPt is a Python 3 software package and QIIME 2 plugin for reproducible generation and management of reference sequence taxonomy databases, including dedicated functions that streamline creating databases from popular sources, and functions for evaluating, comparing, and interactively exploring qualitative and quantitative characteristics across reference databases.

::DEVELOPER

Laboratory of Food Systems Biotechnology

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  •  Linux
  • Python
  • QIIME
  • conda

:: DOWNLOAD

RESCRIPt

:: MORE INFORMATION

Citation:

Robeson MS 2nd, O’Rourke DR, Kaehler BD, Ziemski M, Dillon MR, Foster JT, Bokulich NA.
RESCRIPt: Reproducible sequence taxonomy reference database management.
PLoS Comput Biol. 2021 Nov 8;17(11):e1009581. doi: 10.1371/journal.pcbi.1009581. Epub ahead of print. PMID: 34748542.

WGA-LP – A Pipeline for Whole Genome Assembly

WGA-LP

:: DESCRIPTION

WGA-LP is a pipeline for whole genome assembly that simplifies the usage of different tools and helps the user in evaluating his results.

::DEVELOPER

WGA-LP team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • Python

:: DOWNLOAD

WGA-LP

:: MORE INFORMATION

Citation

Rossi N, Colautti A, Iacumin L, Piazza C.
WGA-LP: a pipeline for Whole Genome Assembly of contaminated reads.
Bioinformatics. 2021 Oct 20:btab719. doi: 10.1093/bioinformatics/btab719. Epub ahead of print. PMID: 34668528.

Parallel-META 3.6 – A Parallel Metagenomic Analysis Pipeline

Parallel-META 3.6

:: DESCRIPTION

Parallel-META is a GPGPU and Multi-Core CPU based software which can parallelly analyze massive metagenomic data structures, report the classification, construction and distribution on phylogenetic & taxonomic and functional level.

::DEVELOPER

Bioinformatics Group , Single-cell Reseearch Center of Qingdao Institute of BioEnergy and Bioprocess Technology, Chinese Academy of Sciences (QIBEBT-CAS).

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOs
  • C Compiler
  • R

:: DOWNLOAD

 Parallel-META

:: MORE INFORMATION

Citation

Parallel-META 2.0: enhanced metagenomic data analysis with functional annotation, high performance computing and advanced visualization.
Su X, Pan W, Song B, Xu J, Ning K.
PLoS One. 2014 Mar 3;9(3):e89323. doi: 10.1371/journal.pone.0089323.

Parallel-META: efficient metagenomic data analysis based on high-performance computation.
Su X, Xu J, Ning K.
BMC Syst Biol. 2012 Jul 16;6 Suppl 1:S16. doi: 10.1186/1752-0509-6-S1-S16.