Tag: Microbial Community

Seed – Exploring and Visualizing Microbial Community Data

Seed

:: DESCRIPTION

Seed (Simple Exploration of Ecological Datasets) is an R/Shiny package for visualizing ecological data. It provides a visual interface for generating a wide variety of plots, including histograms, scatterplots, bar plots, stacked bar plots, PCoA plots, cluster dendrograms, and heatmaps.

::DEVELOPER

Daniel Beck , Christopher Dennis at christozoan@gmail.com.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • R

:: DOWNLOAD

 Seed

:: MORE INFORMATION

Citation

Seed: a user-friendly tool for exploring and visualizing microbial community data.
Beck D, Dennis C, Foster JA.
Bioinformatics. 2014 Oct 20. pii: btu693.

iVikodak – A Modular Framework for Inferring Functional Potential of Microbial Communities from 16S Metagenomic Datasets

iVikodak

:: DESCRIPTION

iVikodak (a word derived from Sanskrit language, which refers to ‘Decoder’) is  a multi-modular web-platform that hosts a logically inter-connected repertoire of functional inference and analysis tools, coupled with a comprehensive visualization interface.

::DEVELOPER

iVikodak team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • WEb browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Nagpal S, Haque MM, Singh R, Mande SS.
iVikodak-A Platform and Standard Workflow for Inferring, Analyzing, Comparing, and Visualizing the Functional Potential of Microbial Communities.
Front Microbiol. 2019 Jan 14;9:3336. doi: 10.3389/fmicb.2018.03336. PMID: 30692979; PMCID: PMC6339920.

Vikodak – A Modular Framework for Inferring Functional Potential of Microbial Communities from 16S Metagenomic Datasets.
Nagpal S, Haque MM, Mande SS.
PLoS One. 2016 Feb 5;11(2):e0148347. doi: 10.1371/journal.pone.0148347.

QIIME2 2021.8 – Analysis of Microbial Communities

QIIME2 2021.8

:: DESCRIPTION

QIIME (Quantitative Insights Into Microbial Ecology) is a pipeline for performing microbial community analysis that integrates many third party tools which have become standard in the field.

::DEVELOPER

Knight Lab / Jeff Werner Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 QIIME

:: MORE INFORMATION

Citation

J Gregory Caporaso et al.
QIIME allows analysis of high-throughput community sequencing data
Nature Methods, 2010; doi:10.1038/nmeth.f.303

Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2.
Bolyen E, et al.
Nat Biotechnol. 2019 Aug;37(8):852-857. doi: 10.1038/s41587-019-0209-9.

RAMI – Identification and Characterization of Phylogenetic Clusters in Microbial Communities

RAMI

:: DESCRIPTION

RAMI clusters related nodes in a phylogenetic tree based on the patristic distance. RAMI also produces indices of cluster properties and other indices used in population and community studies on-the-fly.

::DEVELOPER

RAMI team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Bioinformatics. 2009 Mar 15;25(6):736-42. doi: 10.1093/bioinformatics/btp051. Epub 2009 Feb 17.
RAMI: a tool for identification and characterization of phylogenetic clusters in microbial communities.
Pommier T1, Canbäck B, Lundberg P, Hagström A, Tunlid A.

MGKit 0.4.2 – Metagenomic Framewotk for the Study of Microbial Communities

MGKit 0.4.2

:: DESCRIPTION

MGKit library is to provide a series of useful modules and packages to make it easier to build custom pipelines for metagenomics or any kind of bioinformatics analysis.

::DEVELOPER

The Creevey Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

MGKit

:: MORE INFORMATION

Citation

Rubino, F. and Creevey, C.J. 2014.
MGkit: Metagenomic Framework For The Study Of Microbial Communities. 
figshare [doi:10.6084/m9.figshare.1269288].`

eLSA 1.0.2 – Finding Time-Dependent Associations in Time Series Datasets

eLSA 1.0.2

:: DESCRIPTION

eLSA (Extended local similarity analysis) is a software of Finding Time-Dependent Associations in Time Series Datasets.

::DEVELOPER

Ji Research Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX
  • Python

:: DOWNLOAD

 eLSA

:: MORE INFORMATION

Citation

Li C Xia, Joshua A Steele, Jacob A Cram, Zoe G Cardon, Sheri L Simmons, Joseph J Vallino, Jed A Fuhrman and Fengzhu Sun
Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates
BMC Systems Biology 2011, 5(Suppl 2):S15

Quansong Ruan, Debojyoti Dutta, Michael S. Schwalbach, Joshua A. Steele, Jed A. Fuhrman, Fengzhu Sun (2006),
Local Similarity Analysis Reveals Unique Associations Among Marine Bacterioplankton Species and Environmental Factors.
Bioinformatics (2006) 22 (20): 2532-2538.

SIAMCAT 1.5.0 – Statistical Inference of Associations between Microbial Communities

SIAMCAT 1.5.0

:: DESCRIPTION

SIAMCAT is a modular framework for the statistical inference of associations between microbial communities and host phenotypes, such as disease states in clinical case-control studies. SIAMCAT is based on LASSO models, which offer distinctive advantages for model interpretation and microbial biomarker selection and avoid overfitting issues that can arise in naive combinations of feature selection and cross-validation.

::DEVELOPER

Bork Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOs
  • R

:: DOWNLOAD

SIAMCAT

:: MORE INFORMATION

CLcommunity 3.30 – Microbial Community Analysis

CLcommunity 3.30

:: DESCRIPTION

CLcommunity is a standalone application developed to analyze various microbial populations present in environmental samples. This software uses ChunLab’s proprietary analysis pipeline (generating clc data files), provides a simple interface that allows researchers without bioinformatics expertise to easily perform complex analyses, and creates publication-caliber figures suited to various users’ research needs.

::DEVELOPER

Chunlab, Inc.

:: SCREENSHOTS

CLmicrobiome

:: REQUIREMENTS

  • Linux/ Windows/ MacOsX
  • Java

:: DOWNLOAD

CLcommunity

:: MORE INFORMATION

DynBin – Binning Microbial Community Profiles

DynBin

:: DESCRIPTION

DynBin is a dynamic programming algorithm based binning method for ARISA data analysis which minimizes the overall differences between replicates from the same sampling spot. Data preprocessing identifies several outliers which are later found to be due to systematic errors. Clustering analysis of the time spots based on the binned data reveals important features of the biodiversity of the microbial communities.

::DEVELOPER

Fengzhu Sun

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 DynBin

:: MORE INFORMATION

Citation

Quansong Ruan, Joshua A. Steele, Michael S. Schwalbach, Jed A. Fuhrman, Fengzhu Sun (2006),
A Dynamic Programming Algorithm for Binning Microbial Community Profiles.
Bioinformatics 22:1508-1514

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