Deeplasmid – Identifying Plasmids in Microbial Isolates and Assembled Metagenomes

Deeplasmid

:: DESCRIPTION

Deeplasmid is a deep learning tool for distinguishing plasmids from bacterial chromosomes based on the DNA sequence and its encoded biological data.

::DEVELOPER

Deeplasmid team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Docker

:: DOWNLOAD

Deeplasmid

:: MORE INFORMATION

Citation

Andreopoulos WB, Geller AM, Lucke M, Balewski J, Clum A, Ivanova NN, Levy A.
Deeplasmid: deep learning accurately separates plasmids from bacterial chromosomes.
Nucleic Acids Res. 2021 Dec 6:gkab1115. doi: 10.1093/nar/gkab1115. Epub ahead of print. PMID: 34871418.

MetaCherchant – Analysing Genomic Environment of a Nucleotide Sequence within a Metagenome

MetaCherchant

:: DESCRIPTION

MetaCherchant is a tool for analyzing genomic context of antibiotic resistance genes in gut microbiota.

::DEVELOPER

Computer Technologies Laboratory, National Research University of Information Technologies,

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / MacOs
  • JRE
:: DOWNLOAD

MetaCherchant

:: MORE INFORMATION

Citation

Olekhnovich EI, Vasilyev AT, Ulyantsev VI, Kostryukova ES, Tyakht AV.
MetaCherchant: analyzing genomic context of antibiotic resistance genes in gut microbiota.
Bioinformatics. 2018 Feb 1;34(3):434-444. doi: 10.1093/bioinformatics/btx681. PMID: 29092015.

Metafast v1.0.0 – METAgenome FAST Analysis Toolkit

Metafast v1.0.0

:: DESCRIPTION

Metafast is a tool for fast graph-based reference-free comparison of shotgun metagenomic data.

::DEVELOPER

Computer Technologies Laboratory, National Research University of Information Technologies,

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / MacOs
  • Java
:: DOWNLOAD

Metafast

:: MORE INFORMATION

Citation

Ulyantsev VI, Kazakov SV, Dubinkina VB, Tyakht AV, Alexeev DG.
MetaFast: fast reference-free graph-based comparison of shotgun metagenomic data.
Bioinformatics. 2016 Sep 15;32(18):2760-7. doi: 10.1093/bioinformatics/btw312. Epub 2016 Jun 3. PMID: 27259541.

HipMer 1.2.2 / MetaHipMer 2.0.1 – Extreme Scale De Novo Genome and MetaGenome Assembler

HipMer 1.2.2 / MetaHipMer 2.0.1

:: DESCRIPTION

HipMer is a high-performance application that produces high-quality de novo assemblies for very large-scale genomes.

The MetaHipMer extension is a recent addition to HipMer that is geared to large metagenomes and leverages iterative kmer sizes and a specialized scaffolding algorithm to produce increased contiguity and accuracy in metagenomic assemblies. It is able to reconstruct rRNA elements via a separate algorithm which relies on reference SSU and LSU Hidden Markov Models to help traverse the contig graph around ribosomal RNA regions.

::DEVELOPER

Berkeley Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

HipMer / MetaHipMer

:: MORE INFORMATION

Citation

E. Georganas et al.,
HipMer: an extreme-scale de novo genome assembler,
SC ’15: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, 2015, pp. 1-11, doi: 10.1145/2807591.2807664.

Hofmeyr S, Egan R, Georganas E, Copeland AC, Riley R, Clum A, Eloe-Fadrosh E, Roux S, Goltsman E, Buluç A, Rokhsar D, Oliker L, Yelick K.
Terabase-scale metagenome coassembly with MetaHipMer.
Sci Rep. 2020 Jul 1;10(1):10689. doi: 10.1038/s41598-020-67416-5. PMID: 32612216; PMCID: PMC7329831.

FCMM 2.0.0 – Functional Characterization of Multiple Metagenome samples

FCMM 2.0.0

:: DESCRIPTION

FCMM is a pipeline for top-k based functional characterization of multiple metagenome samples to infer the major functions as well as their quantitative scores in a comparative metagenomics manner.

::DEVELOPER

Bioinformatics Laboratory, Konkuk University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Perl
  • awk (GNU awk 3.1.7)
  • sed (GNU sed 4.2.1)
  • BWA (v0.7.10)
  • DIAMOND (v0.7.8.57)
  • Trimmomatic (v0.33)

:: DOWNLOAD

 FCMM

:: MORE INFORMATION

Citation

FCMM: A comparative metagenomic approach for functional characterization of multiple metagenome samples.
Lee J, Lee HT, Hong WY, Jang E, Kim J.
J Microbiol Methods. 2015 Aug;115:121-8. doi: 10.1016/j.mimet.2015.05.023

MEGAN 6.21.14 – Metagenome analysis

MEGAN 6.21.14

:: DESCRIPTION

MEGAN (MEta Genome ANalyzer) allows laptop analysis of large metagenomic data sets. In a preprocessing step, the set of DNA sequences is compared against databases of known sequences using BLAST or another comparison tool. MEGAN is then used to compute and explore the taxonomical content of the data set, employing the NCBI taxonomy to summarize and order the results. A simple lowest common ancestor algorithm assigns reads to taxa such that the taxonomical level of the assigned taxon reflects the level of conservation of the sequence. The software allows large data sets to be dissected without the need for assembly or the targeting of specific phylogenetic markers. It provides graphical and statistical output for comparing different data sets.

::DEVELOPER

the Algorithms in Bioinformatics lab.

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows / MacOsX / Linux
  • Java

:: DOWNLOAD

 MEGAN

:: MORE INFORMATION

Citation

Analysis of 16S rRNA environmental sequences using MEGAN.
Mitra S, Stärk M, Huson DH.
BMC Genomics. 2011 Nov 30;12 Suppl 3:S17. doi: 10.1186/1471-2164-12-S3-S17.

MetaMeta 1.2.0 – Integrating Metagenome analysis tools to improve Taxonomic Profiling

MetaMeta 1.2.0

:: DESCRIPTION

MetaMeta is a pipeline to execute and integrate results from metagenome analysis tools. It provides an easy workflow to run multiple tools with multiple samples, producing a single enhanced output profile for each sample.

::DEVELOPER

Vitor C. Piro

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

MetaMeta

:: MORE INFORMATION

Citation

Piro VC, Matschkowski M, Renard BY.
MetaMeta: integrating metagenome analysis tools to improve taxonomic profiling.
Microbiome. 2017 Aug 14;5(1):101. doi: 10.1186/s40168-017-0318-y. PMID: 28807044; PMCID: PMC5557516.

Taxy / Taxy-Pro – Fast Estimation of Metagenomic Taxon Abundances

Taxy / Taxy-Pro

:: DESCRIPTION

Taxy is a software for taxonomic profiling based on mixture modeling of the overall oligonucleotide distribution of a sample. Inferring the taxonomic composition of a microbial community from a large collection of anonymous DNA sequencing reads is a challenging task in computational biology. Because existing methods for taxonomic profiling of metagenomes are all based on the assignment of fragmental sequences to phylogenetic categories, the accuracy of results largely depends on fragment length. This dependency complicates comparative analysis of data originating from different sequencing platforms or preprocessing pipelines. We have developed a read length-independent method for taxonomic profiling and we provide a freely available Matlab/Octave toolbox which includes an ultra-fast implementation of that method. Besides the platform-independent toolbox we also provide a prototype tool implementation for Windows that allows the user to compare a large number of preprocessed metagenomes within a graphical environment.Our tests indicate that Taxy results compare well with taxonomic profiles obtained with other methods. However, in contrast to the existing methods, Taxy provides a nearly constant profiling accuracy across all kinds of read lengths and it operates at an unrivaled speed. As input, DNA sequences in terms of multi-FASTA files of any size can be used for the estimation of metagenomic profiles. The analysis of a large sequence file with a Gbp volume typically requires less than a minute of processing time and can even be performed on a standard notebook.

In contrast to the oligonucleotide-based Taxy method, Taxy-Pro is based on mixture model analysis of protein signatures in terms of protein domain frequencies.

::DEVELOPER

the Department of Bioinformatics of the University of Göttingen

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux / Mac OS X / Windows
  • Matlab

:: DOWNLOAD

 Taxy / Taxy-Pro

:: MORE INFORMATION

Citation

Bioinformatics. 2013 Apr 15;29(8):973-80. doi: 10.1093/bioinformatics/btt077. Epub 2013 Feb 15.
Protein signature-based estimation of metagenomic abundances including all domains of life and viruses.
Klingenberg H1, Aßhauer KP, Lingner T, Meinicke P.

P. Meinicke, K. Asshauer and T. Lingner.
Mixture models for analysis of the taxonomic composition of metagenomes“,
Bioinformatics May 15, 2011 27 (10)

DAS tool 1.1.3 – Recovery of Genomes from Metagenomes via Dereplication, Aggregation, and Scoring Strategy

DAS tool 1.1.3

:: DESCRIPTION

DAS Tool is an automated method that integrates the results of a flexible number of binning algorithms to calculate an optimized, non-redundant set of bins from a single assembly.

::DEVELOPER

Banfield Lab

:: SCREENSHOTS

N/a

:: REQUIREMENTS

  • Linux
  • R

:: DOWNLOAD

DAS Tool

:: MORE INFORMATION

Citation

Sieber CMK, Probst AJ, Sharrar A, Thomas BC, Hess M, Tringe SG, Banfield JF.
Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy.
Nat Microbiol. 2018 Jul;3(7):836-843. doi: 10.1038/s41564-018-0171-1. Epub 2018 May 28. PMID: 29807988; PMCID: PMC6786971.

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