Metainformation Template
CV terms
| Issue 84 metadata key | Corresponding bio.tools CV term | Type of value or list of values |
|---|---|---|
| Homepage | ||
| Developmental stage of the tool | maturity | (Emerging, Mature , Legacy) |
| Topic | (could be ELIXIR Community) | |
| Software or data license | license | string |
| Description | multiline varchar (max. 50 lines) | |
| Input format | see EBI | 1 |
| Tool operation (function) | see link | 2 |
| Output data | see EBI | |
| Credits and support | entity type, entity role |
Template
Homepage
Maturity
License
Description
Input format
Tool operation
Output data
Credits, support
- code: github/gitlab/sourceforge link
- doi: 10.XXXX/YYYYYY
Bioinformatic tools
Disclaimer: Changes in the tool display
We recently created a NFDI4Microbiota domain on the life sciences software registry bio.tools and we will soon be displaying all the tools that NFDI4Microbiota created as well as the ones that NFDI4Microbiota consortium members endorse and highly recommend.
Thank you for your patience while we sort out editing rights and collection labels. Below you can browse tool recommendations from our consortium members.
bakta
Description
Bakta is a tool for the rapid & standardized annotation of bacterial genomes and plasmids from both isolates and MAGs. It provides dbxref-rich, sORF-including and taxon-independent annotations in machine-readable JSON & bioinformatics standard file formats for automated downstream analysis.
Output data
- .tsv: annotations as simple human readble TSV
- .gff3: annotations & sequences in GFF3 format
- .gbff: annotations & sequences in (multi) GenBank format
- .embl: annotations & sequences in (multi) EMBL format
- .fna: replicon/contig DNA sequences as FASTA
- .ffn: feature nucleotide sequences as FASTA
- .faa: CDS/sORF amino acid sequences as FASTA
- .hypotheticals.tsv: further information on hypothetical protein CDS as simple human readble tab separated values
- .hypotheticals.faa: hypothetical protein CDS amino acid sequences as FASTA
- .json: all (internal) annotation & sequence information as JSON
- .txt: summary as TXT
- .png: circular genome annotation plot as PNG
- .svg: circular genome annotation plot as SVG
Credits, support
- code: oschwengers@github
- doi: 10.1099/mgen.0.000685
andi
Homepage
http://github.com/evolbioinf/andi/
maturity
Mature
topic
Phylogenetic analysis
license
Description
Andi estimates the evolutionary distance between closely related genomes. These distances can be used to rapidly infer phylogenies for big sets of genomes. Because andi does not compute full alignments, it is so efficient that it scales even up to thousands of bacterial genomes.
Input
2 fasta files
Tool operation
Calculation
Output data
txt (distance matrix)
Credits, support
- github: @haubold
- doi: 10.1093/bioinformatics/btu815
xenoseq
Homepage
https://github.com/bramvandijk88/xenoseq
maturity
Mature
topic
Metagenomics, horizontal gene transfer
license
Description
Pipeline to automate the comparison between short-read libraries to detect foreign (“xenotypic”) sequences.
Input format
fastq, tsv
Tool operation
Sequence analysis
Output data
fasta, tsv
Credits, support
- github: @bramvandijk88
syntenet
Homepage
https://doi.org/doi:10.18129/B9.bioc.syntenet
maturity
Mature
topic
Inference And Analysis Of Synteny Networks
license
Description
syntenet can be used to infer synteny networks from whole-genome protein sequences and analyze them. Anchor pairs are detected with the MCScanX algorithm, which was ported to this package with the Rcpp framework for R and C++ integration. Anchor pairs from synteny analyses are treated as an undirected unweighted graph (i.e., a synteny network), and users can perform: i. network clustering; ii. phylogenomic profiling (by identifying which species contain which clusters) and; iii. microsynteny-based phylogeny reconstruction with maximum likelihood.
Input format
fasta, gff, gtf
Tool operation
Phylogenetic Inference
Output data
R data.frame
Credits, support
- github: @almeidasilvaf
- doi:10.1093/bioinformatics/btac806
DeLTA2
Homepage
https://gitlab.com/dunloplab/delta
maturity
Mature
topic
Bioimage segmentation and tracking
license
Description
DeLTA (Deep Learning for Time-lapse Analysis) is a deep learning-based image processing pipeline for segmenting and tracking single cells in time-lapse microscopy movies.
Input format
czi, tiff, png, jpg
Tool operation
Classication
Output data
tiff, tsv
Credits, support
- gitlab: @jblugagne
- doi:10.1371/journal.pcbi.1009797
Omnipose
Homepage
https://github.com/kevinjohncutler/omnipose
maturity
Mature
topic
Bioimage segmentation
license
Omnipose NonCommercial License
Description
Omnipose is a general image segmentation tool that builds on Cellpose in a number of ways described in our paper. It works for both 2D and 3D images and on any imaging modality or cell shape, so long as you train it on representative images.
Input format
czi, tiff, png, jpg
Tool operation
Classification
Output data
tiff, hdf5
Credits, support
- github: @kevinjohncutler
- doi:10.1038/s41592-022-01639-4
RAREFAN
Homepage
http://rarefan.evolbio.mpg.de
maturity
Mature
topic
Genome sequence analysis
license
Description
The RAREFAN webserver aims to identify and analyze RAYT transposases and their associated REPIN (Repetitive Extragenic PalINdromic sequences) in bacterial species. The input to the server is a selection of closely related strains (less than 5% divergence). Our service provides an analysis of REPIN population size, how it relates to REPIN replication rate and the presence and absence of RAYTs across all submitted genomes.
Input
fasta
Tool operation
Genome annotation
Output data
gff3, html, R
Credits, support
- github: @CFGrote
- doi:10.24072/pcjournal.244
ProteinOrtho
Homepage
https://gitlab.com/paulklemm_PHD/proteinortho
maturity
Mature
topic
Comparative Genomics, Orthology
license
Description
Proteinortho is a tool to detect orthologous genes within different species. For doing so, it compares similarities of given gene sequences and clusters them to find significant groups. Input: Multiple fasta files (orange boxes) with many proteins/genes (circles). Output: Groups (.proteinortho) and pairs (.proteinortho-graph) of orthologs proteins/genes. The algorithm was designed to handle large-scale data and can be applied to hundreds of species at one. To enhance the prediction accuracy, the relative order of genes (synteny) can be used as additional feature for the discrimination of orthologs. The corresponding extension, namely PoFF (doi:10.1371/journal.pone.0105015), is already build in Proteinortho.
Input
FASTA
Tool operation
Analysis, Clustering
Output data
tsv, graph, html, xml
Credits, support
- gitlab: @paulklemm_PHD
- doi:10.1186/1471-2105-12-124
vConTACT
Homepage
### maturity Mature
topic
Taxonomy
license
Description
vConTACT2 is a tool to perform guilt-by-contig-association classification of viral genomic sequence data. It’s designed to cluster and provide taxonomic context of viral metagenomic sequencing data.
Input
FASTA
Tool operation
classification
Output data
TSV, network file, annotation file
Credits, support
- code: https://bitbucket.org/MAVERICLab/vcontact2/src/master/
- doi: 10.1038/s41587-019-0100-8
VirFinder
Homepage
https://github.com/jessieren/VirFinder
maturity
Mature
topic
Prediction of viral sequences
license
USC-RL v1.0
Description
The package provides functions to predict viral sequences in a fasta file, such as the assembled contigs from metagenomic data. The method has good prediction accuracy for short (~1kb) and noval viral sequences. The prediction method is based on the sequence signatures (k-tuple word frequencies) that distinguish virus from host sequences. The model was trained using equal number of known viral and host sequences. For a query sequence, the number of occurrences of k-tuple words are first counted by a c++ program using a hash table. Then the sequence is predicted based on the k-tuple word frequencies using a logistic regression model trained with previously known sequences.
Input
FASTA
Tool operation
R
Output data
TSV
Credits, support
- code: https://github.com/jessieren/VirFinder
- doi:
deepARG
Homepage
https://github.com/gaarangoa/deeparg
Maturity
Mature
Topic
Antibiotic Resistance Genes Prediction from metagenomes
License
MIT The databases used may have commercial restrictions
Description
The deepARG tool leverages deep learning techniques to predict antibiotic resistance genes (ARGs) from genetic sequences. It is designed to handle both DNA and protein sequences, making it versatile for various research applications. By using advanced machine learning algorithms, deepARG can accurately identify and categorize ARGs. Detailed documentation and usage instructions are provided in the GitHub repository, including how to install, run the tool, and interpret its outputs.
Input
- DNA sequences (fasta)
- Protein sequences (faa)
- Short reads (fast)
Tool Operation
- Genome annotation
Output Data
- tsv
- Predictions of antibiotic resistance genes with associated metadata
Credits, Support
- Creator: Gustavo Arango-Argoty
- github: @gaarangoa
- doi: 10.1186/s40168-018-0401-z
Qiita
Homepage
maturity
Mature
topic
Multiomics databasing analysis
license
Description
Qiita (canonically pronounced cheetah) is an entirely open-source microbial study management platform. It allows users to keep track of multiple studies with multiple ‘omics data. Additionally, Qiita is capable of supporting multiple analytical pipelines through a 3rd-party plugin system, allowing the user to have a single entry point for all of their analyses. Qiita provides database and compute resources to the global community, alleviating the technical burdens that are typically limiting for researchers studying microbial ecology (e.g. familiarity with the command line or access to compute power). Qiita’s platform allows for quick reanalysis of the datasets that have been deposited using the latest analytical technologies. This means that Qiita’s internal datasets are living data that is periodically re-annotated according to current best practices.
Input format
FASTQ, SAM
Tool operation
Analysis, Data handling
Output data
SAM, Tables, visualizations
Credits, support
GTDBTk
Homepage
https://github.com/Ecogenomics/GTDBTk https://ecogenomics.github.io/GTDBTk/
Maturity
Mature and under constant development
Topic
Bacterial and Archaeal Genome Taxonomic classification, Phylogenetic Analysis
License
Description
GTDBTk (Genome Taxonomy Database Toolkit) is a software toolkit designed to classify bacterial and archaeal genomes based on the Genome Taxonomy Database (GTDB). It is designed to work with recent advances that allow hundreds or thousands of metagenome-assembled genomes (MAGs) to be obtained directly from environmental samples. It can also be applied to isolate and single-cell genomes. GTDBTk aims to standardize microbial taxonomy, facilitating consistent and reproducible microbial diversity studies.
Input
- Genomic sequences (fasta, both complete and draft genomes)
Tool Operation
- Taxonomic classification based on the Genome Taxonomy Database
- Phylogenetic tree construction
Output Data
- tsv, fasta, tree
Credits, Support
- Chaumeil PA, et al. 2022. GTDB-Tk v2: memory friendly classification with the Genome Taxonomy Database. Bioinformatics, btac672.
- doi: 10.1093/bioinformatics/btac672
PlasFlow
Homepage
https://github.com/smaegol/PlasFlow
Maturity
Mature
Topic
Plasmid Prediction in Metagenomics contigs
License
Description
PlasFlow is a set of scripts used for prediction of plasmid sequences in metagenomic contigs. It relies on the neural network models trained on full genome and plasmid sequences and is able to differentiate between plasmids and chromosomes with accuracy reaching 96%. It outperforms other available solutions for plasmids recovery from metagenomes and incorporates the thresholding which allows for exclusion of incertain predictions. PlasFlow has been published in Nucleic Acids Research (https://doi.org/10.1093/nar/gkx1321).
Input
- Nucleotide sequences (fasta format)
Tool Operation
- Sequence Classification
Output Data
- tsv (Classification results indicating plasmid or chromosomal origin)
Credits, Support
- Created by S. Maegol
- Krawczyk PS, Lipinski L, Dziembowski A. Nucleic Acids Res. 2018 Apr 6;46(6):e35.
- doi: 10.1093/nar/gkx1321
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FASTA, SAM, TSV, CSV, XLSX, Image ↩
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Alignment, Analysis, Annotation, Calculation, Classification, Clustering, Comparison, Conversion, Correlation, Data handling, Design, Generation, Indexing, Mapping, Modelling and simulation, Optimisation and refinement, Prediction and recognition, Quantification, Service management, Validation, Visualisation ↩