BinfoTools

SprayNPray is a user-friendly bioinformatics tool for taxonomic profiling and classification of genome and metagenome contigs. It separates contigs based on taxonomy and key genomic metrics like gene density, GC content, read coverage, tetra/penta/hexanucleotide frequencies, and codon-usage bias. Using DIAMOND and Prodigal, SprayNPray efficiently assigns taxonomic identities to predicted genes and presents results in an intuitive format.

This tool is ideal for identifying contaminants in genome assemblies, validating metagenome bins, detecting symbionts, and investigating horizontal gene transfer

The output includes a data_table_for_binarena.tsv file, which should be dragged and dropped into the BinaRena tool interface for visualization and further analysis.

Fill out the form below to run the SprayNPray tool. You will receive an email with the results once the analysis is complete.

Please be sure to cite SprayNPray if you use this in your publication:
Garber, A. I., Armbruster, C. R., Lee, S. E., Cooper, V. S., Bomberger, J. M., & McAllister, S. M. (2022). SprayNPray: user-friendly taxonomic profiling of genome and metagenome contigs. BMC Genomics, 23(1), 202. https://doi.org/10.1186/s12864-022-08382-2

Pseudofinder is a tool for identifying pseudogenes in bacterial and archaeal genomes

BinaRena is a dedicated interactive platform for human-guided exploration and binning of metagenomes. This tool allows users to analyze metagenomic bins interactively.

Click the link below to open the BinaRena interface and drag and drop the data_table_for_binarena.tsv file generated by the SprayNPray tool.

Please be sure to cite BinaRena if you use this in your publication:
Pavia, M. J., Chede, A., Wu, Z., Cadillo-Quiroz, H., & Zhu, Q. (2023). BinaRena: a dedicated interactive platform for human-guided exploration and binning of metagenomes. Microbiome, 11(1), 186. BMC Genomics, 23(1), 202. https://doi.org/10.1186/s40168-023-01625-8

MHCscan detects multi-heme cytochromes (MHCs), proteins involved in extracellular electron transfer (EET) in bacteria and archaea. It is commonly used in microbial genomics to explore potential electroactive enzymes and investigate EET mechanisms across diverse microbial lineages.

GToTree generates phylogenomic trees using genomic data by automating steps like gene selection, alignment, and tree inference. It is used in evolutionary biology research for genome-level evolutionary studies and comparative genomics.

Name: Email: Select a single-copy gene set: Single-column file of NCBI assembly accessions:

Upload Protein Files (amino acid FASTA):

Drag and drop files here or click to select files.

Upload GenBank Files (.gbk, .gbff, .gb, or .gbf files):

Drag and drop files here or click to select files.

Upload Genome Files (nucleotide FASTA):

Drag and drop files here or click to select files.

Please be sure to cite GToTree if you use it for your publication:
Lee, M. D. (2019). GToTree: a user-friendly workflow for phylogenomics. Bioinformatics , 35(20), 4162–4164.
https://doi.org/10.1093/bioinformatics/btz188

FeGenie identifies genes related to iron metabolism using a hidden Markov model (HMM) database. It is frequently used in environmental microbiology and microbial ecology to explore genetic markers associated with iron acquisition, storage, and redox processes.

Name: Email Address To Send Results: Data Input (drag and drop one or more genome assemblies in FASTA format)

Drag and drop files here or click to select files.

Please be sure to cite FeGenie if you use it for your publication:
Garber, A. I., Nealson, K. H., Okamoto, A., McAllister, S. M., Chan, C. S., Barco, R. A., & Merino, N. (2020). FeGenie: A Comprehensive Tool for the Identification of Iron Genes and Iron Gene Neighborhoods in Genome and Metagenome Assemblies. Frontiers in Microbiology, 11, 37. https://doi.org/10.3389/fmicb.2020.00037

QIIME 2 (Quantitative Insights Into Microbial Ecology 2) is an open-source bioinformatics platform used for analyzing and interpreting microbial community data from environmental DNA sequences. It supports tasks like sequence quality control, taxonomic classification, diversity analysis, and statistical comparisons.

Name: Email Address To Send Results:

Sequencing Target

Metadata (max 12 samples): Learn more about metadata

Data Input (drag and drop paired FASTQ reads)

Drag and drop files here or click to select files (max 100 Mb per file)

Please be sure to cite Qiime2 if you use it for your publication:
Bolyen, E., Rideout, J. R., Dillon, M. R., Bokulich, N. A., Abnet, C. C., Al-Ghalith, G. A., Alexander, H., Alm, E. J., Arumugam, M., Asnicar, F., Bai, Y., Bisanz, J. E., Bittinger, K., Brejnrod, A., Brislawn, C. J., Brown, C. T., Callahan, B. J., Caraballo-Rodríguez, A. M., Chase, J., … Caporaso, J. G. (2019). Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotechnology, 37(8), 852–857. https://doi.org/10.1038/s41587-019-0209-9

Bakta annotates prokaryotic genomes by predicting genes, proteins, and RNA features