Skip to content

Home

Jump to bottom
pedroq edited this page Sep 16, 2020 · 35 revisions

MANTIS

overview_small

This tool can be used for protein function annotation, it is a standalone tool that uses HMMER to match sequences against multiple reference datasets. It accepts as input an aminoacids sequence fasta.
The main goals of this tool are to:

  • consider multiple protein domains
  • annotate with taxonomy resolution
  • use different reference datasets and provide a consensus annotation
  • be easy to setup and/or customize
  • scale well with multiple samples and/or metagenomes

If you have only loose reads, you need to assemble them first; when you have assembled reads/genomes you need to predict the protein coding regions (gene prediction - e.g. prodigal) to convert your data into a protein fasta that Mantis can then use.

Mantis is compatible with genomes and metagenomes.

Paper data

Click here!

Workflow overview

overview_small

Straight to the point

Requirements

  • Python, tested with v3.7.3 but anything above v3 should be fine
  • requests, tested with v2.22.0
  • numpy, tested with v1.18.1
  • nltk, tested with v3.4.4
  • sqlite, tested with v3.30.1
  • psutil, tested with 5.6.7
  • HMMER, tested with v3.2.1

Mantis can only run on Linux-based systems

Quick configuration

  1. git clone git@github.com:PedroMTQ/mantis.git
  2. Go to cloned mantis folder and run conda env create -f mantis_env.yml
  3. Run conda activate mantis_env
  4. Go up one folder and run python mantis setup_databases
  5. Run python mantis run_mantis -t target_faa

Custom hmms

    custom_hmms_folder=/path/to/mantis/hmm/custom_hmms/   
    custom_hmm=/path/to/HMM_folder/file.hmm

Custom hmms can be added in MANTIS.config by adding their absolute path, alternatively you may add them to the custom_hmms folder. Mantis will read the folders within the custom hmms folder and use the .hmm stored in each of those folders.

Functions

1. Help

python  mantis/  -h

2. Setup databases

python  mantis/  setup_databases

3. Check installation

python  mantis/  check_installation

4. Merge HMM folder

python  mantis/  merge_hmm_folder -t target

5. Annotate one sample

python mantis/ run_mantis -t target.faa -o output_folder-od organism_details -et evalue_threshold -ar acceptable_range -ov overlap_value -mc custom_MANTIS.config

example: python mantis run_mantis -t mantis/tests/test_sample.faa -od "Escherichia coli"

6. Annotate multiple samples

python mantis/ run_mantis -t target.tsv -o output_folder -et evalue_threshold -ar acceptable_range -ov overlap_value -mc custom_MANTIS.config

example: python mantis run_mantis -t mantis/tests/test_file.tsv

Output files

There are 3 output files:

  • output_annotation.tsv, which has all hits and their coordinates and e-values;
  • interpreted_annotation.tsv which has all hits, their coordinates and e-value, as well as the respective hit metadata;
  • consensus_annotation.tsv which has all hits and their respective metadata from the best hmm sources consensus.

The first two files can have the same query sequence in several lines (query sequence/hmm source) while the consensus_annotation.tsv will only have one line per query sequence (consensus/query).

Further details

Copyright

This project is available under the MIT license.

References and acknowledgements

  1. S. R. Eddy. HMMER: biosequence analysis using profile hidden Markov models. HMMER v.3.2.1 www.hmmer.org
  2. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Jaime Huerta-Cepas, Damian Szklarczyk, Davide Heller, Ana Hernández-Plaza, Sofia K Forslund, Helen Cook, Daniel R Mende, Ivica Letunic, Thomas Rattei, Lars J Jensen, Christian von Mering, Peer Bork Nucleic Acids Res. 2019 Jan 8; 47(Database issue): D309–D314. https://doi.org/10.1093/nar/gky1085
  3. The Pfam protein families database in 2019: S. El-Gebali, J. Mistry, A. Bateman, S.R. Eddy, A. Luciani, S.C. Potter, M. Qureshi, L.J. Richardson, G.A. Salazar, A. Smart, E.L.L. Sonnhammer, L. Hirsh, L. Paladin, D. Piovesan, S.C.E. Tosatto, R.D. Finn Nucleic Acids Research (2019) https://doi.org/10.1093/nar/gky995
  4. Haft DH, Loftus BJ, Richardson DL, et al. TIGRFAMs: a protein family resource for the functional identification of proteins. Nucleic Acids Res. 2001;29(1):41–43. https://doi.org/10.1093/nar/29.1.41
  5. Aramaki T., Blanc-Mathieu R., Endo H., Ohkubo K., Kanehisa M., Goto S., Ogata H. KofamKOALA: KEGG ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics. 2019 Nov 19. pii: btz859. https://doi.org/10.1093/bioinformatics/btz859.
  6. Han Zhang, Tanner Yohe, Le Huang, Sarah Entwistle, Peizhi Wu, Zhenglu Yang, Peter K Busk, Ying Xu, Yanbin Yin, dbCAN2: a meta server for automated carbohydrate-active enzyme annotation, Nucleic Acids Research, Volume 46, Issue W1, 2 July 2018, Pages W95–W101, https://doi.org/10.1093/nar/gky418
  7. Yanbin Yin, Xizeng Mao, Jincai Yang, Xin Chen, Fenglou Mao, Ying Xu, dbCAN: a web resource for automated carbohydrate-active enzyme annotation, Nucleic Acids Research, Volume 40, Issue W1, 1 July 2012, Pages W445–W451, https://doi.org/10.1093/nar/gks479
  8. Gibson MK, Forsberg KJ, Dantas G. Improved annotation of antibiotic resistance functions reveals microbial resistomes cluster by ecology. The ISME Journal. 2014, https://doi.org/ISMEJ.2014.106
  9. Albertsen, M., Hugenholtz, P., Skarshewski, A. et al. Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes. Nat Biotechnol 31, 533–538 (2013). https://doi.org/10.1038/nbt.2579
  10. W. Arndt, "Modifying HMMER3 to Run Efficiently on the Cori Supercomputer Using OpenMP Tasking," 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), Vancouver, BC, 2018, pp. 239-246. https://doi.org/10.1109/IPDPSW.2018.00048

Contents

Clone this wiki locally