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Pedro Q edited this page Sep 8, 2021 · 35 revisions

MANTIS

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This tool can be used for protein function annotation, it is a standalone tool that uses HMMER or Diamond 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 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.

Citation

If you use Mantis, please make sure you cite the respective paper https://doi.org/10.1093/gigascience/giab042

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
  • GCC, for compilation of cython code (most systems should have it by default)

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 references

Custom references can be added in MANTIS.config by adding their absolute path or folder path, for example:

    custom_ref=/path/to/ref_folder/file.hmm
    custom_ref=/path/to/ref_folder/file.dmnd
    custom_ref=/path/to/ref_folder/

Alternatively you may add them to the custom_refs folder, for example:

    Mantis/References/Custom_references/custom1/custom1.hmm
    Mantis/References/Custom_references/custom2/custom2.dmnd

You may also redifine the custom_refs folder path by adding your preferred path to custom_refs_folder in the MANTIS.config file, for example:

    custom_refs_folder=path/to/custom_refs/

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 -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 -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;
  • integrated_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

License and copyright

This project is available under the MIT license.

References and acknowledgements

Queirós, Pedro, Novikova, Polina, Wilmes, Paul and May, Patrick. "Unification of functional annotation descriptions using text mining" Biological Chemistry, vol. , no. , 2021. https://doi.org/10.1515/hsz-2021-0125

S. R. Eddy. HMMER: biosequence analysis using profile hidden Markov models. HMMER v.3.2.1 www.hmmer.org

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

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

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.

Lu S, Wang J, Chitsaz F, Derbyshire MK, Geer RC, Gonzales NR, Gwadz M, Hurwitz DI, Marchler GH, Song JS, Thanki N, Yamashita RA, Yang M, Zhang D, Zheng C, Lanczycki CJ, Marchler-Bauer A. CDD/SPARCLE: the conserved domain database in 2020. Nucleic Acids Res. 2020 Jan 8;48(D1):D265-D268. doi: 10.1093/nar/gkz991. PMID: 31777944; PMCID: PMC6943070.

Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Jaime Huerta-Cepas, Kristoffer Forslund, Luis Pedro Coelho, Damian Szklarczyk, Lars Juhl Jensen, Christian von Mering and Peer Bork. Mol Biol Evol (2017). doi:10.1093/molbev/msx148

Saier MH, Reddy VS, Moreno-Hagelsieb G, Hendargo KJ, Zhang Y, Iddamsetty V, Lam KJK, Tian N, Russum S, Wang J, Medrano-Soto A. The Transporter Classification Database (TCDB): 2021 update. Nucleic Acids Res. 2021 Jan 8;49(D1):D461-D467. doi: 10.1093/nar/gkaa1004. PMID: 33170213; PMCID: PMC7778945.

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