Description

MyToSex is a novel tool for in silico sex determination based on the content of the mitochondrial genome. Some mussels and clams species have a different system of mitochondrial inheritance termed double uniparental inheritance, which involves the transmission of two different sex-associated mitogenomes haplotypes to the offspring. Females contain only F-type mitogenomes (mtF), whereas males carry mtF and M-type (mtM) haplotypes. This tool works in three acts:

1. Mitogenomes detection and quantification: We mapped all the reads to both mitotypes of reference to detect the mitogenomes presence. Afterwards, we calculate the mitogenomes coverage, sequencing depth and depth uniformity.
2. Sex prediction: We implemented an artificial neural network that takes those three parameters of each mitogenome as input to predict the sex of the sample.
3. Supporting analysis: We also implemented three additional analyses to complement and bring more support to the results.
   1. We clustered the samples applying dimensional reduction techniques to verify if the resultant groups agree with the prediction.
   2. We used the reads mapped to the mitogenomes to assemble de novo the mitogenes, and after annotating them, we performed a phylogenetic analysis building the different gene trees.
   3. We calculate a sexual index based on the detection of both mitotypes that can supply more support or complement the prediction done by the ANN.

Installation and use

MyToSex is an open-source tools written in Python3 that requires multiple modules. This tool can be installed as follows.

# Clone the repository
git clone https://github.com/manuelsmendoza/mytosex.git

# Create the conda environment
cd mytosex && conda create --name mytosex_test --file environment.txt 

# Activate the environment
conda activate mytosex_test 

To use MyToSex, create a file containing the information required for the analysis and pass it as a positional argument as we have specified below. To see more information about the settings file, check it in the wiki.

python3 mytosex.py settings.yaml

Resources

This tool was tested with the following resources:

• High-performance computer:
  • OS: Gentoo Base System release 2.7 x86_64
  • CPU: Intel Xeon E5-2680 v3 (24) @ 3.300GHz
  • GPU: ASPEED Technology, Inc. ASPEED Graphics Family
  • Memory: 10237MiB / 128666MiB
• Personal computer:
  • OS: Ubuntu 18.04.6 LTS x86_64
  • CPU: AMD Ryzen 9 3950X (32) @ 3.500GHz
  • GPU: AMD ATI 10:00.0 Device 731f
  • Memory: 697MiB / 32041MiB

Note: The alignments consume much space in the disk (150GB per sample approx. in some cases). Please, check if you have enough space available for the analysis. To run the example proposed, you will need 650GB. However, after removing the temporal files, the final output will have less than 100BM.

Citation

If you only use MyToSex cite us as follows:

Mendoza M. and Canchaya A., MyToSex: Sexual inference based on mitochondrial genome content [...]

Please, also include to:

• Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie2. Nat Methods. 2012;9(4):357-359. doi:10.1038/nmeth.1923.

  @article{langmead2012bowtie2, 
    title={Fast gapped-read alignment with Bowtie2},
    author={Langmead, Ben and Salzberg, Steven L},
    journal={Nature methods},
    volume={9},
    number={4},
    pages={357--359},
    year={2012},
    publisher={Nature Publishing Group}
  }
  

• Li H, Handsaker B, Wysoker A, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25(16):2078-2079. doi:10.1093/bioinformatics/btp352.

  @article{li2009samtools,
    title={The sequence alignment/map format and SAMtools},
    author={Li, Heng and Handsaker, Bob and Wysoker, Alec and Fennell, Tim and Ruan, Jue and Homer, Nils and Marth, Gabor and Abecasis, Goncalo and Durbin, Richard},
    journal={Bioinformatics},
    volume={25},
    number={16},
    pages={2078--2079},
    year={2009},
    publisher={Oxford University Press}
  }
  

If you also perform the supporting analysis, please cite them too.

• Samples clustering:

  • McInnes, L., Healy, J. and Melville, J. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv preprint, arXiv:1802.03426.

    @misc{mcinnes2020umap,
      title={UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction}, 
      author={Leland McInnes and John Healy and James Melville},
      year={2020},
      eprint={1802.03426},
      archivePrefix={arXiv}
    } 
    

• Phylogenetic analysis:

  • Haas BJ, Papanicolaou A, Yassour M, et. al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat Protoc. 2013;8(8):1494-1512. doi:10.1038/nprot.2013.084

    @article{haas2013trinity,
      title={De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis},
      author={Haas, Brian J and Papanicolaou, Alexie and Yassour, Moran and Grabherr, Manfred and Blood, Philip D and Bowden, Joshua and Couger, Matthew Brian and Eccles, David and Li, Bo and Lieber, Matthias and others},
      journal={Nature protocols},
      volume={8},
      number={8},
      pages={1494--1512},
      year={2013},
      publisher={Nature Publishing Group}
    }
    

  • Camacho C, Coulouris G, Avagyan V, et al. BLAST+: architecture and applications. BMC Bioinformatics. 2009;10:421. doi:10.1186/1471-2105-10-421.

    @article{camacho2009blast+,
      title={BLAST+: architecture and applications},
      author={Camacho, Christiam and Coulouris, George and Avagyan, Vahram and Ma, Ning and Papadopoulos, Jason and Bealer, Kevin and Madden, Thomas L},
      journal={BMC bioinformatics},
      volume={10},
      number={1},
      pages={421--429},
      year={2009},
      publisher={Springer}
    }
    

  • Katoh K, Kuma K, Toh H, Miyata T. MAFFT version 5: improvement in accuracy of multiple sequence alignment. Nucleic Acids Res. 2005;33(2):511-518. doi:10.1093/nar/gki198.

    @article{katoh2005mafft,
      title={MAFFT version 5: improvement in accuracy of multiple sequence alignment},
      author={Katoh, Kazutaka and Kuma, Kei-ichi and Toh, Hiroyuki and Miyata, Takashi},
      journal={Nucleic acids research},
      volume={33},
      number={2},
      pages={511--518},
      year={2005},
      publisher={Oxford University Press}
    }
    

  • Darriba D, Posada D, Kozlov AM, Stamatakis A, Morel B, Flouri T. ModelTest-NG: A New and Scalable Tool for the Selection of DNA and Protein Evolutionary Models. Mol Biol Evol. 2020;37(1):291-294. doi:10.1093/molbev/msz189.

    @article{darriba2020modeltest,
      title={ModelTest-NG: a new and scalable tool for the selection of DNA and protein evolutionary models},
      author={Darriba, Diego and Posada, David and Kozlov, Alexey M and Stamatakis, Alexandros and Morel, Benoit and Flouri, Tomas},
      journal={Molecular Biology and Evolution},
      volume={37},
      number={1},
      pages={291--294},
      year={2020},
      publisher={Oxford University Press}
    }
    

  • Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30(9):1312-1313. doi:10.1093/bioinformatics/btu033.

    @article{stamatakis2014raxml,
      title={RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies},
      author={Stamatakis, Alexandros},
      journal={Bioinformatics},
      volume={30},
      number={9},
      pages={1312--1313},
      year={2014},
      publisher={Oxford University Press}
    }
    

Acknowledgement

This work was supported by the European Social Fund and the Government of Xunta de Galicia (Scholarship reference ED481A-2018/305 awarded by Manuel Mendoza).

We developed this tools using the computational resources of the Supercomputing Center of Galicia (CESGA) using Pycharm with an Academic License freely provided for JetBrain.