@article{katoh2013mafft,
    title={MAFFT multiple sequence alignment software version 7: improvements in performance and usability},
    author={Katoh, Kazutaka and Standley, Daron M},
    journal={Molecular biology and evolution},
    volume={30},
    number={4},
    pages={772--780},
    year={2013},
    publisher={Society for Molecular Biology and Evolution},
    doi={10.1093/molbev/mst010}
}

@article{Wang2014,
    abstract = {Sequence analysis of large protein families can produce sub-clusters even within the same family. In some cases, it is of interest to know precisely which amino acid position variations are most responsible for driving separation into sub-clusters. In large protein families composed of large proteins, it can be quite challenging to assign the relative importance to specific amino acid positions. Principal components analysis (PCA) is ideal for such a task, since the problem is posed in a large variable space, i.e. the number of amino acids that make up the protein sequence, and PCA is powerful at reducing the dimensionality of complex problems by projecting the data into an eigenspace that represents the directions of greatest variation. However, PCA of aligned protein sequence families is complicated by the fact that protein sequences are traditionally represented by single letter alphabetic codes, whereas PCA of protein sequence families requires conversion of sequence information into a numerical representation. Here, we introduce a new amino acid sequence conversion algorithm optimized for PCA data input. The method is demonstrated using a small artificial dataset to illustrate the characteristics and performance of the algorithm, as well as a small protein sequence family consisting of nine members, COG2263, and finally with a large protein sequence family, Pfam04237, which contains more than 1,800 sequences that group into two sub-clusters. {\textcopyright} 2014 Springer Science+Business Media Dordrecht.},
    author = {Wang, Bo and Kennedy, Michael A.},
    doi = {10.1007/s10969-014-9173-2},
    issn = {15700267},
    journal = {Journal of Structural and Functional Genomics},
    keywords = {PCA,Principal components analysis,Protein sequence analysis},
    month = {feb},
    number = {1},
    pages = {1--11},
    pmid = {24496727},
    publisher = {Kluwer Academic Publishers},
    title = {{Principal components analysis of protein sequence clusters}},
    url = {http://www.ncbi.nlm.nih.gov/},
    volume = {15},
    year = {2014}
}

@article{Sehnal2017,
    author = {Sehnal, David and Deshpande, Mandar and Vařekov{\'{a}}, Radka Svobodov{\'{a}} and Mir, Saqib and Berka, Karel and Midlik, Adam and Pravda, Luk{\'{a}}{\v{s}} and Velankar, Sameer and Ko{\v{c}}a, Jaroslav},
    doi = {10.1038/nmeth.4499},
    issn = {15487105},
    journal = {Nature Methods},
    keywords = {Protein analysis,Proteins,Software,Structure determination},
    month = {dec},
    number = {12},
    pages = {1121--1122},
    pmid = {29190272},
    publisher = {Nature Publishing Group},
    title = {{LiteMol suite: Interactive web-based visualization of large-scale macromolecular structure data}},
    url = {http://autode.sk/natmethods},
    volume = {14},
    year = {2017}
}
