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A python package to compute clusters of sequence feature variant types (SFVTs) based upon user-selected subsequence.

License: Creative Commons Zero v1.0 Universal

Python 0.22% Jupyter Notebook 0.37% Clarion 99.42%
python bioinformatics sequence-alignment motif-discovery nucleotides protein-sequences clustering

feavar's Introduction

FeaVar

Documentation Status Updates

FeaVar is a python package that computes clusters of unique substrings or "variant types" for user selected sequence features in a set of aligned sequences. Sequences can be nucleotide (DNA or RNA), or amino acid. Gradient takes as input a reference sequence, a list of one or more sequence positions and a multiple sequence alignments of related sequences. It produces a list of the different variant types as well as a list of which variant type each input sequence belongs to.

This package is a generalization of the sequence feature variant type (SFVT) work undertaken in the laboratory of Dr. Richard Scheuermann while at UT Southwestern. Please see the references below.

References:

  • Noronha, J. M., Liu, M., Squires, R. B., Pickett, B. E., Hale, B. G., Air, G. M., et al. (2012). Influenza virus sequence feature variant type analysis: evidence of a role for NS1 in influenza virus host range restriction. Journal of Virology, 86(10), 5857โ€“5866. http://doi.org/10.1128/JVI.06901-11
  • Karp, D. R., Marthandan, N., Marsh, S. G. E., Ahn, C., Arnett, F. C., DeLuca, D. S., et al. (2009). Novel sequence feature variant type analysis of the HLA genetic association in systemic sclerosis. Human Molecular Genetics, 19(4), 707โ€“719. http://doi.org/10.1093/hmg/ddp521
  • Thomson, Glenys & Marthandan, Nishanth & Hollenbach, Jill & Mack, St & A Erlich, Henry & Single, Richard & Waller, Matthew & Marsh, Steven & A Guidry, Paula & R Karp, David & Scheuermann, Richard & Thompson, Susan & N Glass, David & Helmberg, Wolfgang. (2010). Sequence feature variant type (SFVT) analysis of the HLA genetic association in juvenile idiopathic arthritis. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing. 359-70. 10.1142/9789814295291_0038

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