Skip to content

References

Jump to bottom
Alexander McFarland edited this page Jul 7, 2021 · 2 revisions

  1. Cock PJA, Antao T, Chang JT, Chapman BA, Cox CJ, Dalke A, et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics. 2009 Jun 1;25(11):1422–3.

  2. Buitinck L, Louppe G, Blondel M, Pedregosa F, Mueller A, Grisel O, et al. API design for machine learning software: experiences from the scikit-learn project. arXiv:13090238 [cs] [Internet]. 2013 Sep 1 [cited 2021 Apr 5]; Available from: http://arxiv.org/abs/1309.0238

  3. McKerns MM, Strand L, Sullivan T, Fang A, Aivazis MAG. Building a Framework for Predictive Science. arXiv:12021056 [cs] [Internet]. 2012 Feb 6 [cited 2021 Apr 5]; Available from: http://arxiv.org/abs/1202.1056

  4. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinformatics. 2009 Dec 15;10(1):421.

  5. Steinegger M, Söding J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nature Biotechnology. 2017 Nov;35(11):1026–8.

  6. Enright AJ, Van Dongen S, Ouzounis CA. An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res. 2002 Apr 1;30(7):1575–84.

  7. gggenes @ METACRAN [Internet]. [cited 2021 Apr 5]. Available from: https://www.r-pkg.org/pkg/gggenes

  8. O’Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D, McVeigh R, et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 2016 Jan 4;44(Database issue):D733–45.

  9. Steinegger M, Söding J. Clustering huge protein sequence sets in linear time. Nature Communications. 2018 Jun 29;9(1):2542.

  10. Ester M, Kriegel H-P, Xu X. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. :6.

  11. Caliński T, Harabasz J. A dendrite method for cluster analysis. Communications in Statistics. 1974 Jan 1;3(1):1–27.

Clone this wiki locally