KABOOM! A new suffix-array based algorithm for clustering expression data

Hazelhurst S, Lipták Z (2011)
Bioinformatics 27(24): 3348-3355.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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DOI
Autor*in
Hazelhurst, Scott; Lipták, Zsuzsanna
Einrichtung
Technische Fakultät > AG Genominformatik
Centrum für Biotechnologie > Institut für Bioinformatik
Erscheinungsjahr
2011
Zeitschriftentitel
Bioinformatics
Band
27
Ausgabe
24
Seite(n)
3348-3355
ISSN
1367-4803
eISSN
1460-2059
Page URI
https://pub.uni-bielefeld.de/record/2401665

Zitieren

Hazelhurst S, Lipták Z. KABOOM! A new suffix-array based algorithm for clustering expression data. Bioinformatics. 2011;27(24):3348-3355.
Hazelhurst, S., & Lipták, Z. (2011). KABOOM! A new suffix-array based algorithm for clustering expression data. Bioinformatics, 27(24), 3348-3355. https://doi.org/10.1093/bioinformatics/btr560
Hazelhurst, Scott, and Lipták, Zsuzsanna. 2011. “KABOOM! A new suffix-array based algorithm for clustering expression data”. Bioinformatics 27 (24): 3348-3355.
Hazelhurst, S., and Lipták, Z. (2011). KABOOM! A new suffix-array based algorithm for clustering expression data. Bioinformatics 27, 3348-3355.
Hazelhurst, S., & Lipták, Z., 2011. KABOOM! A new suffix-array based algorithm for clustering expression data. Bioinformatics, 27(24), p 3348-3355.
S. Hazelhurst and Z. Lipták, “KABOOM! A new suffix-array based algorithm for clustering expression data”, Bioinformatics, vol. 27, 2011, pp. 3348-3355.
Hazelhurst, S., Lipták, Z.: KABOOM! A new suffix-array based algorithm for clustering expression data. Bioinformatics. 27, 3348-3355 (2011).
Hazelhurst, Scott, and Lipták, Zsuzsanna. “KABOOM! A new suffix-array based algorithm for clustering expression data”. Bioinformatics 27.24 (2011): 3348-3355.

5 Zitationen in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

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A bioinformatician's guide to the forefront of suffix array construction algorithms.
Shrestha AM, Frith MC, Horton P., Brief Bioinform 15(2), 2014
PMID: 24413184
EasyCluster2: an improved tool for clustering and assembling long transcriptome reads.
Bevilacqua V, Pietroleonardo N, Giannino E, Stroppa F, Simone D, Pesole G, Picardi E., BMC Bioinformatics 15 Suppl 15(), 2014
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Ultrafast clustering algorithms for metagenomic sequence analysis.
Li W, Fu L, Niu B, Wu S, Wooley J., Brief Bioinform 13(6), 2012
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A hybrid distance measure for clustering expressed sequence tags originating from the same gene family.
Ng KH, Ho CK, Phon-Amnuaisuk S., PLoS One 7(10), 2012
PMID: 23071763

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