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Items: 1 to 20 of 131

1.

Comparative analysis of DNA word abundances in four yeast genomes using a novel statistical background model.

Hariharan R, Simon R, Pillai MR, Taylor TD.

PLoS One. 2013;8(3):e58038. doi: 10.1371/journal.pone.0058038. Epub 2013 Mar 5.

2.

Enrichment of transcriptional regulatory sites in non-coding genomic region.

Xue W, Wang J, Shen Z, Zhu H.

Bioinformatics. 2004 Mar 1;20(4):569-75. Epub 2004 Jan 22.

3.

Genomic DNA k-mer spectra: models and modalities.

Chor B, Horn D, Goldman N, Levy Y, Massingham T.

Genome Biol. 2009;10(10):R108. doi: 10.1186/gb-2009-10-10-r108. Epub 2009 Oct 8.

4.

Distribution patterns of over-represented k-mers in non-coding yeast DNA.

Hampson S, Kibler D, Baldi P.

Bioinformatics. 2002 Apr;18(4):513-28.

5.

A new method to compute K-mer frequencies and its application to annotate large repetitive plant genomes.

Kurtz S, Narechania A, Stein JC, Ware D.

BMC Genomics. 2008 Oct 31;9:517. doi: 10.1186/1471-2164-9-517.

6.

The complex task of choosing a de novo assembly: lessons from fungal genomes.

Gallo JE, Muñoz JF, Misas E, McEwen JG, Clay OK.

Comput Biol Chem. 2014 Dec;53 Pt A:97-107. doi: 10.1016/j.compbiolchem.2014.08.014. Epub 2014 Aug 29.

PMID:
25262360
7.

Comparative genomics reveals long, evolutionarily conserved, low-complexity islands in yeast proteins.

Romov PA, Li F, Lipke PN, Epstein SL, Qiu WG.

J Mol Evol. 2006 Sep;63(3):415-25. Epub 2006 Aug 21.

PMID:
16927006
8.

Clustering of DNA words and biological function: a proof of principle.

Hackenberg M, Rueda A, Carpena P, Bernaola-Galván P, Barturen G, Oliver JL.

J Theor Biol. 2012 Mar 21;297:127-36. doi: 10.1016/j.jtbi.2011.12.024. Epub 2011 Dec 30.

PMID:
22226985
9.

Segmentation of yeast DNA using hidden Markov models.

Peshkin L, Gelfand MS.

Bioinformatics. 1999 Dec;15(12):980-6.

10.

Optimization of de novo transcriptome assembly from high-throughput short read sequencing data improves functional annotation for non-model organisms.

Haznedaroglu BZ, Reeves D, Rismani-Yazdi H, Peccia J.

BMC Bioinformatics. 2012 Jul 18;13:170. doi: 10.1186/1471-2105-13-170.

11.

PhyloGibbs: a Gibbs sampling motif finder that incorporates phylogeny.

Siddharthan R, Siggia ED, van Nimwegen E.

PLoS Comput Biol. 2005 Dec;1(7):e67. Epub 2005 Dec 9.

12.

Rare k-mer DNA: Identification of sequence motifs and prediction of CpG island and promoter.

Mohamed Hashim EK, Abdullah R.

J Theor Biol. 2015 Dec 21;387:88-100. doi: 10.1016/j.jtbi.2015.09.014. Epub 2015 Sep 30.

PMID:
26427337
13.

Comparative analysis using K-mer and K-flank patterns provides evidence for CpG island sequence evolution in mammalian genomes.

Chae H, Park J, Lee SW, Nephew KP, Kim S.

Nucleic Acids Res. 2013 May;41(9):4783-91. doi: 10.1093/nar/gkt144. Epub 2013 Mar 21.

14.

Robust k-mer frequency estimation using gapped k-mers.

Ghandi M, Mohammad-Noori M, Beer MA.

J Math Biol. 2014 Aug;69(2):469-500. doi: 10.1007/s00285-013-0705-3. Epub 2013 Jul 17.

15.

A dictionary based informational genome analysis.

Castellini A, Franco G, Manca V.

BMC Genomics. 2012 Sep 17;13:485. doi: 10.1186/1471-2164-13-485.

16.

Using hidden Markov models and observed evolution to annotate viral genomes.

McCauley S, Hein J.

Bioinformatics. 2006 Jun 1;22(11):1308-16. Epub 2006 Apr 13.

17.
18.

How independent are the appearances of n-mers in different genomes?

Fofanov Y, Luo Y, Katili C, Wang J, Belosludtsev Y, Powdrill T, Belapurkar C, Fofanov V, Li TB, Chumakov S, Pettitt BM.

Bioinformatics. 2004 Oct 12;20(15):2421-8. Epub 2004 Apr 15.

19.

Alignment uncertainty and genomic analysis.

Wong KM, Suchard MA, Huelsenbeck JP.

Science. 2008 Jan 25;319(5862):473-6. doi: 10.1126/science.1151532.

20.

K-mer natural vector and its application to the phylogenetic analysis of genetic sequences.

Wen J, Chan RH, Yau SC, He RL, Yau SS.

Gene. 2014 Aug 1;546(1):25-34. doi: 10.1016/j.gene.2014.05.043. Epub 2014 May 22.

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