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Items: 1 to 20 of 476


Comparison of metagenomic samples using sequence signatures.

Jiang B, Song K, Ren J, Deng M, Sun F, Zhang X.

BMC Genomics. 2012 Dec 27;13:730. doi: 10.1186/1471-2164-13-730.


Comparison of metatranscriptomic samples based on k-tuple frequencies.

Wang Y, Liu L, Chen L, Chen T, Sun F.

PLoS One. 2014 Jan 2;9(1):e84348. doi: 10.1371/journal.pone.0084348. eCollection 2014.


Assessment of k-mer spectrum applicability for metagenomic dissimilarity analysis.

Dubinkina VB, Ischenko DS, Ulyantsev VI, Tyakht AV, Alexeev DG.

BMC Bioinformatics. 2016 Jan 16;17:38. doi: 10.1186/s12859-015-0875-7.


Estimating the composition of species in metagenomes by clustering of next-generation read sequences.

Seok HS, Hong W, Kim J.

Methods. 2014 Oct 1;69(3):213-9. doi: 10.1016/j.ymeth.2014.07.009. Epub 2014 Jul 27.


New developments of alignment-free sequence comparison: measures, statistics and next-generation sequencing.

Song K, Ren J, Reinert G, Deng M, Waterman MS, Sun F.

Brief Bioinform. 2014 May;15(3):343-53. doi: 10.1093/bib/bbt067. Epub 2013 Sep 23. Review.


Evaluation of short read metagenomic assembly.

Charuvaka A, Rangwala H.

BMC Genomics. 2011;12 Suppl 2:S8. doi: 10.1186/1471-2164-12-S2-S8. Epub 2011 Jul 27.


Alignment-free supervised classification of metagenomes by recursive SVM.

Cui H, Zhang X.

BMC Genomics. 2013 Sep 22;14:641. doi: 10.1186/1471-2164-14-641.


Estimating the extent of horizontal gene transfer in metagenomic sequences.

Tamames J, Moya A.

BMC Genomics. 2008 Mar 24;9:136. doi: 10.1186/1471-2164-9-136.


Effect of k-tuple length on sample-comparison with high-throughput sequencing data.

Wang Y, Lei X, Wang S, Wang Z, Song N, Zeng F, Chen T.

Biochem Biophys Res Commun. 2016 Jan 22;469(4):1021-7. doi: 10.1016/j.bbrc.2015.11.094. Epub 2015 Dec 22.


Transposon fingerprinting using low coverage whole genome shotgun sequencing in cacao (Theobroma cacao L.) and related species.

Sveinsson S, Gill N, Kane NC, Cronk Q.

BMC Genomics. 2013 Jul 24;14:502. doi: 10.1186/1471-2164-14-502.


MetaID: a novel method for identification and quantification of metagenomic samples.

Srinivasan SM, Guda C.

BMC Genomics. 2013;14 Suppl 8:S4. doi: 10.1186/1471-2164-14-S8-S4. Epub 2013 Dec 9.


Reconstructing mitochondrial genomes directly from genomic next-generation sequencing reads--a baiting and iterative mapping approach.

Hahn C, Bachmann L, Chevreux B.

Nucleic Acids Res. 2013 Jul;41(13):e129. doi: 10.1093/nar/gkt371. Epub 2013 May 9.


Optimizing hybrid assembly of next-generation sequence data from Enterococcus faecium: a microbe with highly divergent genome.

Wang Y, Yu Y, Pan B, Hao P, Li Y, Shao Z, Xu X, Li X.

BMC Syst Biol. 2012;6 Suppl 3:S21. doi: 10.1186/1752-0509-6-S3-S21. Epub 2012 Dec 17.


Resolving prokaryotic taxonomy without rRNA: longer oligonucleotide word lengths improve genome and metagenome taxonomic classification.

Alsop EB, Raymond J.

PLoS One. 2013 Jul 1;8(7):e67337. doi: 10.1371/journal.pone.0067337. Print 2013.


Assembly-free genome comparison based on next-generation sequencing reads and variable length patterns.

Comin M, Schimd M.

BMC Bioinformatics. 2014;15 Suppl 9:S1. doi: 10.1186/1471-2105-15-S9-S1. Epub 2014 Sep 10.


Fast identification and removal of sequence contamination from genomic and metagenomic datasets.

Schmieder R, Edwards R.

PLoS One. 2011 Mar 9;6(3):e17288. doi: 10.1371/journal.pone.0017288.


Comparative metagenomic analysis of plasmid encoded functions in the human gut microbiome.

Jones BV, Sun F, Marchesi JR.

BMC Genomics. 2010 Jan 19;11:46. doi: 10.1186/1471-2164-11-46.


FASTQSim: platform-independent data characterization and in silico read generation for NGS datasets.

Shcherbina A.

BMC Res Notes. 2014 Aug 15;7:533. doi: 10.1186/1756-0500-7-533.


A comparison of random sequence reads versus 16S rDNA sequences for estimating the biodiversity of a metagenomic library.

Manichanh C, Chapple CE, Frangeul L, Gloux K, Guigo R, Dore J.

Nucleic Acids Res. 2008 Sep;36(16):5180-8. doi: 10.1093/nar/gkn496. Epub 2008 Aug 5.

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