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Items: 1 to 20 of 167

1.

Probabilistic error correction for RNA sequencing.

Le HS, Schulz MH, McCauley BM, Hinman VF, Bar-Joseph Z.

Nucleic Acids Res. 2013 May 1;41(10):e109. doi: 10.1093/nar/gkt215.

2.

Rcorrector: efficient and accurate error correction for Illumina RNA-seq reads.

Song L, Florea L.

Gigascience. 2015 Oct 19;4:48. doi: 10.1186/s13742-015-0089-y.

3.

Optimizing de novo assembly of short-read RNA-seq data for phylogenomics.

Yang Y, Smith SA.

BMC Genomics. 2013 May 14;14:328. doi: 10.1186/1471-2164-14-328.

4.

Optimizing de novo common wheat transcriptome assembly using short-read RNA-Seq data.

Duan J, Xia C, Zhao G, Jia J, Kong X.

BMC Genomics. 2012 Aug 14;13:392. doi: 10.1186/1471-2164-13-392.

5.

Transcriptome assembly and quantification from Ion Torrent RNA-Seq data.

Mangul S, Caciula A, Al Seesi S, Brinza D, MÓ‘ndoiu I, Zelikovsky A.

BMC Genomics. 2014;15 Suppl 5:S7. doi: 10.1186/1471-2164-15-S5-S7.

6.

Hybrid error correction and de novo assembly of single-molecule sequencing reads.

Koren S, Schatz MC, Walenz BP, Martin J, Howard JT, Ganapathy G, Wang Z, Rasko DA, McCombie WR, Jarvis ED, Adam M Phillippy.

Nat Biotechnol. 2012 Jul 1;30(7):693-700. doi: 10.1038/nbt.2280.

7.

A comparison of next generation sequencing technologies for transcriptome assembly and utility for RNA-Seq in a non-model bird.

Finseth FR, Harrison RG.

PLoS One. 2014 Oct 3;9(10):e108550. doi: 10.1371/journal.pone.0108550.

8.

FDM: a graph-based statistical method to detect differential transcription using RNA-seq data.

Singh D, Orellana CF, Hu Y, Jones CD, Liu Y, Chiang DY, Liu J, Prins JF.

Bioinformatics. 2011 Oct 1;27(19):2633-40. doi: 10.1093/bioinformatics/btr458.

9.

Optimization of de novo transcriptome assembly from next-generation sequencing data.

Surget-Groba Y, Montoya-Burgos JI.

Genome Res. 2010 Oct;20(10):1432-40. doi: 10.1101/gr.103846.109.

10.

Bias detection and correction in RNA-Sequencing data.

Zheng W, Chung LM, Zhao H.

BMC Bioinformatics. 2011 Jul 19;12:290. doi: 10.1186/1471-2105-12-290.

11.

Accurate inference of isoforms from multiple sample RNA-Seq data.

Tasnim M, Ma S, Yang EW, Jiang T, Li W.

BMC Genomics. 2015;16 Suppl 2:S15. doi: 10.1186/1471-2164-16-S2-S15.

12.

Prediction of novel long non-coding RNAs based on RNA-Seq data of mouse Klf1 knockout study.

Sun L, Zhang Z, Bailey TL, Perkins AC, Tallack MR, Xu Z, Liu H.

BMC Bioinformatics. 2012 Dec 13;13:331. doi: 10.1186/1471-2105-13-331.

13.

Efficient frequency-based de novo short-read clustering for error trimming in next-generation sequencing.

Qu W, Hashimoto S, Morishita S.

Genome Res. 2009 Jul;19(7):1309-15. doi: 10.1101/gr.089151.108.

14.

Correction of sequencing errors in a mixed set of reads.

Salmela L.

Bioinformatics. 2010 May 15;26(10):1284-90. doi: 10.1093/bioinformatics/btq151.

15.

Characterizing short read sequencing for gene discovery and RNA-Seq analysis in Crassostrea gigas.

Gavery MR, Roberts SB.

Comp Biochem Physiol Part D Genomics Proteomics. 2012 Jun;7(2):94-9. doi: 10.1016/j.cbd.2011.12.003.

PMID:
22244882
16.
17.

Single read and paired end mRNA-Seq Illumina libraries from 10 nanograms total RNA.

Sengupta S, Bolin JM, Ruotti V, Nguyen BK, Thomson JA, Elwell AL, Stewart R.

J Vis Exp. 2011 Oct 27;(56):e3340. doi: 10.3791/3340.

18.

A comprehensive evaluation of alignment algorithms in the context of RNA-seq.

Lindner R, Friedel CC.

PLoS One. 2012;7(12):e52403. doi: 10.1371/journal.pone.0052403.

19.

Comparative analysis of RNA-Seq alignment algorithms and the RNA-Seq unified mapper (RUM).

Grant GR, Farkas MH, Pizarro AD, Lahens NF, Schug J, Brunk BP, Stoeckert CJ, Hogenesch JB, Pierce EA.

Bioinformatics. 2011 Sep 15;27(18):2518-28. doi: 10.1093/bioinformatics/btr427.

20.

IsoLasso: a LASSO regression approach to RNA-Seq based transcriptome assembly.

Li W, Feng J, Jiang T.

J Comput Biol. 2011 Nov;18(11):1693-707. doi: 10.1089/cmb.2011.0171.

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