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Items: 1 to 20 of 101

1.

SERE: single-parameter quality control and sample comparison for RNA-Seq.

Schulze SK, Kanwar R, Gölzenleuchter M, Therneau TM, Beutler AS.

BMC Genomics. 2012 Oct 3;13:524. doi: 10.1186/1471-2164-13-524.

2.

Blind spots of quantitative RNA-seq: the limits for assessing abundance, differential expression, and isoform switching.

Rehrauer H, Opitz L, Tan G, Sieverling L, Schlapbach R.

BMC Bioinformatics. 2013 Dec 24;14:370. doi: 10.1186/1471-2105-14-370.

3.

Impact of library preparation on downstream analysis and interpretation of RNA-Seq data: comparison between Illumina PolyA and NuGEN Ovation protocol.

Sun Z, Asmann YW, Nair A, Zhang Y, Wang L, Kalari KR, Bhagwate AV, Baker TR, Carr JM, Kocher JP, Perez EA, Thompson EA.

PLoS One. 2013 Aug 19;8(8):e71745. doi: 10.1371/journal.pone.0071745. eCollection 2013.

4.

A flexible count data model to fit the wide diversity of expression profiles arising from extensively replicated RNA-seq experiments.

Esnaola M, Puig P, Gonzalez D, Castelo R, Gonzalez JR.

BMC Bioinformatics. 2013 Aug 21;14:254. doi: 10.1186/1471-2105-14-254.

5.

Efficient experimental design and analysis strategies for the detection of differential expression using RNA-Sequencing.

Robles JA, Qureshi SE, Stephen SJ, Wilson SR, Burden CJ, Taylor JM.

BMC Genomics. 2012 Sep 17;13:484. doi: 10.1186/1471-2164-13-484.

6.

Synthetic spike-in standards for RNA-seq experiments.

Jiang L, Schlesinger F, Davis CA, Zhang Y, Li R, Salit M, Gingeras TR, Oliver B.

Genome Res. 2011 Sep;21(9):1543-51. doi: 10.1101/gr.121095.111. Epub 2011 Aug 4.

7.

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.

8.

mRNA enrichment protocols determine the quantification characteristics of external RNA spike-in controls in RNA-Seq studies.

Qing T, Yu Y, Du T, Shi L.

Sci China Life Sci. 2013 Feb;56(2):134-42. doi: 10.1007/s11427-013-4437-9. Epub 2013 Feb 8.

PMID:
23393029
9.

Systematic comparison of RNA-Seq normalization methods using measurement error models.

Sun Z, Zhu Y.

Bioinformatics. 2012 Oct 15;28(20):2584-91. doi: 10.1093/bioinformatics/bts497. Epub 2012 Aug 22.

PMID:
22914217
10.

A two-parameter generalized Poisson model to improve the analysis of RNA-seq data.

Srivastava S, Chen L.

Nucleic Acids Res. 2010 Sep;38(17):e170. doi: 10.1093/nar/gkq670. Epub 2010 Jul 29.

11.

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.

12.

Sparse linear modeling of next-generation mRNA sequencing (RNA-Seq) data for isoform discovery and abundance estimation.

Li JJ, Jiang CR, Brown JB, Huang H, Bickel PJ.

Proc Natl Acad Sci U S A. 2011 Dec 13;108(50):19867-72. doi: 10.1073/pnas.1113972108. Epub 2011 Dec 1.

13.

Identifying differentially spliced genes from two groups of RNA-seq samples.

Wang W, Qin Z, Feng Z, Wang X, Zhang X.

Gene. 2013 Apr 10;518(1):164-70. doi: 10.1016/j.gene.2012.11.045. Epub 2012 Dec 8.

PMID:
23228854
14.

Comparison of normalization and differential expression analyses using RNA-Seq data from 726 individual Drosophila melanogaster.

Lin Y, Golovnina K, Chen ZX, Lee HN, Negron YL, Sultana H, Oliver B, Harbison ST.

BMC Genomics. 2016 Jan 5;17:28. doi: 10.1186/s12864-015-2353-z.

15.

A simple strand-specific RNA-Seq library preparation protocol combining the Illumina TruSeq RNA and the dUTP methods.

Sultan M, Dökel S, Amstislavskiy V, Wuttig D, Sültmann H, Lehrach H, Yaspo ML.

Biochem Biophys Res Commun. 2012 Jun 15;422(4):643-6. doi: 10.1016/j.bbrc.2012.05.043. Epub 2012 May 15.

16.

NBLDA: negative binomial linear discriminant analysis for RNA-Seq data.

Dong K, Zhao H, Tong T, Wan X.

BMC Bioinformatics. 2016 Sep 13;17(1):369. doi: 10.1186/s12859-016-1208-1.

17.

Identifying differentially expressed transcripts from RNA-seq data with biological variation.

Glaus P, Honkela A, Rattray M.

Bioinformatics. 2012 Jul 1;28(13):1721-8. doi: 10.1093/bioinformatics/bts260. Epub 2012 May 3.

18.

Statistical models for RNA-seq data derived from a two-condition 48-replicate experiment.

Gierliński M, Cole C, Schofield P, Schurch NJ, Sherstnev A, Singh V, Wrobel N, Gharbi K, Simpson G, Owen-Hughes T, Blaxter M, Barton GJ.

Bioinformatics. 2015 Nov 15;31(22):3625-30. doi: 10.1093/bioinformatics/btv425. Epub 2015 Jul 23.

19.

Higher order asymptotics for negative binomial regression inferences from RNA-sequencing data.

Di Y, Emerson SC, Schafer DW, Kimbrel JA, Chang JH.

Stat Appl Genet Mol Biol. 2013 Mar 26;12(1):49-70. doi: 10.1515/sagmb-2012-0071.

20.

RSeQC: quality control of RNA-seq experiments.

Wang L, Wang S, Li W.

Bioinformatics. 2012 Aug 15;28(16):2184-5. doi: 10.1093/bioinformatics/bts356. Epub 2012 Jun 27.

PMID:
22743226

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