401 related articles for article (PubMed ID: 22965124)
1. A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae.
Nookaew I; Papini M; Pornputtapong N; Scalcinati G; Fagerberg L; Uhlén M; Nielsen J
Nucleic Acids Res; 2012 Nov; 40(20):10084-97. PubMed ID: 22965124
[TBL] [Abstract][Full Text] [Related]
2. Whole genome sequencing of Saccharomyces cerevisiae: from genotype to phenotype for improved metabolic engineering applications.
Otero JM; Vongsangnak W; Asadollahi MA; Olivares-Hernandes R; Maury J; Farinelli L; Barlocher L; Osterås M; Schalk M; Clark A; Nielsen J
BMC Genomics; 2010 Dec; 11():723. PubMed ID: 21176163
[TBL] [Abstract][Full Text] [Related]
3. Evaluating gene expression in C57BL/6J and DBA/2J mouse striatum using RNA-Seq and microarrays.
Bottomly D; Walter NA; Hunter JE; Darakjian P; Kawane S; Buck KJ; Searles RP; Mooney M; McWeeney SK; Hitzemann R
PLoS One; 2011 Mar; 6(3):e17820. PubMed ID: 21455293
[TBL] [Abstract][Full Text] [Related]
4. Complete genomic and transcriptional landscape analysis using third-generation sequencing: a case study of Saccharomyces cerevisiae CEN.PK113-7D.
Jenjaroenpun P; Wongsurawat T; Pereira R; Patumcharoenpol P; Ussery DW; Nielsen J; Nookaew I
Nucleic Acids Res; 2018 Apr; 46(7):e38. PubMed ID: 29346625
[TBL] [Abstract][Full Text] [Related]
5. Parallel comparison of Illumina RNA-Seq and Affymetrix microarray platforms on transcriptomic profiles generated from 5-aza-deoxy-cytidine treated HT-29 colon cancer cells and simulated datasets.
Xu X; Zhang Y; Williams J; Antoniou E; McCombie WR; Wu S; Zhu W; Davidson NO; Denoya P; Li E
BMC Bioinformatics; 2013; 14 Suppl 9(Suppl 9):S1. PubMed ID: 23902433
[TBL] [Abstract][Full Text] [Related]
6. Next-generation sequencing facilitates quantitative analysis of wild-type and Nrl(-/-) retinal transcriptomes.
Brooks MJ; Rajasimha HK; Roger JE; Swaroop A
Mol Vis; 2011; 17():3034-54. PubMed ID: 22162623
[TBL] [Abstract][Full Text] [Related]
7. Nanopore sequencing enables near-complete de novo assembly of Saccharomyces cerevisiae reference strain CEN.PK113-7D.
Salazar AN; Gorter de Vries AR; van den Broek M; Wijsman M; de la Torre Cortés P; Brickwedde A; Brouwers N; Daran JG; Abeel T
FEMS Yeast Res; 2017 Nov; 17(7):. PubMed ID: 28961779
[TBL] [Abstract][Full Text] [Related]
8. Comparative transcriptome analysis of epithelial and fiber cells in newborn mouse lenses with RNA sequencing.
Hoang TV; Kumar PK; Sutharzan S; Tsonis PA; Liang C; Robinson ML
Mol Vis; 2014; 20():1491-517. PubMed ID: 25489224
[TBL] [Abstract][Full Text] [Related]
9. Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells.
Zhao S; Fung-Leung WP; Bittner A; Ngo K; Liu X
PLoS One; 2014; 9(1):e78644. PubMed ID: 24454679
[TBL] [Abstract][Full Text] [Related]
10. 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; 9(10):e108550. PubMed ID: 25279728
[TBL] [Abstract][Full Text] [Related]
11. A comparison of per sample global scaling and per gene normalization methods for differential expression analysis of RNA-seq data.
Li X; Brock GN; Rouchka EC; Cooper NGF; Wu D; O'Toole TE; Gill RS; Eteleeb AM; O'Brien L; Rai SN
PLoS One; 2017; 12(5):e0176185. PubMed ID: 28459823
[TBL] [Abstract][Full Text] [Related]
12. Comparing the normalization methods for the differential analysis of Illumina high-throughput RNA-Seq data.
Li P; Piao Y; Shon HS; Ryu KH
BMC Bioinformatics; 2015 Oct; 16():347. PubMed ID: 26511205
[TBL] [Abstract][Full Text] [Related]
13. 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; 31(22):3625-30. PubMed ID: 26206307
[TBL] [Abstract][Full Text] [Related]
14. Challenges and strategies in transcriptome assembly and differential gene expression quantification. A comprehensive in silico assessment of RNA-seq experiments.
Vijay N; Poelstra JW; Künstner A; Wolf JB
Mol Ecol; 2013 Feb; 22(3):620-34. PubMed ID: 22998089
[TBL] [Abstract][Full Text] [Related]
15. Analysis of RNA-Seq Data Using TopHat and Cufflinks.
Ghosh S; Chan CK
Methods Mol Biol; 2016; 1374():339-61. PubMed ID: 26519415
[TBL] [Abstract][Full Text] [Related]
16. Analysis of RNA-Seq data with TopHat and Cufflinks for genome-wide expression analysis of jasmonate-treated plants and plant cultures.
Pollier J; Rombauts S; Goossens A
Methods Mol Biol; 2013; 1011():305-15. PubMed ID: 23616006
[TBL] [Abstract][Full Text] [Related]
17. A nested parallel experiment demonstrates differences in intensity-dependence between RNA-seq and microarrays.
Robinson DG; Wang JY; Storey JD
Nucleic Acids Res; 2015 Nov; 43(20):e131. PubMed ID: 26130709
[TBL] [Abstract][Full Text] [Related]
18. 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; 17():28. PubMed ID: 26732976
[TBL] [Abstract][Full Text] [Related]
19. An optimized protocol for generation and analysis of Ion Proton sequencing reads for RNA-Seq.
Yuan Y; Xu H; Leung RK
BMC Genomics; 2016 May; 17():403. PubMed ID: 27229683
[TBL] [Abstract][Full Text] [Related]
20. Comprehensive evaluation of AmpliSeq transcriptome, a novel targeted whole transcriptome RNA sequencing methodology for global gene expression analysis.
Li W; Turner A; Aggarwal P; Matter A; Storvick E; Arnett DK; Broeckel U
BMC Genomics; 2015 Dec; 16():1069. PubMed ID: 26673413
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
