These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

528 related articles for article (PubMed ID: 26732976)

  • 1. 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]  

  • 2. 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]  

  • 3. 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]  

  • 4. Differential expression analysis of RNA sequencing data by incorporating non-exonic mapped reads.
    Chen HI; Liu Y; Zou Y; Lai Z; Sarkar D; Huang Y; Chen Y
    BMC Genomics; 2015; 16 Suppl 7(Suppl 7):S14. PubMed ID: 26099631
    [TBL] [Abstract][Full Text] [Related]  

  • 5. It's DE-licious: A Recipe for Differential Expression Analyses of RNA-seq Experiments Using Quasi-Likelihood Methods in edgeR.
    Lun AT; Chen Y; Smyth GK
    Methods Mol Biol; 2016; 1418():391-416. PubMed ID: 27008025
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Gene dispersion is the key determinant of the read count bias in differential expression analysis of RNA-seq data.
    Yoon S; Nam D
    BMC Genomics; 2017 May; 18(1):408. PubMed ID: 28545404
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Power analysis and sample size estimation for RNA-Seq differential expression.
    Ching T; Huang S; Garmire LX
    RNA; 2014 Nov; 20(11):1684-96. PubMed ID: 25246651
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Choice of library size normalization and statistical methods for differential gene expression analysis in balanced two-group comparisons for RNA-seq studies.
    Li X; Cooper NGF; O'Toole TE; Rouchka EC
    BMC Genomics; 2020 Jan; 21(1):75. PubMed ID: 31992223
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments.
    Bullard JH; Purdom E; Hansen KD; Dudoit S
    BMC Bioinformatics; 2010 Feb; 11():94. PubMed ID: 20167110
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Statistical detection of differentially expressed genes based on RNA-seq: from biological to phylogenetic replicates.
    Gu X
    Brief Bioinform; 2016 Mar; 17(2):243-8. PubMed ID: 26108230
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Benchmarking differential expression analysis tools for RNA-Seq: normalization-based vs. log-ratio transformation-based methods.
    Quinn TP; Crowley TM; Richardson MF
    BMC Bioinformatics; 2018 Jul; 19(1):274. PubMed ID: 30021534
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. A generalized dSpliceType framework to detect differential splicing and differential expression events using RNA-Seq.
    Zhu D; Deng N; Bai C
    IEEE Trans Nanobioscience; 2015 Mar; 14(2):192-202. PubMed ID: 25680210
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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; 14():254. PubMed ID: 23965047
    [TBL] [Abstract][Full Text] [Related]  

  • 15. An evaluation of RNA-seq differential analysis methods.
    Li D; Zand MS; Dye TD; Goniewicz ML; Rahman I; Xie Z
    PLoS One; 2022; 17(9):e0264246. PubMed ID: 36112652
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Bootstrap-based differential gene expression analysis for RNA-Seq data with and without replicates.
    Al Seesi S; Tiagueu YT; Zelikovsky A; Măndoiu II
    BMC Genomics; 2014; 15 Suppl 8(Suppl 8):S2. PubMed ID: 25435284
    [TBL] [Abstract][Full Text] [Related]  

  • 17. CORNAS: coverage-dependent RNA-Seq analysis of gene expression data without biological replicates.
    Low JZB; Khang TF; Tammi MT
    BMC Bioinformatics; 2017 Dec; 18(Suppl 16):575. PubMed ID: 29297307
    [TBL] [Abstract][Full Text] [Related]  

  • 18. EPIG-Seq: extracting patterns and identifying co-expressed genes from RNA-Seq data.
    Li J; Bushel PR
    BMC Genomics; 2016 Mar; 17():255. PubMed ID: 27004791
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Microenvironmental Gene Expression Plasticity Among Individual Drosophila melanogaster.
    Lin Y; Chen ZX; Oliver B; Harbison ST
    G3 (Bethesda); 2016 Dec; 6(12):4197-4210. PubMed ID: 27770026
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Trimming of sequence reads alters RNA-Seq gene expression estimates.
    Williams CR; Baccarella A; Parrish JZ; Kim CC
    BMC Bioinformatics; 2016 Feb; 17():103. PubMed ID: 26911985
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 27.