BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

529 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. deGPS is a powerful tool for detecting differential expression in RNA-sequencing studies.
    Chu C; Fang Z; Hua X; Yang Y; Chen E; Cowley AW; Liang M; Liu P; Lu Y
    BMC Genomics; 2015 Jun; 16(1):455. PubMed ID: 26070955
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. 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]  

  • 9. 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]  

  • 10. 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]  

  • 11. 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]  

  • 12. 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]  

  • 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. 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]  

  • 15. 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]  

  • 16. 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]  

  • 17. 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]  

  • 18. 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]  

  • 19. 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]  

  • 20. 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]  

    [Next]    [New Search]
    of 27.