BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

486 related articles for article (PubMed ID: 27008025)

  • 1. 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]  

  • 2. From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline.
    Chen Y; Lun AT; Smyth GK
    F1000Res; 2016; 5():1438. PubMed ID: 27508061
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. No counts, no variance: allowing for loss of degrees of freedom when assessing biological variability from RNA-seq data.
    Lun ATL; Smyth GK
    Stat Appl Genet Mol Biol; 2017 Apr; 16(2):83-93. PubMed ID: 28599403
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. A Guide for Designing and Analyzing RNA-Seq Data.
    Chatterjee A; Ahn A; Rodger EJ; Stockwell PA; Eccles MR
    Methods Mol Biol; 2018; 1783():35-80. PubMed ID: 29767357
    [TBL] [Abstract][Full Text] [Related]  

  • 7. RNA-Seq Analysis Pipeline Based on Oshell Environment.
    Li J; Hu J; Newman M; Liu K; Ge H
    IEEE/ACM Trans Comput Biol Bioinform; 2014; 11(5):973-8. PubMed ID: 26356868
    [TBL] [Abstract][Full Text] [Related]  

  • 8. SARTools: A DESeq2- and EdgeR-Based R Pipeline for Comprehensive Differential Analysis of RNA-Seq Data.
    Varet H; Brillet-Guéguen L; Coppée JY; Dillies MA
    PLoS One; 2016; 11(6):e0157022. PubMed ID: 27280887
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 10. Analysis of Technical and Biological Variability in Single-Cell RNA Sequencing.
    Kim B; Lee E; Kim JK
    Methods Mol Biol; 2019; 1935():25-43. PubMed ID: 30758818
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A Bioinformatics Pipeline for the Identification of CHO Cell Differential Gene Expression from RNA-Seq Data.
    Monger C; Motheramgari K; McSharry J; Barron N; Clarke C
    Methods Mol Biol; 2017; 1603():169-186. PubMed ID: 28493130
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A fuzzy method for RNA-Seq differential expression analysis in presence of multireads.
    Consiglio A; Mencar C; Grillo G; Marzano F; Caratozzolo MF; Liuni S
    BMC Bioinformatics; 2016 Nov; 17(Suppl 12):345. PubMed ID: 28185579
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Molecular Profiling of RNA Tumors Using High-Throughput RNA Sequencing: From Raw Data to Systems Level Analyses.
    da Silveira WA; Hazard ES; Chung D; Hardiman G
    Methods Mol Biol; 2019; 1908():185-204. PubMed ID: 30649729
    [TBL] [Abstract][Full Text] [Related]  

  • 14. The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads.
    Liao Y; Smyth GK; Shi W
    Nucleic Acids Res; 2019 May; 47(8):e47. PubMed ID: 30783653
    [TBL] [Abstract][Full Text] [Related]  

  • 15. VIPER: Visualization Pipeline for RNA-seq, a Snakemake workflow for efficient and complete RNA-seq analysis.
    Cornwell M; Vangala M; Taing L; Herbert Z; Köster J; Li B; Sun H; Li T; Zhang J; Qiu X; Pun M; Jeselsohn R; Brown M; Liu XS; Long HW
    BMC Bioinformatics; 2018 Apr; 19(1):135. PubMed ID: 29649993
    [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. Experimental Design and Power Calculation for RNA-seq Experiments.
    Wu Z; Wu H
    Methods Mol Biol; 2016; 1418():379-90. PubMed ID: 27008024
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. 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]  

  • 20. READemption-a tool for the computational analysis of deep-sequencing-based transcriptome data.
    Förstner KU; Vogel J; Sharma CM
    Bioinformatics; 2014 Dec; 30(23):3421-3. PubMed ID: 25123900
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 25.