494 related articles for article (PubMed ID: 30281477)
1. Differential Expression Analysis of RNA-seq Reads: Overview, Taxonomy, and Tools.
Chowdhury HA; Bhattacharyya DK; Kalita JK
IEEE/ACM Trans Comput Biol Bioinform; 2020; 17(2):566-586. PubMed ID: 30281477
[TBL] [Abstract][Full Text] [Related]
2. SPARTA: Simple Program for Automated reference-based bacterial RNA-seq Transcriptome Analysis.
Johnson BK; Scholz MB; Teal TK; Abramovitch RB
BMC Bioinformatics; 2016 Feb; 17():66. PubMed ID: 26847232
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. Bias, robustness and scalability in single-cell differential expression analysis.
Soneson C; Robinson MD
Nat Methods; 2018 Apr; 15(4):255-261. PubMed ID: 29481549
[TBL] [Abstract][Full Text] [Related]
5. From RNA-seq reads to differential expression results.
Oshlack A; Robinson MD; Young MD
Genome Biol; 2010; 11(12):220. PubMed ID: 21176179
[TBL] [Abstract][Full Text] [Related]
6. Polyester: simulating RNA-seq datasets with differential transcript expression.
Frazee AC; Jaffe AE; Langmead B; Leek JT
Bioinformatics; 2015 Sep; 31(17):2778-84. PubMed ID: 25926345
[TBL] [Abstract][Full Text] [Related]
7. Analysis of ChIP-Seq and RNA-Seq Data with BioWardrobe.
Vallabh S; Kartashov AV; Barski A
Methods Mol Biol; 2018; 1783():343-360. PubMed ID: 29767371
[TBL] [Abstract][Full Text] [Related]
8. Data Analysis in Single-Cell Transcriptome Sequencing.
Gao S
Methods Mol Biol; 2018; 1754():311-326. PubMed ID: 29536451
[TBL] [Abstract][Full Text] [Related]
9. RNA CoMPASS: a dual approach for pathogen and host transcriptome analysis of RNA-seq datasets.
Xu G; Strong MJ; Lacey MR; Baribault C; Flemington EK; Taylor CM
PLoS One; 2014; 9(2):e89445. PubMed ID: 24586784
[TBL] [Abstract][Full Text] [Related]
10. The promise of single-cell RNA sequencing for kidney disease investigation.
Wu H; Humphreys BD
Kidney Int; 2017 Dec; 92(6):1334-1342. PubMed ID: 28893418
[TBL] [Abstract][Full Text] [Related]
11. Group A Streptococcus Transcriptome Analysis.
Sanson M; Flores AR
Methods Mol Biol; 2020; 2136():113-133. PubMed ID: 32430816
[TBL] [Abstract][Full Text] [Related]
12. Normalization of Single-Cell RNA-Seq Data.
Risso D
Methods Mol Biol; 2021; 2284():303-329. PubMed ID: 33835450
[TBL] [Abstract][Full Text] [Related]
13. Nanopore RNA Sequencing Analysis.
Leonardi T; Leger A
Methods Mol Biol; 2021; 2284():569-578. PubMed ID: 33835464
[TBL] [Abstract][Full Text] [Related]
14. RNA-Seq in Nonmodel Organisms.
Chalifa-Caspi V
Methods Mol Biol; 2021; 2243():143-167. PubMed ID: 33606257
[TBL] [Abstract][Full Text] [Related]
15. An accessible, interactive GenePattern Notebook for analysis and exploration of single-cell transcriptomic data.
Mah CK; Wenzel AT; Juarez EF; Tabor T; Reich MM; Mesirov JP
F1000Res; 2018; 7():1306. PubMed ID: 31316748
[TBL] [Abstract][Full Text] [Related]
16. In Silico HLA Typing Using Standard RNA-Seq Sequence Reads.
Boegel S; Scholtalbers J; Löwer M; Sahin U; Castle JC
Methods Mol Biol; 2015; 1310():247-58. PubMed ID: 26024640
[TBL] [Abstract][Full Text] [Related]
17. QuickRNASeq lifts large-scale RNA-seq data analyses to the next level of automation and interactive visualization.
Zhao S; Xi L; Quan J; Xi H; Zhang Y; von Schack D; Vincent M; Zhang B
BMC Genomics; 2016 Jan; 17():39. PubMed ID: 26747388
[TBL] [Abstract][Full Text] [Related]
18. RNA-Seq Data Analysis in Galaxy.
Batut B; van den Beek M; Doyle MA; Soranzo N
Methods Mol Biol; 2021; 2284():367-392. PubMed ID: 33835453
[TBL] [Abstract][Full Text] [Related]
19. Identification of Cell Types from Single-Cell Transcriptomic Data.
Shekhar K; Menon V
Methods Mol Biol; 2019; 1935():45-77. PubMed ID: 30758819
[TBL] [Abstract][Full Text] [Related]
20. Pseudotime Reconstruction Using TSCAN.
Ji Z; Ji H
Methods Mol Biol; 2019; 1935():115-124. PubMed ID: 30758823
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
