-
1
-
-
84903193221
-
RNA-Seq transcriptome profiling identifies CRISPLD2 as a glucocorticoid responsive gene that modulates cytokine function in airway smooth muscle cells
-
24926665, 4057123
-
Himes BE Jiang X Wagner P : RNA-Seq transcriptome profiling identifies CRISPLD2 as a glucocorticoid responsive gene that modulates cytokine function in airway smooth muscle cells. PLoS One. 2014;9(6):e99625. 24926665 10.1371/journal.pone.0099625 4057123
-
(2014)
PLoS One
, vol.9
, Issue.6
-
-
Himes, B.E.1
Jiang, X.2
Wagner, P.3
-
2
-
-
84924629414
-
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
-
25516281, 4302049
-
Love MI Huber W Anders S : Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. 25516281 10.1101/002832 4302049
-
(2014)
Genome Biol
, vol.15
, Issue.12
, pp. 550
-
-
Love, M.I.1
Huber, W.2
Anders, S.3
-
3
-
-
75249087100
-
edgeR: a Bioconductor package for differential expression analysis of digital gene expression data
-
19910308, 2796818
-
Robinson MD McCarthy DJ Smyth GK : edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26(1):139-140. 19910308 10.1093/bioinformatics/btp616 2796818
-
(2010)
Bioinformatics
, vol.26
, Issue.1
, pp. 139-140
-
-
Robinson, M.D.1
McCarthy, D.J.2
Smyth, G.K.3
-
4
-
-
84896735766
-
voom: Precision weights unlock linear model analysis tools for RNA-seq read counts
-
24485249, 4053721
-
Law CW Chen Y Shi W : voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 2014;15(2):R29. 24485249 10.1186/gb-2014-15-2-r29 4053721
-
(2014)
Genome Biol
, vol.15
, Issue.2
, pp. R29
-
-
Law, C.W.1
Chen, Y.2
Shi, W.3
-
5
-
-
84874912212
-
A new shrinkage estimator for dispersion improves differential expression detection in RNA-seq data
-
23001152, 3590927
-
Wu H Wang C Wu Z : A new shrinkage estimator for dispersion improves differential expression detection in RNA-seq data. Biostatistics. 2013;14(2):232-243. 23001152 10.1093/biostatistics/kxs033 3590927
-
(2013)
Biostatistics
, vol.14
, Issue.2
, pp. 232-243
-
-
Wu, H.1
Wang, C.2
Wu, Z.3
-
6
-
-
84876263777
-
EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments
-
23428641, 3624807
-
Leng N Dawson JA Thomson JA : EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics. 2013;29(8):1035-1043. 23428641 10.1093/bioinformatics/btt087 3624807
-
(2013)
Bioinformatics
, vol.29
, Issue.8
, pp. 1035-1043
-
-
Leng, N.1
Dawson, J.A.2
Thomson, J.A.3
-
7
-
-
77955298482
-
baySeq: empirical Bayesian methods for identifying differential expression in sequence count data
-
20698981, 2928208
-
Hardcastle TJ Kelly KA : baySeq: empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinformatics. 2010;11(1):422. 20698981 10.1186/1471-2105-11-422 2928208
-
(2010)
BMC Bioinformatics
, vol.11
, Issue.1
, pp. 422
-
-
Hardcastle, T.J.1
Kelly, K.A.2
-
8
-
-
84900328038
-
Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms
-
24752080, 4077321
-
Patro R Mount SM Kingsford C : Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms. Nat Biotechnol. 2014;32(5):462-464. 24752080 10.1038/nbt.2862 4077321
-
(2014)
Nat Biotechnol
, vol.32
, Issue.5
, pp. 462-464
-
-
Patro, R.1
Mount, S.M.2
Kingsford, C.3
-
9
-
-
84976335724
-
Salmon provides accurate, fast, and bias-aware transcript expression estimates using dual-phase inference
-
Patro R Duggal G Love MI : Salmon provides accurate, fast, and bias-aware transcript expression estimates using dual-phase inference. bioRxiv. 2016. 10.1101/021592
-
(2016)
bioRxiv
-
-
Patro, R.1
Duggal, G.2
Love, M.I.3
-
10
-
-
84966283954
-
Near-optimal probabilistic rna-seq quantification
-
27043002
-
Bray NL Pimentel H Melsted P : Near-optimal probabilistic rna-seq quantification. Nat Biotechnol. 2016;34(5):525-527. 27043002 10.1038/nbt.3519
-
(2016)
Nat Biotechnol
, vol.34
, Issue.5
, pp. 525-527
-
-
Bray, N.L.1
Pimentel, H.2
Melsted, P.3
-
11
-
-
79961123152
-
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
-
21816040, 3163565
-
Li B Dewey CN : RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011;12:323. 21816040 10.1186/1471-2105-12-323 3163565
-
(2011)
BMC Bioinformatics
, vol.12
, pp. 323
-
-
Li, B.1
Dewey, C.N.2
-
12
-
-
84872198346
-
Differential analysis of gene regulation at transcript resolution with RNA-seq
-
23222703, 3869392
-
Trapnell C Hendrickson DG Sauvageau M : Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat Biotechnol. 2013;31(1):46-53. 23222703 10.1038/nbt.2450 3869392
-
(2013)
Nat Biotechnol
, vol.31
, Issue.1
, pp. 46-53
-
-
Trapnell, C.1
Hendrickson, D.G.2
Sauvageau, M.3
-
13
-
-
84941261782
-
Errors in RNA-Seq quantification affect genes of relevance to human disease
-
26335491, 4558956
-
Robert C Watson M : Errors in RNA-Seq quantification affect genes of relevance to human disease. Genome Biol. 2015;16(1):177. 26335491 10.1186/s13059-015-0734-x 4558956
-
(2015)
Genome Biol
, vol.16
, Issue.1
, pp. 177
-
-
Robert, C.1
Watson, M.2
-
14
-
-
85010908291
-
Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences [version 2; referees: 2 approved]
-
26925227, 4712774
-
Soneson C Love MI Robinson MD : Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences [version 2; referees: 2 approved]. F1000Res. 2015;4:1521. 26925227 10.12688/f1000research.7563.2 4712774
-
(2015)
F1000Res
, vol.4
, pp. 1521
-
-
Soneson, C.1
Love, M.I.2
Robinson, M.D.3
-
15
-
-
84891768365
-
Ensembl 2014
-
24316576, 3964975
-
Flicek P Amode MR Barrell D : Ensembl 2014. Nucleic Acids Res. 2014;42(Database issue):D749-D755. 24316576 10.1093/nar/gkt1196 3964975
-
(2014)
Nucleic Acids Res
, vol.42
, Issue.DATABASE ISSUE
, pp. D749-D755
-
-
Flicek, P.1
Amode, M.R.2
Barrell, D.3
-
16
-
-
84871809302
-
STAR: ultrafast universal RNA-seq aligner
-
23104886, 3530905
-
Dobin A Davis CA Schlesinger F : STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15-21. 23104886 10.1093/bioinformatics/bts635 3530905
-
(2013)
Bioinformatics
, vol.29
, Issue.1
, pp. 15-21
-
-
Dobin, A.1
Davis, C.A.2
Schlesinger, F.3
-
17
-
-
68549104404
-
The Sequence Alignment/Map format and SAMtools
-
19505943, 2723002
-
Li H Handsaker B Wysoker A : The Sequence Alignment/Map format and SAMtools. Bioinformatics (Oxford, England). 2009;25(16):2078-2079. 19505943 10.1093/bioinformatics/btp352 2723002
-
(2009)
Bioinformatics (Oxford, England)
, vol.25
, Issue.16
, pp. 2078-2079
-
-
Li, H.1
Handsaker, B.2
Wysoker, A.3
-
18
-
-
84883368195
-
Software for computing and annotating genomic ranges
-
23950696, 3738458
-
Lawrence M Huber W Pagès H : Software for computing and annotating genomic ranges. PLoS Comput Biol. 2013;9(8):e1003118. 23950696 10.1371/journal.pcbi.1003118 3738458
-
(2013)
PLoS Comput Biol
, vol.9
, Issue.8
-
-
Lawrence, M.1
Huber, W.2
Pagès, H.3
-
19
-
-
84897397058
-
featureCounts: an efficient general purpose program for assigning sequence reads to genomic features
-
24227677
-
Liao Y Smyth GK Shi W : featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30(7):923-930. 24227677 10.1093/bioinformatics/btt656
-
(2014)
Bioinformatics
, vol.30
, Issue.7
, pp. 923-930
-
-
Liao, Y.1
Smyth, G.K.2
Shi, W.3
-
20
-
-
84928987900
-
HTSeq--a Python framework to work with high-throughput sequencing data
-
25260700, 4287950
-
Anders S Pyl PT Huber W : HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31(2):166-169. 25260700 10.1093/bioinformatics/btu638 4287950
-
(2015)
Bioinformatics
, vol.31
, Issue.2
, pp. 166-169
-
-
Anders, S.1
Pyl, P.T.2
Huber, W.3
-
21
-
-
0036079158
-
The human genome browser at UCSC
-
12045153, 186604
-
Kent WJ Sugnet CW Furey TS : The human genome browser at UCSC. Genome Res. 2002;12(6):996-1006. 12045153 10.1101/gr.229102 186604
-
(2002)
Genome Res
, vol.12
, Issue.6
, pp. 996-1006
-
-
Kent, W.J.1
Sugnet, C.W.2
Furey, T.S.3
-
22
-
-
68449101067
-
Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt
-
19617889, 3159387
-
Durinck S Spellman PT Birney E : Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat Protoc. 2009;4(8):1184-1191. 19617889 10.1038/nprot.2009.97 3159387
-
(2009)
Nat Protoc
, vol.4
, Issue.8
, pp. 1184-1191
-
-
Durinck, S.1
Spellman, P.T.2
Birney, E.3
-
23
-
-
84961289551
-
Orchestrating high-throughput genomic analysis with Bioconductor
-
25633503, 4509590
-
Huber W Carey VJ Gentleman R : Orchestrating high-throughput genomic analysis with Bioconductor. Nat Methods. 2015;12(2):115-121. 25633503 10.1038/nmeth.3252 4509590
-
(2015)
Nat Methods
, vol.12
, Issue.2
, pp. 115-121
-
-
Huber, W.1
Carey, V.J.2
Gentleman, R.3
-
24
-
-
77958471357
-
Differential expression analysis for sequence count data
-
20979621, 3218662
-
Anders S Huber W : Differential expression analysis for sequence count data. Genome Biol. 2010;11(10):R106. 20979621 10.1186/gb-2010-11-10-r106 3218662
-
(2010)
Genome Biol
, vol.11
, Issue.10
, pp. R106
-
-
Anders, S.1
Huber, W.2
-
25
-
-
84867897914
-
Classification and clustering of sequencing data using a Poisson model
-
Witten DM : Classification and clustering of sequencing data using a Poisson model. Ann Appl Stat. 2011;5(4):2493-2518. 10.1214/11-AOAS493
-
(2011)
Ann Appl Stat
, vol.5
, Issue.4
, pp. 2493-2518
-
-
Witten, D.M.1
-
26
-
-
77749296886
-
ggplot2
-
Springer, New York, NY,.
-
Wickham H : ggplot2.Springer, New York, NY,2009. 10.1007/978-0-387-98141-3
-
(2009)
-
-
Wickham, H.1
-
27
-
-
83455238345
-
GC-Content Normalization for RNA-Seq Data
-
22177264, 3315510
-
Risso D Schwartz K Sherlock G : GC-Content Normalization for RNA-Seq Data. BMC Bioinformatics. 2011;12(1):480. 22177264 10.1186/1471-2105-12-480 3315510
-
(2011)
BMC Bioinformatics
, vol.12
, Issue.1
, pp. 480
-
-
Risso, D.1
Schwartz, K.2
Sherlock, G.3
-
28
-
-
84858068675
-
Removing technical variability in RNA-seq data using conditional quantile normalization
-
22285995, 3297825
-
Hansen KD Irizarry RA Wu Z : Removing technical variability in RNA-seq data using conditional quantile normalization. Biostatistics. 2012;13(2):204-216. 22285995 10.1093/biostatistics/kxr054 3297825
-
(2012)
Biostatistics
, vol.13
, Issue.2
, pp. 204-216
-
-
Hansen, K.D.1
Irizarry, R.A.2
Wu, Z.3
-
29
-
-
84909644283
-
Normalization of RNA-seq data using factor analysis of control genes or samples
-
25150836, 4404308
-
Risso D Ngai J Speed TP : Normalization of RNA-seq data using factor analysis of control genes or samples. Nat Biotechnol. 2014;32(9):896-902. 25150836 10.1038/nbt.2931 4404308
-
(2014)
Nat Biotechnol
, vol.32
, Issue.9
, pp. 896-902
-
-
Risso, D.1
Ngai, J.2
Speed, T.P.3
-
30
-
-
84925226706
-
svaseq: removing batch effects and other unwanted noise from sequencing data
-
25294822, 4245966
-
Leek JT : svaseq: removing batch effects and other unwanted noise from sequencing data. Nucleic Acids Res. 2014;42(21):e161. 25294822 10.1093/nar/gku864 4245966
-
(2014)
Nucleic Acids Res
, vol.42
, Issue.21
-
-
Leek, J.T.1
-
31
-
-
84970949920
-
How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?
-
27022035, 4878611
-
Schurch NJ Schofield P Gierliński M : How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use? RNA. 2016;22(6):839-851. 27022035 10.1261/rna.053959.115 4878611
-
(2016)
RNA
, vol.22
, Issue.6
, pp. 839-851
-
-
Schurch, N.J.1
Schofield, P.2
Gierliński, M.3
-
32
-
-
84959222823
-
Inferential considerations for low-count RNA-seq transcripts: a case study on the dominant prairie grass Andropogon gerardii
-
26919855, 4769568
-
Raithel S Johnson L Galliart M : Inferential considerations for low-count RNA-seq transcripts: a case study on the dominant prairie grass Andropogon gerardii. BMC Genomics. 2016;17(1):140. 26919855 10.1186/s12864-016-2442-7 4769568
-
(2016)
BMC Genomics
, vol.17
, Issue.1
, pp. 140
-
-
Raithel, S.1
Johnson, L.2
Galliart, M.3
-
33
-
-
85027941571
-
Modeling overdispersion heterogeneity in differential expression analysis using mixtures
-
26683201
-
Bonafede E Picard F Robin S : Modeling overdispersion heterogeneity in differential expression analysis using mixtures. Biometrics. 2016;72(3):804-14. 26683201 10.1111/biom.12458
-
(2016)
Biometrics
, vol.72
, Issue.3
, pp. 804-814
-
-
Bonafede, E.1
Picard, F.2
Robin, S.3
-
34
-
-
84965043970
-
Sample size calculation while controlling false discovery rate for differential expression analysiswith RNA-sequencing experiments
-
27029470, 4815167
-
Bi R Liu P : Sample size calculation while controlling false discovery rate for differential expression analysis with RNA-sequencing experiments. BMC Bioinformatics. 2016;17(1):146. 27029470 10.1186/s12859-016-0994-9 4815167
-
(2016)
BMC Bioinformatics
, vol.17
, Issue.1
, pp. 146
-
-
Bi, R.1
Liu, P.2
-
35
-
-
0001677717
-
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
-
Reference Source
-
Benjamini Y Hochberg Y : Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J Roy Stat Soc B Met. 1995;57(1):289-300. Reference Source
-
(1995)
J Roy Stat Soc B Met
, vol.57
, Issue.1
, pp. 289-300
-
-
Benjamini, Y.1
Hochberg, Y.2
-
36
-
-
0036376993
-
Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments
-
Reference Source, In .
-
Dudoit S Yang YH Callow MJ : Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments.In Statistica Sinica. 2002;12:111-139. Reference Source
-
(2002)
Statistica Sinica
, vol.12
, pp. 111-139
-
-
Dudoit, S.1
Yang, Y.H.2
Callow, M.J.3
-
37
-
-
77953095629
-
Independent filtering increases detection power for high-throughput experiments
-
20460310, 2906865
-
Bourgon R Gentleman R Huber W : Independent filtering increases detection power for high-throughput experiments. Proc Natl Acad Sci U S A. 2010;107(21):9546-9551. 20460310 10.1073/pnas.0914005107 2906865
-
(2010)
Proc Natl Acad Sci U S A
, vol.107
, Issue.21
, pp. 9546-9551
-
-
Bourgon, R.1
Gentleman, R.2
Huber, W.3
-
38
-
-
84973098846
-
Data-driven hypothesis weighting increases detection power in genome-scale multiple testing
-
27240256, 4930141
-
Ignatiadis N Klaus B Zaugg JB : Data-driven hypothesis weighting increases detection power in genome-scale multiple testing. Nat Methods. 2016;13(7):577-580. 27240256 10.1038/nmeth.3885 4930141
-
(2016)
Nat Methods
, vol.13
, Issue.7
, pp. 577-580
-
-
Ignatiadis, N.1
Klaus, B.2
Zaugg, J.B.3
-
39
-
-
84900378568
-
ReportingTools: an automated result processing and presentation toolkit for high-throughput genomic analyses
-
24078713
-
Huntley MA Larson JL Chaivorapol C : ReportingTools: an automated result processing and presentation toolkit for high-throughput genomic analyses. Bioinformatics. 2013;29(24):3220-3221. 24078713 10.1093/bioinformatics/btt551
-
(2013)
Bioinformatics
, vol.29
, Issue.24
, pp. 3220-3221
-
-
Huntley, M.A.1
Larson, J.L.2
Chaivorapol, C.3
-
40
-
-
84901439351
-
A global non-coding RNA system modulates fission yeast protein levels in response to stress
-
24853205, 4050258
-
Leong HS Dawson K Wirth C : A global non-coding RNA system modulates fission yeast protein levels in response to stress. Nat Commun. 2014;5: 3947. 24853205 10.1038/ncomms4947 4050258
-
(2014)
Nat Commun
, vol.5
-
-
Leong, H.S.1
Dawson, K.2
Wirth, C.3
|