1 Metadata

1.1 Configuration

Analysis approach:

approach <- "kmer"

Species:

species <- "human"

Graphical output format:

graphics.format <- "SVG"

Sequence region:

sequence.type <- "3UTR"

P-value adjustment method:

p.adjust.method <- "BH"

P-value combining method:

p.combining.method <- "SL"

k-mer length:

k <- 6

Significance threshold for k-mers:

kmer.significance.threshold <- 0.01

Motif database:

motif.db <- "transite"

1.2 Sequence data retrieval

  • retrieve requested sequence regions (3’ UTRs, 5’ UTRs, or mature mRNAs) for each RefSeq identifier using
    • GRCh38/hg38 genome assembly for human platforms
    • GRCm38/mm10 genome assembly for mouse platforms
  • draw histogram of length distribution of retrieved sequences

The latest genome assembly releases (GRCh38/hg38 for human platforms, GRCm38/mm10 for mouse platforms) are used to retrieve the requested sequence regions for all specified transcripts.

Sequence data retrieval summary:

Property Value
number of valid RefSeq identifiers 17716
number of invalid RefSeq identifiers 79
number of RefSeq identifiers with associated sequence 16146
species human
sequence type 3UTR
platform custom

Length distribution of retrieved sequences:

2 k-mer-based Transcript Set Motif Analysis

Warning: The presented results are speculative and should be used with caution. Especially the link between the RNA-binding protein and its sequence motif (i.e., the set of k-mers) is not well established. It is recommended to interpret the results with the underlying k-mer set in mind, instead of only looking at the associated RNA-binding proteins. Sequence motif logos and associated hexamers and heptamers of all RNA-binding proteins in the database can be found in the Motif Database section of the Transite website.

2.1 k-mer enrichment

2.1.1 mock

Number of statistically significantly (q < 0.01) enriched or depleted k-mers in mock: 3065

Total number of distinct k-mers in mock: 4096

2.1.2 ZFP36 overexpression

Number of statistically significantly (q < 0.01) enriched or depleted k-mers in ZFP36 overexpression: 1724

Total number of distinct k-mers in ZFP36 overexpression: 4096

2.2 Motif enrichment

2.2.1 Summary

2.2.1.1 mock

2.2.1.2 ZFP36 overexpression

2.2.2 Detailed analysis

Volcano plots for the ten motifs with the lowest p-value in each foreground set are shown here. Volcano plots for all other motifs can be found in the plots ZIP archive.

2.2.2.1 A1CF

2.2.2.1.1 ZFP36 overexpression

k-mer volcano plots

Summary of motif k-mer enrichment values

Geometric mean: \(\bar{x} = 1.349781\)

two-tailed probability estimate: \(\hat{p} = 2e-05\) \((0, 0)_{0.95}\)

Combined p-value of enriched k-mers: \(0\)

Combined p-value of depleted k-mers: \(NA\)

Empirical distribution of mean of enrichment values

2.2.2.1.2 ZFP36 overexpression

k-mer volcano plots

Summary of motif k-mer enrichment values

Geometric mean: \(\bar{x} = 0.810897\)

two-tailed probability estimate: \(\hat{p} = 2e-05\) \((0, 0)_{0.95}\)

Combined p-value of enriched k-mers: \(NA\)

Combined p-value of depleted k-mers: \(0\)

Empirical distribution of mean of enrichment values

2.2.2.2 CPEB3, CPEB2

2.2.2.2.1 ZFP36 overexpression

k-mer volcano plots

Summary of motif k-mer enrichment values

Geometric mean: \(\bar{x} = 1.250836\)

two-tailed probability estimate: \(\hat{p} = 2e-05\) \((0, 0)_{0.95}\)

Combined p-value of enriched k-mers: \(0\)

Combined p-value of depleted k-mers: \(NA\)

Empirical distribution of mean of enrichment values

2.2.2.2.2 ZFP36 overexpression

k-mer volcano plots

Summary of motif k-mer enrichment values

Geometric mean: \(\bar{x} = 0.943061\)

two-tailed probability estimate: \(\hat{p} = 2e-05\) \((0, 0)_{0.95}\)

Combined p-value of enriched k-mers: \(0.043716\)

Combined p-value of depleted k-mers: \(0\)

Empirical distribution of mean of enrichment values

2.2.2.3 DAZAP1

2.2.2.3.1 ZFP36 overexpression

k-mer volcano plots

Summary of motif k-mer enrichment values

Geometric mean: \(\bar{x} = 1.155402\)

two-tailed probability estimate: \(\hat{p} = 2e-05\) \((0, 0)_{0.95}\)

Combined p-value of enriched k-mers: \(0\)

Combined p-value of depleted k-mers: \(NA\)

Empirical distribution of mean of enrichment values

2.2.2.3.2 ZFP36 overexpression

k-mer volcano plots

Summary of motif k-mer enrichment values

Geometric mean: \(\bar{x} = 0.963542\)

two-tailed probability estimate: \(\hat{p} = 0.031939\) \((0.03, 0.033)_{0.95}\)

Combined p-value of enriched k-mers: \(0.178665\)

Combined p-value of depleted k-mers: \(0.001353\)

Empirical distribution of mean of enrichment values

2.2.2.4 FMR1

2.2.2.4.1 ZFP36 overexpression

k-mer volcano plots

Summary of motif k-mer enrichment values

Geometric mean: \(\bar{x} = 0.786655\)

two-tailed probability estimate: \(\hat{p} = 2e-05\) \((0, 0)_{0.95}\)

Combined p-value of enriched k-mers: \(NA\)

Combined p-value of depleted k-mers: \(0\)

Empirical distribution of mean of enrichment values

2.2.2.4.2 ZFP36 overexpression

k-mer volcano plots

Summary of motif k-mer enrichment values

Geometric mean: \(\bar{x} = 1.058611\)

two-tailed probability estimate: \(\hat{p} = 2e-05\) \((0, 0)_{0.95}\)

Combined p-value of enriched k-mers: \(0\)

Combined p-value of depleted k-mers: \(0.949279\)

Empirical distribution of mean of enrichment values

2.2.2.5 FXR2

2.2.2.5.1 ZFP36 overexpression

k-mer volcano plots

Summary of motif k-mer enrichment values

Geometric mean: \(\bar{x} = 0.777517\)

two-tailed probability estimate: \(\hat{p} = 2e-05\) \((0, 0)_{0.95}\)

Combined p-value of enriched k-mers: \(0.16163\)

Combined p-value of depleted k-mers: \(0\)

Empirical distribution of mean of enrichment values

2.2.2.5.2 ZFP36 overexpression

k-mer volcano plots

Summary of motif k-mer enrichment values

Geometric mean: \(\bar{x} = 1.053852\)

two-tailed probability estimate: \(\hat{p} = 6e-05\) \((0, 0)_{0.95}\)

Combined p-value of enriched k-mers: \(0\)

Combined p-value of depleted k-mers: \(0.347265\)

Empirical distribution of mean of enrichment values

2.2.2.6 HNRNPA1L2, HNRNPA3, ENSG00000215492, ENSG00000231942

2.2.2.6.1 ZFP36 overexpression

k-mer volcano plots

Summary of motif k-mer enrichment values

Geometric mean: \(\bar{x} = 0.954979\)

two-tailed probability estimate: \(\hat{p} = 0.0149\) \((0.014, 0.016)_{0.95}\)

Combined p-value of enriched k-mers: \(0.906444\)

Combined p-value of depleted k-mers: \(0.007306\)

Empirical distribution of mean of enrichment values

2.2.2.6.2 ZFP36 overexpression

k-mer volcano plots

Summary of motif k-mer enrichment values

Geometric mean: \(\bar{x} = 1.067292\)

two-tailed probability estimate: \(\hat{p} = 2e-05\) \((0, 0)_{0.95}\)

Combined p-value of enriched k-mers: \(0.000361\)

Combined p-value of depleted k-mers: \(NA\)

Empirical distribution of mean of enrichment values

2.2.2.7 HNRNPC

2.2.2.7.1 ZFP36 overexpression

k-mer volcano plots

Summary of motif k-mer enrichment values

Geometric mean: \(\bar{x} = 1.296025\)

two-tailed probability estimate: \(\hat{p} = 2e-05\) \((0, 0)_{0.95}\)

Combined p-value of enriched k-mers: \(0\)

Combined p-value of depleted k-mers: \(NA\)

Empirical distribution of mean of enrichment values

2.2.2.7.2 ZFP36 overexpression

k-mer volcano plots

Summary of motif k-mer enrichment values

Geometric mean: \(\bar{x} = 0.922782\)

two-tailed probability estimate: \(\hat{p} = 2e-05\) \((0, 0)_{0.95}\)

Combined p-value of enriched k-mers: \(NA\)

Combined p-value of depleted k-mers: \(0\)

Empirical distribution of mean of enrichment values

2.2.2.8 hnRNPK

2.2.2.8.1 ZFP36 overexpression

k-mer volcano plots

Summary of motif k-mer enrichment values

Geometric mean: \(\bar{x} = 0.780361\)

two-tailed probability estimate: \(\hat{p} = 2e-05\) \((0, 0)_{0.95}\)

Combined p-value of enriched k-mers: \(NA\)

Combined p-value of depleted k-mers: \(0\)

Empirical distribution of mean of enrichment values

2.2.2.8.2 ZFP36 overexpression

k-mer volcano plots

Summary of motif k-mer enrichment values

Geometric mean: \(\bar{x} = 1.043853\)

two-tailed probability estimate: \(\hat{p} = 2e-05\) \((0, 0)_{0.95}\)

Combined p-value of enriched k-mers: \(0.000248\)

Combined p-value of depleted k-mers: \(0.782356\)

Empirical distribution of mean of enrichment values

2.2.2.9 HNRNPL, ENSG00000215042

2.2.2.9.1 ZFP36 overexpression

k-mer volcano plots

Summary of motif k-mer enrichment values

Geometric mean: \(\bar{x} = 0.915527\)

two-tailed probability estimate: \(\hat{p} = 8e-05\) \((0, 0)_{0.95}\)

Combined p-value of enriched k-mers: \(0.035648\)

Combined p-value of depleted k-mers: \(0\)

Empirical distribution of mean of enrichment values

2.2.2.9.2 ZFP36 overexpression

k-mer volcano plots

Summary of motif k-mer enrichment values

Geometric mean: \(\bar{x} = 0.925598\)

two-tailed probability estimate: \(\hat{p} = 2e-05\) \((0, 0)_{0.95}\)

Combined p-value of enriched k-mers: \(NA\)

Combined p-value of depleted k-mers: \(0\)

Empirical distribution of mean of enrichment values

2.2.2.10 ELAVL1, ELAVL3

2.2.2.10.1 ZFP36 overexpression

k-mer volcano plots

Summary of motif k-mer enrichment values

Geometric mean: \(\bar{x} = 1.322345\)

two-tailed probability estimate: \(\hat{p} = 2e-05\) \((0, 0)_{0.95}\)

Combined p-value of enriched k-mers: \(0\)

Combined p-value of depleted k-mers: \(NA\)

Empirical distribution of mean of enrichment values

2.2.2.10.2 ZFP36 overexpression

k-mer volcano plots

Summary of motif k-mer enrichment values

Geometric mean: \(\bar{x} = 0.852687\)

two-tailed probability estimate: \(\hat{p} = 2e-05\) \((0, 0)_{0.95}\)

Combined p-value of enriched k-mers: \(NA\)

Combined p-value of depleted k-mers: \(0\)

Empirical distribution of mean of enrichment values

2.2.2.11 IGF2BP2

2.2.2.11.1 ZFP36 overexpression

k-mer volcano plots

Summary of motif k-mer enrichment values

Geometric mean: \(\bar{x} = 1.122804\)

two-tailed probability estimate: \(\hat{p} = 2e-05\) \((0, 0)_{0.95}\)

Combined p-value of enriched k-mers: \(0\)

Combined p-value of depleted k-mers: \(0.89548\)

Empirical distribution of mean of enrichment values

2.2.2.11.2 ZFP36 overexpression

k-mer volcano plots

Summary of motif k-mer enrichment values

Geometric mean: \(\bar{x} = 0.908966\)

two-tailed probability estimate: \(\hat{p} = 2e-05\) \((0, 0)_{0.95}\)

Combined p-value of enriched k-mers: \(NA\)

Combined p-value of depleted k-mers: \(0\)

Empirical distribution of mean of enrichment values

2.2.2.12 KHDRBS3

2.2.2.12.1 ZFP36 overexpression

k-mer volcano plots

Summary of motif k-mer enrichment values

Geometric mean: \(\bar{x} = 1.1268\)

two-tailed probability estimate: \(\hat{p} = 2e-05\) \((0, 0)_{0.95}\)

Combined p-value of enriched k-mers: \(0\)

Combined p-value of depleted k-mers: \(NA\)

Empirical distribution of mean of enrichment values

2.2.2.12.2 ZFP36 overexpression

k-mer volcano plots

Summary of motif k-mer enrichment values

Geometric mean: \(\bar{x} = 0.915828\)

two-tailed probability estimate: \(\hat{p} = 2e-05\) \((0, 0)_{0.95}\)

Combined p-value of enriched k-mers: \(NA\)

Combined p-value of depleted k-mers: \(0\)

Empirical distribution of mean of enrichment values

3 Download data

Download input data

Download output data

Download full HTML report

Download plots only

4 Session info

4.1 Loaded motifs

4.2 R Session info

## R version 3.5.1 (2018-07-02)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.5 LTS
## 
## Matrix products: default
## BLAS: /usr/lib/libblas/libblas.so.3.6.0
## LAPACK: /usr/lib/lapack/liblapack.so.3.6.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] bindrcpp_0.2.2       transite_0.99.3      DT_0.4              
##  [4] knitr_1.20           AnnotationDbi_1.42.1 IRanges_2.14.11     
##  [7] S4Vectors_0.18.3     Biobase_2.40.0       BiocGenerics_0.26.0 
## [10] rmarkdown_1.10       futile.logger_1.4.3 
## 
## loaded via a namespace (and not attached):
##  [1] httr_1.3.1                             
##  [2] jsonlite_1.5                           
##  [3] bit64_0.9-7                            
##  [4] shiny_1.1.0                            
##  [5] assertthat_0.2.0                       
##  [6] blob_1.1.1                             
##  [7] GenomeInfoDbData_1.1.0                 
##  [8] Rsamtools_1.32.3                       
##  [9] yaml_2.2.0                             
## [10] progress_1.2.0                         
## [11] lattice_0.20-35                        
## [12] pillar_1.3.0                           
## [13] RSQLite_2.1.1                          
## [14] backports_1.1.2                        
## [15] glue_1.3.0                             
## [16] digest_0.6.17                          
## [17] promises_1.0.1                         
## [18] RColorBrewer_1.1-2                     
## [19] GenomicRanges_1.32.6                   
## [20] XVector_0.20.0                         
## [21] colorspace_1.3-2                       
## [22] httpuv_1.4.5                           
## [23] Matrix_1.2-14                          
## [24] htmltools_0.3.6                        
## [25] plyr_1.8.4                             
## [26] TxDb.Hsapiens.UCSC.hg38.knownGene_3.4.0
## [27] XML_3.98-1.16                          
## [28] pkgconfig_2.0.2                        
## [29] biomaRt_2.36.1                         
## [30] zlibbioc_1.26.0                        
## [31] xtable_1.8-3                           
## [32] purrr_0.2.5                            
## [33] scales_1.0.0                           
## [34] later_0.7.4                            
## [35] BiocParallel_1.14.2                    
## [36] tibble_1.4.2                           
## [37] ggplot2_3.0.0                          
## [38] SummarizedExperiment_1.10.1            
## [39] GenomicFeatures_1.32.2                 
## [40] TFMPvalue_0.0.8                        
## [41] lazyeval_0.2.1                         
## [42] mime_0.5                               
## [43] magrittr_1.5                           
## [44] crayon_1.3.4                           
## [45] memoise_1.1.0                          
## [46] evaluate_0.11                          
## [47] tools_3.5.1                            
## [48] prettyunits_1.0.2                      
## [49] hms_0.4.2                              
## [50] org.Hs.eg.db_3.6.0                     
## [51] matrixStats_0.54.0                     
## [52] formatR_1.5                            
## [53] stringr_1.3.1                          
## [54] munsell_0.5.0                          
## [55] DelayedArray_0.6.6                     
## [56] lambda.r_1.2.3                         
## [57] Biostrings_2.48.0                      
## [58] compiler_3.5.1                         
## [59] GenomeInfoDb_1.16.0                    
## [60] rlang_0.2.2                            
## [61] grid_3.5.1                             
## [62] RCurl_1.95-4.11                        
## [63] htmlwidgets_1.2                        
## [64] crosstalk_1.0.0                        
## [65] labeling_0.3                           
## [66] bitops_1.0-6                           
## [67] gtable_0.2.0                           
## [68] DBI_1.0.0                              
## [69] R6_2.2.2                               
## [70] GenomicAlignments_1.16.0               
## [71] gridExtra_2.3                          
## [72] dplyr_0.7.6                            
## [73] rtracklayer_1.40.6                     
## [74] bit_1.1-14                             
## [75] bindr_0.1.1                            
## [76] rprojroot_1.3-2                        
## [77] futile.options_1.0.1                   
## [78] stringi_1.2.4                          
## [79] Rcpp_0.12.18                           
## [80] dbplyr_1.2.2                           
## [81] tidyselect_0.2.4