logo

Summary

This report was generated with RLSeq v0.99.3.

Sample information

Sample name: SRX1070676

Sample type: DRIP

Label: POS

Genome: hg38

Time: Thu Oct 7 09:38:01 2021

Results

1. RLFS Analysis

Z-Score distribution

R-loop forming sequences (RLFS) were compared to the ranges in SRX1070676 to measure enrichment. The resulting Z-score distribution is visualized below:

Note: for samples which map R-loop successfully, enrichment is expected. See representative examples for POS and NEG sample types here.

Details

Additional details

RLFS were derived across the genome using QmRLFS-finder.py. R-loop broad peaks were called with macs and then compared with RLFS using permTest from the regioneR R package. An empirical distribution of RLFS was generated using the circularRandomizeRegions method and compared to the peaks in order to calculate enrichment p value and zscore (effect size of enrichment).

From this analysis, the empirically-determined p value was 0.009901 (with 100 permutations, the minimum possible p value was 0.009901). The enrichment z-score was 37.0012.


2. Sample classification

Predicted label for sample SRX1070676 is “POS” (i.e., robust R-loop mapping).

Details

Additional Details

To evaluate sample quality, a binary classifier was developed via the online-learning approach described in the RLSuite manuscript. The classifier evaluates features engineered from the RLFS Z score distribution, specifically, the following features:

Abbreviations: Z, Z-score distribution; ACF, autocorrelation function; FT, Fourier Transform.
feature description raw_value processed_value
Z1 mean of Z 0.3115924 21.4719243
Z2 variance of Z 0.2000516 334.6593397
Zacf1 mean of Z ACF 0.0373472 0.2391912
Zacf2 variance of Z ACF 0.3868038 582.5396127
ReW1 mean of FT of Z (real part) 0.2909243 12.2412218
ReW2 variance of FT of Z (real part) 0.2049347 4744.2587764
ImW1 mean of FT of Z (imaginary part) 0.8892123 0.0000000
ImW2 variance of FT of Z (imaginary part) -0.7306497 58.1400446
ReWacf1 mean of FT of Z ACF (real part) 0.3259641 96.1548430
ReWacf2 variance of FT of Z ACF (real part) 0.3919846 6270.2386954
ImWacf1 mean of FT of Z ACF (imaginary part) -0.0883951 0.0000000
ImWacf2 variance of FT of Z ACF (imaginary part) 0.3647679 5375.3082857

From these features, classification was performed to derive a prediction (predicted label) regarding whether the sample mapped R-loops or not. In short, “POS” indicates any sample for which all the following are true:

  1. Criteria 1: The RLFS Permutation test P value is significant (p < .05)
  2. Criteria 2: The Z-score distribution middle is > 0.
  3. Criteria 3: The Z-score distribution middle is > the start and the end.
  4. Criteria 4: The model predicts a label of “POS”.

The criteria for SRX1070676 are shown below:

Results from quality analysis of SRX1070676
Criteria Result
  1. PVal Significant
TRUE
  1. ZApex > 0
TRUE
  1. ZApex > ZEdges
TRUE
  1. Predicted ‘POS’
TRUE

These results led to the final prediction: “POS” (i.e., robust R-loop mapping).


3. Feature enrichment test

Enrichment plots

The results were then visualized with the plotEnrichment() function:

CpG_Islands

Encode_CREs

G4Qpred

knownGene_RNAs

PolyA

Repeat_Masker

skewr

snoRNA_miRNA_scaRNA

Transcript_Features

tRNAs

Note: If < 200 peaks in user-supplied sample, ◇ will be missing from plots.

Summary table

Additional Details

Annotations were derived from a variety of sources and accessed using RLHub (unless custom annotations were supplied by the user). Detailed explanations of each database and type can be found here. The valr R package was implemented to test the enrichment of these features within the supplied ranges for SRX1070676.



4. Correlation analysis

Unavailable. Run corrAnalyze() and then report() again to view this result.


5. Gene Annotations

hg38 Gene annotations were downloaded from AnnotationHub and overlapped with R-loop ranges in SRX1070676. The resulting gene table was then filtered for the top 2000 peaks (by p-adjusted value) and is observed here:


6. RL-Regions Test

RL-Regions are consensus R-loop sites derived from a meta-analysis of all high-confidence R-loop mapping samples in RLBase (see the RLSuite manuscript for a full description). The ranges supplied for SRX1070676 were compared to the RL-Regions to determine the degree and significance of overlap.



Other

For more information about RLSeq please visit the package homepage here.

Note: if you use RLSeq in published research, please reference:

Miller et al., RLSeq, (2021), GitHub repository, Bishop-Laboratory/RLSeq

Session info

Session info
## R version 4.1.1 (2021-08-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.3 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
## 
## 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] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] RLHub_0.99.4   RLSeq_0.99.3   dplyr_1.0.7    magrittr_2.0.1
## 
## loaded via a namespace (and not attached):
##   [1] AnnotationHub_3.1.5           BiocFileCache_2.1.1          
##   [3] systemfonts_1.0.2             plyr_1.8.6                   
##   [5] splines_4.1.1                 BiocParallel_1.27.10         
##   [7] crosstalk_1.1.1               listenv_0.8.0                
##   [9] GenomeInfoDb_1.29.8           ggplot2_3.3.5                
##  [11] digest_0.6.28                 yulab.utils_0.0.2            
##  [13] foreach_1.5.1                 htmltools_0.5.2              
##  [15] fansi_0.5.0                   memoise_2.0.0                
##  [17] BSgenome_1.61.0               recipes_0.1.16               
##  [19] globals_0.14.0                Biostrings_2.61.2            
##  [21] gower_0.2.2                   matrixStats_0.61.0           
##  [23] svglite_2.0.0                 colorspace_2.0-2             
##  [25] rappdirs_0.3.3                blob_1.2.2                   
##  [27] rvest_1.0.1                   xfun_0.26                    
##  [29] crayon_1.4.1                  RCurl_1.98-1.5               
##  [31] jsonlite_1.7.2                survival_3.2-13              
##  [33] iterators_1.0.13              glue_1.4.2                   
##  [35] kableExtra_1.3.4              gtable_0.3.0                 
##  [37] ipred_0.9-12                  zlibbioc_1.39.0              
##  [39] XVector_0.33.0                webshot_0.5.2                
##  [41] DelayedArray_0.19.4           future.apply_1.8.1           
##  [43] BiocGenerics_0.39.2           scales_1.1.1                 
##  [45] futile.options_1.0.1          DBI_1.1.1                    
##  [47] Rcpp_1.0.7                    xtable_1.8-4                 
##  [49] viridisLite_0.4.0             gridGraphics_0.5-1           
##  [51] bit_4.0.4                     stats4_4.1.1                 
##  [53] lava_1.6.10                   prodlim_2019.11.13           
##  [55] DT_0.19                       htmlwidgets_1.5.4            
##  [57] httr_1.4.2                    ellipsis_0.3.2               
##  [59] pkgconfig_2.0.3               XML_3.99-0.8                 
##  [61] farver_2.1.0                  nnet_7.3-16                  
##  [63] sass_0.4.0                    dbplyr_2.1.1                 
##  [65] utf8_1.2.2                    caret_6.0-88                 
##  [67] ggplotify_0.1.0               AnnotationDbi_1.55.1         
##  [69] later_1.3.0                   tidyselect_1.1.1             
##  [71] labeling_0.4.2                rlang_0.4.11                 
##  [73] reshape2_1.4.4                munsell_0.5.0                
##  [75] BiocVersion_3.14.0            tools_4.1.1                  
##  [77] cachem_1.0.6                  ggprism_1.0.3                
##  [79] generics_0.1.0                RSQLite_2.2.8                
##  [81] ExperimentHub_2.1.4           evaluate_0.14                
##  [83] stringr_1.4.0                 fastmap_1.1.0                
##  [85] yaml_2.2.1                    ModelMetrics_1.2.2.2         
##  [87] knitr_1.34                    bit64_4.0.5                  
##  [89] purrr_0.3.4                   KEGGREST_1.33.0              
##  [91] pbapply_1.5-0                 future_1.22.1                
##  [93] nlme_3.1-153                  mime_0.11                    
##  [95] formatR_1.11                  xml2_1.3.2                   
##  [97] caretEnsemble_2.0.1           compiler_4.1.1               
##  [99] rstudioapi_0.13               png_0.1-7                    
## [101] interactiveDisplayBase_1.31.2 filelock_1.0.2               
## [103] curl_4.3.2                    tibble_3.1.4                 
## [105] bslib_0.3.0                   stringi_1.7.4                
## [107] futile.logger_1.4.3           highr_0.9                    
## [109] lattice_0.20-44               Matrix_1.3-4                 
## [111] vctrs_0.3.8                   pillar_1.6.2                 
## [113] lifecycle_1.0.1               BiocManager_1.30.16          
## [115] jquerylib_0.1.4               data.table_1.14.0            
## [117] bitops_1.0-7                  httpuv_1.6.3                 
## [119] rtracklayer_1.53.1            GenomicRanges_1.45.0         
## [121] R6_2.5.1                      BiocIO_1.3.0                 
## [123] promises_1.2.0.1              gridExtra_2.3                
## [125] IRanges_2.27.2                parallelly_1.28.1            
## [127] codetools_0.2-18              lambda.r_1.2.4               
## [129] MASS_7.3-54                   assertthat_0.2.1             
## [131] SummarizedExperiment_1.23.4   rjson_0.2.20                 
## [133] withr_2.4.2                   regioneR_1.25.1              
## [135] GenomicAlignments_1.29.0      Rsamtools_2.9.1              
## [137] S4Vectors_0.31.3              GenomeInfoDbData_1.2.7       
## [139] parallel_4.1.1                VennDiagram_1.6.20           
## [141] grid_4.1.1                    rpart_4.1-15                 
## [143] timeDate_3043.102             class_7.3-19                 
## [145] rmarkdown_2.11                MatrixGenerics_1.5.4         
## [147] pROC_1.18.0                   shiny_1.7.0                  
## [149] Biobase_2.53.0                lubridate_1.7.10             
## [151] restfulr_0.0.13
 

RLSeq © 2021, Bishop Lab, UT Health San Antonio

RLSeq maintainer: Henry Miller

 

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