TransCistor is an analytical framework which aims to classify cis- or trans-acting long non-coding RNAs(lncRNAs). It includes two modules; digital and analogue. There is only one file required as input: a processed whole-transcriptome list of target/not-target genes (“regulation file”). https://www.biorxiv.org/content/10.1101/2022.09.18.508380v1
TransCistor-digital tests for statistical enrichment in proximal genes using the hypergeometric distribution. Proximity is defined based on Topologically Associating Domains (TADs).
TransCistor-analogue, defines a distance statistic as the mean TSS-to-TSS distance of all same-chromosome targets of a given lncRNA. To estimate statistical significance, a null distribution is calculated by randomisation of target labels.
TransCistor is built as a webserver (https://transcistor.unibe.ch/) or standalone script.
- The webserver is a shiny app and has no dependencies.
- The standalone version is built in R and the main dependencies are R packages: tidyverse, dplyr, rlang, rlist, etc.
- For single-case studies we highly recommend to use the webserver version of TransCistor (https://transcistor.unibe.ch/).
- Standalone version is recommended for analysing multiple datasets. Executing simply requires the start_TransCistor.R script. The tested dataset can be changed by manipulating the metadata and RegulationFiles directory
TransCistor <- function(input.file, id.type, species, cell = "H1-NPC",
lncRNA.name, lncRNA.chr, lncRNA.tss, lncRNA.strand = "+",
enricher.threshold = 0, simulations = 100000 )
You can use the plots.R file to get results for single lncRNAs. You may perform the analysis as shown below. It should take a few seconds in a normal computer. \
### Input the Identifier for the lncRNA####
input='UMLILO'
####Read the metadata to get the regulation file path. Either read the given metadata or make a custom one based on the given format####
metadata_file <- 'metadata.txt'
metadata <- read.table(metadata_file, header = TRUE, sep = "\t",na.strings=c("","NA"))
file_name <- metadata[metadata$SYMBOL==input,]$File_Name
name <- metadata[i[num],]$SYMBOL
chr <- metadata[i[num],]$CHR
tss <- metadata[i[num],]$TSS
strand <- metadata[i[num],]$strand
file.type <- metadata[i[num],]$FILE_TYPE
species <- metadata[i[num],]$SPECIES
#TransCistor function call
results <- TransCistor(input.file = current_file,
id.type = file.type,
species = species,
lncRNA.name = name,
lncRNA.chr = chr,
lncRNA.tss = tss,
lncRNA.strand = strand,
enricher.threshold = enrichr_threshold
)
...or visit the webserver to get publucation ready figures!
(more people have contributed in this work, see https://www.biorxiv.org/content/10.1101/2022.09.18.508380v1)
Panagiotis Chouvardas, [email protected]
Bhavya Dhaka, [email protected]
Rory Johnson*, [email protected]
R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.1 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
locale:
[1] LC_CTYPE=en_IE.UTF-8
[2] LC_NUMERIC=C
[3] LC_TIME=en_IE.UTF-8
[4] LC_COLLATE=en_IE.UTF-8
[5] LC_MONETARY=en_IE.UTF-8
[6] LC_MESSAGES=en_IE.UTF-8
[7] LC_PAPER=en_IE.UTF-8
[8] LC_NAME=C
[9] LC_ADDRESS=C
[10] LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_IE.UTF-8
[12] LC_IDENTIFICATION=C
attached base packages:
[1] grid stats graphics
[4] grDevices utils datasets
[7] methods base
other attached packages:
[1] scales_1.2.1
[2] ggh4x_0.2.3
[3] reshape_0.8.9
[4] VennDiagram_1.7.3
[5] futile.logger_1.4.3
[6] plyr_1.8.7
[7] pCalibrate_0.2-1
[8] MCMCpack_1.6-3
[9] MASS_7.3-55
[10] coda_0.19-4
[11] exact2x2_1.6.6
[12] exactci_1.4-2
[13] testthat_3.1.5
[14] ssanv_1.1
[15] psych_2.2.5
[16] RColorBrewer_1.1-3
[17] pheatmap_1.0.12
[18] ggpubr_0.5.0
[19] clusterProfiler_4.2.2
[20] hash_2.2.6.2
[21] forcats_0.5.2
[22] stringr_1.4.1
[23] purrr_0.3.4
[24] readr_2.1.2
[25] tidyr_1.2.1
[26] tibble_3.1.8
[27] tidyverse_1.3.2
[28] ggrepel_0.9.1
[29] ggplot2_3.4.0
[30] readxl_1.4.1
[31] data.table_1.14.2
[32] rlist_0.4.6.2
[33] dplyr_1.0.10
[34] rlang_1.0.6
loaded via a namespace (and not attached):
[1] shadowtext_0.1.2
[2] backports_1.4.1
[3] fastmatch_1.1-3
[4] igraph_1.3.5
[5] lazyeval_0.2.2
[6] splines_4.1.2
[7] BiocParallel_1.28.3
[8] GenomeInfoDb_1.30.1
[9] digest_0.6.29
[10] yulab.utils_0.0.6
[11] GOSemSim_2.20.0
[12] viridis_0.6.2
[13] GO.db_3.14.0
[14] fansi_1.0.3
[15] magrittr_2.0.3
[16] memoise_2.0.1
[17] googlesheets4_1.0.1
[18] tzdb_0.3.0
[19] Biostrings_2.62.0
[20] graphlayouts_0.8.4
[21] modelr_0.1.9
[22] enrichplot_1.14.2
[23] colorspace_2.0-3
[24] blob_1.2.3
[25] rvest_1.0.3
[26] haven_2.5.1
[27] crayon_1.5.1
[28] RCurl_1.98-1.9
[29] jsonlite_1.8.0
[30] scatterpie_0.1.8
[31] survival_3.2-13
[32] ape_5.6-2
[33] glue_1.6.2
[34] polyclip_1.10-4
[35] gtable_0.3.1
[36] gargle_1.2.1
[37] zlibbioc_1.40.0
[38] XVector_0.34.0
[39] MatrixModels_0.5-1
[40] car_3.1-1
[41] BiocGenerics_0.40.0
[42] SparseM_1.81
[43] abind_1.4-5
[44] DOSE_3.20.1
[45] futile.options_1.0.1
[46] DBI_1.1.3
[47] rstatix_0.7.1
[48] Rcpp_1.0.9
[49] viridisLite_0.4.1
[50] gridGraphics_0.5-1
[51] tidytree_0.4.2
[52] bit_4.0.4
[53] stats4_4.1.2
[54] httr_1.4.4
[55] fgsea_1.20.0
[56] ellipsis_0.3.2
[57] pkgconfig_2.0.3
[58] farver_2.1.1
[59] dbplyr_2.2.1
[60] utf8_1.2.2
[61] labeling_0.4.2
[62] ggplotify_0.1.0
[63] tidyselect_1.2.0
[64] reshape2_1.4.4
[65] AnnotationDbi_1.56.2
[66] munsell_0.5.0
[67] cellranger_1.1.0
[68] tools_4.1.2
[69] cachem_1.0.6
[70] downloader_0.4
[71] cli_3.4.0
[72] generics_0.1.3
[73] RSQLite_2.2.18
[74] broom_1.0.1
[75] fastmap_1.1.0
[76] mcmc_0.9-7
[77] ggtree_3.2.1
[78] bit64_4.0.5
[79] fs_1.5.2
[80] tidygraph_1.2.2
[81] KEGGREST_1.34.0
[82] ggraph_2.1.0
[83] nlme_3.1-155
[84] quantreg_5.94
[85] formatR_1.12
[86] aplot_0.1.9
[87] DO.db_2.9
[88] xml2_1.3.3
[89] brio_1.1.3
[90] compiler_4.1.2
[91] rstudioapi_0.14
[92] png_0.1-7
[93] ggsignif_0.6.4
[94] reprex_2.0.2
[95] treeio_1.18.1
[96] tweenr_2.0.2
[97] stringi_1.7.8
[98] lattice_0.20-45
[99] Matrix_1.5-1
[100] vctrs_0.5.1
[101] pillar_1.8.1
[102] lifecycle_1.0.3
[103] bitops_1.0-7
[104] patchwork_1.1.2
[105] qvalue_2.26.0
[106] R6_2.5.1
[107] gridExtra_2.3
[108] IRanges_2.28.0
[109] lambda.r_1.2.4
[110] assertthat_0.2.1
[111] withr_2.5.0
[112] mnormt_2.1.0
[113] S4Vectors_0.32.4
[114] GenomeInfoDbData_1.2.7
[115] parallel_4.1.2
[116] hms_1.1.2
[117] ggfun_0.0.9
[118] carData_3.0-5
[119] googledrive_2.0.0
[120] ggforce_0.4.1
[121] Biobase_2.54.0
[122] lubridate_1.8.0
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent