PTR:RNA-modifications
Last updated: 2022-09-30
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Knit directory: funcFinemapping/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 05a3009 | Jing Gu | 2022-08-02 | PTR_m6A |
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intersect, setdiff, setequal, unionBackgrounds
Post-transcriptional regulatory (PTR) processes have been implicated in development and diseases, however, it is largely unknown how genetic variations are mediated through PTR processes. We propose to annotate GWAS variants using both experimental measurements and computational predictions. With this prior knowledge, we can further identify most likely causal variants through fine-mapping and then link them to genes.
Several post-transcriptonal features will be explored:
- alternative splicing
- RNA modification: m6A
- RNA binding
- Polyadenylation
Results
LDSC enrichment across multiple traits
| Version | Author | Date |
|---|---|---|
| 05a3009 | Jing Gu | 2022-08-02 |
| Version | Author | Date |
|---|---|---|
| 05a3009 | Jing Gu | 2022-08-02 |
Adjusting for transcript-level variations across tissues
We compared the enrichment estimates for m6A peaks in fetal tissues conditional on all coding regions or coding regions from differentially expressed genes in each tissue.
Fetal cell atlas for transcriptomics Cao et al.Science 2020:A human cell atlas of fetal gene expression.
The list DE genes obtained from DEG analysis on pseudobulk transcriptomes, comparing the most expressed tissue and the second most expressed tissue.
For heart tissue, 1011 genes show significantly higher expression than that in the second mostly expressed tissue at FDR 0.05.
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'restfulr::download.file' when loading 'rtracklayer'Joint analysis via LDSC
Note: The extended region of $$250 bp away from the coding regions
was included to adjust for the unsure boundary.
7.8% of GWAS SNPs are within coding and the extended regions.
Only 0.2% of GWAS SNPs are within heart-specific DEGs among fetal
samples.
sessionInfo()R version 4.2.0 (2022-04-22)
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attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
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[23] stringr_1.4.0 RCurl_1.98-1.7
[25] munsell_0.5.0 DelayedArray_0.22.0
[27] rtracklayer_1.56.0 compiler_4.2.0
[29] httpuv_1.6.5 xfun_0.30
[31] pkgconfig_2.0.3 htmltools_0.5.2
[33] tidyselect_1.1.2 SummarizedExperiment_1.26.1
[35] tibble_3.1.7 GenomeInfoDbData_1.2.8
[37] workflowr_1.7.0 codetools_0.2-18
[39] matrixStats_0.62.0 XML_3.99-0.9
[41] fansi_1.0.3 crayon_1.5.1
[43] withr_2.5.0 later_1.3.0
[45] GenomicAlignments_1.32.0 bitops_1.0-7
[47] grid_4.2.0 jsonlite_1.8.0
[49] gtable_0.3.0 lifecycle_1.0.1
[51] DBI_1.1.2 git2r_0.30.1
[53] magrittr_2.0.3 scales_1.2.0
[55] cli_3.3.0 stringi_1.7.6
[57] farver_2.1.0 XVector_0.36.0
[59] fs_1.5.2 promises_1.2.0.1
[61] bslib_0.3.1 ellipsis_0.3.2
[63] generics_0.1.2 vctrs_0.4.1
[65] rjson_0.2.21 restfulr_0.0.14
[67] tools_4.2.0 Biobase_2.56.0
[69] glue_1.6.2 purrr_0.3.4
[71] MatrixGenerics_1.8.0 parallel_4.2.0
[73] fastmap_1.1.0 yaml_2.3.5
[75] colorspace_2.0-3 knitr_1.39
[77] BiocIO_1.6.0 sass_0.4.1