Last updated: 2021-04-21

Checks: 7 0

Knit directory: funcFinemapping/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it's best to always run the code in an empty environment.

Seed: set.seed(20210404)

The command set.seed(20210404) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 75dbdd6

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 75dbdd6. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


Ignored files:
    Ignored:    analysis/results.nb.html

Untracked files:
    Untracked:  output/constrained_and_OPC.txt
    Untracked:  output/prop_constrained_variants_with_OCR.txt

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/biology_bkg.Rmd) and HTML (docs/biology_bkg.html) files. If you've configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
html e597d4e Jing Gu 2021-04-21 Build site.
Rmd b93bab1 Jing Gu 2021-04-21 compare with other conservation annotations
html 491ca5c Jing Gu 2021-04-14 Build site.
html ba251cb Jing Gu 2021-04-14 Build site.
html 1a76b29 Jing Gu 2021-04-06 Build site.
Rmd ae12e8a Jing Gu 2021-04-06 evaluate sequence constraints
html 752cb39 Jing Gu 2021-04-05 Build site.
Rmd 1965945 Jing Gu 2021-04-05 literature reading
html f1c5950 Jing Gu 2021-04-05 Build site.
html d16a5e0 Jing Gu 2021-04-05 Build site.
Rmd 6f7214b Jing Gu 2021-04-05 edit index page

TF footprints

Protein 'footprints' are formed when DNA-protein complex prevents DNA from nucleolytic attack and then leaves 'footprints' that can be detected from various experimental approaches, such as in vitro assay or squencing methods for nucleic and whole cells. The in vivo binding of transcription factors (one type of DNA-binding proteins) to their cognate recognition sequences also protect small segments (~6-20nt) of nucleotides from dense cleavage activity such as by Tn5 transposon that usually implying active regulatory DNA (~150-300 nt).(Vierstra et al., 2016 nature review)

What can be studied with in vivo DNA footprinting?

  • Structure, function, evolution of TF occupancy patterns across cell types
  • De novo detection of TF footprints
  • Prediction of the effects of genetic variants on TF occupancy patterns
  • Construction and anlysis of direct TF regulatory network dynamics

Some facts about TF binding:

  • Detection of a footprint reflects the ratio of that TF's affinity for a given binding site versus the relative intrinsic propensity of cleavage agent to cleave at specific sequences or structures.

Methods to detect and analyze TF footprints

(Bentsen et al. 2020)
ATAC-Seq: fast and cheap; examine all accesible regions; but not able to distinguish specific TF binding sites.
Current challenges:
* Discovery of TFs - Tn5 transposases are biased towards certain sequences compositions (ref 9,10) * Compare footprints between studies/conditions * Lack of tools to comprehensively analyze large-scale ATAC-seq footprinting

ChIP-Seq: identify specific TFBS; require high input cell numbers,; one TF per assay; restricted to TFs that have antibodies specifically bind to them;

Conservations

Sequence constraints

Lulio et al. (2018) used large sizes of WGS data and heptamers to build a map of sequence constraints for the human species and found constrained regions are up to 52-fold enriched for known pathogenic variants than unconstrained regions.

Why heptamers?

  • k-mers used to determine the prob. of variation of each nucleotide genome-wide in the context of surrounding nucleotides.(Previous findings of interdependence among nearby nucleotides)
  • 7-mers explain \(>80\%\) heritability in nucleotide substitution
  • Each 7-mer characterized by the rate and freq of variation at the fourth nucleotide of each 7-mer

context-dependent tolerance score (CDTS)

  • A metric to characterize the observed variation for a given noncoding sequence
  • Genome are divided into equally sized regions (550bp)
  • Not based on any existing annotation
  • CDTS defined as \(|obs. variation - expected variation|\)

Main Hypothesis and findings

  1. most constrained regulatory regions both in cis or distal coordinate with genes with essential functions
    • plot medians of pLI scores against CDTS for variants in each CDTS quantile bin
    • Each genomic bin within 15kb of a gene (cis) was assigned the pLI score of the closest gene.
    • Each enhancer was assigned a pLI score of the paired gene based on in situ Hi-C or pcHi-C (distal). The distances between enhancer and gene are up to 2Mb.

  2. Noncoding pathogenic variants associated with Mendelian traits are enriched at the lowest CDTS percentile.

  3. CDTS ranking is a good proxy to score functionality and consequences of mutations for non-coding sequences.
    • benchmark different metrics for noncoding variants: perfomance on detecting Mendelian noncoding variants
    • CDTS captures the highest proportion of variants uniquely detected by a single metric
    • CDTS requires no prior knowledge (no overfitting issue)

Transcription regulation

RNA modification - methylation at N6-adenosine

Intro: m6A is the most aboundant form of RNA modifications and 25-60% of mRNA transcripts found to have this type of modification. More evidence have shown that genes involve in m6A regulation and recognization have been implicated in human diseases. Previous work has provided evidence for the role of m6A variation in the genetics of immune traits. m6A-QTLs identified from 60 LCLs are largely independent of eQTLs (less than 50%) or sQTLs (same level). Additionally, genetic variants that affect m6A levels show cell-type specific effects. m6A is also found to be present in chromatin-associated RNAs, which may implicate its regulatory functions on caRNAs.

Main Hypothesis: 1. Genetic variations that disrupt m6A modification may explain a significant fraction of human disease associations. 2. The effects of genetic variation of m6A may vary across cell types and conditions.

m6A modification dynamics

  • Recognized by Reader Protein: YTH family
  • Methyl group added to N6-adnoesin by writer complex: METTL3/METTL14
  • Methyl group removed by eraser proteins: FTO and ALKBH5

pre-mRNA splicing, RNA degradation, translation and alternative polyadenylation Lulio et al.

This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.

When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:

summary(cars)
     speed           dist       
 Min.   : 4.0   Min.   :  2.00  
 1st Qu.:12.0   1st Qu.: 26.00  
 Median :15.0   Median : 36.00  
 Mean   :15.4   Mean   : 42.98  
 3rd Qu.:19.0   3rd Qu.: 56.00  
 Max.   :25.0   Max.   :120.00  

sessionInfo()
R version 4.0.4 (2021-02-15)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.so

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] workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.6        whisker_0.4       knitr_1.31        magrittr_2.0.1   
 [5] R6_2.5.0          rlang_0.4.10      fansi_0.4.2       stringr_1.4.0    
 [9] tools_4.0.4       xfun_0.21         utf8_1.1.4        git2r_0.28.0     
[13] jquerylib_0.1.3   htmltools_0.5.1.1 ellipsis_0.3.1    rprojroot_2.0.2  
[17] yaml_2.2.1        digest_0.6.27     tibble_3.0.6      lifecycle_1.0.0  
[21] crayon_1.4.1      later_1.1.0.1     sass_0.3.1        vctrs_0.3.6      
[25] promises_1.2.0.1  fs_1.5.0          glue_1.4.2        evaluate_0.14    
[29] rmarkdown_2.7     stringi_1.5.3     bslib_0.2.4       compiler_4.0.4   
[33] pillar_1.5.0      jsonlite_1.7.2    httpuv_1.5.5      pkgconfig_2.0.3