Last updated: 2021-04-05

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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;

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)

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R version 4.0.4 (2021-02-15)
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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    
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[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