# Footprinting controversies: Sequence bias
- [Yardimci et al. 2014](https://doi.org/10.1093/nar/gku810) - Explicit DNase sequence bias modeling enables high-resolution transcription factor footprint detection
- [Madrigal et al. 2015](https://doi.org/10.3389/fbioe.2015.00144) - On accounting for sequence-specific bias in genome-wide chromatin accessibility experiments: recent advances and contradictions
- [Wang et al. 2017](http://dx.doi.org/10.1186/s12859-017-1766-x) - Correcting Nucleotide-Specific Biases in High-Throughput Sequencing Data
- [Martins et al. 2018](http://dx.doi.org/10.1093/nar/gkx1053) - Universal Correction of Enzymatic Sequence Bias Reveals Molecular Signatures of protein/DNA Interactions
- [Calviello et al. 2019](https://doi.org/10.1186/s13059-019-1654-y) - Reproducible inference of transcription factor footprints in ATAC-seq and DNase-seq datasets using protocol-specific bias modeling
Martins et al. 2018
# Footprinting controversies: Factor dynamics
- [Sung et al. 2014](http://10.1016/j.molcel.2014.08.016) - DNase footprint signatures are dictated by factor dynamics and DNA sequence.
- [Sung et al. 2016](https://doi.org/10.1038/nmeth.3766) - Genome-wide footprinting: ready for prime time?
- [Oh et al. 2019](http://10.1186/s13072-019-0277-6) - XL-DNase-seq: improved footprinting of dynamic transcription factors
Sung et al. 2014
# ATAC footprinting
[Li et al. 2019](https://doi.org/10.1186/s13059-019-1642-2) - Identification of transcription factor binding sites using ATAC-seq
HINT-ATAC, which is based on hidden Markov models, uses strand-specific, nucleosome-size decomposed, and bias-corrected signals to identify footprints.
[Ouyang and Boyle 2020](https://doi.org/10.1101/801001)- TRACE: transcription factor footprinting using chromatin accessibility data and DNA sequence
Buenrostro et al. 2015 Xiong et al. 2019 - SCALE method for single-cell ATAC-seq analysis via latent feature extraction
# Take-home guidelines
- Be wary of consensus peaks as the size/diversity in your data increases.
- Count-based statistical approaches for other methods are applicable
- For footprinting, make sure you understand the caveats
- Expect lots of new developments in the near future
# Investigating regions of interest
ROI -> insight
- Top hits
- Distribution across chromosomes
- Distribution of annotated features (genes, enhancers, etc)
- Motif analysis
- Enrichment analysis
# Enrichment analysis for regions of interest
- GREAT: assigns biological meaning to a set of non-coding genomic regions by analyzing the annotations of the nearby genes.
- BART: predicting functional factors (including transcription factors and chromatin regulators) that bind at cis-regulatory regions
- LOLA: enrichment of overlap between a user-provided query region set (a bed file) and a database of region sets.
[Awesome list of ATAC-seq analysis tools](https://github.com/databio/awesome-atac-analysis)