- For a full and comprehensive list of resources see
Overview of experimental and computational considerations for ATAC-seq analysis - good but a bit brief in places, doesn't go into alot of bioinformatic detail link
An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues nature methods paper:
- dounce homogenise
- density gradient centrifugation
Omni-chip - imporoved method - used for human brain slices, do nuclear extract https://www.nature.com/articles/nmeth.4396
esATAC
Wei Z, Zhang W, Fang H, Li Y, Wang X (2018). “esATAC: an easy-to-use systematic pipeline for ATAC-seq data analysis.” Bioinformatics. doi: 10.1093/bioinformatics/bty141. https://www.bioconductor.org/packages/release/bioc/html/esATAC.html
- esATAC easy to use ATAC-seq pipeline - wraps alot of other R packages in an easy to use workflow - from what I see it tend to merge all fastqc of a partivcular replicate at the beginning and then carries them downstream
- does not include IDR
- uses chipseeker for annotation
- uses enrichGO from clusterprofiler for GO enrichment
- also uses motif matcher
- does footprinitng and cutsite analysis
- generates cutsites and filters nucleosome free fragments <100bp
- uses F-seq for peakcalling
Chipseeker
- annotate peaks
- compare replicates
- coverage plot over chromosomes
ATACSeqQC
Ou J, Liu H, Yu J, Kelliher MA, Castilla LH, Lawson ND, Zhu LJ (2018). “ATACseqQC: a Bioconductor package for post-alignment quality assessment of ATAC-seq data.” BMC Genomics, 19(1), 169. ISSN 1471-2164, doi: 10.1186/s12864-018-4559-3, https://doi.org/10.1186/s12864-018-4559-3.
Generates QC plots for ATAC-Seq samples
- IGV snapshot function of specific regions
- library complexity - distinct fragments versus putative sequenced fragments
- frag size distribution & nucleosome phasing plot
- shift reads for peakcalling and footprinting
- coverage of promoter/coverage of transcript body
- NFR score - ratio between cut sifnal adjacent to TSS:
- nucleosome-free, mononucleosome, di nucleosome, trinucleosome fractions
- uses random forest to classify fragments
- top 10% reads <100bp = nucleosome free
- top 10% of reads 180-247bp = mononucleosomes
- this is the training set to classify the rest using randon forest - number of the three is 2*sqrt(len(training_set))
- conservation score can also be inclused
- uses random forest to classify fragments
- heatmap and coverage curve for nucleosome possitions
- average signal across all active TSSs
- nuc free fragements enriched at TSS
- nucleosome bound fragments at upstream and downstream of TSS -factor footprints does not take conservation into account (centipede = an alternative)
- average signal across all active TSSs
- V plot - distance to binding sites
- Blacklist paper - https://www.nature.com/articles/s41598-019-45839-z
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HINT-ATAC https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1642-2
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centerpede: takes PhyloP into account
Basic experimental and computational atac-seq overview https://informatics.fas.harvard.edu/atac-seq-guidelines.html