ATAC-Seq Analysis

Methods

ATAC-seq samples, consisting of two replicates from a human source, were subjected to a comprehensive analysis pipeline. The analysis commenced with quality control (QC) and adapter trimming using FastQC (v0.12.0) and Trimmomatic (v0.39), respectively. The reads were then aligned to the human reference genome (hg38) using Bowtie2 (v2.5.3), with the -X 2000 flag utilized to optimize alignment efficiency. To ensure data integrity, alignments to the mitochondrial chromosome were filtered out using SAMtools (v1.19.2). Additionally, to mitigate bias introduced during the tagmentation process, a read-shifting step was performed using deeptools (v3.5.6). Following read processing, fragment size distributions were assessed using ATACSeqQC using the atacseqqc.R script for quality control evaluation. Peak calling was conducted separately for each replicate using MACS3 (v3.0.1) with default parameters tailored for ATAC-seq data. To generate a set of reproducible peaks, peaks called from individual replicates were intersected using the intersect function in bedtools (v2.31.1). Furthermore, peaks falling within blacklisted regions were filtered out to enhance the reliability of the dataset. Peak annotation was performed using HOMER (v4.11) to associate peaks with nearby genes and genomic features with their proportions being calculated and visualized using the Unique_proportions.R script. Motif analysis was conducted on the reproducible peaks using MEME Suite (v5.5.5) to identify enriched sequence motifs. Lastly, signal coverage plots centered on the transcription start site (TSS) were generated for nucleosome-free regions (NFR) and nucleosome-bound regions (NBR) with PlotProfile from deeptools.

Questions addressed in Final Report.Rmd file

Quality of the sequencing reads and the alignment statistics, specifically mentioning the following:

• Any concerning aspects of the quality control of your sequencing reads?
• Any concerning aspects of the quality control related to alignment?
• Based on all of your quality control, are there any samples to be excluded from further analysis?

After alignment, the following were calcualted:

• Total number of alignments per sample
• Number of alignments against the mitochondrial genome

After performing peak calling analysis, a set of reproducible peaks were produced and filtering of peaks from blacklisted regions was done. The following data were calculated and listed:

• How many peaks are present in each of the replicates?
• How many peaks are present in your set of reproducible peaks? What strategy was used to determine “reproducible” peaks?
• How many peaks remain after filtering out peaks overlapping blacklisted regions?

After performing motif analysis and gene enrichment on the peak annotations:

• Main results of both of these analyses
• What can chromatin accessibility let us infer biologically?

Deliverables

1. Fragment length distribution plot for each of the samples

2. A table of how many alignments for each sample before and after filtering alignments falling on the mitochondrial chromosome

3. Signal coverage plot centered on the TSS for the nucleosome-free regions (NFR) and the nucleosome-bound regions (NBR)

   • I have considered fragments (<100bp) to be those from the NFR and the rest as the NBR.

4. A table containing the number of peaks called in each replicate, and the number of reproducible peaks

5. A single BED file containing the reproducible peaks determined from the experiment.

6. A single table / figure with the most interesting results from the Motif Finding analysis

7. A single table / figure with the most interesting results from gene enrichment analysis on the annotated peaks

8. A figure displaying the proportions of regions that appear to have accessible chromatin called as a peak (Promoter, Intergenic, Intron, Exon, TTS, etc.)

References

1. https://github.com/CebolaLab/ATAC-seq
2. https://github.com/BioinfGuru/memeMotifs?tab=readme-ov-file
3. Mölder F, Jablonski KP, Letcher B et al. Sustainable data analysis with Snakemake [version 1; peer review: 1 approved, 1 approved with reservations]. F1000Research 2021, 10:33 (https://doi.org/10.12688/f1000research.29032.1)
4. Yan, F., Powell, D.R., Curtis, D.J. et al. From reads to insight: a hitchhiker’s guide to ATAC-seq data analysis. Genome Biol 21, 22 (2020). https://doi.org/10.1186/s13059-020-1929-3
5. Andrews, S. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data [Online]. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/
6. Bolger, A. M., Lohse, M., & Usadel, B. (2014). Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics (Oxford, England), 30(15), 2114–2120. https://doi.org/10.1093/bioinformatics/btu170
7. Langmead, B., & Salzberg, S. L. (2012). Fast gapped-read alignment with Bowtie 2. Nature methods, 9(4), 357–359. https://doi.org/10.1038/nmeth.1923
8. Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics (Oxford, England), 25(16), 2078–2079. https://doi.org/10.1093/bioinformatics/btp352
9. Ramírez, F., Dündar, F., Diehl, S., Grüning, B. A., & Manke, T. (2014). deepTools: a flexible platform for exploring deep-sequencing data. Nucleic acids research, 42(Web Server issue), W187–W191. https://doi.org/10.1093/nar/gku365
10. 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.
11. Zhang, Y., Liu, T., Meyer, C.A. et al. Model-based Analysis of ChIP-Seq (MACS). Genome Biol 9, R137 (2008). https://doi.org/10.1186/gb-2008-9-9-r137
12. Heinz, S., Benner, C., Spann, N., Bertolino, E., Lin, Y. C., Laslo, P., Cheng, J. X., Murre, C., Singh, H., & Glass, C. K. (2010). Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Molecular cell, 38(4), 576–589. https://doi.org/10.1016/j.molcel.2010.05.004
13. Timothy L. Bailey, James Johnson, Charles E. Grant, William S. Noble, The MEME Suite, Nucleic Acids Research, Volume 43, Issue W1, 1 July 2015, Pages W39–W49, https://doi.org/10.1093/nar/gkv416
14. Yadon, A. N., Van de Mark, D., Basom, R., Delrow, J., Whitehouse, I., & Tsukiyama, T. (2010). Chromatin remodeling around nucleosome-free regions leads to repression of noncoding RNA transcription. Molecular and cellular biology, 30(21), 5110–5122. https://doi.org/10.1128/MCB.00602-1