Similarly to the results obtained on healthy tissues from Roadmap Epigenomics project, that I described in chapter 5, peakcalling on H3K27me3 ChIP-seq using HERON yielded much longer peaks than using SICER or MACS (see gure 6.2). Analyses of the expres-sion of genes that overlap the identi ed peaks suggest that all three peakcallers return reliable results, i.e. in all the cases the mean expression of those genes was signi cantly lower than the mean expression of all the genes ( gure 6.3). As in this chapter more sam-ples were available than in the case of Roadmap Epigenomics data, I could additionally identify genes that seem to have H3K27me3 modi cation only in a subset of patients; I checked whether in such cases the presence or absence of the modi cation can be used as a predictor of expression level. Indeed, the methylated genes tended to have lower expression than those without the modi cation (see gure 6.5).
Interestingly, as far as the peakcalling on multiple samples is concerned, I obtained dif-ferent results from those in the previous chapter. In chapter 5, peakcalling on multiple samples yielded shorter peaks, and the expression of peaks overlapping those peaks was lower than in the case of peakcalling on a single sample. Here the results were similar only as far as the peak length is concerned. The expression of genes that overlap peaks called using multiple les tended to be similar or even higher than the expression of those that overlap peaks called using a single sample. It might be due to the characteristics of the analysed samples - cancer tissues tend to be much more variable and have more aberrant expression than healthy ones, so assessing results of peakcalling merely based on expression might yield misleading conclusions.
In this chapter I also showed that the negative binomial distribution seems to t to the experimental data better than Gaussian distribution. The results of the peakcalling, however, suggest there is no obvious gain from using it. It may stem from the fact that
on peaks outside of peaks
patient ID
Wasserstein distance
Kullback-Leibler divergence Wasserstein distance
Kullback-Leibler divergence
NB Gaussian NB Gaussian NB Gaussian NB Gaussian
PA01 1.748 7.856 0.061 0.414 0.108 0.598 0.04 7.266
PA02 1.337 1.688 0.226 0.244 0.101 0.728 0.025 6.414
PA03 1.236 5.928 0.046 0.36 0.09 0.525 0.04 7.154
PA04 1.209 6.054 0.048 0.346 0.116 0.63 0.05 7.388
PA05 1.15 5.96 0.044 0.36 0.12 0.583 0.049 7.935
PA06 0.884 4.46 0.04 0.3 0.1 0.539 0.041 8.093
PA07 0.9 4.446 0.043 0.32 0.133 0.685 0.046 7.068
DA01 0.69 3.403 0.034 0.228 0.123 0.55 0.052 8.445
DA02 0.345 0.676 0.059 0.107 0.18 0.626 0.083 8.552
DA03 1.165 5.089 0.053 0.35 0.063 0.439 0.03 8.352
DA04 0.81 3.863 0.032 0.276 0.072 0.443 0.038 8.993
DA05 1.494 5.977 0.053 0.361 0.068 0.43 0.036 8.765
DA06 1.363 6.303 0.046 0.364 0.13 0.742 0.055 7.23
GB01 1.535 7.27 0.053 0.395 0.118 0.658 0.049 6.978
GB02 1.217 6.521 0.05 0.38 0.209 0.853 0.077 6.745
GB03 1.027 5.084 0.05 0.325 0.223 0.766 0.09 6.919
GB04 0.509 3.456 0.026 0.281 0.016 0.284 0.004 9.581
GB05 0.321 2.054 0.023 0.19 0.163 0.642 0.09 7.539
GB06 0.409 2.452 0.025 0.222 0.187 0.718 0.076 7.8
GB07 0.868 4.743 0.041 0.327 0.172 0.767 0.077 7.118
GB08 1.441 6.481 0.052 0.367 0.12 0.621 0.055 6.9
GB09 0.276 0.959 0.015 0.11 0.178 0.702 0.092 8.193
GB10 0.782 4.189 0.045 0.296 0.219 0.724 0.113 8.28
PG11 0.492 1.97 0.029 0.126 0.197 0.586 0.96 9.09
Table 6.3: Accuracy of t for two distributions: Gaussian and negative binomial ("NB" in the header), for data from H3K4me3 peaks and from outside of peaks for every patient.
For both measures the smaller the value, the better the t; in every pair of columns the smaller value is bolded. As one can see, it is always for the negative binomial distribution.
the negative binomial distribution is more di cult to t, and using it can cause various issues with parameters' estimation and algorithm's convergence.
Chapter 7
Summary
Development of High Throughput Sequencing (or Next-Generation Sequencing) and its raising availability at the 2000s opened a lot of new possibilities. During the last twenty years multiple various experimental protocols basing on these methods were developed, like ChIP-seq, ATAC-seq, RNA-seq, CUT&tag or Hi-C. Thanks to their high throughput, they allowed examining various phenomena in the living cells on a huge scale; for exam-ple expression of genes or localisation of proteins bound to DNA. Many projects that collect data from such experiments were launched, probably the most notable of which is ENCODE - Encyclopedia of DNA Elements [15] [13], but also EnhancerAtlas [20], FANTOM (Functional ANnoTation Of the Mammalian genome) [44] [43], TCGA (The Cancer Genome Atlas) [75], Roadmap Epigenomics [37], ENPG (Encyclopedia of Plant Genome) [17] and countless more. NGS-based experiments tend to produce a lot of data that require further downstream analyses; this in turn caused a need to create many new tools designed to work with such data. Many of such tools were created, sometimes as a part of the projects mentioned above; some of them are designed to work only with some speci c type of data, some are meant to work equally well with any type of input, however in practice there always are cases in which these supposedly universal tools tend to fail. As a result, even though there are more and more tools created every year, there is still an ongoing need to develop and implement new ones.
In this dissertation, I've described several methods of performing peakcalling - a proce-dure aiming at discovering enrichment in signal from various NGS-based experiments.
I've shown that especially for very long and poorly enriched domains the existing tools often give unsatisfactory results. I've presented a new tool called HERON, that uses Hidden Markov Models with continuous emissions, from either Gaussian or negative bi-nomial distribution. HERON proved to work better than other tested peakcallers under the conditions described above. I've shown it on experimental data from two dierent published projects [37] [70], speci cally from ChIP-seq experiment against H3K27me3 modi cation performed on healthy and cancer human tissues.
Additionally, I've developed a package for simulating data from NGS-based experiments;
it allows assessing in a controlled way how various data features in uence peakcalling.
In particular, using this package I've shown for dierent tested peakcallers how quality of the obtained results depends on the width of the sought peaks and their enrichment.
The conclusions drawn from these simulations support the ones drawn from analysing experimental data - HERON outperforms other peakcallers when the data is poorly enriched and the peaks are long. Furthermore, the analyses show the importance of choosing the right tool for dierent types of data. I've tested four tools on a broad range of parameters regarding data quality and characteristics; as it turned out, various tools give satisfactory results only within some narrow subspace of parameters. While it is tempting to use just one tool for every kind of data, these simulations prove that in many cases such approach will yield poor and biased results.
The peakcaller HERON was published in 2021 in International Journal of Molecular Sciences [46]. Both HERON and the package for data simulation are publicly available on GitHub under beerware licence (see https://github.com/maciosz/HERON and https:
//github.com/maciosz/NGS_simulation).
Acknowledgements
The results presented in this dissertation were obtained as part of a grant "An atlas of brain regulatory regions and regulatory networks a novel systems biology approach to pathogenesis of selected neurological disorders" funded by National Science Centre in Poland (DEC-2015/16/W/NZ2/00314).
Figures 2.2 and 2.3 are reprinted from papers [87] and [69] respectively, where they are published under Creative Commons license. All other gures were created by me, using Inkscape [28], Integrated Genome Browser [54] and JBrowse [68] (for screenshots from a genome browser) and ggplot2 library [83] for R (for plots).
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