Transcriptional Profiling of Double Stranded Break-Protected Regions Flanking rDNA in Saccharomyces cerevisiae

INDEX

ABSTRACT ……………………………………………………………………….. 1

RESEARCH QUESTION …………………………………………………………. 1

HYPOTHESIS …………………………………………………………………….. 1

BACKGROUND ………………………………………………………………….. 1

MOTIVATION ………………………………………………………………….. 1

DATA SOURCES ………………………………………………………………… 2

METHODS ……………………………………………………………………….. 2

READ PROCESSING

IDENTIFY GENES OF INTEREST / SAMPLING ANALYSIS

EXPECTED RESULTS ………………………………………………………….. 3

CODE/RESULTS………………………………………………………………….. 3

SAMPLING ANALYSIS

VISUALIZE SAMPLING DISTRIBUTIONS DURING VEGETATIVE GROWTH

VISUALIZE SAMPLING DISTRIBUTION DURING EARLY MEIOSIS

VISUALIZE TIME-RESOLVED GENE EXPRESSION

GENE ONTOLOGY ANALYSES

GSEA ON REGION OF INTEREST

GSEA ALONG CHROMOSOME XII

GSEA ON CHROMOSOME XII USING UNIDIRECTIONAL DISTANCE

SPO11 MUTANT ANALYSIS

DISCUSSION/CONCLUSSION ………………………………………………….. 19

LIMITATIONS AND ALTERNATIVE INTERPRETATIONS ………………………… 19

REFERENCE AND CITATIONS ………………………………………………….. 20

Abstract:

Proper meiotic progression requires genotoxic double-strand DNA breaks (DSBs) to occur after DNA replication. During meiosis, DSBs are suppressed across the repetitive ribosomal DNA (rDNA) region on chromosome XII in Saccharomyces cerevisiae. Interestingly, the 50 kb regions upstream and downstream of rDNA are also protected from DSBs (Vader et al 2011). Our goal is to compare the transcriptional activity of these 50 kb regions flanking rDNA to the rest of the genome to determine if they differ during meiosis. We also seek to determine if the genes within these regions have a shared function related to meiosis. Together, these could explain why this genomic region is protected from DSBs during meiosis.

Research Question:

Does the transcriptional activity of the regions 50kb upstream and downstream of rDNA differ from the rest of the genome during meiosis?

Hypothesis:

We hypothesize that the genes inside the flanking rDNA will display increased transcript abundance (TA) during meiosis compared to genes inside the non-protected regions of the genome. In addition, flanking rDNA will share a biological function related to meiosis.

Background:

Meiosis is a specialized type of cell division where the genome duplicates, gets condensed, and experiences double-strand breaks (DSB). These biological hallmarks in meiosis constitute dramatic changes the genome must encounter to ensure proper meiotic progression. Errors during meiosis can result in miscarriage, infertility, and developmental pathologies.

During prophase I, the topoisomerase-like Spo11 generates DSB and is involved in the first step towards meiotic recombination. DSBs are fundamental for cells to proceed with meiotic division. Also, Spo11 forms DNA-protein-driven condensates across the meiotic axis with the help of axis proteins and DSB factors. Nevertheless, Spo11 targets distinct genomics segments defined as hotspots for nuclease attack. A set of genomic regions possessed long-lived DSB (Subramanian et al., 2017) and were defined as ear-adjacent regions (EARs). These regions englobe sub-telomeric regions and the borders of the rDNA. However, the regions adjacent to the rDNA do not exhibit a higher presence of total double-strand break compared to the rest of the genome (Vader et al., 2011). The introduction of DSB towards ribosomal genes provokes transient transcriptional repression (Kruhlak et al., 2007). However, the effects of DSB protection in the regions flanking rDNA are unknown; thus, we seek to characterize the transcriptional activity of DSB-protected regions flanking the rDNA.

Motivation:

An understanding of the transcriptional effects of DSB protection could have meaningful applications for genetic modification. For example, if preventing DSBs near a given gene enhances its gene expression, then inserting a gene into a DSB-protected region may represent a novel mechanism for over-expressing a gene/maintaining the activity of this gene during meiosis.

Data sources:

Brar et al: Riboseq and RNA-seq in vegetative and meiotic growth phases.

Brar GA, Yassour M, Friedman N, Regev A, Ingolia NT, Weissman JS. High-resolution view of the yeast meiotic program revealed by ribosome profiling. Science. 2012;335(6068):552-557. doi:10.1126/science.1215110

spo11 mutant RNA-seq Analysis: (Funda Kar, unpublished; meiotic growth phase: prophase).

Methods:

Read Processing:

FASTQC quality filtered RNA-seq reads will be aligned to genes in the SGD SK1 strain reference genome with HISAT2 and counted with HT-seq to generate Transcript Abundance (TA) data. The Brar et al dataset contains single end reads while the Spo11YF dataset contains paired end reads. Both will be aligned to the same genome and counted using the same gff file. We will also align these fastq files to the randomly sampled contigous regions described below.

Identify Genes of Interest/Sampling Analysis

We will first identify all the genes within the 50kb regions upstream and downstream of rDNA. We will then randomly sample 1000 contiguous 100-kb regions from each S.c chromosome and extract the names of the genes present within each of these 100 kb regions. This will result in a library of 16,000 (1000 samplings per chromosome X 16 chromosomes) lists of genes, each list representing the genes present within a random contiguous 100 kb region of the genome. With these samplings, we will calculate the mean TA of each gene list to generate a sampling distribution of TA, representing the genome wide transcriptional activity of random 100 kb regions. We will compare the mean TA of the actual r-DNA flanking regions to the corresponding sampling distributions by calculating Z-scores. Separately, we will prepare GTF files containing the randomly sampled regions and align count reads using these new GTFs, which will allow us to compare transcriptional activity at the 100kb and 50kb resolutions.

Gene Set Enrichment/Gene Ontology Analysis

              To determine if there is a shared function among the genes of interest, we will run gene ontology on the genes contained within the ProtecDSB and query for enriched biological processes. Also, we will complete a gene-set enrichment analysis using distance from rDNA to rank the genes on chromosome XII, which could tell us that as genes get closer to the rDNA (more protected from SPO11) they become more enriched for a given process.

Expected Results:

              We expect the TA and TE of the genes in ProtecDSB to be higher during meiosis compared to the rest of the genome. We would expect to see this increased activity in meiotic samples, but not in samples taken from non-meiotic, exponentially growing cells. We expect the genes within the rDNA-flanking regions to be enriched for processes related to meiosis.

Code/Results

Sampling Analysis

###Read in the GTF file, subset genes, and cut excess annotation around the gene names. 
#Genome and corresponding GTF for the SK1 strain were obtained from ##https://yjx1217.github.io/Yeast_PacBio_2016/data/
GTF <- read.table("SK1.all_feature.gff") 
GTF<-subset(GTF,V3 == "gene") 
GTF$V9<-gsub(".*Name=", "", GTF$V9)
##Split GTF by chromosome number into separate data frames, then extract the second gene coordinate column (end of the gene, which we use to determine the end of the last gene on the chromosome)
data_list <- split(GTF, f = GTF$V1) 
#rDNA is 451575 to 468931 on chromosome 12
rDNA_Start = 451575
rDNA_Stop = 468931
###Extract genes from within the regions of interest. V4 is the start coordinate of each gene in the GTF, so this is obtaining genes that have a start coordinate within the regions of interest. 
GOI_Region<-rbind(subset(data_list[["chrXII"]], V4 > rDNA_Start-50000 & V4 < rDNA_Start),subset(data_list[["chrXII"]], V4 > rDNA_Stop & V4 < rDNA_Stop+50000))
Downstream<-subset(data_list[["chrXII"]], V4 > rDNA_Start-50000 & V4 < rDNA_Start)
Upstream<- subset(data_list[["chrXII"]], V4 > rDNA_Stop & V4 < rDNA_Stop+50000)
rDNA<- subset(data_list[["chrXII"]], V4 > rDNA_Start & V4 < rDNA_Stop)
write.csv(GOI_Region, file = "GOI.csv")

write.csv(data_list[["chrXII"]], file = "Chrom12.csv")
#Define ranges that we will randomly select from. Since I am adding and subtracting 50kb to each randomly sampled number to generate a contiguous 100kb fragment, I am subtracting 50 kb from the end of each chromosome and starting the sampling at 50,000 so the contigs don't exceed chromosome boundaries. We are considering the end of the last gene the end of the chromosome for sampling, so we aren't sampling regions that don't contain genes. 
Ranges<- as.data.frame(lapply(data_list,"[",5,drop=FALSE) %>% sapply(max) %>% -50000)
Ranges$Start<- 50000 
names(Ranges)[1] <- "Stop"
###Define Sampling size (N, per chromosome), make a list object to store the random samplings in.
N = 1000
Range_Lists <- list("any", N*length(Ranges$Stop))
###Randomly sample a number within range for each data frame (chromosome), add and subtract 50k to each number, remove the middle number so you are left with the start and stop of a 100 kb contiguous region, and store that sampling in a list of lists separated by chromosome. here, b = chromosome 
for(b in seq(1,length(Ranges$Stop))) {
DF<-as.data.frame(sample(Ranges[1,2]:Ranges[b,1], N, replace=FALSE))
DF$Start <- DF[,1] -50000 
DF$Stop<- DF[,1] +50000 
DF[,1] = NULL
Range_Lists[[b]]<-DF
}
###Remoove the last DF that was saved and add chromosome names as list names in the ranges object. I confirmed that the order is consistent between files. 
rm(DF)
names(Range_Lists) <-names(data_list)
####Generate a result object to store data in: Loop one, which takes the sampling size(N), and multiplies it by the number of genes on each chromosome so the output list file is the correct size for X number of genes by N number of samplings per chromosome. 
#Loop 2: Take the GTF separated into a list by chromosome (Data_List). For each chromosome, ask if the Start coordinate of each gene falls within the randomly sampled ranges in Range_list for that same chromosome, and if it does, return the gene names of the genes within that given sampling. Then repeat for each sampling in that chromosome and move on and repeat for each chromosome. This results in N number of gene lists per chromosome, representing the genes present, in order, in that random 100 kb fragment.
result = NULL
for(i in seq(1,length(Range_Lists))) {
result[[i]] <- list("any", N*length(data_list[[i]]$V4))
for(L in seq(1,N)) {
result[[i]][[L]]<-ifelse(data_list[[i]]$V4 %in% (Range_Lists[[i]][L,1]:Range_Lists[[i]][L,2]),data_list[[i]]$V9, NA)
}
}
###Turn the nested list into one long "library", that isnt separated by chromosome. We will now randomly sample from this list. 
library<-lapply(rapply(result, enquote, how="unlist"), eval)
###This library gets used later on in the KB_Dist Loop, which takes the RNA-count matrix from Brar et al transcriptomes and subsets counts for a given time point, and then returns a distribution of the mean gene-level transcript abundance per 100kb fragment with which you can compare the mean of our fragment of interest to. 

After generating separate lists of random 100 and 50 kb fragments, we were able to analyze data using two approaches. For one, a separate loop was written (KB_Dist) that takes the library above and plots the mean gene-level transcript abundance for all random fragments at a given time point in the Brar et al analysis, and allows one to plot the mean of our region of interest against the gene-level sampling distribution. We use gene-level when we refer to the count matrix generated by aligning/counting reads with the gene annotations in the SK1.all_feature.gff (Brar_SK1_RNA_Counts.xlsx). A second approach was to make a GTF out of the random fragments generated above and align reads to these entire random fragments. We make the GTF below; Pseudo_GTF_100 contains 100kb fragments while Pseudo_GTF_50 contains 50kb fragments. We then re-counted reads using these new GTFs (100kb_Aligments_With_Goi.xlsx and Brar_50kb_RNA_Counts.xlsx). We annotate as “with GOI” with the first because we included annotation for the regions of interest in that file, thus the results contain counted reads to the entire 50kb upstream and downstream regions, as well as the entire 100kb region of interest. We include the KB_Dist loop and an example plot, but focus on the second approach for subsequent analyses. As expected, the results are largely the same.

Visualize Sampling Distributions During Vegetative Growth

              There appears to be higher transcriptional activity in our 100kb region of interest compared to the distribution of randomly sampled 100 kb fragments. Interestingly, when analyzed as independent 50 kb regions, the increase in transcriptional activity is exclusively in the upstream region. The z-score indicates the upstream 50kb region had significantly greater read alignment compared to the rest of the genome (p = 0.0000071). We are specifically interested in the activity of this region during early meiosis however, so we repeated this analysis below in an early meiotic time point.

Visualize Sampling Distributions During Early Meiosis

During Meiosis, the upstream region again appears to have higher transcriptional activity. This would suggest that DSBs inhibit gene expressoin. We wanted to visualize this on the gene level, as we hypothesized that there may be a relationship between gene expression and distance to rDNA. What we found however is that looking at the 100kb resolution was not adequate. Only select genes were highly induced, while the rest of the region appeared to be silenced just like the rDNA. The plot on the left shows the distributions of the sum of reads aligned to entire 100 kb fragments, genome wide. The plot on the right shows the expression of individual genes, in the order they appear in our region of interest. We plotted these data in a time resolved manner to show that when there are genome wide fluctuations in gene expression, these fluctuations do not occur in our region of interest, except for a few genes in the upstream region. For more inforamtion on how different stages correspond to different meiotic time points, please refer to the subsequent annotation gif.

Visualize Time-Resolved Gene Expression

There are genome wide fluctuations in gene expression (100 kb level, left plot) that do not occur in our region of interest (gene level, right plot) Opening the RMD html in a web browser will synchronize the GIFs. This tells us that there is some transient high expression of a few genes in the upstream region during early meiosis, however most of the region remains transcriptionally silenced. The figures below help show which stage corresponds to which part of meiosis. The stage annotation information was obtained from the supplemental data of Brar et al (Supplemental Figure 1B). The figure below on the left was copied from their supplemental data, and not created by us.

Together these suggest that the 50kb regions flanking rDNA are transcriptionaly silenced throughout meiosis, with a few exceptions. There are large genome-wide fluctuations in gene expression during the later stages of meiosis that do not occurr in our region of interest. We next wanted to better understand the function of these genes. Since only a select few genes in the upstream are active, we hypothesized they may have a shared function that could justify their transient induction.

Gene Ontology Analyses

Gene Ontology was used to determine if the genes in the regions flanking rDNA are enriched for any specific biological processes. The gene ontology tool on SGD was used with an FDR correction to study each side as an independent 50kb region. https://www.yeastgenome.org/goTermFinder

The upstream 50kb region is enriched for phosphatidylcholine biosynthesis and is weakly enriched for rRNA processing. Also, almost all of the ORF products coded from genes in the upstream region are translocated to the cytoplasm. We next looked for enrichment in the downstream 50kb region. The downstream 50kb region is enriched for heterocycle catabolic process. These genes, along with organic cyclic compound catabolism and aromatic compound catabolism (all of which were the same gene lists) are all present in the 50kb downstream region, thus are silenced throughout meiosis. The compounds made from these processes may have some inhibitory effects on cell growth/meiosis. We took this information and color coded the gene expression profile by gene ontology, which showed that most of the genes induced during meisois in our region of interest are involved in rRNA processing. The heterocycle catabolism genes in the downstream region are silenced throughout meiosis.

This shows that the region is silenced, except for genes involved in rRNA processing. We also noticed at early stages (6-8), there appeared to be a relationship between gene expression and distance from rDNA. It appeared that as rRNA processing genes were closer to rDNA, their gene expression became higher. This led us to repeat te gene ontology analysis, using distance to tank genes. The first analysis, shown below, takes just the genes within out 100kb region of interest and ranks them by their start coordinate on chromosome 12. Iniitially, every time I would run this analysis I would get a different result, even with setting seed = TRUE. To fix this, I set nPerm to 1000000 and it seems to have stabilized the results. There are no results if we use a pAdjustment method, meaning our results represent only mediocre enrichment.

GSEA on Region of Interest

organism = "org.Sc.sgd.db"
library(organism, character.only = TRUE)
Chrom12<-read.csv("Chrom12_anno.csv")
df = subset(Chrom12, V9 %in% GOI_Region$V9)
#instead of inputting log 2 FC I am putting location. Each number is the location of the start coordinate of the gene *10-6. This only includes the genes within our region of interest. 
original_gene_list <- df$adjust
names(original_gene_list) <- df$V9
gene_list<-na.omit(original_gene_list)
gene_list = sort(gene_list, decreasing = TRUE)

organism = org.Sc.sgd.db
gse1 <- gseGO(geneList=gene_list, 
             ont ="ALL", 
             keyType = "ORF", 
             nPerm = 1000000, 
             minGSSize = 3, 
             maxGSSize = 50, 
             pvalueCutoff = 0.1, 
             verbose = TRUE, 
             seed = TRUE,
             OrgDb = organism, 
             pAdjustMethod = "none")

ridgeplot(gse1) + labs(x = "Chromosome Location (Nucleotide Coordinates along Chromosome XII:Gene Start Coordinate X e-6)")

Here, enrichment distribution shows enrichment along the region of interest, with rDNA at the center (~0.046). Cellular Anatomical Entity, cytoplasm, and intracellular all appear to be enriched around rDNA, but not at the actual rDNA region. There are distinctive gaps where the rDNA region is within these enrichment lines. A few other terms also appear to become more enriched as they become closer to rDNA, for example macromolecule catabolic process and regulation of molecular function. While interesting, we felt this analysis may be limited by only including genes within our region of interest, so we repeated this analysis using all genes on chromosome 12. This time, instead of just supplying their start coordinates, we calculated the distance of each gene from rDNA to rank. Each number is the distance of a gene from either the start or stop coordinate of rDNA, depending on which side of the rDNA the gene is on. We then made all downstream genes negative to keep the two sides distinct.

GSEA Along Chromosome XII

Enrichment Distribution represents distance from rDNA, with negative values representing distance of genes downstream of rDNA and positive enrichment representing distance of upstream genes. This generated slightly different results than the first analysis, indicating distance from rDNA may relate to gene function. As genes downstream of rDNA get closer to rDNA, they become more enriched for membrane related terms. As genes upstream of rDNA become closer to rDNA, they become more enriched for cell wall organisation/cell component size regulation. There may be a relationship between these processes and gene distance to rDNA. However if the relationship between distance to rDNA and gene function is based on transcription, then the side of the chromosome that each gene is on should not matter. We predicted this relationship would be based on a function of distance and gene expression, as opposed to downstream/upstream orientation. Thus we repeated this analysis using the absolute value of each genes distance from rDNA to rank.

GSEA on Chromosme XII Using Unidirectional Distance

Enrichment distribution here represents distance from rDNA (0.00), regardless of which side of the rDNA a gene is on. As expected, there is enrichment based on distance to rDNA, regardless of orientation. In most situations, as genes get further away from the transcriptionally silenced region, they become more enriched for cell metabolism related terms. Interestingly, two new GO processes appear enriched as they near the rDNA: Nuclease activity and golgi membrane. These results suggest that distance from rDNA is a factor in gene function on chromosome 12. A more sophisticated analysis based on gene expression with multiple hypothesis testing corrections would be needed to confirm this. Still, since as genes get closer to the transcriptionally silenced rDNA and 50kb flanking regions, they become more enriched for specific processes, we hypothesize the expression of genes related to these enriched processes are meant to be silenced during early meiosis.

Considering the regions exposed to SPO11 have higher gene expression compared to the regions protected from SPO11, we predicted that SPO11 acts to promote gene expression. Funda Kar previously generated RNA-seq data including WT and a SPO11 mutant during prophase in the SK1 strain. Funda was generous enough to share bam files representing the alignment of these libraries to the SK1 genome. We counted these alignments using the SK1 GTF with HT-seq and compared global gene expression between WT and SPO11.

The results support our hypothesis that SPO11 promotes gene expression by showing there is a global decrease in gene expression in the SPO11 mutant compared to WT. Using a T-Test we show a transcriptome-wide difference, indicating the gene expression populations are different. We also analyzed these data using a paired T-test which tested to see if the gene-level changes between WT and spo11 were significant. An ANOVA was also used to confirm that there was a significant difference between WT and the spo11 mutant, which was a similar analysis to the unpaired t-test. Regardless of how we analyze the data, gene expression in the spo11 mutant is significantly decreased compared to WT.

SPO11 Mutant Analysis

Counts<-read.xlsx("Spo11_Counts.xlsx", rowNames =  TRUE)
Counts$WT_Mean <- rowMeans(Counts[1:3])
Counts$Spo_Mean <- rowMeans(Counts[4:6])
t.test(Counts$WT_Mean, Counts$Spo_Mean)$p.value

## [1] 0.00026066

t.test(Counts$WT_Mean, Counts$Spo_Mean, paired = TRUE)$p.value

## [1] 1.02673e-72

Means <- data.frame(
  type = c( rep("WT", length(Counts$WT1)), rep("spo11", length(Counts$SPO1)) ),
  value = c(Counts$WT_Mean, Counts$Spo_Mean) )

summary(aov( value ~ type, data = Means))

##                Df   Sum Sq Mean Sq F value   Pr(>F)    
## type            1   118645  118645   13.34 0.000261 ***
## Residuals   10818 96192001    8892                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

ggplot(Means,aes(y = log2(value), fill = type)) + geom_boxplot() + theme_bw()  +scale_fill_manual(values=c("#E69F00", "#56B4E9"))+
  annotate("text",x=0,y=6,label="p<0.00026")

## Warning: Removed 738 rows containing non-finite values (stat_boxplot).

Discussion/Conclusion

The 100kb region ( 50kb downstream and 50 kb upstream) that defines the boundaries of the rDNA, demonstrated no difference in TA compared to the rest of the genome during vegetative cell cycle. Gene Ontology enrichment analysis demonstrated peptidase regulation which is involved in protein translation. Therefore, we don’t see a transcriptional difference when the 100kb is analyzed combined.

Nevertheless, when rDNA boundaries are analyzed separated ( 50kb sections), significant difference was exhibited in the upstream region (centromere-distal) compared to the downstream region (centromere-proximal) during the vegetative cycle. Likewise, Gene Ontology (GO) enrichment analysis outputted a different result. GO Terms for rRNA processing and cytoplasm localization in the centromere-distal region were exposed in the analysis. All of these vegetative data suggest that the centromere-distal region holds distinct biological functions than the centromere-proximal region. Furthermore, the gene bodies are mostly silenced and suggest that there are other chromatin phenomena occurring inside these regions.

The meiosis time points analysis, exhibited a group of genes involved in rRNA processing expressed during the DNA recombination step, mostly localized in the centromere-distal. These genes had the compartment cytoplasmic annotation, which suggested that during DNA recombination, translation is happening in the cytoplasm. This data is very interesting with high potential for further exploration, since cytoplasmic protein translation would not interfere nuclear meiotic genes.

In addition, we analyzed spo11 transcriptome data from a Hochwagen Lab member and found that there is a reduction of transcription overall the genome. Nevertheless, the rDNA boundaries did not present significant changes in transcription. This result is well integrated with the silencing properties of the rDNA boundaries and would suggest that these regions is not affected by DNA double-strand breaks to not go further into DSB processing. DSB processing directly regulates transcription by positioning RNAPII (Sharma et al. 2021)

These data strongly correlate with the findings in (Paul et., al 2018), where they showed condensin enrichment in the centromere-proximal regions. This suggestS that condensin enrichment correlates with the transcriptional program. Condensin characterizes by synthesizing higher order structure and inducing gene silencing.

Limitations and alternative interpretations

Further experiments would consist on performing spike-in experiments to validate the RNA-seq data. Also, additional experiments would be to excise the boundaries and observe if there is the proximity of the downstream region to the centromeres relates to a regional preferene. CHIP-SEQ data at the Hochwagen lab demonstrated a preference for condensin at pericentromere regions on a spo11 background. Perhaps the centromere-proximal region would serve as a genomic highway to bridge the distribution from pericentromeres to the rDNA resulting in silencing the centromere-proximal region and not the centromere-distal region.

References

1. Subramanian, V. V., Markowitz, T. E., Vale-Silva, L. A., Hochwagen, A., Zhu, X., Keeney, S., San-Segundo, P. A., & Hollingsworth, N. M. (2019). Persistent DNA-break potential near telomeres increases initiation of meiotic recombination on short chromosomes. Nature Communications, 10(1). https://doi.org/10.1038/s41467-019-08875-x
2. Keeney, S., Kleckner, N, & Giroux, C. N. (1997). Meiosis-specific DNA double-strand breaks are catalyzed by Spo11, a member of a widely conserved protein family. Cell, 88(3), 375–384. https://doi.org/10.1016/S0092-8674(00)81876-0
3. Vader, G., Blitzblau, H. G., Tame, M. A., Falk, J. E., Curtin, L., & Hochwagen, A. (2011). Protection of repetitive DNA borders from self-induced meiotic instability. Nature, 477(7362), 115–121. https://doi.org/10.1038/nature10331
4. Kruhlak, M., Crouch, E. E., Orlov, M., Montaño, C., Gorski, S. A., Nussenzweig, A., Misteli, T., Phair, R. D., & Casellas, R. (2007). The ATM repair pathway inhibits RNA polymerase I transcription in response to chromosome breaks. Nature, 447(7145), 730–734. https://doi.org/10.1038/nature05842
5. Brar Gloria A., Yassour Moran, Friedman Nir, Regev Aviv, Ingolia Nicholas T., & Weissman Jonathan S. (2012). High-Resolution View of the Yeast Meiotic Program Revealed by Ribosome Profiling. Science, 335(6068), 552–557. https://doi.org/10.1126/science.1215110 6.Sharma, S., Anand, R., Zhang, X., Francia, S., Michelini, F., Galbiati, A., Williams, H., Ronato, D. A., Masson, J.-Y., Rothenberg, E., Cejka, P., & d’Adda di Fagagna, F. (2021). MRE11-RAD50-NBS1 Complex Is Sufficient to Promote Transcription by RNA Polymerase II at Double-Strand Breaks by Melting DNA Ends. Cell Reports, 34(1), 108565. https://doi.org/10.1016/j.celrep.2020.108565
6. Paul, M. R., Markowitz, T. E., Hochwagen, A., & Ercan, S. (2018). Condensin Depletion Causes Genome Decompaction Without Altering the Level of Global Gene Expression in Saccharomyces cerevisiae. Genetics, 210(1), 331–344. https://doi.org/10.1534/genetics.118.301217

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##     author = {Hadley Wickham and Romain François and Lionel Henry and Kirill Müller},
##     year = {2021},
##     note = {R package version 1.0.5},
##     url = {https://CRAN.R-project.org/package=dplyr},
##   }

citation("openxlsx")

## 
## To cite package 'openxlsx' in publications use:
## 
##   Philipp Schauberger and Alexander Walker (2020). openxlsx: Read,
##   Write and Edit xlsx Files. R package version 4.2.3.
##   https://CRAN.R-project.org/package=openxlsx
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {openxlsx: Read, Write and Edit xlsx Files},
##     author = {Philipp Schauberger and Alexander Walker},
##     year = {2020},
##     note = {R package version 4.2.3},
##     url = {https://CRAN.R-project.org/package=openxlsx},
##   }

citation("readxl")

## 
## To cite package 'readxl' in publications use:
## 
##   Hadley Wickham and Jennifer Bryan (2019). readxl: Read Excel Files. R
##   package version 1.3.1. https://CRAN.R-project.org/package=readxl
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {readxl: Read Excel Files},
##     author = {Hadley Wickham and Jennifer Bryan},
##     year = {2019},
##     note = {R package version 1.3.1},
##     url = {https://CRAN.R-project.org/package=readxl},
##   }

citation("gganimate")

## 
## To cite package 'gganimate' in publications use:
## 
##   Thomas Lin Pedersen and David Robinson (2020). gganimate: A Grammar
##   of Animated Graphics. R package version 1.0.7.
##   https://CRAN.R-project.org/package=gganimate
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {gganimate: A Grammar of Animated Graphics},
##     author = {Thomas Lin Pedersen and David Robinson},
##     year = {2020},
##     note = {R package version 1.0.7},
##     url = {https://CRAN.R-project.org/package=gganimate},
##   }

citation("gifski")

## 
## To cite package 'gifski' in publications use:
## 
##   Jeroen Ooms (2021). gifski: Highest Quality GIF Encoder. R package
##   version 1.4.3. https://CRAN.R-project.org/package=gifski
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {gifski: Highest Quality GIF Encoder},
##     author = {Jeroen Ooms},
##     year = {2021},
##     note = {R package version 1.4.3},
##     url = {https://CRAN.R-project.org/package=gifski},
##   }

citation("magick")

## 
## To cite package 'magick' in publications use:
## 
##   Jeroen Ooms (2021). magick: Advanced Graphics and Image-Processing in
##   R. R package version 2.7.1. https://CRAN.R-project.org/package=magick
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {magick: Advanced Graphics and Image-Processing in R},
##     author = {Jeroen Ooms},
##     year = {2021},
##     note = {R package version 2.7.1},
##     url = {https://CRAN.R-project.org/package=magick},
##   }

citation("DESeq2")

## 
##   Love, M.I., Huber, W., Anders, S. Moderated estimation of fold change
##   and dispersion for RNA-seq data with DESeq2 Genome Biology 15(12):550
##   (2014)
## 
## A BibTeX entry for LaTeX users is
## 
##   @Article{,
##     title = {Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2},
##     author = {Michael I. Love and Wolfgang Huber and Simon Anders},
##     year = {2014},
##     journal = {Genome Biology},
##     doi = {10.1186/s13059-014-0550-8},
##     volume = {15},
##     issue = {12},
##     pages = {550},
##   }

citation("clusterProfiler")

## 
## Please cite G. Yu (2012) for using clusterProfiler. In addition, please
## cite G. Yu (2010) when using GOSemSim, G. Yu (2015) when using DOSE and
## G. Yu (2015) when using ChIPseeker.
## 
##   Guangchuang Yu, Li-Gen Wang, Yanyan Han and Qing-Yu He.
##   clusterProfiler: an R package for comparing biological themes among
##   gene clusters. OMICS: A Journal of Integrative Biology 2012,
##   16(5):284-287
## 
## A BibTeX entry for LaTeX users is
## 
##   @Article{,
##     title = {clusterProfiler: an R package for comparing biological themes among gene clusters},
##     author = {Guangchuang Yu and Li-Gen Wang and Yanyan Han and Qing-Yu He},
##     journal = {OMICS: A Journal of Integrative Biology},
##     year = {2012},
##     volume = {16},
##     number = {5},
##     pages = {284-287},
##     pmid = {22455463},
##     doi = {10.1089/omi.2011.0118},
##   }

citation("enrichplot")

## 
## To cite package 'enrichplot' in publications use:
## 
##   Guangchuang Yu (2020). enrichplot: Visualization of Functional
##   Enrichment Result. R package version 1.8.1.
##   https://github.com/GuangchuangYu/enrichplot
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {enrichplot: Visualization of Functional Enrichment Result},
##     author = {Guangchuang Yu},
##     year = {2020},
##     note = {R package version 1.8.1},
##     url = {https://github.com/GuangchuangYu/enrichplot},
##   }

sessionInfo()

## R version 4.0.4 (2021-02-15)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur 10.16
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] org.Sc.sgd.db_3.11.4        AnnotationDbi_1.50.3       
##  [3] enrichplot_1.8.1            clusterProfiler_3.16.1     
##  [5] DESeq2_1.28.1               SummarizedExperiment_1.18.2
##  [7] DelayedArray_0.14.1         matrixStats_0.58.0         
##  [9] Biobase_2.48.0              GenomicRanges_1.40.0       
## [11] GenomeInfoDb_1.24.2         IRanges_2.22.2             
## [13] S4Vectors_0.26.1            BiocGenerics_0.34.0        
## [15] magick_2.7.1                gifski_1.4.3               
## [17] gganimate_1.0.7             readxl_1.3.1               
## [19] openxlsx_4.2.3              reshape2_1.4.4             
## [21] lubridate_1.7.10            forcats_0.5.1              
## [23] stringr_1.4.0               dplyr_1.0.5                
## [25] purrr_0.3.4                 readr_1.4.0                
## [27] tidyr_1.1.3                 tibble_3.1.0               
## [29] ggplot2_3.3.3               tidyverse_1.3.0            
## 
## loaded via a namespace (and not attached):
##   [1] backports_1.2.1        fastmatch_1.1-0        plyr_1.8.6            
##   [4] igraph_1.2.6           splines_4.0.4          BiocParallel_1.22.0   
##   [7] urltools_1.7.3         digest_0.6.27          htmltools_0.5.1.1     
##  [10] GOSemSim_2.14.2        viridis_0.5.1          GO.db_3.11.4          
##  [13] fansi_0.4.2            magrittr_2.0.1         memoise_2.0.0         
##  [16] annotate_1.66.0        graphlayouts_0.7.1     modelr_0.1.8          
##  [19] prettyunits_1.1.1      colorspace_2.0-0       blob_1.2.1            
##  [22] rvest_1.0.0            ggrepel_0.9.1          haven_2.3.1           
##  [25] xfun_0.22              crayon_1.4.1           RCurl_1.98-1.3        
##  [28] jsonlite_1.7.2         scatterpie_0.1.5       genefilter_1.70.0     
##  [31] survival_3.2-10        glue_1.4.2             polyclip_1.10-0       
##  [34] gtable_0.3.0           zlibbioc_1.34.0        XVector_0.28.0        
##  [37] scales_1.1.1           DOSE_3.14.0            DBI_1.1.1             
##  [40] Rcpp_1.0.6             viridisLite_0.3.0      xtable_1.8-4          
##  [43] progress_1.2.2         gridGraphics_0.5-1     bit_4.0.4             
##  [46] europepmc_0.4          httr_1.4.2             fgsea_1.14.0          
##  [49] RColorBrewer_1.1-2     ellipsis_0.3.1         pkgconfig_2.0.3       
##  [52] XML_3.99-0.6           farver_2.1.0           sass_0.3.1            
##  [55] dbplyr_2.1.0           locfit_1.5-9.4         utf8_1.2.1            
##  [58] labeling_0.4.2         ggplotify_0.0.5        tidyselect_1.1.0      
##  [61] rlang_0.4.10           munsell_0.5.0          cellranger_1.1.0      
##  [64] tools_4.0.4            cachem_1.0.4           downloader_0.4        
##  [67] cli_2.3.1              generics_0.1.0         RSQLite_2.2.5         
##  [70] ggridges_0.5.3         broom_0.7.5            evaluate_0.14         
##  [73] fastmap_1.1.0          yaml_2.2.1             knitr_1.31            
##  [76] bit64_4.0.5            fs_1.5.0               tidygraph_1.2.0       
##  [79] zip_2.1.1              ggraph_2.0.5           DO.db_2.9             
##  [82] xml2_1.3.2             compiler_4.0.4         rstudioapi_0.13       
##  [85] png_0.1-7              reprex_2.0.0           tweenr_1.0.2          
##  [88] geneplotter_1.66.0     bslib_0.2.4            stringi_1.5.3         
##  [91] highr_0.8              lattice_0.20-41        Matrix_1.3-2          
##  [94] vctrs_0.3.7            pillar_1.5.1           lifecycle_1.0.0       
##  [97] BiocManager_1.30.12    triebeard_0.3.0        jquerylib_0.1.3       
## [100] data.table_1.14.0      cowplot_1.1.1          bitops_1.0-6          
## [103] qvalue_2.20.0          R6_2.5.0               gridExtra_2.3         
## [106] MASS_7.3-53.1          assertthat_0.2.1       withr_2.4.1           
## [109] GenomeInfoDbData_1.2.3 hms_1.0.0              grid_4.0.4            
## [112] rvcheck_0.1.8          rmarkdown_2.7          ggforce_0.3.3