DOI: 10.5061/dryad.zpc866tkv
title: DNA gains and losses in gigantic genomes do not track differences in transposable element-host silencing interactions

description: Size evolution among gigantic genomes involves gain and loss of many
 gigabases of transposable elements (TEs), sequences that parasitize host
 genomes. Vertebrates suppress TEs using piRNA and KRAB-ZFP pathways. TEs
 and hosts coevolve in an arms race, where suppression strength reflects TE
 fitness costs. In enormous genomes, additional TE costs become miniscule.
 How, then, do TEs and host suppression invoke further addition of massive
 DNA amounts? We analyze TE proliferation histories, deletion rates, and
 community diversities in six salamander genomes (21.3 - 49.9 Gb),
 alongside gonadal expression of TEs and suppression pathways. We show that
 TE activity is higher in testes than ovaries, attributable to lower
 KRAB-ZFP suppression. Unexpectedly, genome size and expansion are
 uncorrelated with TE deletion rate, proliferation history, expression, and
 host suppression. Also, TE community diversity increases with genome size,
 contrasting theoretical predictions. We infer that TE-host antagonism in
 gigantic genomes produces stochastic TE accumulation, reflecting noisy
 intermolecular interactions in huge genomes and cells.
description: # DNA gains and losses in gigantic genomes do not track differences in
 transposable element-host silencing interactions Dataset DOI:
 [10.5061/dryad.zpc866tkv](10.5061/dryad.zpc866tkv) Our manuscript
 described differences in TE composition, TE diversity, TE proliferation
 history based on histograms of sequence divergence, and TE ectopic
 recombination-mediated deletion from the genomes of six salamander species
 with different genome sizes. In addition, we quantified expresion of TEs
 and piRNAs that target TEs, as well as genes in TE silencing pathways. ##
 Description of the data and file structure The data we include here in
 this DRYAD submission are those that we used to generate the five figures
 in our manuscript. For each of the five figures, we include a folder that
 includes an excel spreadsheet with the relevant data, as well as the R
 code for producing the figure itself. When relevant, we also include tree
 files. ## Sharing/Access information Genomic shotgun and transcriptome
 sequences have been deposited in the Genome Sequence Archive at the
 National Genomics Data Center, Beijing Institute of Genomics, Chinese
 Academy of Sciences/China National Center for Bioinformation (GSA:
 CRA008892, CRA008899, CRA008900, CRA009444, CRA009736, CRA009747,
 CRA009748, CRA009749), and are publicly accessible at
 [http://bigd.big.ac.cn/gsa](http://bigd.big.ac.cn/gsa). ### Files and
 variables #### File Folders: Fig1, Fig2, Fig3, Fig4, Fig5
 **Description:** Each figure is associated with its own folder, which
 includes all files with the underlying data as well as R code for
 generating the figure. Figure 1 includes a spreadsheet with percent
 sequencing reads mapped in bins of different percent sequence divergence
 for each TE superfamily in each salamander species. In addition, Figure 1
 includes a spreadsheet that contains the percentage of the genome made up
 of different TE classes in each species, as well as the percentage of the
 genome made up of each different TE superfamily. There is also a newick
 file that contains the phylogeny and branch lengths depicted on the left
 side of the figure. Finally, there is an R script that will generate all
 of the graphs included in Figure 1, and the salamander image files
 included in the figure are also included in the folder. Figure 2 includes
 a spreadsheet with TE community diversity calculated using both
 Shannon's (SI) and Gini-Simpson (GSI) indices, as well as genome size
 (GS) and membership in major vertebrate clades/taxonomic groups. There is
 an R script that will generate the graphs included in Figure 2. Figure 3
 includes a spreadsheet that contains information on the ratio of terminal
 to internal sequences for between 12 and 25 individual TE contigs for each
 of 5 salamander species. There is also an R script that will generate the
 graph in Figure 3.  Figure 4 includes two spreadsheets that include
 species name, TE superfamily, and for each individual sampled, whether it
 is male or female, the percentage of the genome consisting of the TE
 superfamily, and the average expression level of each TE superfamily in
 TPM (transcripts per million). This workbook is entitled
 "genome_transcriptTE." In addition, there is a sheet called
 "Data TE exp piRNA mapping" that includes species name, TE
 superfamily, male or female for each individual, and then the piRNA
 expression levels (piRNA map TE RPM, or Reads Per Million) and the TE
 expression level (as TPM, or transcripts per million).There is also an R
 script that will generate the graph in Figure 4. Figure 5 includes a
 spreadsheet summarizing expression of genes involved in different TE
 silencing pathways. The columns specify the TE silencing pathway and the
 rows specify total expression levels of the genes in that pathway
 normalized by expression of genes in the miRNA pathway. There is also an R
 script that will generate the graphs in Figure 5. List of files in the
 “Figures_data_and_code” folder： Figures_data_and_code ├── Fig_1 │   ├──
 Data_divergence dynamics.xlsx │   ├── Data_phylogeny_tree_species_name.txt
 │   ├── Data_TE_superfamily_coverage.xlsx │   ├──
 Data_TE_type_percentage.xlsx │   ├── Fig1_April_16ps_final.pdf │   ├──
 Fig1_code.R │   ├── Fig1_R_output_April_16.pdf │   ├── Photo_Andrias.jpg
 │   ├── Photo_Cynops.jpg │   ├── Photo_Pachytriton.jpg │   ├──
 Photo_Paramesotriton.jpg │   ├── Photo_Ranodon.jpg │   └──
 Photo_Tylototriton.jpg │   ├── Fig_2 │   ├── Data_TE_diversity.xlsx │  
 ├── Fig2_April_17ps_final.pdf │   ├── Fig2_code.R │   └──
 Fig2_R_output_April_17.pdf │  ├── Fig_3 │   ├── Data_depth_TE.xlsx │   ├──
 Fig3_April_17ps_final.pdf │   ├── Fig3_code.R │   └──
 Fig3_R_output_April_17.pdf │  ├── Fig_4 │   ├──
 Data_genome_transcript_TE.xlsx │   ├── Data_TE_exp_piRNA_mapping.xlsx │  
 ├── Fig4_April_17ps_final.pdf │   ├── Fig4_code.R │   └──
 Fig4_R_output_April_16.pdf │  └── Fig_5     ├── Data_pathway_gene.xlsx    
 ├── Fig5_April_17ps_final.pdf     ├── Fig5_code.R └──
 Fig5_R_output_April_16.pdf   **File descriptions:** All figure files in
 the .pdf format were generated in R using the corresponding *_code.R
 scripts. All files named “Data_***.xlsx” or “Data_.txt” serve as the input
 files for R operations. ## Code/software None