Dataset Information

High satellite repeat turnover in great apes studied with short- and long-read technologies.

ABSTRACT: Satellite repeats are a structural component of centromeres and telomeres, and in some instances their divergence is known to drive speciation. Due to their highly repetitive nature, satellite sequences have been understudied and underrepresented in genome assemblies. To investigate their turnover in great apes, we studied satellite repeats of unit sizes up to 50 bp in human, chimpanzee, bonobo, gorilla, and Sumatran and Bornean orangutans, using unassembled short and long sequencing reads. The density of satellite repeats, as identified from accurate short reads (Illumina), varied greatly among great ape genomes. These were dominated by a handful of abundant repeated motifs, frequently shared among species, which formed two groups: (1) the (AATGG)n repeat (critical for heat shock response) and its derivatives; and (2) subtelomeric 32-mers involved in telomeric metabolism. Using the densities of abundant repeats, individuals could be classified into species. However clustering did not reproduce the accepted species phylogeny, suggesting rapid repeat evolution. Several abundant repeats were enriched in males vs. females; using Y chromosome assemblies or FIuorescent In Situ Hybridization, we validated their location on the Y. Finally, applying a novel computational tool, we identified many satellite repeats completely embedded within long Oxford Nanopore and Pacific Biosciences reads. Such repeats were up to 59 kb in length and consisted of perfect repeats interspersed with other similar sequences. Our results based on sequencing reads generated with three different technologies provide the first detailed characterization of great ape satellite repeats, and open new avenues for exploring their functions.

SUBMITTER: Cechova M

PROVIDER: S-EPMC6805231 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundDe novo genome assembly is essential to modern genomics studies. As it is not biased by a reference, it is also a useful method for studying genomes with high variation, such as cancer genomes. De novo short-read assemblers commonly use de Bruijn graphs, where nodes are sequences of equal length k, also known as k-mers. Edges in this graph are established between nodes that overlap by [Formula: see text] bases, and nodes along unambiguous walks in the graph are subsequently merged. The selection of k is influenced by multiple factors, and optimizing this value results in a trade-off between graph connectivity and sequence contiguity. Ideally, multiple k sizes should be used, so lower values can provide good connectivity in lesser covered regions and higher values can increase contiguity in well-covered regions. However, current approaches that use multiple k values do not address the scalability issues inherent to the assembly of large genomes.ResultsHere we present RResolver, a scalable algorithm that takes a short-read de Bruijn graph assembly with a starting k as input and uses a k value closer to that of the read length to resolve repeats. RResolver builds a Bloom filter of sequencing reads which is used to evaluate the assembly graph path support at branching points and removes paths with insufficient support. RResolver runs efficiently, taking only 26 min on average for an ABySS human assembly with 48 threads and 60 GiB memory. Across all experiments, compared to a baseline assembly, RResolver improves scaffold contiguity (NGA50) by up to 15% and reduces misassemblies by up to 12%.ConclusionsRResolver adds a missing component to scalable de Bruijn graph genome assembly. By improving the initial and fundamental graph traversal outcome, all downstream ABySS algorithms greatly benefit by working with a more accurate and less complex representation of the genome. The RResolver code is integrated into ABySS and is available at https://github.com/bcgsc/abyss/tree/master/RResolver .

Project description:RNA sequencing using next-generation sequencing technologies (NGS) is currently the standard approach for gene expression profiling, particularly for large-scale high-throughput studies. NGS technologies comprise high throughput, cost efficient short-read RNA-Seq, while emerging single molecule, long-read RNA-Seq technologies have enabled new approaches to study the transcriptome and its function. The emerging single molecule, long-read technologies are currently commercially available by Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT), while new methodologies based on short-read sequencing approaches are also being developed in order to provide long range single molecule level information-for example, the ones represented by the 10x Genomics linked read methodology. The shift toward long-read sequencing technologies for transcriptome characterization is based on current increases in throughput and decreases in cost, making these attractive for de novo transcriptome assembly, isoform expression quantification, and in-depth RNA species analysis. These types of analyses were challenging with standard short sequencing approaches, due to the complex nature of the transcriptome, which consists of variable lengths of transcripts and multiple alternatively spliced isoforms for most genes, as well as the high sequence similarity of highly abundant species of RNA, such as rRNAs. Here we aim to focus on single molecule level sequencing technologies and single-cell technologies that, combined with perturbation tools, allow the analysis of complete RNA species, whether short or long, at high resolution. In parallel, these tools have opened new ways in understanding gene functions at the tissue, network, and pathway levels, as well as their detailed functional characterization. Analysis of the epi-transcriptome, including RNA methylation and modification and the effects of such modifications on biological systems is now enabled through direct RNA sequencing instead of classical indirect approaches. However, many difficulties and challenges remain, such as methodologies to generate full-length RNA or cDNA libraries from all different species of RNAs, not only poly-A containing transcripts, and the identification of allele-specific transcripts due to current error rates of single molecule technologies, while the bioinformatics analysis on long-read data for accurate identification of 5' and 3' UTRs is still in development.

Dataset Information

High satellite repeat turnover in great apes studied with short- and long-read technologies.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets