The human genome is generally organized into stable chromosomes, and only tumor cells are known to accumulate kilobase (kb)-sized extrachromosomal circular DNA elements (eccDNAs). However, it must be expected that kb eccDNAs exist in normal cells as a result of mutations. Here, we purify and sequence eccDNAs from muscle and blood samples from 16 healthy men, detecting ~100,000 unique eccDNA types from 16 million nuclei. Half of these structures carry genes or gene fragments and the majority are smaller than 25 kb. Transcription from eccDNAs suggests that eccDNAs reside in nuclei and recurrence of certain eccDNAs in several individuals implies DNA circularization hotspots. Gene-rich chromosomes contribute to more eccDNAs per megabase and the most transcribed protein-coding gene in muscle, TTN (titin), provides the most eccDNAs per gene. Thus, somatic genomes are rich in chromosome-derived eccDNAs that may influence phenotypes through altered gene copy numbers and transcription of full-length or truncated genes.

The molecular landscape in non-muscle-invasive bladder cancer (NMIBC) is characterized by large biological heterogeneity with variable clinical outcomes. Here, we perform an integrative multi-omics analysis of patients diagnosed with NMIBC (n = 834). Transcriptomic analysis identifies four classes (1, 2a, 2b and 3) reflecting tumor biology and disease aggressiveness. Both transcriptome-based subtyping and the level of chromosomal instability provide independent prognostic value beyond established prognostic clinicopathological parameters. High chromosomal instability, p53-pathway disruption and APOBEC-related mutations are significantly associated with transcriptomic class 2a and poor outcome. RNA-derived immune cell infiltration is associated with chromosomally unstable tumors and enriched in class 2b. Spatial proteomics analysis confirms the higher infiltration of class 2b tumors and demonstrates an association between higher immune cell infiltration and lower recurrence rates. Finally, the independent prognostic value of the transcriptomic classes is documented in 1228 validation samples using a single sample classification tool. The classifier provides a framework for biomarker discovery and for optimizing treatment and surveillance in next-generation clinical trials.

Hundreds of thousands of human genomes are now being sequenced to characterize genetic variation and use this information to augment association mapping studies of complex disorders and other phenotypic traits 1-4 . Genetic variation is identified mainly by mapping short reads to the reference genome or by performing local assembly 2,5-7 . However, these approaches are biased against discovery of structural variants and variation in the more complex parts of the genome. Hence, large-scale de novo assembly is needed. Here we show that it is possible to construct excellent de novo assemblies from high-coverage sequencing with mate-pair libraries extending up to 20 kilobases. We report de novo assemblies of 150 individuals (50 trios) from the GenomeDenmark project. The quality of these assemblies is similar to those obtained using the more expensive long-read technology 4,8-13 . We use the assemblies to identify a rich set of structural variants including many novel insertions and demonstrate how this variant catalogue enables further deciphering of known association mapping signals. We leverage the assemblies to provide 100 completely resolved major histocompatibility complex haplotypes and to resolve major parts of the Y chromosome. Our study provides a regional reference genome that we expect will improve the power of future association mapping studies and hence pave the way for precision medicine initiatives, which now are being launched in many countries including Denmark.Using a combination of high-depth (average 78× ) Illumina pairedend and mate-pair libraries, we applied Allpaths-LG 14 to create de novo assemblies of high quality and coverage for each of the 150 individuals with a median scaffold N50 of ~ 21 megabases (Mb; maximum ~ 30 Mb) (Supplementary Table 1). The 100 largest scaffolds in each of the 140 best assemblies typically covered more than 75% (median 77%, Extended Data Fig. 1a) of the genome, with the largest scaffolds exceeding 110 Mb in size (Supplementary Table 1). To evaluate the accuracy of the assemblies, we subsequently aligned the scaffolds for each individual to the human reference genome (GRCh38) 15 . Figure 1 shows an example individual where the euchromatic part of each chromosome was almost completely covered by a few large scaffolds and in several cases scaffolds covered almost entire chromosome arms. Only rarely did we find that large scaffolds break and align to more than one chromosome (Extended Data Fig. 1b), suggesting that even the largest scaffolds are seldom chimaeric. We also compared our de novo assemblies with a published long-read assembly based on BioNano mapping and PacBio sequencing 16 . Extended Data Figs 2a and 3 show that this assembly was less complete than our assemblies, but with similar scaffold lengths. The long-read assembly had 5.38% missing data compared with our median of 4.25% (Extended Data Fig. 3a), but the missing data in our assemblies were found in smaller gaps (Extended Data Fig. 3b, c), and the median contig length was therefore much smaller th...

As sequencing technologies become more affordable, it is now realistic to propose studying the evolutionary history of virtually any organism on a genomic scale. However, when dealing with non-model organisms it is not always easy to choose the best approach given a specific biological question, a limited budget, and challenging sample material. Furthermore, although recent advances in technology offer unprecedented opportunities for research in non-model organisms, they also demand unprecedented awareness from the researcher regarding the assumptions and limitations of each method. In this review we present an overview of the current sequencing technologies and the methods used in typical high-throughput data analysis pipelines. Subsequently, we contextualize high-throughput DNA sequencing technologies within their applications in non-model organism biology. We include tips regarding managing unconventional sample material, comparative and population genetic approaches that do not require fully assembled genomes, and advice on how to deal with low depth sequencing data.