Extrachromosomal circular DNA (eccDNA) of chromosomal origin is found in many eukaryotic species and cell types, including cancer, where eccDNAs with oncogenes drive tumorigenesis. Most studies of eccDNA employ short-read sequencing for their identification. However, short-read sequencing cannot resolve the complexity of genomic repeats, which can lead to missing eccDNA products. Long-read sequencing technologies provide an alternative to constructing complete eccDNA maps. We present a software suite, Construction-based Rolling-circle-amplification for eccDNA Sequence Identification and Location (CReSIL), to identify and characterize eccDNA from long-read sequences. CReSIL's performance in identifying eccDNA, with a minimum F1 score of 0.98, is superior to the other bioinformatic tools based on simulated data. CReSIL provides many useful features for genomic annotation, which can be used to infer eccDNA function and Circos visualization for eccDNA architecture investigation. We demonstrated CReSIL's capability in several long-read sequencing datasets, including datasets enriched for eccDNA and whole genome datasets from cells containing large eccDNA products. In conclusion, the CReSIL suite software is a versatile tool for investigating complex and simple eccDNA in eukaryotic cells.

BACKGROUND: Circular DNA has recently been identified across different species including human normal and cancerous tissue, but short-read mappers are unable to align many of the reads crossing circle junctions hence limiting their detection from short-read sequencing data. RESULTS: Here, we propose a new method, Circle-Map that guides the realignment of partially aligned reads using information from discordantly mapped reads to map the short unaligned portions using a probabilistic model. We compared Circle-Map to similar up-to-date methods for circular DNA and RNA detection and we demonstrate how the approach implemented in Circle-Map dramatically increases sensitivity for detection of circular DNA on both simulated and real data while retaining high precision. CONCLUSION: Circle-Map is an easy-to-use command line tool that implements the required pipeline to accurately detect circular DNA from circle enriched next generation sequencing experiments. Circle-Map is implemented in python3.6 and it is freely available at https://github.com/iprada/Circle-Map.

MOTIVATION: Hierarchical and relocation clustering (e.g. K-means and self-organizing maps) have been successful tools in the display and analysis of whole genome DNA microarray expression data. However, the results of hierarchical clustering are sensitive to outliers, and most relocation methods give results which are dependent on the initialization of the algorithm. Therefore, it is difficult to assess the significance of the results. We have developed a consensus clustering algorithm, where the final result is averaged over multiple clustering runs, giving a robust and reproducible clustering, capable of capturing small signal variations. The algorithm preserves valuable properties of hierarchical clustering, which is useful for visualization and interpretation of the results. RESULTS: We show for the first time that one can take advantage of multiple clustering runs in DNA microarray analysis by collecting re-occurring clustering patterns in a co-occurrence matrix. The results show that consensus clustering obtained from clustering multiple times with Variational Bayes Mixtures of Gaussians or K-means significantly reduces the classification error rate for a simulated dataset. The method is flexible and it is possible to find consensus clusters from different clustering algorithms. Thus, the algorithm can be used as a framework to test in a quantitative manner the homogeneity of different clustering algorithms. We compare the method with a number of state-of-the-art clustering methods. It is shown that the method is robust and gives low classification error rates for a realistic, simulated dataset. The algorithm is also demonstrated for real datasets. It is shown that more biological meaningful transcriptional patterns can be found without conservative statistical or fold-change exclusion of data. AVAILABILITY: Matlab source code for the clustering algorithm ClusterLustre, and the simulated dataset for testing are available upon request from T.G. and O.W.