Accurate Identification of Subclones in Tumor Genomes.

Understanding intratumor heterogeneity is critical for studying tumorigenesis and designing personalized treatments. To decompose the mixed cell population in a tumor, subclones are inferred computationally based on variant allele frequency (VAF) from bulk sequencing data. In this study, we showed that sequencing depth, mean VAF, and variance of VAF of a subclone are confounded. Without considering this effect, current methods require deep-sequencing data (>300× depth) to reliably infer subclones. Here, we present a novel algorithm that incorporates depth-variance and mean-variance dependencies in a clustering error model and successfully identifies subclones in tumors sequenced at depths of as low as 30×. We implemented the algorithm as a model-based adaptive grouping of subclones (MAGOS) method. Analyses of computer simulated data and empirical sequencing data showed that MAGOS outperformed existing methods on minimum sequencing depth, decomposition accuracy, and computation efficiency. The most prominent improvements were observed in analyzing tumors sequenced at depths between 30× and 200×, whereas the performance was comparable between MAGOS and existing methods on deeply sequenced tumors. MAGOS supports analysis of single-nucleotide variants and copy number variants from a single sample or multiple samples of a tumor. We applied MAGOS to whole-exome data of late-stage liver cancers and discovered that high subclone count in a tumor was a significant risk factor of poor prognosis. Lastly, our analysis suggested that sequencing multiple samples of the same tumor at standard depth is more cost-effective and robust for subclone characterization than deep sequencing a single sample. MAGOS is available at github (https://github.com/liliulab/magos).

Trichodesmium genome maintains abundant, widespread noncoding DNA in situ, despite oligotrophic lifestyle.

Understanding the evolution of the free-living, cyanobacterial, diazotroph Trichodesmium is of great importance because of its critical role in oceanic biogeochemistry and primary production. Unlike the other >150 available genomes of free-living cyanobacteria, only 63.8% of the Trichodesmium erythraeum (strain IMS101) genome is predicted to encode protein, which is 20-25% less than the average for other cyanobacteria and nonpathogenic, free-living bacteria. We use distinctive isolates and metagenomic data to show that low coding density observed in IMS101 is a common feature of the Trichodesmium genus, both in culture and in situ. Transcriptome analysis indicates that 86% of the noncoding space is expressed, although the function of these transcripts is unclear. The density of noncoding, possible regulatory elements predicted in Trichodesmium, when normalized per intergenic kilobase, was comparable and twofold higher than that found in the gene-dense genomes of the sympatric cyanobacterial genera Synechococcus and Prochlorococcus, respectively. Conserved Trichodesmium noncoding RNA secondary structures were predicted between most culture and metagenomic sequences, lending support to the structural conservation. Conservation of these intergenic regions in spatiotemporally separated Trichodesmium populations suggests possible genus-wide selection for their maintenance. These large intergenic spacers may have developed during intervals of strong genetic drift caused by periodic blooms of a subset of genotypes, which may have reduced effective population size. Our data suggest that transposition of selfish DNA, low effective population size, and high-fidelity replication allowed the unusual "inflation" of noncoding sequence observed in Trichodesmium despite its oligotrophic lifestyle.