The ability to inexpensively describe taxonomic diversity is critical in this era of rapid climate and biodiversity changes. The recent genome-skimming approach extends current barcoding practices beyond short markers by applying low-pass sequencing and recovering whole organelle genomes computationally. This approach discards the nuclear DNA, which constitutes the vast majority of the data. In contrast, we suggest using all unassembled reads. We introduce an assembly-free and alignment-free tool, Skmer, to compute genomic distances between the query and reference genome skims. Skmer shows excellent accuracy in estimating distances and identifying the closest match in reference datasets. Electronic supplementary material The online version of this article (10.1186/s13059-019-1632-4) contains supplementary material, which is available to authorized users.

Placing a new species on an existing phylogeny has increasing relevance to several applications. Placement can be used to update phylogenies in a scalable fashion and can help identify unknown query samples using (meta-)barcoding, skimming, or metagenomic data. Maximum likelihood (ML) methods of phylogenetic placement exist, but these methods are not scalable to reference trees with many thousands of leaves, limiting their ability to enjoy benefits of dense taxon sampling in modern reference libraries. They also rely on assembled sequences for the reference set and aligned sequences for the query. Thus, ML methods cannot analyze data sets where the reference consists of unassembled reads, a scenario relevant to emerging applications of genome skimming for sample identification. We introduce APPLES, a distance-based method for phylogenetic placement. Compared to ML, APPLES is an order of magnitude faster and more memory efficient, and unlike ML, it is able to place on large backbone trees (tested for up to 200,000 leaves). We show that using dense references improves accuracy substantially so that APPLES on dense trees is more accurate than ML on sparser trees, where it can run. Finally, APPLES can accurately identify samples without assembled reference or aligned queries using kmer-based distances, a scenario that ML cannot handle. APPLES is available publically at github.com/balabanmetin/apples.

Highlights d The overlap between genome annotations is often used to study biological association d We describe a tool for computing the statistical significance of annotations' overlap d Our method corrects p values reported in previous experiments by orders of magnitude

The cost of sequencing the genome is dropping at a much faster rate compared to assembling and finishing the genome. The use of lightly sampled genomes (genome-skims) could be transformative for genomic ecology, and results using k-mers have shown the advantage of this approach in identification and phylogenetic placement of eukaryotic species. Here, we revisit the basic question of estimating genomic parameters such as genome length, coverage, and repeat structure, focusing specifically on estimating the k-mer repeat spectrum. We show using a mix of theoretical and empirical analysis that there are fundamental limitations to estimating the k-mer spectra due to ill-conditioned systems, and that has implications for other genomic parameters. We get around this problem using a novel constrained optimization approach (Spline Linear Programming), where the constraints are learned empirically. On reads simulated at 1X coverage from 66 genomes, our method, REPeat SPECTra Estimation (RESPECT), had 2.2% error in length estimation compared to 27% error previously achieved. In shotgun sequenced read samples with contaminants, RESPECT length estimates had median error 4%, in contrast to other methods that had median error 80%. Together, the results suggest that low-pass genomic sequencing can yield reliable estimates of the length and repeat content of the genome. The RESPECT software will be publicly available at https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_shahab-2Dsarmashghi_RESPECT.git&d=DwIGAw&c=-35OiAkTchMrZOngvJPOeA&r=ZozViWvD1E8PorCkfwYKYQMVKFoEcqLFm4Tg49XnPcA&m=f-xS8GMHKckknkc7Xpp8FJYw_ltUwz5frOw1a5pJ81EpdTOK8xhbYmrN4ZxniM96&s=717o8hLR1JmHFpRPSWG6xdUQTikyUjicjkipjFsKG4w&e=.

The ability to quickly and inexpensively describe the taxonomic diversity in an environment is critical in this era of rapid climate and biodiversity changes. The currently preferred molecular technique is (meta)barcoding in which taxonomically informative plasmid/mitochondrial markers are sequenced. It is low-cost, and widely used, but has drawbacks. As sequencing costs continue to fall, an alternative approach based on genome-skimming has been proposed. This approach first applies low-pass (100Mb -several Gb per sample) sequencing to voucher and/or query samples and then recovers marker genes and/or organelle genomes computationally. In contrast, we suggest the use of the unassembled sequence data for taxonomic identification using an alignment-free approach based on the k-mer decomposition of the sequencing reads. Our approach is motivated by earlier work that connects genomic distance to the Jaccard index on k-mer collections, but improves upon prior work through a careful modeling of the impact of low-coverage, sequencing error, and other factors on the Jaccard index. Our tool, Skmer, estimates genomic distance between two organisms represented by their k-mer collections obtained from the genome-skims, and uses distance estimates to match a genome-skim query to a reference collection. Skmer shows excellent performance in our simulation studies, and makes the assembly-free approach to genome-skimming a viable alternative to the traditional barcoding. The Skmer software is made publicly available on https://github.com/shahab-sarmashghi/Skmer.git