Conserved, ultraconserved and other classes of constrained non-coding elements (referred as CNEs) represent one of the mysteries of current comparative genomics. These elements are defined using various degrees of sequence similarity between organisms and several thresholds of minimal length and are often marked by extreme conservation that frequently exceeds the one observed for protein-coding sequences. We here explore the distribution of different classes of CNEs in entire chromosomes, in the human genome. We employ two complementary methodologies, the scaling of block entropy and box-counting, with the aim to assess fractal characteristics of different CNE datasets. Both approaches converge to the conclusion that well-developed fractality is characteristic of elements that are either marked by extreme conservation between two or more organisms or are of ancient origin, i.e. conserved between distant organisms across evolution. Given that CNEs are often clustered around genes, especially those that regulate developmental processes, we verify by appropriate gene masking that fractal-like patterns emerge irrespectively of whether elements found in proximity or inside genes are excluded or not. An evolutionary scenario is proposed, involving genomic events, that might account for fractal distribution of CNEs in the human genome as indicated through numerical simulations.

A pattern in which nucleotide transitions are favored several-fold over transversions is common in molecular evolution. When this pattern occurs among amino acid replacements, explanations often invoke an effect of selection, on the grounds that transitions are more conservative in their effects on proteins. However, the underlying hypothesis of conservative transitions has never been tested directly. Here we assess support for this hypothesis using direct evidence: the fitness effects of mutations in actual proteins, measured via individual or paired growth experiments. We assembled data from 8 published studies, ranging in size from 24 to 757 single-nucleotide mutations that change an amino acid. Every study has the statistical power to reveal significant effects of amino acid exchangeability, and most studies have the power to discern a binary conservative-vs-radical distinction. However, only one study suggests that transitions are significantly more conservative than transversions. In the combined set of 1239 replacements, the chance that a transition is more conservative than a transversion is 53 % (95 % confidence interval, 50 % to 56 %), compared to the null expectation of 50 %. We show that this effect is not large compared to that of most biochemical factors, and is not large enough to explain the several-fold bias observed in evolution. In short, available data have the power to verify the “conservative transitions” hypothesis if true, but suggest instead that selection on proteins plays at best a minor role in the observed bias.

We have developed an algorithm for genetic analysis of complex traits using genome-wide SNPs in a linear mixed model framework. Compared to current standard REML software, our method could be more than 1000 times faster. The advantage is largest when there is only a single genetic covariance structure. The method is particularly useful for multivariate analysis, including random regression models for studying reaction norms. We applied our proposed method to publicly available mice and human data and discuss advantages and limitations.

The accurate identification of low-frequency variants in tumors remains an unsolved problem. To support characterization of the issues in a realistic setting, we have developed software tools and a reference dataset for diagnosing variant calling pipelines. The dataset contains millions of variants at frequencies ranging from 0.05 to 1.0. To generate the dataset, we performed whole-genome sequencing of a mixture of two Corriel cell lines, NA19240 and NA12878, the mothers of YRI (Y) and CEU (C) HapMap trios, respectively. The cells were mixed in three different proportions, 10Y/90C, 50Y/50C and 90Y/10C, in an effort to simulate the heterogeneity found in tumor samples. We sequenced three biological replicates for each mixture, yielding approximately 1.4 billion reads per mixture for an average of 64X coverage. Using the published genotypes as our reference, we evaluate the performance of a general variant calling algorithm, constructed as a demonstration of our flexible toolset, and make comparisons to a standard GATK pipeline. We estimate the overall FDR to be 0.028 and the FNR (when coverage exceeds 20X) to be 0.019 in the 50Y/50C mixture. Interestingly, even with these relatively well studied individuals, we predict over 475,000 new variants, validating in well-behaved coding regions at a rate of 0.97, that were not included in the published genotypes.

Analysis of environmental DNA (eDNA) enables detection of specific species from water and soil samples. Typically, these analyses are performed by amplifying a short DNA sequence using species-specific primers. Alternatively, primers amplifying many species within a taxonomic group are used and amplicons are subsequently sequenced. Here, we describe a method to quickly characterize the biodiversity of a given environment by amplification of eDNA using a combination of primer pairs targeting a wide range of taxa and species identification by high-throughput sequencing. We tested this approach by analyzing 91 water samples of 40mL collected along the Cuyahoga River (Ohio, USA). We amplified eDNA extracted from each water sample using 12 primer pairs targeting mammals, fish, amphibians, birds, bryophytes, arthropods, copepods, plants and several microorganism taxa and simultaneously sequenced all PCR products by high-throughput sequencing. Despite the small sample volumes analyzed, we identified DNA sequences from 15 species of fish, 17 species of mammals, 8 species of birds, 15 species of arthropods, one turtle and one salamander. Interestingly, in addition to aquatic and semi-aquatic animal species, we identified DNA from many terrestrial species that live near the banks of the Cuyahoga River. We also identified DNA from one Asian carp species invasive to the Great Lakes but that had not been reported in the Cuyahoga River. Our study shows that analysis of eDNA extracted from a small sample of water using wide-range PCR amplification combined with massively parallel sequencing can provide a broad perspective on the biological diversity of a given environment.

In this study, we adapt a protocol for the growth of previously uncultured environmental bacterial isolates, to make it compatible with whole genome sequencing. We demonstrate that in combination with the MinION sequencing device, complete assemblies can be derived, allowing genomic comparisons to be made. This approach allows rapid, inexpensive and straightforward discovery, and genomic analysis, of previously uncultured prokaryotic genomes, and brings greater ownership of all parts of the sequencing process back to individual researchers.