H2 metabolism is proposed to be the most ancient and diverse mechanism of energy-conservation. The metalloenzymes mediating this metabolism, hydrogenases, are encoded by over 60 microbial phyla and are present in all major ecosystems. We developed a classification system and web tool, HydDB, for the structural and functional analysis of these enzymes. We show that hydrogenase function can be predicted by primary sequence alone using an expanded classification scheme (comprising 29 [NiFe], 8 [FeFe], and 1 [Fe] hydrogenase classes) that defines 11 new classes with distinct biological functions. Using this scheme, we built a web tool that rapidly and reliably classifies hydrogenase primary sequences using a combination of k-nearest neighbors’ algorithms and CDD referencing. Demonstrating its capacity, the tool reliably predicted hydrogenase content and function in 12 newly-sequenced bacteria, archaea, and eukaryotes. HydDB provides the capacity to browse the amino acid sequences of 3248 annotated hydrogenase catalytic subunits and also contains a detailed repository of physiological, biochemical, and structural information about the 38 hydrogenase classes defined here. The database and classifier are freely and publicly available at http://services.birc.au.dk/hyddb/

RNA molecules are sequences of nucleotides that serve as more than mere intermediaries between DNA and proteins, e.g., as catalytic molecules. Computational prediction of RNA secondary structure is among the few structure prediction problems that can be solved satisfactorily in polynomial time. Most work has been done to predict structures that do not contain pseudoknots. Allowing pseudoknots introduces modeling and computational problems. In this paper we consider the problem of predicting RNA secondary structures with pseudoknots based on free energy minimization. We first give a brief comparison of energy-based methods for predicting RNA secondary structures with pseudoknots. We then prove that the general problem of predicting RNA secondary structures containing pseudoknots is NP complete for a large class of reasonable models of pseudoknots.

Building a population-specific catalogue of single nucleotide variants (SNVs), indels and structural variants (SVs) with frequencies, termed a national pan-genome, is critical for further advancing clinical and public health genetics in large cohorts. Here we report a Danish pan-genome obtained from sequencing 10 trios to high depth (50 × ). We report 536k novel SNVs and 283k novel short indels from mapping approaches and develop a population-wide de novo assembly approach to identify 132k novel indels larger than 10 nucleotides with low false discovery rates. We identify a higher proportion of indels and SVs than previous efforts showing the merits of high coverage and de novo assembly approaches. In addition, we use trio information to identify de novo mutations and use a probabilistic method to provide direct estimates of 1.27e−8 and 1.5e−9 per nucleotide per generation for SNVs and indels, respectively.

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...

Abstract. The neighbour-joining method reconstructs phylogenies by iteratively joining pairs of nodes until a single node remains. The criterion for which pair of nodes to merge is based on both the distance between the pair and the average distance to the rest of the nodes. In this paper, we present a new search strategy for the optimisation criteria used for selecting the next pair to merge and we show empirically that the new search strategy is superior to other state-of-the-art neighbourjoining implementations.