Scientists have access to artifacts of evolutionary history (namely, the fossil record and genomic sequences of living organisms) but they have limited means with which to infer the exact evolutionary events that occurred to produce today s living world. An intriguing question to arise from this historical limitation is whether the evolutionary paths of organisms are dominated by internal or external controlled processes (i.e., Life as a factory) or whether they are inherently random and subject to completely different outcomes if repeated under identical conditions (i.e., Life as a casino parlor). Two experimental approaches, ancestral sequence reconstruction and experimental evolution with microorganisms, can be used to recapitulate ancient adaptive pathways and provide valuable insights into the mutational steps that constitute an organism s genetic heritage. Ancestral sequence reconstruction follows a backwards-from-present-day strategy in which various ancestral forms of a modern gene or protein are reconstructed and then studied mechanistically. Experimental evolution, by contrast, follows a forward-from-present day strategy in which microbial populations are evolved in the laboratory under defined conditions in which their evolutionary paths may be closely monitored. Here I describe a novel hybrid of these two methods, in which synthetic components constructed from inferred ancestral gene or protein sequences are placed into the genomes of modern organisms that are then experimentally evolved. Through this system, we aim to establish the comparative study of ancient phenotypes as a novel, statistically rigorous methodology with which to explore the respective impacts of biophysics and chance in evolution within the scope of the Extended Synthesis.

Beneficial mutations fuel adaptation by altering phenotypes that enhance the fit of organisms to their environment. However, the phenotypic effects of mutations often depend on ecological context, making the distribution of effects across multiple environments essential to understanding the true nature of beneficial mutations. Studies that address both the genetic basis and ecological consequences of adaptive mutations remain rare. Here, we characterize the direct and pleiotropic fitness effects of a collection of 21 first-step beneficial mutants derived from naive and adapted genotypes used in a long-term experimental evolution of Escherichia coli. Whole-genome sequencing was used to identify most beneficial mutations. In contrast to previous studies, we find diverse fitness effects of mutations selected in a simple environment and few cases of genetic parallelism. The pleiotropic effects of these mutations were predominantly positive but some mutants were highly antagonistic in alternative environments. Further, the fitness effects of mutations derived from the adapted genotypes were dramatically reduced in nearly all environments. These findings suggest that many beneficial variants are accessible from a single point on the fitness landscape, and the fixation of alternative beneficial mutations may have dramatic consequences for niche breadth reduction via metabolic erosion.

For the past 15 years, the UCSC Genome Browser (http://genome.ucsc.edu/) has served the international research community by offering an integrated platform for viewing and analyzing information from a large database of genome assemblies and their associated annotations. The UCSC Genome Browser has been under continuous development since its inception with new data sets and software features added frequently. Some release highlights of this year include new and updated genome browsers for various assemblies, including bonobo and zebrafish; new gene annotation sets; improvements to track and assembly hub support; and a new interactive tool, the “Data Integrator”, for intersecting data from multiple tracks. We have greatly expanded the data sets available on the most recent human assembly, hg38/GRCh38, to include updated gene prediction sets from GENCODE, more phenotype- and disease-associated variants from ClinVar and ClinGen, more genomic regulatory data, and a new multiple genome alignment.

Animals and plants have evolved a range of mechanisms for recognizing noxious substances and organisms. A particular challenge, most successfully met by the adaptive immune system in vertebrates, is the specific recognition of potential pathogens, which themselves evolve to escape recognition. A variety of genomic and evolutionary mechanisms shape large families of proteins dedicated to detecting pathogens and create the diversity of binding sites needed for epitope recognition. One family involved in innate immunity are the NACHT-domain-and Leucine-Rich-Repeat-containing (NLR) proteins. Mammals have a small number of NLR proteins, which are involved in first-line immune defense and recognize several conserved molecular patterns. However, there is no evidence that they cover a wider spectrum of differential pathogenic epitopes. In other species, mostly those without adaptive immune systems, NLRs have expanded into very large families. A family of nearly 400 NLR proteins is encoded in the zebrafish genome. They are subdivided into four groups defined by their NACHT and effector domains, with a characteristic overall structure that arose in fishes from a fusion of the NLR domains with a domain used for immune recognition, the B30.2 domain. The majority of the genes are located on one chromosome arm, interspersed with other large multi-gene families, including a new family encoding proteins with multiple tandem arrays of Zinc fingers. This chromosome arm may be a hot spot for evolutionary change in the zebrafish genome. NLR genes not on this chromosome tend to be located near chromosomal ends. Extensive duplication, loss of genes and domains, exon shuffling and gene conversion acting differentially on the NACHT and B30.2 domains have shaped the family. Its four groups, which are conserved across the fishes, are homogenised within each group by gene conversion, while the B30.2 domain is subject to gene conversion across the groups. Evidence of positive selection on diversifying mutations in the B30.2 domain, probably driven by pathogen interactions, indicates that this domain rather than the LRRs acts as a recognition domain. The NLR-B30.2 proteins represent a new family with diversity in the specific recognition module that is present in fishes in spite of the parallel existence of an adaptive immune system.

There has been much interest in analyzing genome-scale DNA sequence data to infer population histories, but the inference methods developed hitherto are limited in model complexity and computational scalability. Here, we present an efficient, flexible statistical method that can utilize whole-genome sequence data from multiple populations to infer complex demographic models involving population size changes, population splits, admixture, and migration. We demonstrate through an extensive simulation study that our method can accurately and efficiently infer demographic parameters in realistic biological scenarios. The algorithms described here are implemented in a new version of the software package diCal, which is available for download at https://sourceforge.net/projects/dical2.

DNA assembly is a core methodological step in metagenomic pipelines used to study the structure and function within microbial communities. Here we investigate the utility of Pacific Biosciences long and high accuracy circular consensus sequencing (CCS) reads for metagenomics projects. We compared the application and performance of both PacBio CCS and Illumina HiSeq data with assembly and taxonomic binning algorithms using metagenomic samples representing a complex microbial community. Eight SMRT cells produced approximately 94 Mb of CCS reads from a biogas reactor microbiome sample, which averaged 1319 nt in length and 99.7 % accuracy. CCS data assembly generated a comparative number of large contigs greater than 1 kb, to those assembled from a ~190x larger HiSeq dataset (~18 Gb) produced from the same sample (i.e approximately 62 % of total contigs). Hybrid assemblies using PacBio CCS and HiSeq contigs produced improvements in assembly statistics, including an increase in the average contig length and number of large contigs. The incorporation of CCS data produced significant enhancements in taxonomic binning and genome reconstruction of two dominant phylotypes, which assembled and binned poorly using HiSeq data alone. Collectively these results illustrate the value of PacBio CCS reads in certain metagenomics applications.

Characterization of genetic variations in maize has been challenging, mainly due to deterioration of collinearity between individual genomes in the species and the fact the B73 genome used as the reference only represents a fraction of all haplotypes. An international consortium of maize research groups combined resources to develop the maize haplotype version 3 (HapMap3), built from whole genome sequencing data from 916 maize lines, covering pre-domestication and domesticated Zea mays varieties across the world. A new computational pipeline was set up to process over 7 trillion bp of sequencing data, and a set of population genetics filters were applied to identify over 60 million variant sites in regions where collinearity is largely preserved in the maize species.