Temperature gradient focusing (TGF) is a recently developed technique for the simultaneous concentration and electrophoretic separation of ionic analytes in microfluidic channels. One drawback to TGF as it has previously been described is the limited peak capacity; only a small number of analyte peaks (approximately 2-3) can be simultaneously focused and separated. In this paper, we report on a variation of the TGF method whereby the bulk flow rate is varied over time so that a large number of analytes can be sequentially focused, moved past a fixed detection point, and flushed to waste. In addition to improved peak capacity, the detection limits of the scanning TGF method can be adjusted on-the-fly, as needed for different samples. Finally, scanning TGF provides a technique by which high-resolution, high-peak-capacity electrophoretic separations can be performed in simple, straight, and short microfluidic channels.

In considering evolution of transcribed regions, regulatory sequences, and other genomic loci, we are often faced with a situation in which the number of allelic states greatly exceeds the size of the population. In this limit, the population eventually adopts a steady state characterized by mutation-selection-drift balance. Although new alleles continue to be explored through mutation, the statistics of the population, and in particular the probabilities of seeing specific allelic configurations in samples taken from the population, do not change with time. In the absence of selection, the probabilities of allelic configurations are given by the Ewens sampling formula, widely used in population genetics to detect deviations from neutrality. Here we develop an extension of this formula to arbitrary fitness distributions. Although our approach is general, we focus on the class of fitness landscapes, inspired by recent high-throughput genotype-phenotype maps, in which alleles can be in several distinct phenotypic states. This class of landscapes yields sampling probabilities that are computationally more tractable and can form a basis for inference of selection signatures from genomic data. Using an efficient numerical implementation of the sampling probabilities, we demonstrate that, for a sizable range of mutation rates and selection coefficients, the steady-state allelic diversity is not neutral. Therefore, it may be used to infer selection coefficients, as well as other evolutionary parameters from population data. We also carry out numerical simulations to challenge various approximations involved in deriving our sampling formulas, such as the infinite-allele limit and the “full connectivity” assumption inherent in the Ewens theory, in which each allele can mutate into any other allele. We find that, at least for the specific numerical examples studied, our theory remains sufficiently accurate even if these assumptions are relaxed. Thus our framework establishes both theoretical and practical foundations for inferring selection signatures from population-level genomic sequence samples.

No abstract

In considering evolution of transcribed regions, regulatory modules, and other genomic loci of interest, we are often faced with a situation in which the number of allelic states greatly exceeds the population size. In this limit, the population eventually adopts a steady state characterized by mutation-selection-drift balance. Although new alleles continue to be explored through mutation, the statistics of the population, and in particular the probabilities of seeing specific allelic configurations in samples taken from a population, do not change with time. In the absence of selection, probabilities of allelic configurations are given by the Ewens sampling formula, widely used in population genetics to detect deviations from neutrality. Here we develop an extension of this formula to arbitrary, possibly epistatic, fitness landscapes. Although our approach is general, we focus on the class of landscapes in which alleles are grouped into two, three, or several fitness states. This class of landscapes yields sampling probabilities that are computationally more tractable, and can form a basis for the inference of selection signatures from sequence data. We demonstrate that, for a sizeable range of mutation rates and selection coefficients, the steady-state allelic diversity is not neutral. Therefore, it may be used to infer selection coefficients, as well as other key evolutionary parameters, using high-throughput sequencing of evolving populations to collect data on locus polymorphisms. We also carry out numerical investigation of various approximations involved in deriving our sampling formulas, such as the infinite allele limit and the "full connectivity" assumption in which each allele can mutate into any other allele. We find that our theory remains sufficiently accurate even if these assumptions are relaxed. Thus, our framework establishes a theoretical foundation for inferring selection signatures from samples of sequences produced by evolution on epistatic fitness landscapes.