Aesthetic preference for art emerges from a weighted integration over hierarchically structured visual features in the brain, bioRxiv, 2020-02-10

AbstractIt is an open question whether preferences for visual art can be lawfully predicted from the basic constituent elements of a visual image. Moreover, little is known about how such preferences are actually constructed in the brain. Here we developed and tested a computational framework to gain an understanding of how the human brain constructs aesthetic value. We show that it is possible to explain human preferences for a piece of art based on an analysis of features present in the image. This was achieved by analyzing the visual properties of drawings and photographs by multiple means, ranging from image statistics extracted by computer vision tools, subjective human ratings about attributes, to a deep convolutional neural network. Crucially, it is possible to predict subjective value ratings not only within but also across individuals, speaking to the possibility that much of the variance in human visual preference is shared across individuals. Neuroimaging data revealed that preference computations occur in the brain by means of a graded hierarchical representation of lower and higher level features in the visual system. These features are in turn integrated to compute an overall subjective preference in the parietal and prefrontal cortex. Our findings suggest that rather than being idiosyncratic, human preferences for art can be explained at least in part as a product of a systematic neural integration over underlying visual features of an image. This work not only advances our understanding of the brain-wide computations underlying value construction but also brings new mechanistic insights to the study of visual aesthetics and art appreciation.

biorxiv neuroscience 0-100-users 2020

BOSS-RUNS a flexible and practical dynamic read sampling framework for nanopore sequencing, bioRxiv, 2020-02-08

AbstractReal-time selective sequencing of individual DNA fragments, or ‘Read Until’, allows the focusing of Oxford Nanopore Technology sequencing on pre-selected genomic regions. This can lead to large improvements in DNA sequencing performance in many scenarios where only part of the DNA content of a sample is of interest. This approach is based on the idea of deciding whether to sequence a fragment completely after having sequenced only a small initial part of it. If, based on this small part, the fragment is not deemed of (sufficient) interest it is rejected and sequencing is continued on a new fragment. To date, only simple decision strategies based on location within a genome have been proposed to determine what fragments are of interest. We present a new mathematical model and algorithm for the real-time assessment of the value of prospective fragments. Our decision framework is based not only on which genomic regions are a priori interesting, but also on which fragments have so far been sequenced, and so on the current information available regarding the genome being sequenced. As such, our strategy can adapt dynamically during each run, focusing sequencing efforts in areas of highest uncertainty (typically areas currently low coverage). We show that our approach can lead to considerable savings of time and materials, providing high-confidence genome reconstruction sooner than a standard sequencing run, and resulting in more homogeneous coverage across the genome, even when entire genomes are of interest.Author SummaryAn existing technique called ‘Read Until’ allows selective sequencing of DNA fragments with an Oxford Nanopore Technology (ONT) sequencer. With Read Until it is possible to enrich coverage of areas of interest within a sequenced genome. We propose a new use of this technique combining a mathematical model of read utility and an algorithm to select an optimal dynamic decision strategy (i.e. one that can be updated in real time, and so react to the data generated so far in an experiment), we show that it possible to improve the efficiency of a sequencing run by focusing effort on areas of highest uncertainty.

biorxiv genomics 0-100-users 2020

Clonal Evolution of Acute Myeloid Leukemia Revealed by High-Throughput Single-Cell Genomics, bioRxiv, 2020-02-08

SummaryOne of the pervasive features of cancer is the diversity of mutations found in malignant cells within the same tumor; a phenomenon called clonal diversity or intratumor heterogeneity. Clonal diversity allows tumors to adapt to the selective pressure of treatment and likely contributes to the development of treatment resistance and cancer recurrence. Thus, the ability to precisely delineate the clonal substructure of a tumor, including the evolutionary history of its development and the co-occurrence of its mutations, is necessary to understand and overcome treatment resistance. However, DNA sequencing of bulk tumor samples cannot accurately resolve complex clonal architectures. Here, we performed high-throughput single-cell DNA sequencing to quantitatively assess the clonal architecture of acute myeloid leukemia (AML). We sequenced a total of 556,951 cells from 77 patients with AML for 19 genes known to be recurrently mutated in AML. The data revealed clonal relationship among AML driver mutations and identified mutations that often co-occurred (e.g., NPM1FLT3-ITD, DNMT3ANPM1, SRSF2IDH2, and WT1FLT3-ITD) and those that were mutually exclusive (e.g., NRASKRAS, FLT3-D835ITD, and IDH1IDH2) at single-cell resolution. Reconstruction of the tumor phylogeny uncovered history of tumor development that is characterized by linear and branching clonal evolution patterns with latter involving functional convergence of separately evolved clones. Analysis of longitudinal samples revealed remodeling of clonal architecture in response to therapeutic pressure that is driven by clonal selection. Furthermore, in this AML cohort, higher clonal diversity (≥4 subclones) was associated with significantly worse overall survival. These data portray clonal relationship, architecture, and evolution of AML driver genes with unprecedented resolution, and illuminate the role of clonal diversity in therapeutic resistance, relapse and clinical outcome in AML.

biorxiv cancer-biology 0-100-users 2020

Widespread divergent transcription from prokaryotic promoters, bioRxiv, 2020-02-03

ABSTRACTPromoters are DNA sequences that stimulate the initiation of transcription. In all prokaryotes, promoters are believed to drive transcription in a single direction. Here we show that prokaryotic promoters are frequently bidirectional and drive divergent transcription. Mechanistically, this occurs because key promoter elements have inherent symmetry and often coincide on opposite DNA strands. Reciprocal stimulation between divergent transcription start sites also contributes. Horizontally acquired DNA is enriched for bidirectional promoters suggesting that they represent an early step in prokaryotic promoter evolution.

biorxiv molecular-biology 0-100-users 2020

A Systematic Evaluation of Single-cell RNA-sequencing Imputation Methods, bioRxiv, 2020-01-30

ABSTRACTThe rapid development of single-cell RNA-sequencing (scRNA-seq) technology, with increased sparsity compared to bulk RNA-sequencing (RNA-seq), has led to the emergence of many methods for preprocessing, including imputation methods. Here, we systematically evaluate the performance of 18 state-of-the-art scRNA-seq imputation methods using cell line and tissue data measured across experimental protocols. Specifically, we assess the similarity of imputed cell profiles to bulk samples as well as investigate whether methods recover relevant biological signals or introduce spurious noise in three downstream analyses differential expression, unsupervised clustering, and inferring pseudotemporal trajectories. Broadly, we found significant variability in the performance of the methods across evaluation settings. While most scRNA-seq imputation methods recover biological expression observed in bulk RNA-seq data, the majority of the methods do not improve performance in downstream analyses compared to no imputation, in particular for clustering and trajectory analysis, and thus should be used with caution. Furthermore, we find that the performance of scRNA-seq imputation methods depends on many factors including the experimental protocol, the sparsity of the data, the number of cells in the dataset, and the magnitude of the effect sizes. We summarize our results and provide a key set of recommendations for users and investigators to navigate the current space of scRNA-seq imputation methods.

biorxiv genomics 0-100-users 2020

Copy number variants outperform SNPs to reveal genotype-temperature association in a marine species, bioRxiv, 2020-01-30

AbstractCopy number variants (CNVs) are a major component of genotypic and phenotypic variation in genomes. Yet, our knowledge on genotypic variation and evolution is often limited to single nucleotide polymorphism (SNPs) and the role of CNVs has been overlooked in non-model species, partly due to their challenging identification until recently. Here, we document the usefulness of reduced-representation sequencing data (RAD-seq) to detect and investigate copy number variants (CNVs) alongside SNPs in American lobster (Homarus americanus) populations. We conducted a comparative study to examine the potential role of SNPs and CNVs in local adaptation by sequencing 1141 lobsters from 21 sampling sites within the southern Gulf of St. Lawrence which experiences the highest yearly thermal variance of the Canadian marine coastal waters. Our results demonstrated that CNVs accounts for higher genetic differentiation than SNP markers. Contrary to SNPs for which no association was found, genetic-environment association revealed that 48 CNV candidates were significantly associated with the annual variance of sea surface temperature, leading to the genetic clustering of sampling locations despite their geographic separation. Altogether, we provide a strong empirical case that CNVs putatively contribute to local adaptation in marine species and unveil stronger spatial signal than SNPs. Our study provides the means to study CNVs in non-model species and underlines the importance to consider structural variants alongside SNPs to enhance our understanding of ecological and evolutionary processes shaping adaptive population structure.

biorxiv genomics 0-100-users 2020