Naught all zeros in sequence count data are the same, bioRxiv, 2018-11-27

AbstractDue to the advent and utility of high-throughput sequencing, modern biomedical research abounds with multivariate count data. Yet such sequence count data is often extremely sparse; that is, much of the data is zero values. Such zero values are well known to cause problems for statistical analyses. In this work we provide a systematic description of different processes that can give rise to zero values as well as the types of methods for addressing zeros in sequence count studies. Importantly, we systematically review how various models perform on each type of zero generating process. Our results demonstrate that zero-inflated models can have substantial biases in both simulated and real data settings. Additionally, we find that zeros due to biological absences can, for many applications, be approximated as originating from under sampling. Beyond these results, this work provides a paired categorization scheme for models and zero generating processes to facilitate discussions and future research into the analysis of sequence count data.

biorxiv bioinformatics 100-200-users 2018

C. elegans pathogenic learning confers multigenerational pathogen avoidance, bioRxiv, 2018-11-22

AbstractThe ability to pass on learned information to progeny could present an evolutionary advantage for many generations. While apparently evolutionarily conserved1–12, transgenerational epigenetic inheritance (TEI) is not well understood at the molecular or behavioral levels. Here we describe our discovery that C. elegans can pass on a learned pathogenic avoidance behavior to their progeny for several generations through epigenetic mechanisms. Although worms are initially attracted to the gram-negative bacteria P. aeruginosa (PA14)13, they can learn to avoid this pathogen13. We found that prolonged PA14 exposure results in transmission of avoidance behavior to progeny that have themselves never been exposed to PA14, and this behavior persists through the fourth generation. This form of transgenerational inheritance of bacterial avoidance is specific to pathogenic P. aeruginosa, requires physical contact and infection, and is distinct from CREB-dependent long-term associative memory and larval imprinting. The TGF-β ligand daf-7, whose expression increases in the ASJ upon initial exposure to PA1414, is highly expressed in the ASI neurons of progeny of trained mothers until the fourth generation, correlating with transgenerational avoidance behavior. Mutants of histone modifiers and small RNA mediators display defects in naïve PA14 attraction and aversive learning. By contrast, the germline-expressed PRG-1Piwi homolog15 is specifically required for transgenerational inheritance of avoidance behavior. Our results demonstrate a novel and natural paradigm of TEI that may optimize progeny decisions and subsequent survival in the face of changing environmental conditions.

biorxiv genetics 100-200-users 2018

Hunger for Knowledge How the Irresistible Lure of Curiosity is Generated in the Brain, bioRxiv, 2018-11-22

Introductory ParagraphCuriosity is often portrayed as a desirable feature of human faculty. For example, a meta-analysis revealed that curiosity predicts academic performance above and beyond intelligence1, corroborating findings that curiosity supported long-term consolidation of learning2,3. However, curiosity may come at a cost of strong seductive power that sometimes puts people in a harmful situation. Here, with a set of three behavioural and two neuroimaging experiments including novel stimuli that strongly trigger curiosity (i.e. magic tricks), we examined the psychological and neural mechanisms underlying the irresistible lure of curiosity. We consistently demonstrated that across different samples people were indeed willing to gamble to expose themselves to physical risks (i.e. electric shocks) in order to satisfy their curiosity for trivial knowledge that carries no apparent instrumental values. Also, underlying this seductive power of curiosity is its incentive salience properties, which share common neural mechanisms with extrinsic incentives (i.e. hunger for foods). In particular, the two independent fMRI experiments using different kinds of curiosity-stimulating stimuli found replicable results that acceptance (compared to rejection) of curiosityincentive-driven gambles was accompanied with an enhanced activity in the striatum.

biorxiv neuroscience 100-200-users 2018

CRISPR-TSKO facilitates efficient cell type-, tissue-, or organ-specific mutagenesis in Arabidopsis, bioRxiv, 2018-11-21

AbstractDetailed functional analyses of many fundamentally-important plant genes via conventional loss-of-function approaches are impeded by severe pleiotropic phenotypes. In particular, mutations in genes that are required for basic cellular functions andor reproduction often interfere with the generation of homozygous mutant plants, precluding further functional studies. To overcome this limitation, we devised a CRISPR-based tissue-specific knockout system, CRISPR-TSKO, enabling the generation of somatic mutations in particular plant cell types, tissues, and organs. In Arabidopsis, CRISPR-TSKO mutations in essential genes caused well-defined, localized phenotypes in the root cap, stomatal lineage, or entire lateral roots. The underlying modular cloning system allows for efficient selection, identification, and functional analysis of mutant lines directly in the first transgenic generation. The efficacy of CRISPR-TSKO opens new avenues to discover and analyze gene functions in spatial and temporal contexts of plant life while avoiding pleiotropic effects of system-wide loss of gene function.

biorxiv plant-biology 100-200-users 2018

BEAST 2.5 An Advanced Software Platform for Bayesian Evolutionary Analysis, bioRxiv, 2018-11-20

AbstractElaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data. It is increasingly appreciated that some evolutionary questions can only be adequately answered by combining evidence from multiple independent sources of data, including genome sequences, sampling dates, phenotypic data, radiocarbon dates, fossil occurrences, and biogeographic range information among others. Including all relevant data into a single joint model is very challenging both conceptually and computationally. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments.Developing such software frameworks is increasingly a major scientific activity in its own right, and comes with specific challenges, from practical software design, development and engineering challenges to statistical and conceptual modelling challenges. BEAST 2 is one such computational software platform, and was first announced over 4 years ago. Here we describe a series of major new developments in the BEAST 2 core platform and model hierarchy that have occurred since the first release of the software, culminating in the recent 2.5 release.Author summaryBayesian phylogenetic inference methods have undergone considerable development in recent years, and joint modelling of rich evolutionary data, including genomes, phenotypes and fossil occurrences is increasingly common. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing scientific software is increasingly crucial to advancement in many fields of biology. The challenges range from practical software development and engineering, distributed team coordination, conceptual development and statistical modelling, to validation and testing. BEAST 2 is one such computational software platform for phylogenetics, population genetics and phylodynamics, and was first announced over 4 years ago. Here we describe the full range of new tools and models available on the BEAST 2.5 platform, which expand joint evolutionary inference in many new directions, especially for joint inference over multiple data types, non-tree models and complex phylodynamics.

biorxiv evolutionary-biology 100-200-users 2018

Deep Neural Networks and Kernel Regression Achieve Comparable Accuracies for Functional Connectivity Prediction of Behavior and Demographics, bioRxiv, 2018-11-20

AbstractThere is significant interest in the development and application of deep neural networks (DNNs) to neuroimaging data. A growing literature suggests that DNNs outperform their classical counterparts in a variety of neuroimaging applications, yet there are few direct comparisons of relative utility. Here, we compared the performance of three DNN architectures and a classical machine learning algorithm (kernel regression) in predicting individual phenotypes from whole-brain resting-state functional connectivity (RSFC) patterns. One of the DNNs was a generic fully-connected feedforward neural network, while the other two DNNs were recently published approaches specifically designed to exploit the structure of connectome data. By using a combined sample of almost 10,000 participants from the Human Connectome Project (HCP) and UK Biobank, we showed that the three DNNs and kernel regression achieved similar performance across a wide range of behavioral and demographic measures. Furthermore, the generic feedforward neural network exhibited similar performance to the two state-of-the-art connectome-specific DNNs. When predicting fluid intelligence in the UK Biobank, performance of all algorithms dramatically improved when sample size increased from 100 to 1000 subjects. Improvement was smaller, but still significant, when sample size increased from 1000 to 5000 subjects. Importantly, kernel regression was competitive across all sample sizes. Overall, our study suggests that kernel regression is as effective as DNNs for RSFC-based behavioral prediction, while incurring significantly lower computational costs. Therefore, kernel regression might serve as a useful baseline algorithm for future studies.

biorxiv neuroscience 100-200-users 2018