MOMS: A pipeline for scaffolding using multi-optical maps

Here, we report a new multi-optical maps scaffolder (MOMS) aiming at utilizing complementary information among optical maps labelled by distinct enzymes. This pipeline was designed for data structure organization, scaffolding by path traversal, gap-filling and molecule reuse of optical maps. Our testing showed that this pipeline has uncapped enzyme tolerance in scaffolding. This means that there are no inbuilt limits as to the number of maps generated by different enzymes that can be utilized by MOMS. For the genome assembly of the human GM12878 cell line, MOMS significantly improved the contiguity and completeness with an up to 144-fold increase of scaffold N50 compared with initial assemblies. Benchmarking on the genomes of human and O. sativa showed that MOMS is more effective and robust compared with other optical-map-based scaffolders. We believe this pipeline will contribute to high-fidelity chromosome assembly and chromosome-level evolutionary analysis.     

A genealogical estimate of genetic relationships

1. Download : Download high-res image (151KB)
2. Download : Download full-size image

     

Structure-based analyses of gut microbiome-related proteins by neural networks and molecular dynamics simulations

An imbalance in the gut microbiome is linked to immune disorders, such as autoimmune, allergic, and chronic inflammatory disorders. Elucidation of disease mechanisms is a matter of urgency. It requires precise elucidation of the structure-based mechanisms of protein interactions involved in disease onset. In addition, an understanding of the protein dynamics is vital because these fluctuations affect the function and interaction of disease-associated proteins. Experimental evaluation of not only protein interactions, functions, and structures but also the dynamics are time-consuming; therefore, computational predictions are necessary to elucidate disease mechanisms. Here, we introduce recent studies on structure-based analyses of proteins using computational approaches, particularly artificial intelligence (AI) and molecular dynamics (MD) simulations.     

A chromosome-level reference genome and pangenome for barn swallow population genomics

1. Download : Download high-res image (294KB)
2. Download : Download full-size image

     

Reconstructing disease outbreaks from genetic data: a graph approach

Epidemiology and public health planning will increasingly rely on the analysis of genetic sequence data. In particular, genetic data coupled with dates and locations of sampled isolates can be used to reconstruct the spatiotemporal dynamics of pathogens during outbreaks. Thus far, phylogenetic methods have been used to tackle this issue. Although these approaches have proved useful for informing on the spread of pathogens, they do not aim at directly reconstructing the underlying transmission tree. Instead, phylogenetic models infer most recent common ancestors between pairs of isolates, which can be inadequate for densely sampled recent outbreaks, where the sample includes ancestral and descendent isolates. In this paper, we introduce a novel method based on a graph approach to reconstruct transmission trees directly from genetic data. Using simulated data, we show that our approach can efficiently reconstruct genealogies of isolates in situations where classical phylogenetic approaches fail to do so. We then illustrate our method by analyzing data from the early stages of the swine-origin A/H1N1 influenza pandemic. Using 433 isolates sequenced at both the hemagglutinin and neuraminidase genes, we reconstruct the likely history of the worldwide spread of this new influenza strain. The presented methodology opens new perspectives for the analysis of genetic data in the context of disease outbreaks.     

Pseudo‐real‐time retinal layer segmentation for high‐resolution adaptive optics optical coherence tomography

We present a pseudo‐real‐time retinal layer segmentation for high‐resolution Sensorless Adaptive Optics‐Optical Coherence Tomography (SAO‐OCT). Our pseudo‐real‐time segmentation method is based on Dijkstra's algorithm that uses the intensity of pixels and the vertical gradient of the image to find the minimum cost in a geometric graph formulation within a limited search region. It segments six retinal layer boundaries in an iterative process according to their order of prominence. The segmentation time is strongly correlated to the number of retinal layers to be segmented. Our program permits en face images to be extracted during data acquisition to guide the depth specific focus control and depth dependent aberration correction for high‐resolution SAO‐OCT systems. The average processing times for our entire pipeline for segmenting six layers in a retinal B‐scan of 496 × 400 and 240 × 400 pixels are around 25.60 and 13.76 ms, respectively. When reducing the number of layers segmented to only two layers, the time required for a 240 × 400 pixel image is 8.26 ms.     

Model for Heterogeneous Random Networks Using Continuous Latent Variables and an Application to a Tree–Fungus Network

Summary The mixture model is a method of choice for modeling heterogeneous random graphs, because it contains most of the known structures of heterogeneity: hubs, hierarchical structures, or community structure. One of the weaknesses of mixture models on random graphs is that, at the present time, there is no computationally feasible estimation method that is completely satisfying from a theoretical point of view. Moreover, mixture models assume that each vertex pertains to one group, so there is no place for vertices being at intermediate positions. The model proposed in this article is a grade of membership model for heterogeneous random graphs, which assumes that each vertex is a mixture of extremal hypothetical vertices. The connectivity properties of each vertex are deduced from those of the extreme vertices. In this new model, the vector of weights of each vertex are fixed continuous parameters. A model with a vector of parameters for each vertex is tractable because the number of observations is proportional to the square of the number of vertices of the network. The estimation of the parameters is given by the maximum likelihood procedure. The model is used to elucidate some of the processes shaping the heterogeneous structure of a well‐resolved network of host/parasite interactions.