PENG, Y. | bioRxiv Science

bioRxiv2022

Prediction of range expansion and estimation of dispersal routes of water deer (Hydropotes inermis) in the transboundary region between China, the Russian Far East and the Korean Peninsula

Global changes may direct species expansion away from their current range. When such an expansion occurs, and a species colonizes a new region, it is important to monitor the habitat used by the species and use the information for updated management strategies. Water deer is listed as Vulnerable species in IUCN Red List and restricted to east central China and the Korean Peninsula. Since 2017 water deer has expanded its range towards northeast China and the Russian Far East. Our research focuses on data collected in northeast China and the Russian Far East during 2017-2021, with the purpose of providing support for a better understanding of habitat use and provide conservation suggestions. We used MaxEnt to model species niche and distribution and predict habitat suitability for water deer and applied the circuitscape to determine possible dispersal routes for the species. There is good quality habitat for water deer in the boundary area of the Yalu and Tumen River estuaries between China, North Korea, and the Russian Far East, as well as the east and west regions of the Korean Peninsula. Elevation, distance to cropland and water sources, and presence of wetlands were the variables that positively contributed to modelling the suitable habitats. Two possible dispersal routes were determined using the circuit theory, one was across the area from North Korea to the downstream Tumen transboundary region, and the other was across North Korea to the boundary region in China and along the tiger national park in northern China. A series of protected areas in North Korea, China, and Russia may support the dispersal of water deer. The establishment of a Northeast Asia landscape conservation network would help establish monitoring and conservation planning at a broad scale, and this study provides an example for the need for such a network

ecology↗

bioRxiv2021

A Reference-free Approach for Cell Type Classification with scRNA-seq

The single-cell RNA sequencing (scRNA-seq) has become a revolutionary technology to detect and characterize distinct cell populations under different biological conditions. Unlike bulk RNA-seq, the expression of genes from scRNA-seq is highly sparse due to limited sequencing depth per cell. This is worsened by tossing away a significant portion of reads that cannot be mapped during gene quantification. To overcome data sparsity and fully utilize original sequences, we propose scSimClassify, a reference-free and alignment-free approach to classify cell types with k-mer level features derived from raw reads in a scRNA-seq experiment. The major contribution of scSimClassify is the simhash method compressing k-mers with similar abundance profiles into groups. The compressed k-mer groups (CKGs) serve as the aggregated k-mer level features for cell type classification. We evaluate the performance of CKG features for predicting cell types in four scRNA-seq datasets comparing four state-of-the-art classification methods as well as two scRNA-seq specific algorithms. Our experiments demonstrate that the CKG features lend themselves to better performance than traditional gene expression features in scRNA-seq classification accuracy in the majority of cases. Because CKG features can be efficiently derived from raw reads without a resource-intensive alignment process, scSimClassify offers an efficient alternative to help scientists rapidly classify cell types without relying on reference sequences. The current version of scSimClassify is implemented in python and can be found at https://github.com/digi2002/scSimClassify.

bioinformatics↗