Ma, F. | Search bioRxiv

bioRxiv2019

Genome analysis and Hi-C assisted assembly of Elaeagnus angustifolia L., a deciduous tree belonging to Elaeagnaceae

Elaeagnus angustifolia L. is a deciduous tree of the Elaeagnaceae family. It is widely used in the study of abiotic stress tolerance in plants and for the improvement of desertification-affected land due to its characteristics of drought resistance, salt tolerance, cold resistance, wind resistance, and other environmental adaptation. Here, we report the complete genome sequencing using the Pacific Biosciences (PacBio) platform and Hi-C assisted assembly of E. angustifolia. A total of 44.27 Gb raw PacBio sequel reads were obtained after filtering out low-quality data, with an average length of 8.64 Kb. And 39.56 Gb clean reads was obtained, with a sequencing coverage of 75x, and Q30 ratio > 95.46%. The 510.71 Mb genomic sequence was mapped to the chromosome, accounting for 96.94% of the total length of the sequence, and the corresponding number of sequences was 269, accounting for 45.83% of the total number of sequences. The genome sequence study of E. angustifolia can be a valuable source for the comparative genome analysis of the Elaeagnaceae family members, and can help to understand the evolutionary response mechanisms of the Elaeagnaceae to drought, salt, cold and wind resistance, and thereby provide effective theoretical support for the improvement of desertification-affected land.

genetics↗

bioRxiv2019

Automated identification of Cell Types in Single Cell RNA Sequencing

Cell type identification is one of the major goals in single cell RNA sequencing (scRNA-seq). Current methods for assigning cell types typically involve the use of unsupervised clustering, the identification of signature genes in each cluster, followed by a manual lookup of these genes in the literature and databases to assign cell types. However, there are several limitations associated with these approaches, such as unwanted sources of variation that influence clustering and a lack of canonical markers for certain cell types. Here, we present ACTINN (Automated Cell Type Identification using Neural Networks), which employs a neural network with 3 hidden layers, trains on datasets with predefined cell types, and predicts cell types for other datasets based on the trained parameters. We trained the neural network on a mouse cell type atlas (Tabula Muris Atlas) and a human immune cell dataset, and used it to predict cell types for mouse leukocytes, human PBMCs and human T cell sub types. The results showed that our neural network is fast and accurate, and should therefore be a useful tool to complement existing scRNA-seq pipelines. Author SummarySingle cell RNA sequencing (scRNA-seq) provides high resolution profiling of the transcriptomes of individual cells, which inevitably results in high volumes of data that require complex data processing pipelines. Usually, one of the first steps in the analysis of scRNA-seq is to assign individual cells to known cell types. To accomplish this, traditional methods first group the cells into different clusters, then find marker genes, and finally use these to manually assign cell types for each cluster. Thus these methods require prior knowledge of cell type canonical markers, and some level of subjectivity to make the cell type assignments. As a result, the process is often laborious and requires domain specific expertise, which is a barrier for inexperienced users. By contrast, our neural network ACTINN automatically learns the features for each predefined cell type and uses these features to predict cell types for individual cells. This approach is computationally efficient and requires no domain expertise of the tissues being studied. We believe ACTINN allows users to rapidly identify cell types in their datasets, thus rendering the analysis of their scRNA-seq datasets more efficient.

bioinformatics↗