Project description:we studied the functional composition of a packed-bed nitrifying bioreactor inoculated with a co-culture of Nitrosomonas europaea (ATCC 25978) and Nitrobacter winogradskyi (ATCC 25391) after 840 days of operation.
Project description:We compared standard human reference genome GRCh38 and de novo assembled reference genome HX1 in precision medicine applications for specific ethnics. In order to quantify the HX1 misassembled genes and HX1-specific contigs, we performed RNA-seq and RNC-seq on hepatocellular carcinoma cell lines (MHCC97H, MHCCLM3 and MHCCLM6) which were derived from Chinese Han individuals. In which, RNC-seq datasets of MHCC97H and MHCCLM3 had been published. We found that a considerable fraction of HX1 misassembled genes was expressed in the Chinese Han samples. Furthermore, we found no HX1-specific contigs yielded more than 2.27 FPKM (minimun FPKM of 1 copy/cell transcript) in the Chinese Han sampels.
Project description:MotivationBacteriophages (aka phages), which mainly infect bacteria, play key roles in the biology of microbes. As the most abundant biological entities on the planet, the number of discovered phages is only the tip of the iceberg. Recently, many new phages have been revealed using high-throughput sequencing, particularly metagenomic sequencing. Compared to the fast accumulation of phage-like sequences, there is a serious lag in taxonomic classification of phages. High diversity, abundance and limited known phages pose great challenges for taxonomic analysis. In particular, alignment-based tools have difficulty in classifying fast accumulating contigs assembled from metagenomic data.ResultsIn this work, we present a novel semi-supervised learning model, named PhaGCN, to conduct taxonomic classification for phage contigs. In this learning model, we construct a knowledge graph by combining the DNA sequence features learned by convolutional neural network and protein sequence similarity gained from gene-sharing network. Then we apply graph convolutional network to utilize both the labeled and unlabeled samples in training to enhance the learning ability. We tested PhaGCN on both simulated and real sequencing data. The results clearly show that our method competes favorably against available phage classification tools.Availability and implementationThe source code of PhaGCN is available via: https://github.com/KennthShang/PhaGCN.