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Li, L.-G.

Publications and source records attributed to Li, L.-G..

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ARGs-OSP: online searching platform for antibiotic resistance genes distribution in metagenomic database and bacterial whole genome database

BackgroundThe antibiotic resistant genes (ARGs) have been emerging as one of the top global issue s in both medical and environmental fields. The metagenomic analysis has been widely adopted in ARG-related studies, revealing a universal presence of ARGs in diverse environments from medical settings to natural habitats, even in drinking water and ancient permafrost. With the tremendous resources of accessible metagenomic datasets, it would be feasible and beneficial to construct a global profile of antibiotic resistome as a guidance of its phylogenetic and ecological distribution. And such information should be shared by an open webpage to avoid the unnecessary repeat of data processing and the bias caused by incompatible search method.\n\nResultsTwo dataset collections, the Whole Genome Database (WGD, 54,718 complete and draft bacterial genomes) and the Metagenomic Database (MGD, 854 metagenomic datasets of 7 eco-types), were downloaded and analyzed using a standard method of ARG online analysis platform (ARGs-OAP v1.0). The representativeness of WGD and MGD was evaluated to have a comprehensive coverage of ARGs in bacterial genomes and metagenomes. Besides, an ARGs online searching platform (ARGs-OSP, http://args-osp.herokuapp.com/) was developed in this study to make the data accessible to other researchers via the search and download functionality. Finally, flexible usage of the ARGs-OAP was demonstrated by evaluating the co-occurrence of class 1 integrases and total ARGs across different environments.\n\nConclusionsThe ARGs-OSP is presented in this study as the valuable sources and references for future studies with versatile research interests, meanwhile avoiding unnecessary re-computations and re-analysis.

bioinformatics

AQMM: Enabling Absolute Quantification of Metagenome and Metatranscriptome

Metatranscriptome has become increasingly important along with the application of next generation sequencing in the studies of microbial functional gene activity in environmental samples. However, the quantification of target active gene is hindered by the current relative quantification methods, especially when tracking the sharp environmental change. Great needs are here for an easy-to-perform method to obtain the absolute quantification. By borrowing information from the parallel metagenome, an absolute quantification method for both metagenomic and metatranscriptomic data to per gene/cell/volume/gram level was developed. The effectiveness of AQMM was validated by simulated experiments and was demonstrated with a real experimental design of comparing activated sludge with and without foaming. Our method provides a novel bioinformatic approach to fast and accurately conduct absolute quantification of metagenome and metatranscriptome in environmental samples. The AQMM can be accessed from https://github.com/biofuture/aqmm.

bioinformatics