MA, C. | Search bioRxiv

bioRxiv2021

Isolation, screening, degradation characteristics of a quinclorac-degrading bacteria D and its potential of bioremediation for rice field environment polluted by quinclorac

Quinclorac (QNC) is a highly selective, hormonal, and low-toxic herbicide with a long duration. And the growth and development of subsequent crops are easily affected by QNC accumulated in the soil. In this paper, a QNC-degrading strain D was isolated and screened from the rice paddy soil. Through morphology, physiological and biochemical tests and 16Sr DNA gene analysis, strain D was identified as Cellulosimicrobium cellulans sp. And the QNC degradation characteristics of strain D were studied. Under the optimal culture conditions, the QNC-degrading rate was 45.9% after culturing for 21 days. The QNC-degrading efficiency of strain D in the field was evaluated by a simulated pot experiment. The results show that strain D can promote the growth of rice and QNC-degrading effectively. This research could provide a new bacterial species for microbial degradation of QNC and lay a theoretical foundation for further research on QNC remediation. ImportanceAt present, some QNC-degrading bacteria have been isolated from different environments, but there are no reports of Cellulosimicrobium cellulans sp. bacterial that could degrade QNC. In this study, a new QNC-degradation strain was selected from the paddy soil. The degradation characteristics of strain D were studied in detail. The results shown that strain D had a satisfactory quinclorac-degrading efficiency. Two degradation products of QNC by strain D were identified by HPLC-Q-TOF/MS: 3-pyridylacetic acid (138.0548 m/z) and 3-ethylpyridine (108.0805 m/z), which have not been reported before. The strain D had a potential ability of quinclorac-degrading effectively in the quinclorac-polluted paddy field environment.

microbiology↗

bioRxiv2020

precisionFDA Truth Challenge V2: Calling variants from short- and long-reads in difficult-to-map regions

The precisionFDA Truth Challenge V2 aimed to assess the state-of-the-art of variant calling in difficult-to-map regions and the Major Histocompatibility Complex (MHC). Starting with FASTQ files, 20 challenge participants applied their variant calling pipelines and submitted 64 variant callsets for one or more sequencing technologies (~35X Illumina, ~35X PacBio HiFi, and ~50X Oxford Nanopore Technologies). Submissions were evaluated following best practices for benchmarking small variants with the new GIAB benchmark sets and genome stratifications. Challenge submissions included a number of innovative methods for all three technologies, with graph-based and machine-learning methods scoring best for short-read and long-read datasets, respectively. New methods out-performed the 2016 Truth Challenge winners, and new machine-learning approaches combining multiple sequencing technologies performed particularly well. Recent developments in sequencing and variant calling have enabled benchmarking variants in challenging genomic regions, paving the way for the identification of previously unknown clinically relevant variants.

bioinformatics↗