scholarly journals Biological Control of Fusarium oxysporum f. sp. cubense Tropical Race 4 Using Natively Isolated Bacillus spp. YN0904 and YN1419

2021 ◽  
Vol 7 (10) ◽  
pp. 795
Author(s):  
Huacai Fan ◽  
Shu Li ◽  
Li Zeng ◽  
Ping He ◽  
Shengtao Xu ◽  
...  
Keyword(s):  
Biological Control ◽  
Yunnan Province ◽  
Marker Gene ◽  
Marker Genes ◽  
Banana Production ◽  
Physiological And Biochemical Characteristics ◽  
Bacillus Spp ◽  
Fusarium Wilt Of Banana ◽  
Race 4 ◽  
Physiological And Biochemical

Fusarium wilt of banana (FWB) is the main threatening factor for banana production worldwide. To explore bacterial biocontrol resources for FWB, the antagonistic effective strains were isolated from banana-producing areas in Yunnan Province, China. Two isolates (YN0904 and YN1419) displaying strong antagonism against Tropical Race 4 (TR4) were identified from a total of 813 strains of endophytic bacteria. TR4 inhibition rates of YN0904 and YN1419 were 79.6% and 81.3%, respectively. By looking at morphological, molecular, physiological and biochemical characteristics, YN0904 was identified as Bacillus amyloliquefaciens, while YN1419 was identified as as B. subtillis. The control effects of YN0904 and YN1419 on TR4 in greenhouse experiments were 82.6% and 85.6%, respectively. Furthermore, YN0904 obviously promoted the growth of banana plantlets. In addition, biocontrol marker genes related to the biosynthesis of antibiotics synthesized and auxin key synthetase genes could be detected in YN0904. Surprisingly, the marker gene sboA could be exclusively detected in YN1419, while other marker genes were all absent. Molecular characterization results could provide a theoretical basis for expounding the biocontrol mechanisms of these two strains. We concluded that natively antagonistic strains derived from local banana plantations could provide new biological control resources for FWB.

scholarly journals Multi-view feature selection for identifying gene markers: a diversified biological data driven approach

2020 ◽  
Vol 21 (S18) ◽  
Author(s):  
Sudipta Acharya ◽  
Laizhong Cui ◽  
Yi Pan
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Selection ◽  
Marker Gene ◽  
Biological Data ◽  
Protein Interaction Data ◽  
Marker Genes ◽  
Data Sets ◽  
Gene Markers ◽  
Multi Objective

Abstract Background In recent years, to investigate challenging bioinformatics problems, the utilization of multiple genomic and proteomic sources has become immensely popular among researchers. One such issue is feature or gene selection and identifying relevant and non-redundant marker genes from high dimensional gene expression data sets. In that context, designing an efficient feature selection algorithm exploiting knowledge from multiple potential biological resources may be an effective way to understand the spectrum of cancer or other diseases with applications in specific epidemiology for a particular population. Results In the current article, we design the feature selection and marker gene detection as a multi-view multi-objective clustering problem. Regarding that, we propose an Unsupervised Multi-View Multi-Objective clustering-based gene selection approach called UMVMO-select. Three important resources of biological data (gene ontology, protein interaction data, protein sequence) along with gene expression values are collectively utilized to design two different views. UMVMO-select aims to reduce gene space without/minimally compromising the sample classification efficiency and determines relevant and non-redundant gene markers from three cancer gene expression benchmark data sets. Conclusion A thorough comparative analysis has been performed with five clustering and nine existing feature selection methods with respect to several internal and external validity metrics. Obtained results reveal the supremacy of the proposed method. Reported results are also validated through a proper biological significance test and heatmap plotting.

PPIT: an R package for inferring microbial taxonomy from nifH sequences

2021 ◽  
Author(s):  
Bennett J Kapili ◽  
Anne E Dekas
Keyword(s):  
Gene Transfer ◽  
Horizontal Gene Transfer ◽  
Query Sequence ◽  
Marker Gene ◽  
R Package ◽  
Supplementary Information ◽  
Marker Genes ◽  
Pairwise Identity ◽  
Metabolic Marker ◽  
Microbial Taxonomy

Abstract Motivation Linking microbial community members to their ecological functions is a central goal of environmental microbiology. When assigned taxonomy, amplicon sequences of metabolic marker genes can suggest such links, thereby offering an overview of the phylogenetic structure underpinning particular ecosystem functions. However, inferring microbial taxonomy from metabolic marker gene sequences remains a challenge, particularly for the frequently sequenced nitrogen fixation marker gene, nitrogenase reductase (nifH). Horizontal gene transfer in recent nifH evolutionary history can confound taxonomic inferences drawn from the pairwise identity methods used in existing software. Other methods for inferring taxonomy are not standardized and require manual inspection that is difficult to scale. Results We present Phylogenetic Placement for Inferring Taxonomy (PPIT), an R package that infers microbial taxonomy from nifH amplicons using both phylogenetic and sequence identity approaches. After users place query sequences on a reference nifH gene tree provided by PPIT (n = 6317 full-length nifH sequences), PPIT searches the phylogenetic neighborhood of each query sequence and attempts to infer microbial taxonomy. An inference is drawn only if references in the phylogenetic neighborhood are: (1) taxonomically consistent and (2) share sufficient pairwise identity with the query, thereby avoiding erroneous inferences due to known horizontal gene transfer events. We find that PPIT returns a higher proportion of correct taxonomic inferences than BLAST-based approaches at the cost of fewer total inferences. We demonstrate PPIT on deep-sea sediment and find that Deltaproteobacteria are the most abundant potential diazotrophs. Using this dataset we show that emending PPIT inferences based on visual inspection of query sequence placement can achieve taxonomic inferences for nearly all sequences in a query set. We additionally discuss how users can apply PPIT to the analysis of other marker genes. Availability PPIT is freely available to non-commercial users at https://github.com/bkapili/ppit. Installation includes a vignette that demonstrates package use and reproduces the nifH amplicon analysis discussed here. The raw nifH amplicon sequence data have been deposited in the GenBank, EMBL, and DDBJ databases under BioProject number PRJEB37167. Supplementary information Supplementary data are available at Bioinformatics online.

Comparative Characteristics and Desilicon Ability of 10 Strains of Silicate Bacteria Screened from Different Bauxite Ores

2012 ◽  
Vol 524-527 ◽  
pp. 953-956
Author(s):  
Chan Juan Zhong ◽  
De Si Sun ◽  
Hong Bao ◽  
Hao Chen
Keyword(s):  
Henan Province ◽  
Jiangxi Province ◽  
Biochemical Characteristics ◽  
Physiological And Biochemical Characteristics ◽  
Silicate Bacteria ◽  
Bauxite Ore ◽  
Physiological And Biochemical

Ten strains of silicate bacteria were screened from three bauxite samples in Jiangxi Province and Henan Province in China. The ten strains were determined as B. mucilaginosus according to the results of physiological and biochemical characteristics and desilicon ability. Bioleaching tests showed that the ten strains all can decompose bauxite ore and release silicon from bauxite, but had a large difference of desilicon ability among them.

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