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High-throughput sequencing technologies have greatly enabled the study of genomics, transcriptomics and metagenomics. Automated annotation and classification of the vast amounts of generated sequence data has become paramount for facilitating biological sciences. Genomes of viruses can be radically different from all life, both in terms of molecular structure and primary sequence. Alignment-based and profile-based searches are commonly employed for characterization of assembled viral contigs from high-throughput sequencing data. Recent attempts have highlighted the use of machine learning models for the task, but these models rely entirely on DNA genomes and owing to the intrinsic genomic complexity of viruses, RNA viruses have gone completely overlooked. Here, we present a novel short k-mer based sequence scoring method that generates robust sequence information for training machine learning classifiers. We trained 18 classifiers for the task of distinguishing viral RNA from human transcripts. We challenged our models with very stringent testing protocols across different species and evaluated performance against BLASTn, BLASTx and HMMER3 searches. For clean sequence data retrieved from curated databases, our models display near perfect accuracy, outperforming all similar attempts previously reported. On de novo assemblies of raw RNA-Seq data from cells subjected to Ebola virus, the area under the ROC curve varied from 0.6 to 0.86 depending on the software used for assembly. Our classifier was able to properly classify the majority of the false hits generated by BLAST and HMMER3 searches on the same data. The outstanding performance metrics of our model lays the groundwork for robust machine learning methods for the automated annotation of sequence data.

The endophytic bacterium Burkholderia contaminans NZ was isolated from jute, which is an important fiber-producing plant. This bacterium exhibits significant growth promotion activity in in vivo pot experiments, and like other plant growth-promoting (PGP) bacteria fixes nitrogen, produces indole acetic acid (IAA), siderophore, and 1-aminocyclopropane-1-carboxylate (ACC) deaminase activity. B. contaminans NZ is considered to exert a promising growth inhibitory effect on Macrophomina phaseolina, a phytopathogen responsible for infecting hundreds of crops worldwide. This study aimed to identify the possibility of B. contaminans NZ as a safe biocontrol agent and assess its effectiveness in suppressing phytopathogenic fungi, especially M. phaseolina. Co-culture of M. phaseolina with B. contaminans NZ on both solid and liquid media revealed appreciable growth suppression of M. phaseolina and its chromogenic aberration in liquid culture. Genome mining of B. contaminans NZ using NaPDoS and antiSMASH revealed gene clusters that displayed 100% similarity for cytotoxic and antifungal substances, such as pyrrolnitrin. GC-MS analysis of B. contaminans NZ culture extracts revealed various bioactive compounds, including catechol; 9,10-dihydro-12’-hydroxy-2’-methyl-5’-(phenylmethyl)- ergotaman 3’,6’,18-trione; 2,3-dihydro-3,5- dihydroxy-6-methyl-4H-pyran-4-one; 1-(1,6-Dioxooctadecyl)- pyrrolidine; 9-Octadecenamide; and 2- methoxy- phenol. These compounds reportedly exhibit tyrosinase inhibitory, antifungal, and antibiotic activities. Using a more targeted approach, an RP-HPLC purified fraction was analyzed by LC-MS, confirming the existence of pyrrolnitrin in the B. contaminans NZ extract. Secondary metabolites, such as catechol and ergotaman, have been predicted to inhibit melanin synthesis in M. phaseolina. Thus, B. contaminans NZ appears to inhibit phytopathogens by apparently impairing melanin synthesis and other potential biochemical pathways, exhibiting considerable fungistatic activity.

Mohammad Ali Moni) 14 15 ABSTRACT 16 17 An outbreak, caused by a RNA virus, SARS-CoV-2 named COVID-19 has become pandemic with a 18 magnitude which is daunting to all public health institutions in the absence of specific antiviral 19 treatment. Surface glycoprotein and nucleocapsid phosphoprotein are two important proteins of this 20 virus facilitating its entry into host cell and genome replication. Small interfering RNA (siRNA) is a 21 prospective tool of the RNA interference (RNAi) pathway for the control of human viral infections 22 by suppressing viral gene expression through hybridization and neutralization of target 23 complementary mRNA. So, in this study, the power of RNA interference technology was harnessed 24 to develop siRNA molecules against specific target genes namely, nucleocapsid phosphoprotein 25 gene and surface glycoprotein gene. Conserved sequence from 139 SARS-CoV-2 strains from 26 around the globe was collected to construct 78 siRNA that can inactivate nucleocapsid 27 phosphoprotein and surface glycoprotein genes. Finally, based on GC content, free energy of 28folding, free energy of binding, melting temperature and efficacy prediction process 8 siRNA 29molecules were selected which are proposed to exerts the best action. These predicted siRNAs 30 should effectively silence the genes of SARS-CoV-2 during siRNA mediated treatment assisting in 31 the response against SARS-CoV-2 32 33

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