Diamond NCBI Genbank Viral database for SOVAP

<strong>Diamond NCBI Genbank Viral database</strong> Database type: Diamond database Database format version: 3 Label: 2022-08-18_23-00-37 Sequences: 2,253,849 Sum length: 537,674,059 Assembly summary entries: 50,633 -------------------------------------------------------- <strong>SOVAP v.1.3: </strong>GitHub <strong><em>Soil Virome Analysis Pipeline</em></strong> Description The study of viral communities in complex environmental samples, such as <strong>soil</strong>, can provide valuable insights into the diversity and functions of viral communities in the ecosystem. However, processing and analyzing of virome data can be a challenging task that requires the integration of various computational tools and techniques. To address these challenges, we have developed <strong>SOVAP</strong> pipeline that utilizes a suite of state-of-the-art tools for processing, analysis, and annotation viromics and metagenomics data. It utilizes various tools such as <strong>Fastp</strong> and <strong>Centrifuge</strong> for preprocessing and contamination removal, <strong>geNomad</strong>, <strong>Diamond</strong> and <strong>Megan</strong> for identification and annotation of viral contigs which are assembled and clustered using <strong>Megahit</strong> and <strong>CD-HIT</strong>. Additionally, this pipeline provides an <strong>estimate of the abundance</strong> of viral contigs, allowing for a more comprehensive understanding of the virome within the sample. The integration of these tools offers a reliable and effective means of taxonomy classification and annotation of viral contigs, aiding researchers in gaining insight into the composition and function of the virome within the analyzed sample. By integrating the SOVAP pipeline with <strong>IMG/VR</strong> and <strong>geNomad</strong>, it is possible to identify a wider range of viruses, including those that were previously unknown. The <strong>batch-mode</strong> script allows for the processing of multiple datasets using the SOVAP pipeline. This feature is particularly useful for <strong>large-scale</strong> analyses, such as those involving multiple environmental samples or large sequencing datasets.