Paired-end reads of 250 bp generated by MiSeq sequencing were debarcoded using vendor software from Illumina. An in-house analysis pipeline running on a 32-node Linux cluster was used to process the data. Reads were considered duplicates if bases 5 to 55 were identical and only one random copy of duplicates was kept. Clonal reads were removed, and low-sequencing-quality tails were trimmed using Phred. Adaptors were trimmed using VecScreen with the default parameters, which uses NCBI BLASTn with specific parameters designed for adapter removal. The cleaned reads were then compared to an in-house non-virus non-redundant (NVNR) protein database to remove false-positive viral hits using DIAMOND BLASTx search with default parameters [16 (link)]. The NVNR database was compiled using non-viral protein sequences extracted from an NCBI nr fasta file (based on annotation taxonomy, excluding the virus kingdom). Then, taxonomic classification for DIAMOND results was parsed using MEGAN to perform the LCA-assignment algorithm according to default parameters. Gene assembly, prediction, and annotation were completed with Geneious software [17 (link)].
Vendor software
Manufactured by Illumina
Vendor software is a suite of applications designed to facilitate the management and analysis of data generated by Illumina's sequencing instruments. The software provides tools for data processing, visualization, and storage, enabling users to effectively utilize the output from Illumina's lab equipment.
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Lab products found in correlation
11 protocols using vendor software
1
Viral Metagenome Analysis Pipeline
2
Viral Genome Identification and Annotation
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The generated 250-bp paired-end reads were debarcoded for each pool using vendor software from Illumina. Clonal reads were removed, and low sequencing quality tails were trimmed using Phred quality score 30 (Q30) as the threshold. The cleaned reads were then compared to an in-house nonvirus nonredundant (NVNR) protein database to remove false-positive viral hits using DIAMOND BLASTx search with default parameters (46 (link)). Then, taxonomic classification for DIAMOND results was parsed using MEGAN to perform the LCA-assignment algorithm according to default parameters. All viral sequence reads were de novo assembled using the Geneious Prime v2019.2.3 (Biomatters Ltd) (47 (link)). The contigs and singlet sequences were then matched against the viral proteome database using BLASTx (E-value < 10−5) (48 (link)) to confirm the virus types and remove false virus sequences. The open reading frames (ORFs) in the viral genome were predicted by combining Geneious Prime software and the BLASTx search results. Potential exons and introns were predicted by NetGene2 (https://services.healthtech.dtu.dk/service.php?NetGene2-2.42 ). The protein domains were identified and annotated using the NCBI conserved domain search (E-value < 10−5) (49 (link)).
3
Nextera XT Library Preparation and Sequencing
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Nextera XT Sample Preparation Kit (Illumina Inc) was used to construct a DNA library for each sample using dual barcodes. After library preparation each samples was quantified using Nebnext® library quant (Illumina Inc) following the manufacturer's instructions and normalized in equimolar quantities before loading the flow cell. The library was deep-sequenced using the MiSeq Illumina platform with 2 x 75 bp paired ends, which allow us to sequence both ends of a fragment and generate high-quality alignable sequence. Paired-end reads were demultiplexed using the vendor software from Illumina. Demultiplexed Illumina reads were mapped on the KP164568 reference genome using Bowtie2 program with default parameters (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3322381/ ). Final consensus sequences were generated by the consensus module of Integrate Genome Viewer (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3346182/ ) with a 5x minimum read depth coverage. Any nucleotide variants on the primer regions were removed from the final consensus sequence.
4
Viral Sequence Identification Protocol
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For bioinformatics analyses, the generated reads were debarcoded using vendor software from Illumina. Clonal reads were removed, and low sequencing quality tails were trimmed using Phred quality score 30 (Q30) as the threshold. The cleaned reads were de novo assembly using the Geneious Prime (v2019.2.3) [35 (link)]. To find viral-related sequences, the assembled contigs and singlet sequences were then matched against the NCBI non-redundant nucleotide (NT) and protein (NR) databases using BLAST (E-value < 10−5) [36 (link)]. Candidate viral hits were then compared to a non-virus non-redundant protein database to remove false positive viral hits.
5
Viral Sequence Identification from Illumina Data
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The 250 bp paired-end reads generated by HiSeq sequencing were debarcoded using vendor software from Illumina. Clonal reads were abandoned, and low sequencing quality tails were trimmed using Phred quality score 30 as the threshold. The cleaned reads from Illumina sequencing were assembled de novo within each barcode group using the Ensemble assembler to merge them into longer contigs. The assembled contigs, as well as singlets, were compared to an in-house viral proteome database using BLASTx with an E-value cutoff of 10–5 [20 (link), 21 (link)].
6
Viral Metagenome Assembly and Analysis
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Paired-end reads of 250 bp generated by MiSeq sequencing were debarcoded using vendor software from Illumina. An in-house analysis pipeline running on a 32-node Linux cluster was used to process the data. Clonal reads were removed, and low-sequencing-quality tails were trimmed using Phred, with a quality of score ten as the threshold. Adaptors were trimmed using the default parameters in VecScreen, which uses NCBI BLASTn with specialized parameters designed for adapter removal. The cleaned reads from Illumina sequencing were assembled de novo within each barcode group using the Ensemble assembler to combine them into longer contigs. The Ensemble strategy integrates the sequential use of multifarious de Bruijn graph (DBG) and overlap-layout-consensus (OLC) assemblers by using a novel partitioned sub-assembly approach, integrating results from multiple assemblers, including SOAPDenovo2, ABySS, MetaVelvet, and Cap3 (source code available at https://github.com/xutaodeng/EnsembleAssembler ). The assembled contigs, along with singlets, were compared to an in-house viral proteome database using BLASTx with an E-value cutoff of <10−5 [14 (link), 15 (link)].
7
Microbial Metagenome Assembly: Paired-End Preprocessing
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Paired-end reads of 250 bp generated by MiSeq were debarcoded using vendor software from Illumina. Human host reads and bacterial reads are identified and removed by mapping the raw reads to human reference genome hg38 and bacterial genomes release 66 using bowtie2 in local search mode with other parameters set as default, requiring finding 60bp aligned segments with at most 2 mismatches and no gaps [12 (link)]. Reads were considered duplicates if 5bp to 55bp from 5’ end are identical. One random copy of duplicates was kept. Duplicate sequences were replaced with sequence ‘A’ as a place holder; preserving the original order of the paired-end files for paired-end sequence assembly. A paired-end sequence record is removed if both paired reads are deleted duplicates. Low sequencing quality tails were trimmed using Phred quality score 20 as the threshold. Adaptor and primer sequences were trimmed using the default parameters of VecScreen using default parameters [14 (link)].
8
Viral Metagenomics Data Processing
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Paired-end reads of 250 bp were generated by NovaSeq sequencing and then debarcoded for each DNA library by vendor software from Illumina. An in-house analysis pipeline running on a 32-node Linux cluster was utilized to process data. Reads were considered duplicates if bases 5–55 were identical, and then only one random copy of duplicates was kept and clonal reads were removed. Low-sequencing-quality tails were trimmed with a quality of score 30 as the threshold using Phred. Adaptors were trimmed using the default parameters of VecScreen in the National Center for Biotechnology Information (NCBI) (https://www.ncbi.nlm.nih.gov/tools/vecscreen/ ). The cleaned reads were assembled de novo within each barcode using Geneious Prime v2019.2.3.46 (link) Contigs and singlets were matched against an in-house viral proteome database using BLASTx with an E-value of <10−5.47 (link),48 (link)
9
Bioinformatics Analysis of Viral Metagenomes
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For bioinformatics analysis, paired-end reads of 250 bp generated by MiSeq were debarcoded using vendor software from Illumina. An in-house analysis pipeline running on a 32-nodes Linux cluster was used to process the data. Clonal reads were removed and low sequencing quality tails were trimmed using Phred quality score 20 as the threshold. Adaptors were trimmed using the default parameters of VecScreen which is NCBI BLASTn with specialized parameters designed for adapter removal. The cleaned reads were de novo assembled by SOAPdenovo2 version r240 using Kmer size 63 with default settings. The assembled contigs along with singlets were matched to an in-house viral proteome database using BLASTx with an E-value cutoff of < 10− 5 [23 (link), 24 (link)]. These BLASTx results generated by DIAMOND (DAA format) were used to generate rma6 format files by MEGAN6 software, which can be further used for subsequent analysis including species accumulation curve, and Co-occurrence plot [25 (link)].
10
Viral Genome Assembly and Annotation Pipeline
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Paired-end reads of 250 bp generated by HiSeq were debarcoded using vendor software from Illumina. Clonal reads were removed, and low-sequencing-quality tails were trimmed using Phred. Adaptors were removed using the default parameters of VecScreen[21 (link)]. The cleaned reads were assembled de novo within each barcode group utilizing the ENSEMBLE assembler [22 (link)]. The assembled contigs and singlets were compared to an in-house viral proteome database using BLASTx with an E-value cutoff of < 10¯5. Candidate viral hits were then contrasted to an in-house non-redundant (NVNR) protein database to remove false-positive viral hits. The NVNR database was compiled using non-viral protein sequences extracted from an NCBI nr fasta file (based on annotation taxonomy, excluding the virus kingdom). Contigs without significant BLASTx similarity to the viral proteome database were searched against viral protein families in the vFam database using HMMER3 to detect remote viral protein similarities.
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