After trimming off the 5' and 3' adaptor sequences, filtering low quality reads, and cleaning up the contaminated reads, the occurrence of each unique read was counted as tags. The sequences shorter than the cut-off read length 17 and more than 27 nucleotides were removed, as determined by the read length distribution plot. The trimmed sequences were mapped to database and further grouped as known mature miRNA. The completely matched reads were annotated according to their position in the stem-loop structure. These unique tags were mapped to the sequence data in miRBase 15 microRNA Sequence Database and Ensembl Homo_sapiens. GRCh37.57.ncrn using the Illumina 1.8 and later pipeline. Data trimming and initial analysis were carried out using CLC Genomics Workbench 7.0.
Genomics workbench 7
Manufactured by Qiagen
Genomics Workbench 7.0 is a bioinformatics software suite for the analysis and visualization of genomic data. It provides a comprehensive platform for tasks such as sequence assembly, variant calling, and biological data integration.
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Lab products found in correlation
4 protocols using genomics workbench 7
1
Mature miRNA Identification Pipeline
2
Differential miRNA Profiling of Spheroid Cells
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Analysis of the raw NGS data included the trimming, filtering, and cleaning up the contaminated reads was performed using CLC Genomics Workbench 7.0. The sequences with shorter read length of 17 and more than 27 were removed. The normalised trimmed read lengths were aligned to the Ensembl human database (Homo sapiens GRCh 37.57) and known miRNAs database (miRBase-19) using the Illumina 1.8 pipeline. The processed miRNAs data was then visually assessed using quality control plots. The differential profiling of genome-wide miRNAs between the spheroid and parental was then compared using Kal’s Z-test and the resulting p-values were background corrected using Benjamini–Hochberg method. The statistically significant differentially expressed miRNAs (FC > 2, P < 0.05) of the spheroid cells in relative to parental cells were then generated.
3
RNA-Seq Analysis of Ovis aries
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Genomics Server 6.0 and Genomics Workbench 7.0.4 (CLC bio) were used to analyze RNA-Seq data. Using the default parameters, we quality-trimmed the Ion reads (error probability: 0.02) and Illumina reads (error probability: 0.001), and 13 bps were trimmed from the 5ʹ-end of each read. The genome assembly of Ovis aries (Oar_v3.1) was used for mapping the sequencing libraries using Ensembl annotations version 75, requiring paired mapping and using fragment per kilobase of transcript per million (FPKM) value as an expression metric. A box plot of square root-transformed expression values was used to assess the quality control, ensuring that all samples showed similar distributions. Scatter plots were generated in the R programming environment (version 4.0.3) to identify the differentially expressed genes (DEGs) in AKD and BKD TE compared to NTC. Only the genes with |log fold change (logFC) ≥ 2| and FPKM ≥ 5 were considered as the DEGs. The heatmap of RNA expression profiles in different samples was generated using hierarchical clustering by heatmap.2 function of gplots package (version 3.0.1) in the R programming environment. The heatmap facilitated a comparison of expression profiles of the DEGs and transcription factors between different experimental groups.
4
Complete Genome Characterization of DASHV by NGS
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Supernatant corresponding to passage 2 of DASHV infected Vero cells was used for complete genome characterization using Next Generation Sequencing (NGS) as previously described [2 (link)]; viral sequences were identified from the contigs based on the best BLAST similarity against reference databases using the CLC Genomics Workbench 7.0.4. Reads > 30 nucleotides long were trimmed using CLC Genomic Workbench 6.5, with a minimum of 99% quality per base and mapped to reference sequences on Genbank. Parameters were set such that each accepted read had to map to the reference sequence for at least 50% of its length, with a minimum of 80% identity to the reference. Sequence gaps were completed by PCR, designing specific primers based on NGS results and for the extremities using the primers previously defined [14 (link)], and PCR fragments were sequenced either by Sanger sequencing or by NGS. Once the complete genome was revealed, Sanger sequencing was performed through specific primers designed for the confirmation of the complete sequence.
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