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10 protocols using novaseq 6000 pe150

1

Genomic DNA Extraction and WGS of PDX Models

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Genomic DNA was extracted from flash-frozen PDX tumor tissue and germline controls using the Qiagen DNeasy Blood and Tissue Kit (cat no. 69506). WGS of the 51 PDX models (41 with matched germline control) was performed by Novogene (www.novogene.com; NovaSeq 6000 PE150 with 60× coverage). Sequencing reads were aligned to the human reference genome GRCh38.v21 or the mouse reference genome GRCm38.p3 using bwa-0.7.12 (github.com/lh3/bwa). Mouse reads were filtered when the mouse alignment score (AS flag in bam files) was greater than the human alignment score. Duplicated reads were then marked using Picard MarkDuplicates (v2.27.5; https://broadinstitute.github.io/picard/).
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2

Transcriptome Analysis of Neonatal Lung in Mice

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Whole lung from control and mutant neonatal mice at P12 was dissected and briefly washed in cold 1× PBS to clear blood and was immediately snap frozen in liquid nitrogen and stored at –80°C until further analyses. Total RNA isolation from snap-frozen lungs was performed using QIAGEN RNeasy mini kit (catalog 74104) according to manufacturer instructions. Whole lung from each sample homogenized in lysis buffer RLT (Qiagen) provided with the kit and equal aliquots of the lysates from 2 controls and 2 mutants were respectively pooled prior to further downstream steps. RNA was finally eluted with RNase free water, quantified using nanodrop, and sent for sequencing (Novogene) as 3 replicates for each pooled sample. RNA library preparation and sequencing using NovaSeq 6000 PE150 were performed by Novogene Co. Ltd. Raw fastq files were processed using RNA-Seq pipeline of the DolphinNext platform (109 (link)). Paired end reads were mapped to mm10 using STAR (110 (link)) to generate BAM files, which were then used for transcript quantification using RSEM (111 (link)). Using gene-mapped count files from the RSEM output, differential expression analysis was performed in the DEBrowser interface (112 (link)).
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3

Transcriptome Profiling of Plant Treatments

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Total RNA was isolated using an RNeasy Plant Mini Kit (Qiagen, Germany) from 100 mg of ground samples. The quality of extracted RNA was assessed by 1% agarose gel electrophoresis, its purity was monitored using a NanoPhotometer® spectrophotometer (Implen, Germany), and its integrity and quantity were measured with an RNA Nano 6000 Assay Kit on a Bioanalyzer 2100 instrument (Agilent Technologies, CA, USA). One microgram of total RNA per sample (20 samples in total: five different treatments, N0, N1, N2, Org0, and Org2, with four biological replicates) was used to construct libraries with a NEBNext® UltraTM RNA Library Prep Kit for Illumina® (NEB, MA, USA) according to the manufacturer’s recommendations, using poly-T oligo-attached magnetic beads to purify the mRNA. PCR products and libraries were purified using an AMPure XP system (Beckman Coulter, Beverly, USA), and their quality was analyzed with a 2100 Bioanalyzer Instrument (Agilent, CA, USA). Finally, libraries were clustered and subsequently sequenced on an Illumina platform, NovaSeq 6000, PE150, by Novogene Co., Ltd (Cambridge, UK) to generate 150-bp paired-end reads.
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4

Genome Sequencing and Phylogenetic Analysis of Baculoviruses

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Viral DNA of SporNPV and SporGV was obtained from purified NPVs and GVs, respectively. OBs were dissolved by alkaline lysis and DNA was extracted by standard methods [15 (link)]. Whole genome sequencing was then conducted using Illumina platform (NovaSeq 6000 PE150; Novogene, Beijing, China). For quality control and trimming analysis, the FastQC and Trimmomatic programs were used [17 (link)], while Spades v. 3.13.0 in the Galaxy platform (http://usegalaxy.eu (accessed on 25 October 2021) was employed for the sequence assembly with default parameters [18 (link)]. Open reading frames (ORFs) were identified using ARTEMIS [19 (link)], the Fickett’s method [20 (link)], and BlastP [21 (link)]. The concatenated 38 core proteins from 71 baculoviruses plus the two isolates of S. ornithogalli (Table S1) were then used for a phylogeny inference applying the Maximum Likelihood method and the analysis “ModelFinder + tree reconstruction + ultrafast bootstrap (1000 replicates)” in IQ-TREE 2.1.2 [22 (link)]. Moreover, the evolutionary divergence analysis was performed in the MEGAX [23 (link)] using the Kimura 2-parameters (K2P) model and the 38 concatenated core genes from 20 betabaculoviruses and 26 Group II-alphabaculoviruses (Table S1) for SporGV and SporNPV, respectively [24 (link)]. To find putative functional domains in unique genes, the HHpred server was used [25 (link)].
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5

RNA Extraction, Sequencing, and Analysis

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Total RNA was extracted from using TRIzol (Invitrogen) combined with Purelink RNA columns (Fisher) and quantified using a Nano-drop. mRNA was reverse transcribed into cDNA using the ABI High-Capacity cDNA Synthesis kit (ABI). Real-time PCR was performed on an ABI7900HT PCR machine using SYBR green fluorescent dye (Applied Biosystems). Fold changes were calculated using the ΔΔCT method, with Tata Binding Protein (Tbp) mRNA serving as a normalization control. RNaseq libraries were prepared using the NEB Next Ultra RNA Library Prep Kit and sequenced on a NovaSeq 6000 (PE150) (Novogene). RNA-seq reads were aligned to UCSC mm9 genome using STAR aligner (Dobin et al., 2013 (link)) with an option, “–outSAMstrandField intronMotif–outFilterMultimapNmax 1.” Mitochondrial reads were filtered out to avoid sequencing depth bias due to mitochondrial abundance. Then, reads aligned to genes were counted using featureCounts (Liao et al., 2014 (link)). Differential gene expression analysis was performed using edgeR (Robinson et al., 2010 (link)). Hierarchical clustering was performed to identify distinct functional modules of genes using Ward’s criterion and Pearson correlation as a similarity measure. Gene ontology analysis was done using EnrichR (Chen et al., 2013 (link)).
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6

RNA Extraction, Sequencing, and Analysis

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Total RNA was extracted from using TRIzol (Invitrogen) combined with Purelink RNA columns (Fisher) and quantified using a Nano-drop. mRNA was reverse transcribed into cDNA using the ABI High-Capacity cDNA Synthesis kit (ABI). Real-time PCR was performed on an ABI7900HT PCR machine using SYBR green fluorescent dye (Applied Biosystems). Fold changes were calculated using the ΔΔCT method, with Tata Binding Protein (Tbp) mRNA serving as a normalization control. RNaseq libraries were prepared using the NEB Next Ultra RNA Library Prep Kit and sequenced on a NovaSeq 6000 (PE150) (Novogene). RNA-seq reads were aligned to UCSC mm9 genome using STAR aligner (Dobin et al., 2013 (link)) with an option, “–outSAMstrandField intronMotif–outFilterMultimapNmax 1.” Mitochondrial reads were filtered out to avoid sequencing depth bias due to mitochondrial abundance. Then, reads aligned to genes were counted using featureCounts (Liao et al., 2014 (link)). Differential gene expression analysis was performed using edgeR (Robinson et al., 2010 (link)). Hierarchical clustering was performed to identify distinct functional modules of genes using Ward’s criterion and Pearson correlation as a similarity measure. Gene ontology analysis was done using EnrichR (Chen et al., 2013 (link)).
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7

Transcriptomic Analysis of Tomato Biotin Metabolism and Ethylene Regulation

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The tomato leaves from BGW-OE lines, GW2-OE lines, and WT control group were collected for RNA-seq to analyze the biotin anabolism. The GW2-OE tomato leaves with ethylene treatment and control group (treated with dry air) were monitored using RNA-seq to decipher the ethylene regulation. Three biological replicates from independent sampling were included for each point. At least 6 GB of raw data per sample were generated and pretreated from the pair-end sequencing performed on Illumina Novaseq6000 PE150 by Novogene. Clean reads were checked for quality using the threshold Q < 20 and mapped to the tomato reference genomes (ITAG2.4) using TopHat2 (version 2.0.8). Unique alignments with no more than 2 nucleotide mismatches were used to construct transcripts using Cufflinks (version 2.0.2). Differentially expressed genes (DEGs) were confirmed with the selection criteria of | log2fold change| ≥ 1, p-adjust < 0.05. Then, KEGG enrichment analysis of DEGs was performed using KOBAS (version 2.0), based on organism annotation libraries and native BLAST tools. KEGG pathways with p < 0.05 for each sample were visualized using R Project (version 3.4.0). The clean data of these RNA-seq for this study were submitted to the NCBI Sequence Read Archive under accession number PRJNA795585.
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8

Single-cell RNA sequencing of tumor cells

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Cells harvested from 4 tumors from each experimental condition were
pooled and capture was performed at the Stanford Function Genomics Facility
using the 10X Genomics platform. Sequencing libraries were prepared with the
10X Chromium Next GEM Single Cell 3’ v 3.1 kit (10X Genomics)
according to manufacturer’s instructions. Deep sequencing of
single-index libraries was performed by Novogene (Sacramento, CA) on the
llumina NovaSeq 6000 PE150 platform with an average depth of approximately
38,000 reads per cell.
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9

RNA-Seq of Gastrocnemius Muscle

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The RNA Seq of GAS muscle was carried out using Novogene Next Generation Sequencing platform NovaSeq 6000 PE150 (Novogene, Sacramento, CA, USA). Total RNA was extracted from GAS muscle and the RNA quality and integrity were determined. RNA Seq library was then prepared and the cDNA library quality were determined before sequencing with Illumina PE150 platform. The data generated were analysed between groups (n = 3 mice/group) for bioinformatics analysis.
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10

Arapaima Gonad Transcriptome Profiling

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RNA of gonads of one juvenile male (RIN 9.0) and one juvenile female (RIN 7.0) arapaima were extracted using the TRIzol Reagent (Thermo Fisher Scientific, Waltham, USA) according to the supplier’s recommendation. RNA was then cleaned using RNeasy (Qiagen RNeasy Mini Kit cat#: 74104). Library processing and RNAseq were carried out by NOVOGENE (Cambridge, UK) on a NovaSeq 6000 PE 150, generating 10 Gb of data per sample.
Transcriptome sequences of male and female juvenile and adult gonads were mapped to the genome using the RNA-sequence aligner STAR (https://github.com/alexdobin/STAR/releases,—quantMode GeneCounts). Differentially expressed genes between testis and ovary were detected by DESeq281 (link) (Bioconductor/R) for juveniles and adults. Genes were considered to be differentially expressed, if p value ≤ 0.05 AND log2FC ≤ − 2 (higher expression in male) and log2FC ≥ 2 (higher expression in female). Heat maps for sex-related genes were plotted and genes showing comparable regulation between male and female, and between adult and juvenile samples were selected.
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