To analyse RNA-seq data from Caucasian patient GC tumours, we mapped short reads to the human reference genome (hg19) and exon junctions (defined as RefSeq gene annotation), using the whole-transcriptome analysis pipeline in the Life Sciences Bioscope (V.1.2 Life Technologies) with default parameters. We included only properly mapped read pairs and quantified gene expression levels as reads per kilobase of exons per million mapped reads (RPKM), log-transformed. We used paired t tests to detect genes differentially expressed between GC and normal samples, with significance at p<0.05. We obtained another three published GC gene expression datasets, two from the NCBI Gene Expression Omnibus: GSE13861 and GSE3696820 21 (link) and one from TCGA22 (level 3 data obtained from TCGA data portal). We used t tests to detect differentially expressed genes and p<0.0001, p<0.05 and p<0.001 as the respective cut-offs. For 396 genes commonly identified across the four differential analyses, we classified them into five groups based on the directional change (upregulation/downregulation relative to normal) and the average fold change: (1) consistent upregulation with >2 fold; (2) consistent upregulation with <2 fold; (3) consistent downregulation with >2 fold; (4) consistent downregulation with <2 fold; and (5) inconsistent change.
Bioscope
Manufactured by Thermo Fisher Scientific
Sourced in United States
The Bioscope is a versatile lab equipment product from Thermo Fisher Scientific. It is designed to provide high-quality imaging and analysis capabilities for a variety of biological samples. The Bioscope offers advanced optical and imaging technologies to enable detailed observation and examination of specimens.
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Lab products found in correlation
13 protocols using bioscope
1
Transcriptome Analysis of Gastric Cancer
2
Exome sequencing and variant analysis
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Exome was enriched using SureSelect v2−5 (Agilent, Santa Clara, CA) and sequenced on SOLiD, Illumina, or IonProton platforms to at least 30x coverage. All reads were mapped to human reference genome Hg19. SOLiD reads were mapped using Bioscope (Life Technologies) and Life Scope (Life Technologies). Illumina reads were mapped using BWA (Li & Durbin, 2009 ), and IonProton reads were mapped using the Torrent suit software (Life Technologies). All programs used for mapping were run using default settings. Variants were called from SOLiD and Illumina reads using Genome Analysis Toolkit (GATK) and the standard GATK workflow (Broad Institute). Variants were called from IonProton reads using the Torrent suit software (Life Technologies). SNVs were filtered against our in‐house database containing previously identified variants as well as dbSNP V.42 (non‐flagged). Identified DNVs and inherited variants were validated by Sanger sequencing using standard protocols. Validated variants were interpreted according to the ACMG guidelines. The number of DNVs identified in patient cases and controls in a selected set of previous exome sequencing studies were counted.
3
Somatic Variant Identification from Sequencing Data
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The standard SOLiD software BioScope (Life Technologies, Foster City, CA) was carried out to analyze sequencing data including image analysis, mapping to human reference genome (UCSC Hg19). Alignment files were base quality score recalibrated and locally realigned around indels with GATK12 and marked for duplicates using PICARD tools (picard.sourceforge.net ). Consensus genotype calls were generated using SAMtools13 and GATK 2.7‐2 annotated using the Annovar package.14 The somatic variants were distinguished by filtering the 1000 Genomes phase 1.15 We defined the novel SNVs were compared with NCBI dbSNP version 138 (http://www.ncbi.nlm.nih.gov/projects/SNP/ ) to annotate known SNP information. Visualizing genomic data used the Circos.16
4
Comprehensive Variant Detection Pipeline
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For single nucleotide variations (SNVs) detection, SOLiD 4.0 and Illumina reads were aligned to the human reference genome sequence (GRCh37/hg19) using BioScope (Life Technologies) and BWA [66 (link)], respectively. For InDels detection, alignments were performed using NovoAlignCS (www.novocraft.com ). A local pipeline for point mutations was developed using Samtools mpileup and bcftools [67 (link)]. Duplicated reads were removed with rmdup (Samtools) to avoid potential PCR duplicates generated during library construction. Variants were filtered against known germline variations annotated in dbSNP (version #135) and variations present in more than 3 cell lines were manually inspected to distinguish recurrent mutations (eg. EGFR mutations) from false positive mutations due to alignment artifacts. Somatic mutations were validated using PCR amplification and Sanger sequencing using standard protocols (Supplementary information Table S9, S10 ). SIFT [68 (link)], PolyPhen-2 [69 ] and Mutation Assessor [70 (link)] were used to evaluate the impact of non-synonymous substitutions and InDels on protein function. Mutations were annotated as having an impact on protein function when predicted by at least two of these algorithms in the case of non-synonymous substitutions and by SIFT in the case of InDels.
5
DNA-PET Library Construction and Sequencing
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DNA-PET library construction from 1 to 4 kb fragments of genomic DNA, sequencing, mapping and data analysis were performed as described in [13 (link)] with refined bioinformatics filtering as described in [41 (link)]. High throughput sequencing by 2 × 35 bp or 2 × 50 bp was performed on SOLiD sequencers (v3plus and v4, respectively) according to the manufacturer's recommendation (Life Technologies). The short reads were aligned to the NCBI human reference genome build 37 (hg19) using Bioscope (Life Technologies).
6
DNA Sequencing and Structural Variant Analysis
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DNA sequencing was performed using the SOLiD platform (Life Technologies). Mate-pair libraries were generated starting from 5 μg of tumor genomic DNA sheared into 0.6–1.0 kb fragments, according to manufacturer's instructions. Sequence data were mapped against the hg19/GRCh37 human genome reference sequence using Bioscope (Life Technologies). Mapped sequences selected for further analysis were required to match the reference genome uniquely with a mapping quality greater than or equal to 20 (Q > = 20). Selected sequences were analyzed for aberrant mate-pair spacing and orientation using ICRmax [22 (link)]. For the identification of intrachromosomal deletions, read pairs mapping on the same chromosome within distances larger than 4Kb were selected and submitted to ICRmax. Interchromosomal rearrangements and intrachromosomal deletions and inversions were selected when reported by at least 3 independent sequence-pairs and validated by PCR amplification and Sanger sequencing across the breakpoint region using tumor and matched normal DNA as templates to confirm their somatic origin. Primer sequences used for validation are provided as Supplementary Material Table S1 .
7
Sequencing and Alignment of ChIP-seq and RNA-seq
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SOLiD™ (Life Technologies) platform was used for sequencing, the library preparation was done according to SOLiD™ protocol for both ChIP-seq and RNA-seq libraries and raw reads were obtained in color space. ChIP-seq reads were aligned using BioScope (Life technologies, CA) while input and RNA-seq reads were aligned using LifeScope (Life Technologies, CA). The reads (Table S9 ) were mapped uniquely to the reference human genome library hg19. All data have been submitted to the Gene Expression Omnibus (GEO) repository under the accession GSE53215.
8
Differential Gene Expression Analysis of Phagocytic Hemocytes in Oyster
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Read alignment was performed with the software BioScope (Life Technologies) using the C. gigas genome as the reference.1 The expression level of each gene was estimated using the frequency of clean reads in the corresponding sample, and the RPKM method was applied to calculate read density. Gene expression values are summarized in Table S1 in Supplementary Material, and the false discovery rate (FDR) was used to determine p value thresholds in multiple tests. Absolute values of log2 > 1 and FDR ≤ 0.001 were set as thresholds to determine DEGs between phagocytes and non-phagocytic hemocytes. DEG annotations were performed by running this assembly against the genome of C. gigas (Table S2 in Supplementary Material). All blast results were imported into the software Blast2GO to map sequences onto gene ontology (GO) (Table S3 in Supplementary Material). The Kyoto Encyclopedia of Genes and Genomes Ortholog database (KEGG2 ) was applied using the software BLASTx (E < 10−5) to analyze metabolic pathways in C. gigas and the results from the KEGG analysis are presented in Table S4 in Supplementary Material.
9
Trio Family RNA-seq Analysis
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RNA sequencing of the trio family was performed using a SOLiD4 instrument. Each parent was sequenced once and two replicates were sequenced for the child. Sequenced reads were mapped to the HG19 version of the human reference genome using the BioScope software (Applied Biosystems). The number of reads mapping to each RefSeq gene (based on the refFlat table from the UCSC genome browser) was then counted using htseq‐count (http://wwwhuber.embl.de/users/anders/HTSeq/doc/count.html ). Differential expression was calculated using the DESeq2 [Love et al., 2014 ] and EdgeR [Robinson et al., 2010 ], comparing the replicated patient samples to the parents, generating a lists of DEGs. Candidate genes were then chosen using an adjusted P value of 0.0001 as cutoff and required that each gene had a P value below the cut‐off in both analysis programs. Gene ontology (GO) analysis was performed using the R‐package GOseq, and the REVIGO Webserver was used to identify nonredundant GO categories [Supek et al., 2011 ]. The list of Wnt target genes used for enrichment analysis was compiled from the Nousse laboratory (http://web.stanford.edu/group/nusselab/cgi‐bin/wnt/target_genes ), for genes found in both humans and other organisms (if a human orthologue could be identified)
10
Identification of De Novo Mutation in BAZ1A
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Exome sequence data was aligned using BioScope (Applied Biosystems, Foster City, CA). SNP calls were made using DiBAYES with default settings and resulted in 16,723 nonsynonymous single‐nucleotide variants (SNVs) with a sequence read coverage >3 in the patient. Filtering was performed using ANNOVAR [Wang et al., 2010 ] and dbSNP 132 [Sherry et al., 2001 ]. Further filtering was done using the parental samples and our in‐house database of previously analyzed exomes. Manual inspection of each SNP remaining was then performed resulting in three putative de novo coding variant. Sanger sequencing only validated a single de novo mutation in the BAZ1A gene (NM_182648.2:c.4043T>G, g.35231163A>C). The other two variants were found to be false positives. The DNA mutation numbering system is based on genomic DNA. The de novo variant in BAZ1A identified in the patient is submitted to LOVD 3 (www.lovd.nl ).
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