The standard analysis includes the following steps
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Quality assessment of sequencing data, including evaluation of read quality, adapter trimming, and removal of low-quality bases
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Alignment of reads to a reference genome, utilizing a splice aware aligner to ensure accurate mapping to the reference genome.
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Assembly and quantification of transcripts, including identification of transcripts based on the known transcriptome models and estimation of gene expression levels
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Normalization of gene counts, using methods such as TMM (trimmed mean of M-values) or DESeq2 to account for library size and sequencing depth
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Identification of differentially expressed genes, using statistical methods such as edgeR or DESeq2 to detect significant changes in gene expression
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Principal component analysis (PCA) to visualize sample relationships and identify patterns in gene expression
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Significance testing to determine statistical significance of differentially expressed genes
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Generation of detailed reports and visualizations, including heatmaps, volcano plots, and various QC metrics.
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RNASeq data analysis service is designed to provide a comprehensive and efficient analysis of RNASeq data, with the option to add a data consultation for personalized guidance and support. Our team of bioinformatics experts will work closely with you to ensure that your analysis meets your specific research goals and requirements.
Deliverables:
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Raw Sequencing Reads (FASTQ): Original raw sequencing data.
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Genome Alignments (BAM): Alignment files in BAM format, providing the genomic coordinates of each read.
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Gene Count Tables (TSV): A table of gene counts for each sample.
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PCA Plots (PNG): Principal Component Analysis plots for quality control and visualization of sample relationships.
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Volcano Plots (PNG): Volcano plots to visualize the relationship between fold change and statistical significance for each gene.
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Fold Change Tables (TSV): Tables containing fold change values for each gene.
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Significantly Expressed Genes (TSV): A list of genes that are significantly differentially expressed based on specified criteria.
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Analysis Summary Report (HTML): A comprehensive report summarizing the analysis methods, parameters, and key findings.
Optional & Advanced Analysis
Optional Analysis
In addition to the standard analysis, several optional analyses are available, including:
Alternative splicing (AS) analysis: This analysis is available for designs with replicates in a case vs. control setup and provides a detailed view of the alternative splicing events that occur in the samples.
Deliverables: List of alternative splicing events in TSV format.
Gene Fusion Analysis: This analysis is available for single sample onwards and provides a detailed view of the gene fusions that occur in the samples.
Deliverables: List of gene fusions in TSV format.
Variant detection and its effect on protein alteration: This analysis is available for single sample onwards and provides a detailed view of the variants that occur in the samples and their effect on protein function.
Deliverables: List of variants in VCF format.
Advanced Analysis
Gene Set Enrichment Analysis (GSEA): This advanced bioinformatics analysis service utilizes Gene Set Enrichment Analysis (GSEA) to identify biological pathways and gene sets that are significantly enriched in differentially expressed genes. This analysis provides a deeper understanding of the biological processes and pathways that are affected by the changes in gene expression.
The GSEA report and results provide a comprehensive overview of the enriched gene sets and pathways, allowing for a more detailed interpretation of the biological significance of the results.
Deliverables:
- GSEA report in HTML format, including:
- Enriched gene sets and pathways
- Gene set enrichment scores and p-values
- Biological process and pathway annotations
- Heatmap visualization of enriched gene sets
- GSEA results in TSV format, including:
- Enriched gene sets and pathways
- Gene set enrichment scores and p-values
- Biological process and pathway annotations