NGS Services

Whole Transcriptome Sequencing

Next generation sequencing enables gene expression analysis from normal and disease tissues with high-sensitivity. Profiling of coding and non-coding RNAs is important in revealing molecular mechanisms of development and disease. With the several options that exist for gene expression profiling, it is also possible to perform whole transcriptome profiling ranging from a single-cell to thousands of cells in a sample. In addition, current methodologies allow for profiling of transcriptomes from intact RNA and RNA degraded due to the storage and isolation conditions (such as FFPE processed tissue blocks, cell-free nucleic acids etc). At MedGenome we offer solutions, for performing gene expression analysis from a wide-range of sample types. Shown in table 1 is a range of sample types that we process for whole transcriptome profiling.

Sample requirements and library prep information
Service Sample Requirements Library Kit Used
RNA (whole transcriptome) Minimum = 250 pg (DV > 25)
Minimum: 50 ng (RIN < 5)
Single-cell solutions available
TruSeq RNA, SMARTer Pico v2, TruSeq Stranded (No capture probes used)

RNA analysis deliverables
Standard Analysis Offerings Advanced Analysis Offerings
Data QC (read orientation, quality distribution, base distribution and GC distribution for all reads) Gene fusion detection
Alignment to the reference genome & transcriptome Wiggle file for gene expression visualization on genome browser
Gene and isoform expression estimation in RPKM / read count per gene Pathway analysis
Statistical analysis including p-values and coefficient of variance (CV) between replicates SNV & InDel detection
Gene/Transcript expression levels in Excel Splicing analysis
Differential expression comparison

In addition to the NGS library preparation solutions, we offer extensive informatics solutions for whole-transcriptome profiling that ranges from standard gene expression analyses to advanced analyses of splicing, pathways, visualization and tertiary analyses to reveal heterogeneity and cell types in a population as well tumor microenvironment analyses. In addition to that we have solutions using machine learning approaches that utilize gene expression data to predict biomarkers and classify tissue types and diseases.

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