TACGenomics

RNA-seq (RNA sequencing), also called whole transcriptome sequencing, uses next-generation sequencing (NGS) to reveal the presence and quantity of RNA in a biological sample at a given moment in time.
TACGenomics use the cutting-edge pipeline to analyze your RNA-seq data. We put more attention to quality control and data visualization.

The services for RNA seq data analysis includes:

• Standard analysis for RNA-seq data with reference (QC of data quality, mapping, gene expression and differential gene expression analysis, pathway enrichment analysis)
• LincRNA expression analysis
• CircRNA expression analysis
• De novo Transcriptome assembly and differential gene expression analysis
• Differential splicing analysis (if appliable)
• Gene fusion analysis (if appliable)
• SNV/Indel calling analysis (if appliable)

Exome sequencing is a cost-effective approach when whole-genome sequencing is not practical or necessary. It is a quick and effective strategy to identify disease-causing genes for rare Mendelian disorders. It is also being applied to profile all variants in complex diseases such as Cancer and Diabetes.
TACGenomics use the cutting-edge pipeline to analyze your Exome-seq data. We put more attention to quality control and data visualization. (see our work)

The services for RNA seq data analysis includes:

Germline SNV/Indel calling for individual sample
Somatic SNV/Indel/copynumber calling for paired samples
De novo germline variants calling in trio studies
Variant phenotype association test
CoMut and Circos plotting (if appliable)
SNV/Indel calling for Whole genome sequencing

ChIP-seq is a method which combines chromatin immunoprecipitation(ChIP) with massively parallel DNA sequencing to identify the binding sites of DNA-associated proteins.ChIP-seq is used primarily to determine how transcription factors and other chromatin-associated proteins influence phenotype-affecting mechanisms.
TACGenomics use the cutting-edge pipeline to analyze your Chip-seq data. We put more attention to quality control and data visualization.

The services for RNA seq data analysis includes:

• Peak Calling
• Peak annotation
• Differential peak analysis
• Motif analysis
• Pathway and Go analysis