Next-Gen Sequencing Analysis

Data Analysis

Analytical services are offered at $75/hr. The table below is a price breakdown for an RNA-Seq Analysis Experiment with a simple example.

Pricing is specific to your experiment needs, for a more detailed cost estimate please contact the Bioinformatics Core to discuss your experiment.

Service Price Example*
Base RNA-Seq Experiment Cost $750 $750
        Fee Per Sample $30 $30x2
        Fee Per Replicate $15 $15x6
Statistical Experiment $75-$150 $75x1
    Total: $975

*The example column represents an RNA-Seq experiment with 2 sample sets: two conditions and three replicates per condition. This includes one statistical experiment testing differential gene expression.

All off-campus customers are subject to a 2% University Administrative Fee.

DNA Analysis

Genome Resequencing and Variant Detection

We have used CLC to analyze next generation sequencing data for variance against a reference genome. The results can also be loaded into GxSeq for further analysis. Steps include:

  • Screening, trimming, and filtering
  • Read mapping to reference
  • Quality checking
  • Variant detection using CLC

Genome Assembly

We can perform genome assemblies using next generation sequencing data using Velvet or CLC. The sequence can be paired end, mate pairs. The results can also be loaded into GxSeq for further analysis. Steps include:

  • Screening, trimming, and filtering
  • Assembly with Trinity or CLC depending on purpose of experiment
  • Quality checking
  • Annotation

ChIP-Seq

We have used CLC to analyze ChIP-Seq data against a reference genome. The results can also be loaded into GxSeq for further analysis. Steps include:

  • Screening, trimming, and filtering
  • Read mapping to reference
  • Calling peaks
  • Quality checking

RNA Analysis

De novo Transcriptome Assembly

We have completed several de novo assemblies using next generation sequencing data from Illumina using Trinity or CLC. The sequence can be single-stranded, sense or antisense, paired end. The results can also be loaded into GxSeq for further analysis. Steps include:

  • Screening, trimming, and filtering
  • Assembly with Trinity or CLC depending on purpose of experiment
  • Quality checking
  • Read mapping to contigs with CLC
  • Annotation by Blast

Differential Gene Expression

We have several tools for evaluating differential gene expression, including DeSeq2 in the R package and Empirical analysis of DGE in CLC using FPKM values from a read mapping analysis. We can take the results from a DGE experiment and perform a gene ontology analysis using the R package and GOSeq.

RNA-seq Analysis Pipeline Overview

Quantifying gene expression from RNA-Seq data requires several steps that are very time and resource intensive. We have worked to establish a best-practices pipeline for our core customers. This pipeline includes:

  • De novo assembly of next generation sequencing data using Trinity (if required)
  • RNA-seq mapping of reads to contigs in the assembly or a reference using CLC
  • Annotation of contigs by best BLAST hit
  • Data visualization in GxSeq

Data Visualization in GxSeq

One of the hardest tasks in modern research is organizing large data sets from multiple sources, such as those generated from RNA-Seq, ChIP-Seq and variance analysis, in ways that facilitate discovery. We have developed a unique web application designed around community standards for storing and displaying different forms of NGS data, that contains a full suite of features for visualizing transcript expression, exploring the genomic context of transcripts, and linking these results to functional annotation. The software is a Ruby on Rails application freely available as open source. (Live Demo)

 

GxSeq

 

GxSeq