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 the purpose of the 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 the purpose of the 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