Functional Genomics Core - Advanced Analysis
In addition to the basic analysis provided in our report, we can provide several kinds of advanced analysis to help the investigator determine how best to interpret the results, prepare the data for publication, and use the experiment to find leads for the next experiment. If you have performed multiple experiments at the FGC, we can integrate the results of those experiments to gain further insight.
TessLA Genome Browser
For high-throughput sequencing experiments we always provide raw data as well as files suitable for uploading to UCSC. However, we also load the data into our in-house genome browser TessLA. TessLA is the basis for our deeper analyses of genomic data and a platform for sharing data between the FGC and experimenters. TessLA is password protected, but data can be shared between multiple projects in a lab. Click here or on the image to access the site.
We typically load the following tracks for each experiment:
- reads for each lane
- stack-height profile for each lane
- merged reads for replicates
- stack-height profiles for merged reads
- enriched region calls
We can also load the following kinds of data:
- custom annotation such as primer pairs relevant to the experiment
- third-party data
- expression results from your other FGC experiments.
The genome browser usually works with reference genomes such as mouse (mm8, mm9), human (hg18), or zebrafish (so far), but it can also use mRNA sequence, miRNA precursors, or your own custom set of sequences. These last options are especially useful for small RNA experiments. We have access to many of the UCSC gene tracks directly from UCSC, but have also loaded local copies of repeats and conservation tracks for better visualization or use in analysis.
Profiles and Neighboring Features
For ChIP-seq experiments measuring histone modifications we can provide average profiles of modifications at transcription start sites (optionally stratified by expression in related experiments). We can provide summary measures of the amount of modification levels around start sites of genes or any of the tracks in TessLA such as CpG island, conserved regions, or your custom tracks.
Identifying Enriched Regions
For many GA experiments, it is desirable to identify regions where the TF or histone modification is enriched. We run MACS and/or GLITR to identify peaks. These are loaded into TessLA for browsing.
Resequencing Consensus Sequences
We regularly use MAQ to build consensus sequences for your targeted regions.
Cis-Regulatory Modules
Gene regulation is typically accomplished by a cohort of proteins including transcription factors. Because transcription factors leave a footprint in the regulatory regions of target genes, we can search for enriched known or novel motifs in potential regulatory regions. For mRNA expression experiments, we concentrate on the proximal promoter. For TF targeted ChIP-seq experiments, we look in the neighborhood of the binding events identified in the experiment. We can easily identify individually enriched motifs, but with more effort can learn combinations of sites that co-occur more often than expected.
Functional Annotation
Both mRNA expression and ChIP-seq experiments can produce a long list of target genes. To help understand the experimental results, identification of biological pathways in a set of differentially expressed genes using programs such as Ingenuity, GenMapp, EASE, or GSEA can be particularly useful to identify a smaller set of enriched functional groups.
