Analysis - ChIP-Seq - Regional Enrichment

Introduction

This tool is used to identify statistically significant enrichment on regions that are defined relative to annotated regions of the genome, rather than to regions defined by the ChIP-Seq data itself.  It is typically used when the pattern of the ChIP-Seq target is so diffuse that standard peak callers have a difficult time identifying regions of enrichment.  In this case we will use regions that are of a priori interest, such as promoters, gene bodies, CpG islands , etc. that are likely regions to contain enrichment for the ChIP target.  The ngsc-chipseq-RegionalEnrichment tool counts reads on these pre-defined regions of interest for both a ChIP and a control (usually input) sample, then uses a Fisher exact test and Benjamini-Hochberg correction to assess the enrichment of the ChIP signal on the region.  A new track will be loaded with the enrichment ratio as the score and the p-value and FDR.

Details

A pseudocount of 1 is added to each region when computing the Fisher test.

Analysis - Tools - RUM-MultipleComparisons

Introduction

We routinely run the pipeline RUM-MultipleComparisons to assess RNA-Seq data.  Although the tool includes the work 'RUM' in the title, it can work with gene expression values from a variety of RNA-Seq tools.

We are still expanding what analyses RUM-MultipleComparisons performs but at the moment, it includes these basic steps.

1. Assemble a table of the raw data
2. Filter to consider just transcripts
3. Performs quantile normalization of the values
4. Does a series of k-means clustering of the data and displays results as heatmaps
5. Generates MvA plots of averages for all conditions
6. Generates MvA plots of replicates within a condition
7. Tabulates fold-changes between average values for all conditions

What Files Should I Look At?

 First, take a look at the plot, Replicates and Kmeans-heatmap.pdf files so that you can see if the samples have good intra-condition consistency.  In addition, the heatmap file will help you see if the changes between conditions are consistent across samples, and roughly how many sets of expression patterns there are in the set.

Once you can see that the data is ok, turn to the Averages.tab file or the appropriate Kmeans-*-clusters.tab file to see gene IDs.  All of the tab files can be opened from within Excel which can be used to further filter the genes.  Gene lists can also be created for use with functional analysis.

How Do we Usually Run It?

 We usually focus on well-characterized RefSeqs, i.e., those with IDs like NM_* or NR_*.

What Does the Output Look Like?

 Plots

• AllPairs-mva.png - a comparison of all samples in the data set.
• Kmeans-heatmap.pdf - series of heatmaps using different numbers of clusters.  Yellow/white is high expression, red is low.
• Pairs.pdf - MvA plots of all condition comparisons
• Replicates-mva.pdf - MvA plots of replicates within a condition

Tables of Data

• AllTranscriptReadCounts-sql.tab - initial raw data
• AllTranscriptReadCounts.tab - data filtered to just transcripts
• Averages.tab - averages over conditions with fold-changes for all comparison
• Details-Lg2-Qn.tab - quantile normalized values for individual samples
• Kmeans-04-clusters.tab - details of genes in each cluster.
• Kmeans-05-clusters.tab
• Kmeans-06-clusters.tab
• ...
• Kmeans-28-clusters.tab
• Kmeans-29-clusters.tab
• Kmeans-30-clusters.tab
  

Analysis - Tools - ConnectSpanToGene

Introduction

We routinely run the ConnectSpanToGene tool to associate ChIP-Seq peaks with genes, but it can be run to associate any set of regions with genes.

ConnectSpanToGene takes a gene track, a span track (which has the peaks), and parameters (MaxDistBp, ToleranceBp, TolerancePct) controlling how far away from a span we will look for a gene.  It outputs a tab-delimited file containing the results, which we usually convert to an Excel file and load into the database (attached to the region track).

How Does it Work?

 The program considers each region in the region track.  It then reports any gene that overlaps with the span.  It then puts the nearby genes in order by the distance from the span to the transcription start site (TSS), with the closest TSS first.  If the distance (D) to the first gene is less than the distance threshold (MaxDistBp), then the gene is reported.  Any other genes that are closer to the span than D * (1 + TolerancePct/100) and (D +ToleranceBp)  are also reported.  This process is then repeated for each span in the span track.  The value of D is adjusted for each span.

 How do We Usually Run it?

We usually run with the following settings:

MaxDistBp    = 100,000
ToleranceBp  =  50,000
TolerancePct =      50

So the first TSS must be within 100KB.  Note that if the region overlaps the gene, then more distant TSSs may be reported as well.

What Does the Output Look Like?

 Here are the columns in the output file.

1. Span-GenomeRelease - genome of the spans (and the genes)
2. Span-Chromosome - chromosome of spans
3. Span-BeginBp - begin of span
4. Span-EndBp - end of span
5. Span-Strand - '+' or '-' of span
6. Span-Score - score of span - the meaning of the value depends on the span table.
7. Span-Name - name of span
8. GeneI - empty, or 1, 2, 3 etc. Is empty if there are no nearby genes
9. AbsDistanceBp - absolute value of distance from span to gene TSS
10. DistanceBp - distance from span to gene TSS,  Positive values are downstream of the TSS.
11. Overlaps - yes/no, does the span overlap the gene.
12. Gene-GenomeRelease - genome of the gene
13. Gene-Chromosome - chromosome of the gene
14. Gene-BeginBp - beginning of the gene
15. Gene-EndBp - end of the gene
16. Gene-Strand - '+' or '-' of gene
17. Gene-Score - score of gene. Usually meaningless, but could be a differential expression value.
18. Gene-Name - name of gene
19. Span-Pvalue - may be empty, p-value for detection of the span
20. Span-FDR - may be empty, FDR for detection of the span
21. Span-ContentTag - extra stuff about the span.  Varies with the span table.