# Base Pair Quantitation

The Base Pair quantitation closely related to the Read Count Quantitation except that instead of counting the number of reads which overlap with a probe it counts the actual number of bases. This gives a more fine grained view of the data. It can correct the counts according to a few different factors which might bias the result - allowing it to be compared to other data sets.

The base pair quantitaiton is ideally suited for use where you have small probes with split or partial overlaps. It is particularly useful for mRNA-Seq experiments where you are putting probes over exons which will have vastly different sizes, and where you will get spliced reads mapping only a few bases into some exons.

## Options

The options you have for this module are:

1. The types of reads you want to count (All / Forward / Reverse / Unknown)
2. Whether you want to correct for the total read count. If you are comparing different datasets with different total counts then this will normalise the average count per probe. This correction is done relative to either the largest count in the stores being quantitated, such that the largest set will show raw counts and the counts in all other sets will be scaled up proportionally, or per million reads
3. If you want the total read count correction to be applied only using reads inside the current probeset. This would be useful if you wanted to exclude certain regions of the genome from this correction (eg Chr X/Y).
4. Whether you want to correct for the length of your probes. If your probes are of different sizes you would expect to get a higher count in longer probes. This correction adjusts the count to a count of bases of read per base of probe.
5. If you want to log transform your count. If you have a large range of values in your count you can calculate them on a log2 scale to make them easier to view.
6. If you want to ignore duplicated reads. If you select this option then every unique read position (start, end and strand must be the same) will only be counted once and duplicates will be ignored

## Warning

If you choose to quantitate your data on a log scale then any probes which contain no reads will have their initial read count increased to 0.01 bases to avoid problems with infinite values when log transforming. This value is set before any subsequent correction for total read length or probe size. This means that in log transformed data different data stores could end up with different absolute values for probes containing no reads. If probe length correction is applied then probes with no reads will end up with a range of low values reflecting the different lengths of the probes. In these cases you might want to use an initial linear quantitation to allow you to flag and possibly filter out the regions with no reads, before later moving on to quantitate on a log scale.