# Tutorial: StepVector versus StretchVector¶

One of the main uses of HTSeq is to represent data along a genome: coverage, genes and other features, transcription factor (TF) binding, etc. Storing long genomes in memory as a dense array (a la numpy) is not only impractical on most laptop/desktop computers, but also rarely useful. For instance, if you are only interested in gene bodies, you can save a lot of memory by ignoring intergenic regions altogether. For TF binding sites, most of the genome does not harbour a compatible binding motif with any given TF. For chromatin conformation, most pairs of sites in the genome are not in contact. This observation leads to a need for “sparse” data representations.

However the same implementation of sparsity might not suit each and every application. HTSeq offers two main sparse representations for genomic data, through the classes StepVector and StretchVector. This tutorial will give an overview of the concepts and potential applications for both of these interfaces.

## StepVector: Piecewise constant values at low resolution¶

The first way to represent sparse genomic data, implemented by StepVector, is useful for data that are piecewise constant. A typical example are the gene overlaps used in htseq-count: perhaps the first exon starts at 1450972 and occupies 1kb, the second exon is 500bp on its own and 300bp overlapping with another gene, the third exon is 10kb downstream, and so on. StepVector would represent this situation as four steps:

1. [1450972, 1451972) (i.e. from 1450972, 0-based and included to 1451972 excluded): exon 1
2. [1453972, 1454472]: exon 2
3. [1454472, 1454772]: exon 2 and overlapping gene
4. [1464772, ...]: exon 3

Within each step, the data is constant and the step or interval width is much larger than 1bp. In a way, this representation is great for low-resolution information compared to 1bp, just like bedGraph files are. If you imagine this kind of data as a function of genomic coordinate, this representation is ideal for slowly fluctuating functions.

## StretchVector: Islands of rapidly fluctuaring data¶

The second option included in HTSeq to represent sparse genomic data is via a StretchVector. An example would be targeted sequencing of a specific gene or a few genes: most of the genome has no data, but a lot of extremely detailed information, down to the single nucleotide level, is available for the genomic stretches covered by the sequencing.

The core idea behind StretchVector is to store two connected lists, one containing dense numpy arrays representing the data on each stretch, and the second containing the start and end coordinate of each stretch. In a way, each stretch behaves a bit like a step in StepVector, but the value needs not be constant within the stretch.

In its simplest form, when you need only one stretch, StretchVector basically behaves like a numpy array with an associated start coordinate. Keeping track of the start saves you some headaches in offsetting the genomic reference coordinates all the time. If you have several stretches, this bookkeeping can become onerous and lead to bugs, so the infrastructure provided by this class is even more useful.

## ChromVector and GenomicArray¶

Both StepVector and StretchVector are relatively low-level data structures: they cover exactly one linear genomic stretch, have no notion of strandedness, and have their own constructor and methods.

HTSeq also offers higher-level data structures that can make life a little easier. The first is ChromVector, which implement the abstract concept of data on one chromosome. When you create a new ChromVector, you can choose a storage method depending on what kind of data you have:

1. short chromosome, dense data: ndarray (a full numpy array)
2. long chromosome, dense data: memmap (i.e. a disk-stored memory map, which is slow but enables you to store data larger than your computer RAM)
3. long chromosome, piecewise constant sparsity: step, which uses StepVector
4. long chromosome, island sparsity: stretch, which uses StretchVector

The methods of this class will work independently of the storage chosen, even though their speed and memory performance will be affected by the nature of the data.

If you are juggling more than one chromosome at once, including whole genomes, your friend is GenomicArray, which is essentially a dictionary of ChromVector entries, one per chromosome for unstranded data and two (strands) per chromosome if the data is stranded.

Ultimately, GenomicArray is a good starting point if you are just dipping your feet in the HTSeq API, while StepVector and StretchVector might becomemore useful as you become more familiar with the library and want to develop custom analyses.