Until now, biology has had a huge problem that most big data settings don't: far far more features than labels. With enough patients' data, the matrix will become squarish, but that's a long time from now, still.
Not sure if you are referring to the raw sequence data coming from the sequencers, nowadays the standard practice is to align a specific sample's genome (e.g., an individual's genome) to the reference genome (the Human Genome Project) and store only your variations against that reference genome, stored as a BAM (binary alignment) file.
Furthermore, for most clinical cases, if you only care about the current known SNP (single nucleotide polymorphism in coding genes), you can generate a VCF file at those known SNP sites, compressing the data further. This is the way most population genetics is done by comparing the VCF files of cohorts, not doing analysis of single genomes of patients one by one.
Soon we are going to go towards variant graphs and VCFs will disappear again, slowly (IMHO).
There are typically 4-6 million variants discovered through this method of genome sequencing in a normal genome. A simple variant consists of a genome coordinate at ~32 bits (one of 3.2e9), and the change from the reference, which is a x,y index into {A, C, G, T}^2, at ~4 bits. Typically the coordinates are spaced on average ~1k bases apart, so the coordinate could probably be squeezed into ~15bits with clever encoding. So a naive encoding of this information gets to 27MB, and that could probably be shoved down into 10MB if coordinates are deltas from the previous, rather than absolute. 1MB seems feasible, but with diminishing returns computationally.
Pretty much all genomic data uses a reference genome as the basis. This is versioned, and has a bug tracker, etc., for various regions that have been difficult to assemble.
The flow is:
1. BCL (scans of the glass slide) 2. FASTQ (individual short reads and quality scores, unsorted and in random order) 3. BAM (individual short reads aligned to the reference genome) 4. VCF (the "diff" vs. the reference genome)
All of this can be done with <10GB of reference data and code, where the reference data is the current human genome, a burrows-wheeler transform of the human genome, gene locations, and dbSNP (the database of common human variation).
For example loss-of-function mutations can be detected without resorting to other genomes beyond the reference. Inversions, translocations and copy number variations can also give clues to illness.
Source: I am the lead web developer for uniprot.org, and I deal with lack of real info daily as does everyone in the Swiss-Prot team.
Am I right to say that the bandwidth and storage needed to upload whole genomes is prohibitive?
Bandwidth and storage are infrastructure issues that could be solved with enough money. In biology we are still lacking good technical solutions to do the chemistry and even something "simple" like getting a protein crystalized so we can determine its 3D structure is not trivial or cheap yet.
Fast genome and RNA sequencing are massive improvements and really help. But the basic understanding of what all those genes, regulatory and other parts do is still relatively primitive.
We often "complain" that we wish that people stopped sequencing and went back to do more biochemistry for functional characterization instead ;)
Of course, not every mysterious illness can be solved this way...