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Experiment:

• take a set of long-ish paired-end reads from a published rnaseq experiment in a model organism
• run various assemblers with default parameters
• run assemblotron
• compare the results using transrate

Experiment:

• take a set of long-ish paired-end reads from a published rnaseq experiment in a model organism
• reduce the parameter space for soapdt down to use just a few parameters (k, d, e)
• assemblotron without subsampling on the full set of reads
• assemblotron with subsampling
• plot both results

Features of the dataset:

• from a species with a decent reference genome that is well annotated (or at least, as well annotated as any reference, which is not very well)
• fairly small dataset, or with a sample or replicate that is fairly small, so it can be used for full parameter sweep without subsetting
• when assembled, should not knot up the assembler (this will hugely slow down the full sweep)

• Overview of the standard workflow (optimise whole assemblies)
• Overview of the partitioned workflow

Experiment:

• take a set of long-ish paired-end reads from a published rnaseq experiment in a model organism
• reservoir sampling method: make subsets at 80, 50, 20% of the reads (in triplicate?)
• graph sampling method: make subsets at 80, 50, 20% of the partitions (in triplicate?)
• run assemblotron on each subsample varying only K and using only soapdt
• plot the score distribution over k for each subsample

• take a set of long-ish paired-end reads from a published rnaseq experiment in a model organism
• run assemblotron with default method (optimise entire assembly by subset)
• run assemblotron with partition method (optimise each partition independently using full data)
• compare the two assemblers using transrate and look at a spread of both read and reference-based metrics
• possibly produce a figure for comparison