SGA is a memory efficient assembler

It was possible to compute more compressed data

The pipeline changed, since it was not easy to figure out how to run it

It was necessary to make sure the parameters are running

The group assembled one dataset, merged together

SGA indexed each dataset separated

Merging is complicated in a pairwise fashion, then two pairs were merged at a time

Indexing all three sistinct submissions

Pre-processed adapter trimming

Duplicate-removal is later than indexing

One issue the group found: SW018 and 19, same library are optical PCR duplicates that should be removed

The overall duplication level is a problem

Each datset was generated independently

Then, removing duplicates should be done apart for each dataset

The dataset is very complicated - there is big duplication rate across the dataset the group has

Merging indexes - planning on pulling some stats from the grin engine to pull information

The wall time is large

A variant file with the bubble pop counted the contigs

The group is planning on using the mate-pair data

Do adapter removal and index removal - using skewer