High Performance Computational Analysis of Large-scale Proteome Data Sets to Assess Incremental Contribution to Coverage of the Human Genome

Computational analysis of shotgun
proteomics data can now be performed
in a completely automated and statistically rigorous way, as exemplified
by the freely available MaxQuant environment. The sophisticated algorithms
involved and the sheer amount of data translate into very high computational
demands. Here we describe parallelization and memory optimization
of the MaxQuant software with the aim of executing it on a large computer
cluster. We analyze and mitigate bottlenecks in overall performance
and find that the most time-consuming algorithms are those detecting
peptide features in the MS1 data as well as the fragment
spectrum search. These tasks scale with the number of raw files and
can readily be distributed over many CPUs as long as memory access
is properly managed. Here we compared the performance of a parallelized
version of MaxQuant running on a standard desktop, an I/O performance
optimized desktop computer (“game computer”), and a
cluster environment. The modified gaming computer and the cluster
vastly outperformed a standard desktop computer when analyzing more
than 1000 raw files. We apply our high performance platform to investigate
incremental coverage of the human proteome by high resolution MS data
originating from in-depth cell line and cancer tissue proteome measurements.