The major bottlenecks in proteomics research today are related to data analysis to create an environment where computer scientists and biologists and the people who collect data can work closely together, so they can develop the necessary analytical tools that will help interpret the data [182] [183] [184] . Processing and analysis of proteomics data is indeed a very complex multistep process ( Figure 5 ). The meaningful comparison, sharing, and exchange of data or analysis results obtained on different platforms or by different laboratories remain cumbersome mainly due to the lack of standards for data formats, data processing parameters, and data quality assessment. Accurate, consistent, and transparent data processing and analysis are integral and critical parts of proteomics workflows [185] . We can now generate huge amounts of data, and currently there is an enormous challenge to figure out how to actually analyze this data and generate real biological insights. The necessity of an integrated pipeline for processing and analysis of complex proteomics data sets has therefore become critical.