{'fraction_non_alphanumeric': 0.011881188118811881, 'fraction_numerical': 0.0, 'mean_word_length': 5.651315789473684, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Bioinformatics tools for the functional interpretation of quantitative proteomics results.

Proteins are the principal mediators of the functions in the cell; therefore, any abnormal variations on their abundance levels may reflect the presence of pathological processes. In this sense, many researchers rely on the functional interpretation of protein lists generated by quantitative proteomics experiments to analyze, for instance, these variations in the context of diseases' molecular basis and drug discovery. Since no analytical strategy or bioinformatics tool by itself is capable of extract all the information covered by a single experiment; herein we seek to provide the biologists with four groups of different but complementary bioinformatics tools for the functional interpretation of quantitative proteomics results. To this end we will review the basic concepts of a set of different bioinformatics approaches and we will give examples of freely available tools for each one of these approaches.