The Perseus Computational Platform for Comprehensive Analysis of Large-scale (Prote)Omics Data
Abstract
Abstract: Currently, a main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data. It will be shown how the Perseus software supports researchers in interpreting protein quantification, interaction and posttranslational modification data. A comprehensive portfolio of statistical tools for high-dimensional omics data analysis is contained covering normalization, pattern recognition, time series analysis, cross-omics comparisons and multiple hypothesis testing. A machine learning module supports classification and validation of patient groups for diagnosis and prognosis, also detecting predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment providing complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins and users can extend the software by programming their own, which can be shared through a plugin store. Perseus combines a powerful arsenal of algorithms with intuitive usability by biomedical domain experts, making it suitable for interdisciplinary analysis of complex large datasets.
Daegu Gyoengbuk Institute of Science and Technology, Korea
Title
Network analysis of big proteomic data
Abstract
Huge amounts of proteomic data have been generated for a broad spectrum of cells and tissues from various human cancers. Individual datasets have provided useful information regarding protein networks perturbed in the cancers investigated. However, integrative analysis of all proteomic datasets to examine relationships among proteins and post-translational modifications (PTMs) has been hampered due to the lack of a model that can be used for exploration of proteomic datasets. Here, we present a network model for integrative exploration of thousands of proteomic datasets generated from 10 major human cancers defined by The Cancer Genome Atlas (TCGA). Our network model comprises three layers of subnetworks: 1) protein, 2) peptide, and 3) PTM layers. The interactions in the three layers were defined by protein-protein interactions (protein layer), correlations of peptide abundance alterations (peptide layer), and co-occurrence of PTMs (PTM layer). The three layers were further interconnected by relationships between proteins and their sibling peptides (protein-peptide layers) and also between unmodified peptides and detected PTMs (peptide-PTM layers). Exploration of the multilayered network model revealed a set of network motifs that frequently co-occur with correlations of abundance changes in various human cancers, which can serve as the bases to interpret network perturbations in the corresponding cancers. Moreover, these motifs further revealed a new set of PTMs that can cross-talk in their associated signaling pathways, which can be investigated in detailed functional studies. Finally, when a new proteomic dataset was queried, the multilayered network model provided accurate protein quantification by using the peptides with high information contents and also enabled interpretation of perturbations of cancer networks in the queried dataset. Our results demonstrate the utility of the network model for big proteomic data in identifying key network bases and interpretation of new proteomic datasets in terms of network perturbations based on the network bases.
Code / Date
SYM 2-3 / March 30(THUR) 11:57-12:15
Speaker
Emanuele Alpi
Affiliation
EMBL-EBI,UK
Title
Integration of proteomics data into UniProtKB
Abstract
The identification of peptides and proteins in mass spectrometry (MS) based proteomics experiments relies in searching protein sequence databases. Therefore, it is of paramount importance the provision of an up-to-date, stable and complete protein sequence database for a diversity of species.
UniProt provides a broad range of reference protein data sets for a large number of species, specifically tailored for an effective coverage of sequence space while maintaining a high quality level of sequence annotations and mappings to the genomics and proteomics information.
With respect to publicly available bottom-up proteomics data, UniProt started providing mappings to its reference proteomes since release 2015_03 either in the protein entries and made available on the ftp (ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/proteomics_mapping/) via the download section of the website (www.uniprot.org/downloads).
The mappings are recalculated for every UniProt release starting each time from a fresh data retrieval from the collaborating MS proteomics repositories and they contain isoform-specific information.
Since release 2016_05 the proteomics mappings for each canonical sequence of each entry are also graphically displayed on the UniProt website through ProtVista, a dedicated Feature viewer interface.
In addition, the collaborating MS proteomics repositories have been cross-referenced from within UniProt data and website.
Since then the mappings have been expanded both in terms of covered species and collaborating MS proteomics repositories.
Ongoing collaborations have been established to add other MS proteomics repositories as data providers for the mappings also in order to further expand the range of covered species.
Special cases of these collaborations are the ones aimed at global reprocessing of the content of PRIDE (the PRoteomics IDEntifications database) and the ones which will provide data with a specific focus on posttranslational modification (PTM) related studies/data sets.
Another very promising collaboration is the one with the Consortium for Top Down Proteomics (CTDP, www.topdownproteomics.org) which has been cross-referenced from within UniProt data and website since release 2016_03.
The top-down proteomics data available through the CTDP repository is currently used by UniProt for the development of a dedicated pipeline to annotate back the UniProt entries and publicly provide the corresponding mappings on the ftp.
CTDP data include isoform-specific and variant-specific information for whole proteoforms also bearing PTMs.
Korea Research Institute of Bioscience and Biotechnology,Korea
Title
Bioinformatics and Application of Next-Generation Sequencing to Rare Diseases
Abstract
It has been a decade since the advent of next-generation sequencing. It enables one to interrogate various fields of biomedical researches related to DNA sequencing such as exome/genome, transcriptome, and epigenome sequencing. Due to the huge amount of nucleotide data, we need tens of bioinformatics software and pipelines to analyze them. By whole-exome/genome sequencing, causative genes for many genetic disorders has been elucidated. In this talk, I will introduce how to analyze exome/genome sequeucing data in terms of bioinformatics. I will present our recent results genomic variants identified as causative genes for several genetic disorders by familial genome sequencing. Finally, I will discuss how to get sequencing service such as NIPT, DTC, and cancer genomes. In addition, concept and application of proteogenomics will be briefly mentioned.
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