Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body.

Tissue-based map of the human proteome

Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.

Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

The PRoteomics IDEntifications (PRIDE) database is one of the world-leading data repositories of mass spectrometry (MS)-based proteomics data Since the beginning of 2014, PRIDE Archive (http://wwwebiacuk/pride/archive/) is the new PRIDE archival system, replacing the original PRIDE database Here we summarize the developments in PRIDE resources and related tools since the previous update manuscript in the Database Issue in 2013 PRIDE Archive constitutes a complete redevelopment of the original PRIDE, comprising a new storage backend, data submission system and web interface, among other components PRIDE Archive supports the most-widely used PSI (Proteomics Standards Initiative) data standard formats (mzML and mzIdentML) and implements the data requirements and guidelines of the ProteomeXchange Consortium The wide adoption of ProteomeXchange within the community has triggered an unprecedented increase in the number of submitted data sets (around 150 data sets per month) We outline some statistics on the current PRIDE Archive data contents We also report on the status of the PRIDE related stand-alone tools: PRIDE Inspector, PRIDE Converter 2 and the ProteomeXchange submission tool Finally, we will give a brief update on the resources under development 'PRIDE Cluster' and 'PRIDE Proteomes', which provide a complementary view and quality-scored information of the peptide and protein identification data available in PRIDE Archive

2016 update of the PRIDE database and its related tools

Numerous biological processes are concurrently and coordinately active in every living cell. Each of them encompasses synthetic, catalytic and regulatory functions that are, almost always, carried out by proteins organized further into higher-order structures and networks. For decades, the structures and functions of selected proteins have been studied using biochemical and biophysical methods. However, the properties and behaviour of the proteome as an integrated system have largely remained elusive. Powerful mass-spectrometry-based technologies now provide unprecedented insights into the composition, structure, function and control of the proteome, shedding light on complex biological processes and phenotypes.

Mass-spectrometric exploration of proteome structure and function

A proteome-scale map of the human interactome network

Proteomes are characterized by large protein-abundance differences, cell-type- and time-dependent expression patterns and post-translational modifications, all of which carry biological information that is not accessible by genomics or transcriptomics. Here we present a mass-spectrometry-based draft of the human proteome and a public, high-performance, in-memory database for real-time analysis of terabytes of big data, called ProteomicsDB. The information assembled from human tissues, cell lines and body fluids enabled estimation of the size of the protein-coding genome, and identified organ-specific proteins and a large number of translated lincRNAs (long intergenic non-coding RNAs). Analysis of messenger RNA and protein-expression profiles of human tissues revealed conserved control of protein abundance, and integration of drug-sensitivity data enabled the identification of proteins predicting resistance or sensitivity. The proteome profiles also hold considerable promise for analysing the composition and stoichiometry of protein complexes. ProteomicsDB thus enables navigation of proteomes, provides biological insight and fosters the development of proteomic technology.

Mass-spectrometry-based draft of the human proteome

We propose mobile force feedback devices based on actuating the user's muscles using electrical stimulation. Since this allows us to eliminate exoskeletons, motors and reduce battery size, our approach results in devices that are substantially smaller and lighter than traditional motor-based devices, and thus suitable for usage on-the-go. We present a simple prototype that we mount to the back of a mobile phone. It actuates users' forearm muscles via four electrodes, causing the muscles to contract involuntarily, so that users tilt the device sideways. As users resist this motion using their other arm, they perceive force feedback. We demonstrate the interaction at the example of three interactive videogames in which our approach to mobile force feedback provides a richer gaming experience.

Muscle-propelled force feedback: bringing force feedback to mobile devices using electrical stimulation

Enterprise applications such as available-to-promise (ATP), financial accounting, and dunning typically employ a mixed database workload with short-running transactional as well as analytical queries with resource-intensive aggregations. The latter type of queries can be significantly accelerated by using materialized views with pre-calculated aggregates. However, this speed-up comes with the cost of view maintenance which is necessary to guarantee consistency when the underlying data changes. In this paper, we evaluate existing view maintenance strategies in the context of a columnar in-memory database that is designed for mixed workloads. We propose a novel view maintenance strategy that takes the main-delta architecture and resulting merge process of columnar storage into account. A further contribution is a cost model which determines the best maintenance strategy given a specific workload. Our experiments using an ATP application show that our novel strategy outperforms other strategies in mixed workloads with an insert-ratio of more than 40 percent.

Efficient View Maintenance for Enterprise Applications in Columnar In-Memory Databases

Database workloads generated by enterprise applications are comprised of short-running transactional as well as long-running analytical queries with resource-intensive aggregations. The expensive aggregate queries can be significantly accelerated by using materialized views. This speed-up, however, comes with the cost of materialized view maintenance which is necessary to guarantee consistency when the underlying data changes. While several view maintenance strategies are applicable in the context of an in-memory column store, their performance depends on various factors, most importantly the ratio between queries accessing the materialized view and queries altering the base data, called insert ratio. As a contribution in this paper, we propose algorithms that determine the best-performing view maintenance strategy based on the currently monitored factors. Using our novel materialized aggregate engine, we are able to switch between view maintenance strategies on demand. We have created cost models for the identified view maintenance strategies that determine at which insert ratio it is advisable to switch to another strategy. Our benchmarks in SanssouciDB reveal that for all identified workloads, switching between maintenance strategies is more beneficial than staying with a single strategy.

http://prof.ict.ac.cn/bpoe2013/downloads/papers/BigD389_5597.pdf

Workload-aware aggregate maintenance in columnar in-memory databases

One well established method of measuring the success of companies are key performance indicators, whose inter-dependencies can be represented by mathematical models, such as value driver trees. While such models have commonly agreed semantics, they lack the right tool support for business simulations, because a flexible implementation that supports multi-dimensional and hierarchical structures on large data sets is complex and computationally challenging. However, in-memory column stores as the backbone of enterprise applications provide incredible performance that enables to calculate flexible simulation scenarios interactively even on large sets of enterprise data.

Lars Butzmann

Papers

Mass-spectrometry-based draft of the human proteome

Muscle-propelled force feedback: bringing force feedback to mobile devices using electrical stimulation

Efficient View Maintenance for Enterprise Applications in Columnar In-Memory Databases

Workload-aware aggregate maintenance in columnar in-memory databases

Interactive, Flexible, and Generic What-If Analyses Using In-Memory Column Stores