arXiv subjects
Publications and source records attributed to Thomas Rattei.
Over the past years, substantial numbers of microbial species' genomes have been deposited outside of conventional INSDC databases. The GlobDB aggregates 14 independent genomic catalogues to provide a comprehensive database of species-dereplicated microbial genomes, with consistent taxonomy, annotations, and additional analysis resources. The GlobDB is available at https://globdb.org/.
This paper introduces scikit-hubness, a Python package for efficient nearest neighbor search in high-dimensional spaces. Hubness is an aspect of the curse of dimensionality, and is known to impair various learning tasks, including classification, clustering, and visualization. scikit-hubness provides algorithms for hubness analysis ("Is my data affected by hubness?"), hubness reduction ("How can we improve neighbor retrieval in high dimensions?"), and approximate neighbor search ("Does it work for large data sets?"). It is integrated into the scikit-learn environment, enabling rapid adoption by Python-based machine learning researchers and practitioners. Users will find all functionality of the scikit-learn neighbors package, plus additional support for transparent hubness reduction and approximate nearest neighbor search. scikit-hubness is developed using several quality assessment tools and principles, such as PEP8 compliance, unit tests with high code coverage, continuous integration on all major platforms (Linux, MacOS, Windows), and additional checks by LGTM. The source code is available at https://github.com/VarIr/scikit-hubness under the BSD 3-clause license. Install from the Python package index with $ pip install scikit-hubness.
Cellular functions are based on the complex interplay of proteins, therefore the structure and dynamics of these protein-protein interaction (PPI) networks are the key to the functional understanding of cells. In the last years, large-scale PPI networks of several model organisms were investigated. Methodological improvements now allow the analysis of PPI networks of multiple organisms simultaneously as well as the direct modeling of ancestral networks. This provides the opportunity to challenge existing assumptions on network evolution. We utilized present-day PPI networks from integrated datasets of seven model organisms and developed a theoretical and bioinformatic framework for studying the evolutionary dynamics of PPI networks. A novel filtering approach using percolation analysis was developed to remove low confidence interactions based on topological constraints. We then reconstructed the ancient PPI networks of different ancestors, for which the ancestral proteomes, as well as the ancestral interactions, were inferred. Ancestral proteins were reconstructed using orthologous groups on different evolutionary levels. A stochastic approach, using the duplication-divergence model, was developed for estimating the probabilities of ancient interactions from today's PPI networks. The growth rates for nodes, edges, sizes and modularities of the networks indicate multiplicative growth and are consistent with the results from independent static analysis. Our results support the duplication-divergence model of evolution and indicate fractality and multiplicative growth as general properties of the PPI network structure and dynamics.
The similarity between protein sequences is a directly and easly computed quantity from which to deduce information about their evolutionary distance and to detect homologous proteins. The SIMAP database -- Similarity Matrix of Proteins -- provides a pre-computed similarity matrix covering the similarity space formed by about all publicly available amino acid sequences from public databases and completely sequenced genomes. From SIMAP we construct the protein homology network, where the proteins are the nodes and the links represent homology relationships. With more than 5 million nodes and about 70 10^9 edges it is the greatest protein homology network ever been builded. We describe the basic features and we perform a global statistical analysis of the network. Starting from the Smith-Waterman similarity score, we define for each edge a weight w to measure the similarity distance between two nodes. Keeping only edges with a weigth greater than a minimal w_min, and by varying w_min we build a family of networks with different degree of similarity. We investigate the distribution of connected components (clusters) of the networks at different w_min and in particular we find a behaviour similar to a phase transition guided by the formation of a giant component. Moreover we study selected sequence features and protein domains of protein pairs that connect different clusters in the networks at different level of similarity. We observed specific, non-random distributions of the protein features and domains for proteins connecting clusters at certain weight intervals.