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The molecular and cellular basis of novelty is an active area of research in evolutionary biology. Until very recently, the vast majority of cellular phenomena were so difficult to sample that cross-species studies of biochemistry were rare and comparative analysis at the level of biochemical systems was almost impossible. Recent advances in systems biology are changing what is possible, however, and comparative phylogenetic methods that can handle this new data are wanted. Here, we introduce the term "phylogenetic latent variable models" (PLVMs, pronounced "plums") for a class of models that has recently been used to infer the evolution of cellular states from systems-level molecular data, and develop a new parameterization and fitting strategy that is useful for comparative inference of biochemical networks. We deploy this new framework to infer the ancestral states and evolutionary dynamics of protein-interaction networks by analyzing >16,000 predominantly metazoan co-fractionation and affinity-purification mass spectrometry experiments. Based on these data, we estimate ancestral interactions across unikonts, broadly recovering protein complexes involved in translation, transcription, proteostasis, transport, and membrane trafficking. Using these results, we predict an ancient core of the Commander complex made up of CCDC22, CCDC93, C16orf62, and DSCR3, with more recent additions of COMMD-containing proteins in tetrapods. We also use simulations to develop model fitting strategies and discuss future model developments.
Pubmed ID: 31658251
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A database of orthologous groups of genes. The orthologous groups are annotated with functional description lines (derived by identifying a common denominator for the genes based on their various annotations), with functional categories (i.e derived from the original COG/KOG categories). eggNOG's database currently counts 1.7 million orthologous groups in 3686 species, covering over 7.7 million proteins (built from 9.6 million proteins). (Jan 30, 2014)
View all literature mentionsscikit-learn: machine learning in Python
View all literature mentionsSoftware platform for complex network analysis and visualization. Used for visualization of molecular interaction networks and biological pathways and integrating these networks with annotations, gene expression profiles and other state data.
View all literature mentionsRepository for all research outputs from across all fields of science in any file format as well as both positive and negative results. They assign all publicly available uploads a Digital Object Identifier (DOI) to make the upload easily and uniquely citeable. They further support harvesting of all content via the OAI-PMH protocol. They promote peer-reviewed openly accessible research, and curate uploads. ZENODO allows users to create their own collection and accept or reject all uploads to it. They allow for uploading under a multitude of different licenses and access levels.
View all literature mentionsIntergovernmental organisation funded by public research money from its member states in Europe. Groups and laboratories perform basic research in molecular biology and molecular medicine, training for scientists, students and visitors. Provides development of services, new instruments and methods, data and technology in its member states.
View all literature mentionsTool for searching sequence databases for homologs of protein sequences, and for making protein sequence alignments. It implements methods using probabilistic models called profile hidden Markov models (profile HMMs). Compared to BLAST, FASTA, and other sequence alignment and database search tools based on older scoring methodology, HMMER aims to be significantly more accurate and more able to detect remote homologs because of the strength of its underlying mathematical models. In the past, this strength came at significant computational expense, but in the new HMMER3 project, HMMER is now essentially as fast as BLAST.
View all literature mentionsSoftware to: view dicom files and assemble them into 3D volumes. View and convert between Analyze, Nifti, and Interfile. Classify and organize dicoms and 3D volumes using metadata. Search and report on a collection of scans.
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