Inparanoid: a comprehensive database of eukaryotic orthologs

The Inparanoid eukaryotic ortholog database (http://inparanoid.cgb.ki.se/) is a collection of pairwise ortholog groups between 17 whole genomes; Anopheles gambiae, Caenorhabditis briggsae, Caenorhabditis elegans, Drosophila melanogaster, Danio rerio, Takifugu rubripes, Gallus gallus, Homo sapiens, Mus musculus, Pan troglodytes, Rattus norvegicus, Oryza sativa, Plasmodium falciparum, Arabidopsis thaliana, Escherichia coli, Saccharomyces cerevisiae and Schizosaccharomyces pombe. Complete proteomes for these genomes were derived from Ensembl and UniProt and compared pairwise using Blast, followed by a clustering step using the Inparanoid program. An Inparanoid cluster is seeded by a reciprocally best-matching ortholog pair, around which inparalogs (should they exist) are gathered independently, while outparalogs are excluded. The ortholog clusters can be searched on the website using Ensembl gene/protein or UniProt identifiers, annotation text or by Blast alignment against our protein datasets. The entire dataset can be downloaded, as can the Inparanoid program itself

Realizing a deep reinforcement learning agent discovering real-time feedback control strategies for a quantum system

To realize the full potential of quantum technologies, finding good strategies to control quantum information processing devices in real time becomes increasingly important. Usually these strategies require a precise understanding of the device itself, which is generally not available. Model-free reinforcement learning circumvents this need by discovering control strategies from scratch without relying on an accurate description of the quantum system. Furthermore, important tasks like state preparation, gate teleportation and error correction need feedback at time scales much shorter than the coherence time, which for superconducting circuits is in the microsecond range. Developing and training a deep reinforcement learning agent able to operate in this real-time feedback regime has been an open challenge. Here, we have implemented such an agent in the form of a latency-optimized deep neural network on a field-programmable gate array (FPGA). We demonstrate its use to efficiently initialize a superconducting qubit into a target state. To train the agent, we use model-free reinforcement learning that is based solely on measurement data. We study the agent’s performance for strong and weak measurements, and for three-level readout, and compare with simple strategies based on thresholding. This demonstration motivates further research towards adoption of reinforcement learning for real-time feedback control of quantum devices and more generally any physical system requiring learnable low-latency feedback control

FSRD: fungal stress response database

Adaptation to different types of environmental stress is a common part of life for today's fungi. A deeper understanding of the organization, regulation and evolution of fungal stress response systems may lead to the development of novel antifungal drugs and technologies or the engineering of industrial strains with elevated stress tolerance. Here we present the Fungal Stress Response Database (http://internal.med.unideb.hu/fsrd) aimed to stimulate further research on stress biology of fungi. The database incorporates 1985 fungal stress response proteins with verified physiological function(s) and their orthologs identified and annotated in 28 species including human and plant pathogens, as well as important industrial fungi. The database will be extended continuously to cover other fully sequenced fungal species. Our database, as a starting point for future stress research, facilitates the analysis of literature data on stress and the identification of ortholog groups of stress response proteins in newly sequenced fungal genomes. Database URL: http://internal.med.unideb.hu/fsr

Multicenter analysis of sputum microbiota in tuberculosis patients.

The impact of tuberculosis and of anti-tuberculosis therapy on composition and modification of human lung microbiota has been the object of several investigations. However, no clear outcome has been presented so far and the relationship between M. tuberculosis pulmonary infection and the resident lung microbiota remains vague. In this work we describe the results obtained from a multicenter study of the microbiota of sputum samples from patients with tuberculosis or unrelated lung diseases and healthy donors recruited in Switzerland, Italy and Bangladesh, with the ultimate goal of discovering a microbiota-based biomarker associated with tuberculosis. Bacterial 16S rDNA amplification, high-throughput sequencing and extensive bioinformatic analyses revealed patient-specific flora and high variability in taxon abundance. No common signature could be identified among the individuals enrolled except for minor differences which were not consistent among the different geographical settings. Moreover, anti-tuberculosis therapy did not cause any important variation in microbiota diversity, thus precluding its exploitation as a biomarker for the follow up of tuberculosis patients undergoing treatment

Rich-Club Phenomenon in the Interactome of P. falciparum—Artifact or Signature of a Parasitic Life Style?

Recent advances have provided a first experimental protein interaction map of the human malaria parasite P. falciparum, which appears to be remotely related to interactomes of other eukaryotes. Here, we present a comparative topological analysis of this experimentally determined web with a network of conserved interactions between proteins in S. cerevisiae, C. elegans and D. melanogaster that have an ortholog in Plasmodium. Focusing on experimental interactions, we find a significant presence of a “rich-club,” a topological characteristic that features an “oligarchy” of highly connected proteins being intertwined with one another. In complete contrast, the network of interologs and particularly the web of evolutionary-conserved interactions in P. falciparum lack this feature. This observation prompts the question of whether this result points to a topological signature of the parasite's biology, since experimentally obtained interactions widely cover parasite-specific functions. Significantly, hub proteins that appear in such an oligarchy revolve around invasion functions, shaping an island of parasite-specific activities in a sea of evolutionary inherited interactions. This presence of a biologically unprecedented network feature in the human malaria parasite might be an artifact of the quality and the methods to obtain interaction data in this organism. Yet, the observation that rich-club proteins have distinctive and statistically significant functions that revolve around parasite-specific activities point to a topological signature of a parasitic life style

Enrichment of homologs in insignificant BLAST hits by co-complex network alignment

<p>Abstract</p> <p>Background</p> <p>Homology is a crucial concept in comparative genomics. The algorithm probably most widely used for homology detection in comparative genomics, is BLAST. Usually a stringent score cutoff is applied to distinguish putative homologs from possible false positive hits. As a consequence, some BLAST hits are discarded that are in fact homologous.</p> <p>Results</p> <p>Analogous to the use of the genomics context in genome alignments, we test whether conserved functional context can be used to select candidate homologs from insignificant BLAST hits. We make a co-complex network alignment between complex subunits in yeast and human and find that proteins with an insignificant BLAST hit that are part of homologous complexes, are likely to be homologous themselves. Further analysis of the distant homologs we recovered using the co-complex network alignment, shows that a large majority of these distant homologs are in fact ancient paralogs.</p> <p>Conclusions</p> <p>Our results show that, even though evolution takes place at the sequence and genome level, co-complex networks can be used as circumstantial evidence to improve confidence in the homology of distantly related sequences.</p

Algorithm of OMA for large-scale orthology inference

Since the publication of our article (Roth, Gonnet, and Dessimoz: BMC Bioinformatics 2008 9: 518), we have noticed several errors, which we correct in the following

GenomeBlast: a web tool for small genome comparison

BACKGROUND: Comparative genomics has become an essential approach for identifying homologous gene candidates and their functions, and for studying genome evolution. There are many tools available for genome comparisons. Unfortunately, most of them are not applicable for the identification of unique genes and the inference of phylogenetic relationships in a given set of genomes. RESULTS: GenomeBlast is a Web tool developed for comparative analysis of multiple small genomes. A new parameter called "coverage" was introduced and used along with sequence identity to evaluate global similarity between genes. With GenomeBlast, the following results can be obtained: (1) unique genes in each genome; (2) homologous gene candidates among compared genomes; (3) 2D plots of homologous gene candidates along the all pairwise genome comparisons; and (4) a table of gene presence/absence information and a genome phylogeny. We demonstrated the functions in GenomeBlast with an example of multiple herpesviral genome analysis and illustrated how GenomeBlast is useful for small genome comparison. CONCLUSION: We developed a Web tool for comparative analysis of small genomes, which allows the user not only to identify unique genes and homologous gene candidates among multiple genomes, but also to view their graphical distributions on genomes, and to reconstruct genome phylogeny. GenomeBlast runs on a Linux server with 4 CPUs and 4 GB memory. The online version of GenomeBlast is available to public by using a Web browser with the URL