PanCGHweb: a web tool for genotype calling in pangenome CGH data

Summary: A pangenome is the total of genes present in strains of the same species. Pangenome microarrays allow determining the genomic content of bacterial strains more accurately than conventional comparative genome hybridization microarrays. PanCGHweb is the first tool that effectively calls genotype based on pangenome microarray data

Global alignment of protein-protein interaction networks by graph matching methods

Aligning protein-protein interaction (PPI) networks of different species has drawn a considerable interest recently. This problem is important to investigate evolutionary conserved pathways or protein complexes across species, and to help in the identification of functional orthologs through the detection of conserved interactions. It is however a difficult combinatorial problem, for which only heuristic methods have been proposed so far. We reformulate the PPI alignment as a graph matching problem, and investigate how state-of-the-art graph matching algorithms can be used for that purpose. We differentiate between two alignment problems, depending on whether strict constraints on protein matches are given, based on sequence similarity, or whether the goal is instead to find an optimal compromise between sequence similarity and interaction conservation in the alignment. We propose new methods for both cases, and assess their performance on the alignment of the yeast and fly PPI networks. The new methods consistently outperform state-of-the-art algorithms, retrieving in particular 78% more conserved interactions than IsoRank for a given level of sequence similarity. Availability:http://cbio.ensmp.fr/proj/graphm\_ppi/, additional data and codes are available upon request. Contact: [email protected]: Preprint versio

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

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

The Candida genome database incorporates multiple Candida species: multispecies search and analysis tools with curated gene and protein information for Candida albicans and Candida glabrata

The Candida Genome Database (CGD, http://www.candidagenome.org/) is an internet-based resource that provides centralized access to genomic sequence data and manually curated functional information about genes and proteins of the fungal pathogen Candida albicans and other Candida species. As the scope of Candida research, and the number of sequenced strains and related species, has grown in recent years, the need for expanded genomic resources has also grown. To answer this need, CGD has expanded beyond storing data solely for C. albicans, now integrating data from multiple species. Herein we describe the incorporation of this multispecies information, which includes curated gene information and the reference sequence for C. glabrata, as well as orthology relationships that interconnect Locus Summary pages, allowing easy navigation between genes of C. albicans and C. glabrata. These orthology relationships are also used to predict GO annotations of their products. We have also added protein information pages that display domains, structural information and physicochemical properties; bibliographic pages highlighting important topic areas in Candida biology; and a laboratory strain lineage page that describes the lineage of commonly used laboratory strains. All of these data are freely available at http://www.candidagenome.org/. We welcome feedback from the research community at [email protected]

The new COST Action European Venom Network (EUVEN)—synergy and future perspectives of modern venomics

Venom research is a highly multidisciplinary field that involves multiple subfields of biology, informatics, pharmacology, medicine, and other areas. These different research facets are often technologically challenging and pursued by different teams lacking connection with each other. This lack of coordination hampers the full development of venom investigation and applications. The COST Action CA19144–European Venom Network was recently launched to promote synergistic interactions among different stakeholders and foster venom research at the European level

FastBLAST: Homology Relationships for Millions of Proteins

BackgroundAll-versus-all BLAST, which searches for homologous pairs of sequences in a database of proteins, is used to identify potential orthologs, to find new protein families, and to provide rapid access to these homology relationships. As DNA sequencing accelerates and data sets grow, all-versus-all BLAST has become computationally demanding.Methodology/principal findingsWe present FastBLAST, a heuristic replacement for all-versus-all BLAST that relies on alignments of proteins to known families, obtained from tools such as PSI-BLAST and HMMer. FastBLAST avoids most of the work of all-versus-all BLAST by taking advantage of these alignments and by clustering similar sequences. FastBLAST runs in two stages: the first stage identifies additional families and aligns them, and the second stage quickly identifies the homologs of a query sequence, based on the alignments of the families, before generating pairwise alignments. On 6.53 million proteins from the non-redundant Genbank database ("NR"), FastBLAST identifies new families 25 times faster than all-versus-all BLAST. Once the first stage is completed, FastBLAST identifies homologs for the average query in less than 5 seconds (8.6 times faster than BLAST) and gives nearly identical results. For hits above 70 bits, FastBLAST identifies 98% of the top 3,250 hits per query.Conclusions/significanceFastBLAST enables research groups that do not have supercomputers to analyze large protein sequence data sets. FastBLAST is open source software and is available at http://microbesonline.org/fastblast

DODO: an efficient orthologous genes assignment tool based on domain architectures. Domain based ortholog detection

<p>Abstract</p> <p>Background</p> <p>Orthologs are genes derived from the same ancestor gene loci after speciation events. Orthologous proteins usually have similar sequences and perform comparable biological functions. Therefore, ortholog identification is useful in annotations of newly sequenced genomes. With rapidly increasing number of sequenced genomes, constructing or updating ortholog relationship between all genomes requires lots of effort and computation time. In addition, elucidating ortholog relationships between distantly related genomes is challenging because of the lower sequence similarity. Therefore, an efficient ortholog detection method that can deal with large number of distantly related genomes is desired.</p> <p>Results</p> <p>An efficient ortholog detection pipeline DODO (DOmain based Detection of Orthologs) is created on the basis of domain architectures in this study. Supported by domain composition, which usually directly related with protein function, DODO could facilitate orthologs detection across distantly related genomes. DODO works in two main steps. Starting from domain information, it first assigns protein groups according to their domain architectures and further identifies orthologs within those groups with much reduced complexity. Here DODO is shown to detect orthologs between two genomes in considerably shorter period of time than traditional methods of reciprocal best hits and it is more significant when analyzed a large number of genomes. The output results of DODO are highly comparable with other known ortholog databases.</p> <p>Conclusions</p> <p>DODO provides a new efficient pipeline for detection of orthologs in a large number of genomes. In addition, a database established with DODO is also easier to maintain and could be updated relatively effortlessly. The pipeline of DODO could be downloaded from <url>http://140.109.42.19:16080/dodo_web/home.htm</url></p