PanDelos-frags: A methodology for discovering pangenomic content of incomplete microbial assemblies.

Pangenomics was originally defined as the problem of comparing the composition of genes into gene families within a set of bacterial isolates belonging to the same species. The problem requires the calculation of sequence homology among such genes. When combined with metagenomics, namely for human microbiome composition analysis, gene-oriented pangenome detection becomes a promising method to decipher ecosystem functions and population-level evolution. Established computational tools are able to investigate the genetic content of isolates for which a complete genomic sequence is available. However, there is a plethora of incomplete genomes that are available on public resources, which only a few tools may analyze. Incomplete means that the process for reconstructing their genomic sequence is not complete, and only fragments of their sequence are currently available. However, the information contained in these fragments may play an essential role in the analyses. Here, we present PanDelos-frags, a computational tool which exploits and extends previous results in analyzing complete genomes. It provides a new methodology for inferring missing genetic information and thus for managing incomplete genomes. PanDelos-frags outperforms state-of-the-art approaches in reconstructing gene families in synthetic benchmarks and in a real use case of metagenomics. PanDelos-frags is publicly available at https://github.com/InfOmics/PanDelos-frags.

A dictionary based informational genome analysis.

BACKGROUND: In the post-genomic era several methods of computational genomics are emerging to understand how the whole information is structured within genomes. Literature of last five years accounts for several alignment-free methods, arisen as alternative metrics for dissimilarity of biological sequences. Among the others, recent approaches are based on empirical frequencies of DNA k-mers in whole genomes. RESULTS: Any set of words (factors) occurring in a genome provides a genomic dictionary. About sixty genomes were analyzed by means of informational indexes based on genomic dictionaries, where a systemic view replaces a local sequence analysis. A software prototype applying a methodology here outlined carried out some computations on genomic data. We computed informational indexes, built the genomic dictionaries with different sizes, along with frequency distributions. The software performed three main tasks: computation of informational indexes, storage of these in a database, index analysis and visualization. The validation was done by investigating genomes of various organisms. A systematic analysis of genomic repeats of several lengths, which is of vivid interest in biology (for example to compute excessively represented functional sequences, such as promoters), was discussed, and suggested a method to define synthetic genetic networks. CONCLUSIONS: We introduced a methodology based on dictionaries, and an efficient motif-finding software application for comparative genomics. This approach could be extended along many investigation lines, namely exported in other contexts of computational genomics, as a basis for discrimination of genomic pathologies.

Computing by polymerase chain reaction.

A mathematical notation is introduced to represent, at a symbolic level, different mechanisms of DNA recombination, and a 'PCR lemma' is proven by analytically describing the combinatorial properties of the polymerase chain reaction process. This approach led to the discovery of novel techniques, based on a form of PCR which we called cross pairing PCR (briefly XPCR). They were mathematically analyzed and already experimentally proven in different contexts, such as DNA extraction and recombination. Thus, a mathematical analysis of standard methodologies may highlight novel mechanisms of DNA recombination and this can provide new technologies for DNA manipulation.

Knee joint injury and repair modeled by membrane systems.

Tissue reparative processes following tissue injury are modeled by a basic membrane system, dealing only with objects, non-active membranes, and non-deterministic evolution rules. At the biological level, tissue repair is regulated by multiple interactions between cells and macromolecules, the latter acting as signals. Such signals modify cell behavior including proliferation, migration, differentiation, and phagocytosis. The signaling components themselves are produced and removed by the resident cell population, and this set of events may provide additional stimuli for altering cell activities. In this paper we have focused on modeling the biology of events following an injury to the knee joint, and have used hyaluronan (a polymer produced by cartilage and synovial cells) as an example for a signaling component in the healing process. The intrinsic non-determinism of the model is a key feature, which allows a mathematical description of the repair responses as well as a possibility for either functional restoration or chronic degeneration, leading to arthritis.