Inferring Markov Chains to Describe Convergent Tumor Evolution With CIMICE.

The field of tumor phylogenetics focuses on studying the differences within cancer cell populations. Many efforts are done within the scientific community to build cancer progression models trying to understand the heterogeneity of such diseases. These models are highly dependent on the kind of data used for their construction, therefore, as the experimental technologies evolve, it is of major importance to exploit their peculiarities. In this work we describe a cancer progression model based on Single Cell DNA Sequencing data. When constructing the model, we focus on tailoring the formalism on the specificity of the data. We operate by defining a minimal set of assumptions needed to reconstruct a flexible DAG structured model, capable of identifying progression beyond the limitation of the infinite site assumption. Our proposal is conservative in the sense that we aim to neither discard nor infer knowledge which is not represented in the data. We provide simulations and analytical results to show the features of our model, test it on real data, show how it can be integrated with other approaches to cope with input noise. Moreover, our framework can be exploited to produce simulated data that follows our theoretical assumptions. Finally, we provide an open source R implementation of our approach, called CIMICE, that is publicly available on BioConductor.

Environmental surveillance and spatio-temporal analysis of Legionella spp. in a region of northeastern Italy (2002-2017).

Legionella spp. are considered an important cause of potentially preventable morbidity and mortality, making environmental surveillance a crucial component of risk assessment plans. In this work, 20,319 water samples were collected in 3,983 environmental surveys during a 16-year period by ARPA, the Regional Agency for Environmental Protection, Friuli Venezia Giulia, and the results were studied to better understand the diffusion mechanisms of Legionella. The data showed a strong seasonal signal, a prevalence of L. pneumophila serogroup 2-15 in most environments (63% of positive samples), a prevalence of L. pneumophila serogroup 1 in swimming pool-associated environments (82% of positive samples), a persistent presence of Legionella in hospitals and a recurrent presence of Legionella in other facilities such as hotels, possibly years after interventions, highlighting the difficulty of eradicating the bacteria. Retrospective spatio-temporal analyses on geocoded historical data were carried out with SaTScan using an ordinal model with risk as a covariate to identify potential clusters with an excess of cases in the higher-risk categories. Although no outbreaks occurred during the period of study, such analyses identified spatially restricted zones with unusual contamination, which sometimes were also areas in which several surveys triggered by notifications of clinical cases were performed. Simulations of periodic prospective analyses permitted the assessment of the efficacy of the method in early detection of such clusters. The proposed method may be a useful tool in environmental surveillance, prevention and control of Legionella.

Bud set in poplar--genetic dissection of a complex trait in natural and hybrid populations.

• The seasonal timing of growth events is crucial to tree distribution and conservation. The seasonal growth cycle is strongly adapted to the local climate that is changing because of global warming. We studied bud set as one cornerstone of the seasonal growth cycle in an integrative approach. • Bud set was dissected at the phenotypic level into several components, and phenotypic components with most genetic variation were identified. While phenotypic variation resided in the timing of growth cessation, and even so more in the duration from growth cessation to bud set, the timing of growth cessation had a stronger genetic component in both natural and hybrid populations. • Quantitative trait loci (QTL) were identified for the most discriminative phenotypic bud-set components across four poplar pedigrees. The QTL from different pedigrees were recurrently detected in six regions of the poplar genome. • These regions of 1.83-4.25 Mbp in size, containing between 202 and 394 genes, form the basis for further molecular-genetic dissection of bud set.

Analysis of transposons and repeat composition of the sunflower (Helianthus annuus L.) genome.

A sample-sequencing strategy combined with slot-blot hybridization and FISH was used to study the composition of the repetitive component of the sunflower genome. One thousand six hundred thirty-eight sequences for a total of 954,517 bp were analyzed. The fraction of sequences that can be classified as repetitive using computational and hybridization approaches amounts to 62% in total. Almost two thirds remain as yet uncharacterized in nature. Of those characterized, most belong to the gypsy superfamily of LTR-retrotransposons. Unlike in other species, where single families can account for large fractions of the genome, it appears that no transposon family has been amplified to very high levels in sunflower. All other known classes of transposable elements were also found. One family of unknown nature (contig 61) was the most repeated in the sunflower genome. The evolution of the repetitive component in the Helianthus genus and in other Asteraceae was studied by comparative analysis of the hybridization of total genomic DNAs from these species to the sunflower small-insert library and compared to gene-based phylogeny. Very little similarity is observed between Helianthus species and two related Asteraceae species outside of the genus. Most repetitive elements are similar in annual and perennial Helianthus species indicating that sequence amplification largely predates such divergence. Gypsy-like elements are more represented in the annuals than in the perennials, while copia-like elements are similarly represented, attesting a different amplification history of the two superfamilies of LTR-retrotransposons in the Helianthus genus.

Structured motifs search.

In this paper, we describe an algorithm for the localization of structured models, i.e. sequences of (simple) motifs and distance constraints. It basically combines standard pattern matching procedures with a constraint satisfaction solver, and it has the ability, not present in similar tools, to search for partial matches. A significant feature of our approach, especially in terms of efficiency for the application context, is that the (potentially) exponentially many solutions to the considered problem are represented in compact form as a graph. Moreover, the time and space necessary to build the graph are linear in the number of occurrences of the component patterns.