FitTetra 2.0-improved genotype calling for tetraploids with multiple population and parental data support

BackgroundGenetic studies in tetraploids are lagging behind in comparison with studies of diploids as the complex genetics of tetraploids require much more elaborated computational methodologies. Recent advancements in development of molecular techniques and computational tools facilitate new methods for automated, high-throughput genotype calling in tetraploid species. We report on the upgrade of the widely-used fitTetra software aiming to improve its accuracy, which to date is hampered by technical artefacts in the data.ResultsOur upgrade of the fitTetra package is designed for a more accurate modelling of complex collections of samples. The package fits a mixture model where some parameters of the model are estimated separately for each sub-collection. When a full-sib family is analyzed, we use parental genotypes to predict the expected segregation in terms of allele dosages in the offspring. More accurate modelling and use of parental data increases the accuracy of dosage calling. We tested the package on data obtained with an Affymetrix Axiom 60k array and compared its performance with the original version and the recently published ClusterCall tool, showing that at least 20% more SNPs could be called with our updated.ConclusionOur updated software package shows clearly improved performance in genotype calling accuracy. Estimation of mixing proportions of the underlying dosage distributions is separated for full-sib families (where mixture proportions can be estimated from the parental dosages and inheritance model) and unstructured populations (where they are based on the assumption of Hardy-Weinberg equilibrium). Additionally, as the distributions of signal ratios of the dosage classes can be assumed to be the same for all populations, including parental data for some subpopulations helps to improve fitting other populations as well. The R package fitTetra 2.0 is freely available under the GNU Public License as Additional file with this article.</p

Machine learning on the road to unlocking microbiota's potential for boosting immune checkpoint therapy

The intestinal microbiota is a complex and diverse ecological community that fulfills multiple functions and substantially impacts human health. Despite its plasticity, unfavorable conditions can cause perturbations leading to so-called dysbiosis, which have been connected to multiple diseases. Unfortunately, understanding the mechanisms underlying the crosstalk between those microorganisms and their host is proving to be difficult. Traditionally used bioinformatic tools have difficulties to fully exploit big data generated for this purpose by modern high throughput screens. Machine Learning (ML) may be a potential means of solving such problems, but it requires diligent application to allow for drawing valid conclusions. This is especially crucial as gaining insight into the mechanistic basis of microbial impact on human health is highly anticipated in numerous fields of study. This includes oncology, where growing amounts of studies implicate the gut ecosystems in both cancerogenesis and antineoplastic treatment outcomes. Based on these reports and first signs of clinical benefits related to microbiota modulation in human trials, hopes are rising for the development of microbiome-derived diagnostics and therapeutics. In this mini-review, we're inspecting analytical approaches used to uncover the role of gut microbiome in immune checkpoint therapy (ICT) with the use of shotgun metagenomic sequencing (SMS) data

Evaluation of pulsed laser deposited thin films properties on the basis of the nanoindentation test

The overall goal of the research, is development of the numerical model capable of replicating local heterogenous material behavior of thin film materials under loading conditions. This particular work is focused on determination of flow stress characteristics of investigated TiN thin film based on the nanoindentation test. To properly recalculate measured load-displacement values into the required stress-strain curve an inverse analysis techniques are used. Subsequent stages including deposition process of TiN layer, room temperature nanoindentation tests and development of direct problem numerical model for the inverse analysis are described. Capabilities of the approach are also discussed within the work

Computational analysis of the modular architecture of secondary metabolite biosynthesis gene clusters.

Szlaki biosyntetyczne znacznej większości metabolitów drugorzędowych posiadających zastosowanie farmaceutyczne kodowane są przed olbrzymie klastry genowe, zwane klastrami genów metabolitów drugorzędowych (ang. SMGCs - Secondary Metabolite Gene Clusters). Wewnątrz SMGCs geny są zgrupowane w konserwowane wielogenowy moduły. Kodują one białka posiadające szeroki zakres funkcji między innymi regulacyjne oraz transportowe. Występują również moduły biosyntetyczne, z których każdy odpowiada za biosyntezę fragmentu końcowego produktu. Aby otrzymać wgląd w ewolucję tych elementów genetycznych oraz stworzyć możliwość odkrycia nowych molekuł, została wykonana wysokoprzepustowa analiza obliczeniowa modularności SMGCs.Punktem początkowym analizy jest lista wszystkich SMGCs pochodzących ze wszystkich dostępnych sekwencji nukleotydowych bakterii z rzędu Actinomyces. Lista ta została utworzona przy użyciu programu antiSMASH [1]. W celu zidentyfikowania silnie konserwowanych modułów, zbadana została kokonserwacja genów wewnątrz klastrów. W oparciu o podział genów na Klastry Grup Ortologicznych (ang COGs - Clusters of Orthologous Groups), zrekonstruowano sieć interakcji łączących COGs poprzez syntenię genów oraz kolokalizację wewnątrz SMGCs. Prosty algorytm został użyty do identyfikacji grup COGs połączonych znaczną ilością interakcji, reprezentujących konserwowane moduły, które mogą zostać przyporządkowane do chemicznych grup składających się na molekuły metabolitów drugorzędowych. W uzyskanym zbiorze danych możliwa jest identyfikacja licznych SMGCs posiadających zestawy modułów, których nie znano wcześniej i które mogą być odpowiedzialne ze produkcję nie znanych dotychczas molekuł. Co ciekawe, otrzymany katalog konserwowanych modułów umożliwia dogłębną analizę ewolucji SMGCs poprzez porównanie klastrów będących homologami, pod kątem zawartych w nich modułów oraz zaproponować najbardziej prawdopodobną ścieżkę ewolucji, która skutkowałaby obserwowaną różnorodnością molekuł. Dodatkowo, po integracji z programem antiSMASH, uzyskany katalog pozwoli na lepsze przewidywanie struktury końcowego produktu nowoodkrytych SMGCs.The biosynthetic pathways of the great majority of secondary metabolites with pharmaceutical activities are encoded by huge clusters of genes, termed secondary metabolite gene clusters (SMGCs). Inside SMGCs, genes are further grouped into conserved multigene modules. These modules encode proteins with a range of functions like regulation and transport. There are also biosynthetic modules, each of which is responsible for the biosynthesis of a part of the end product. To gain deeper insight into the evolution of SMGCs and to open up new ways to discover novel compounds, we analyzed the modularity of SMGCs computationally in a high-throughput fashion.As a starting point of our analysis, we created a list of SMGCs from all available actinomycete nucleotide data using our previously published software pipeline antiSMASH [1]. To identify all highly conserved modules, we then studied co-conservation of the genes within these gene clusters. Based on a classification of all SMGC genes into Clusters of Orthologous Groups (COGs), we reconstructed interaction networks linking COGs by gene synteny and by co-localization of genes within the same gene clusters. A simple algorithm that overlaid the two networks could then identify highly connected motifs of COGs. These motifs represent conserved modules that can be directly linked to the chemical moieties of the secondary metabolite end product. Using these data, we were able to identify a number of gene clusters with conserved architectures (module compositions) that had not been reported earlier, which may be responsible for the biosynthesis of compounds with novel chemical structures.Intriguingly, our catalogue of conserved modules enables a deep analysis of the evolution of SMGCs, by comparing module compositions of homologous gene clusters from different species and using parsimony or likelihood methods to infer the most probable evolutionary route that has resulted in the observed variety of architectures. It also enables screening for novel types of gene clusters on an unprecedented scale. Finally, this new approach can be integrated into the antiSMASH pipeline to allow more detailed predictions of the secondary metabolite end products of unknown SMGCs from their module compositions