MTR-Viewer: Identifying regions within genes under purifying selection

Advances in genomic sequencing have enormous potential to revolutionise personalised medicine, however distinguishing disease-causing from benign variants remains a challenge. The increasing number of human genome and exome sequences available has revealed areas where unfavourable variation is removed through purifying selection. Here we present the MTR-Viewer, a web-server enabling easy visualisation at the gene or variant level of the Missense Tolerance Ratio (MTR), a measure of regional intolerance to missense variation calculated using variation from 220,000 exome and genome sequences. The MTR-Viewer enables exploration of MTR calculations, using different sliding windows, for over 18,000 human protein-coding genes and 85,000 alternative transcripts. Users can also view MTR scores calculated for specific ethnicities, to enable easy exploration of regions that may be under different selective pressure. The spatial distribution of population and known disease variants is also displayed on the protein’s domain structure. Intolerant regions were found to be highly enriched for ClinVar pathogenic and COSMIC somatic missense variants (Mann-Whitney U test p < 2.2x10-16). As the MTR is not biased by known domains and protein features, it can highlight functionally important regions within genes overlooked or inaccessible by traditional methods. MTR-Viewer is freely available via a user friendly web-server at http://biosig.unimelb.edu.au/mtr-viewer/.M.S. was supported by the Australian Government Research Training Program. S.P. was supported by an NHMRC R.D. Wright Career Development Fellowship (1126877). S.P. is an employee of AstraZeneca. D.B.A. was supported by the Jack Brockhoff Foundation [JBF 4186, 2016], a Newton Fund RCUK-CONFAP Grant awarded by The Medical Research Council (MRC) [MR/M026302/1]; the National Health and Medical Research Council of Australia [APP1072476]; and the Department of Biochemistry and Molecular Biology, University of Melbourne

The ability of four strains of Streptococcus uberis to induce clinical mastitis after intramammary inoculation in lactating cows

AIM: To compare the ability of four strains of Streptococcus uberis at two doses to induce clinical mastitis in lactating dairy cows after intramammary inoculation in order to evaluate their usefulness for future experimental infection models. MATERIALS AND METHODS: Four field strains of Streptococcus uberis (26LB, S418, and S523 and SR115) were obtained from cows with clinical mastitis in the Wairarapa and Waikato regions of New Zealand. Twenty-four crossbred lactating cows, with no history of mastitis and absence of major pathogens following culture of milk samples, were randomly allocated to four groups (one per strain) of six cows. Each cow was infused (Day 0) in one quarter with approximately 104 cfu and in the contralateral quarter with approximately 106 cfu of the same strain. The other two quarters remained unchallenged. All four quarters were then inspected for signs of clinical mastitis, by palpation and observation of the foremilk, twice daily from Days 0–9, and composite milk samples were collected from Days 0–8 for analysis of somatic cell counts (SCC). Quarters were treated with penicillin when clinical mastitis was observed. Duplicate milk samples were collected and cultured on presentation of each clinical case and on Day 4 from challenged quarters with no clinical signs. RESULTS: Clinical mastitis was diagnosed in 26/48 (54%) challenged quarters. Challenge with strain S418 resulted in more cases of mastitis (12/12 quarters) than strains SR115 (7/12), 26LB (6/12) or S523 (1/12), and the mean interval from challenge to first diagnosis of mastitis was shorter for S418 than the other strains (p<0.001). The proportion of quarters from which S. uberis could be isolated after challenge was less for strain 26LB (1/6) than SR115 (6/7) (p<0.05), and SCC following challenge was lower for strain S523 than the other strains (p<0.05). CONCLUSIONS: There were significant differences between the strains in the proportion of quarters developing clinical mastitis, the interval to mastitis onset, SCC following challenge and the proportion of clinical cases from which S. uberis could be isolated. These results illustrate the difference in the ability of S. uberis strains to cause mastitis and the severity of the infections caused. CLINICAL RELEVANCE: Experimental challenge models can be used to compare infectivity and pathogenicity of different strains of mastitis-causing bacteria, the efficacy of pharmaceutical products and host-responses in a cost-effective manner.S Notcovich, G deNicolo, NB Williamson, A Grinberg, N Lopez-Villalobos, KR Petrovsk

The Impact of Feature Vector Length on Activity Recognition Accuracy on Mobile Phone

A key challenge for large scale activity recognition on mobile phones is the requirement for producing non-static classifiers that cater for differences in individual user characteristics when performing similar activities in a diverse environment. A static classifier is fixed throughout the system lifetime and does not adapt to different users or environmental changes. Therefore, a personalized recognition model is desirable for each user of the system to ensure accurate recognition in a diverse population of people. One of the main approaches for personalization of activity recognition is the generation of the classification model from user annotated data on mobile itself. However, giving the resource constraints on such devices there is a need to examine the effects of system parameters such as the feature extraction parameter that can affect the performance of the system. Thus, this paper examines the effects of feature vector lengths and varying data set sizes on the classification accuracy of four selected supervised machine learning algorithms running on off the shelf mobile phones. Our results show that out of the three feature vector lengths of 32, 64 and 128 considered, the 128 vector length yields the best accuracy for all the algorithms tested. Also, the time taken to train the algorithms with samples of this length is minimal compare to 64 and 32 feature lengths

Optimizing genomic medicine in epilepsy through a gene-customized approach to missense variant interpretation

Gene panel and exome sequencing have revealed a high rate of molecular diagnoses among diseases where the genetic architecture has proven suitable for sequencing approaches, with a large number of distinct and highly penetrant causal variants identified among a growing list of disease genes. The challenge is, given the DNA sequence of a new patient, to distinguish disease-causing from benign variants. Large samples of human standing variation data highlight regional variation in the tolerance to missense variation within the protein-coding sequence of genes. This information is not well captured by existing bioinformatic tools, but is effective in improving variant interpretation. To address this limitation in existing tools, we introduce the missense tolerance ratio (MTR), which summarizes available human standing variation data within genes to encapsulate population level genetic variation. We find that patient-ascertained pathogenic variants preferentially cluster in low MTR regions (P < 0.005) of well-informed genes. By evaluating 20 publicly available predictive tools across genes linked to epilepsy, we also highlight the importance of understanding the empirical null distribution of existing prediction tools, as these vary across genes. Subsequently integrating the MTR with the empirically selected bioinformatic tools in a gene-specific approach demonstrates a clear improvement in the ability to predict pathogenic missense variants from background missense variation in disease genes. Among an independent test sample of case and control missense variants, case variants (0.83 median score) consistently achieve higher pathogenicity prediction probabilities than control variants (0.02 median score; Mann-Whitney U test, P < 1 × 10(-16)). We focus on the application to epilepsy genes; however, the framework is applicable to disease genes beyond epilepsy

A 2:1 cocrystal of the cis and trans isomers of bis­[1,1,1,5,5,5-hexa­fluoro­pentane-2,4-dionato(1−)-κ2 O,O′]bis­(4-phenyl­pyridine N-oxide-κO)copper(II)

The title compound is a co-crystal of the cis and trans isomers, namely cis-bis­[1,1,1,5,5,5-hexa­fluoro­pentane-2,4-dionato(1−)-κ2 O,O′]bis­(4-phenyl­pyridine N-oxide-κO)copper(II)–trans-bis­[1,1,1,5,5,5-hexa­fluoro­pentane-2,4-dionato(1−)-κ2 O,O′]bis(4-phenyl­pyridine N-oxide-κO)copper(II) (2/1), [Cu(C5HF6O2)2(C11H9NO)2]. In both isomers, the coordination geometry of the Cu2+ atom is octa­hedral, exhibiting typical Jahn–Teller distortion. The metal atom of the trans isomer is located on an inversion centre. In the cis isomer, the phenyl ring in one 4-phenyl­pyridine N-oxide ligand is disordered over two orientations in a 1:1 ratio. In the crystal, weak inter­molecular C—H⋯F and C—H⋯O contacts establish connections between the cis and trans isomers

Ionic Liquids Based on Oxidoperoxido-Molybdenum(VI) Complexes with a Chelating Picolinate Ligand for Catalytic Epoxidation

UID/QUI/50006/2019 UID/Multi/04378/2019 UIDB/50011/2020 UIDP/50011/2020 contract foreseen in the numbers 4, 5 and 6 of article 23 of the Decree-Law 57/2016 of 29 August, changed by Law 57/2017 of 19 July.Ionic oxidoperoxido-molybdenum(VI) complexes of the type [Cat][MoO(O2)2(pic)], with pic = N,O-chelated picolinate ligand and Cat = monocation, were prepared in high yields (82–95%) from the precursor complex [H3O][MoO(O2)2(pic)] via [H]+ cation exchange for 1-ethyl-3-methylimidazolium [EMIM]+, 1-butyl-3-methylimidazolium [BMIM]+, 1-octyl-3-methylimidazolium [OMIM]+, N-cetylpyridinium [C16Py]+, and N-methyl-N,N,N-trioctylammonium [Aliquat]+. The structure and purity of the ionic compounds were assessed by 1H and 13C NMR, FT-IR, and elemental analysis (C, H, N), and the electrochemical properties were studied by differential pulse voltammetry (DPV) and cyclic voltammetry (CV). The [Cat][MoO(O2)2(pic)] compounds showed promising catalytic epoxidation activity based on the model reaction of cis-cyclooctene with tert-butyl hydroperoxide as oxidant. The type of cation influenced the physical state of the compound and the catalytic performance.publishersversionpublishe

Paragraph: A graph-based structural variant genotyper for short-read sequence data

Accurate detection and genotyping of structural variations (SVs) from short-read data is a long-standing area of development in genomics research and clinical sequencing pipelines. We introduce Paragraph, an accurate genotyper that models SVs using sequence graphs and SV annotations. We demonstrate the accuracy of Paragraph on whole-genome sequence data from three samples using long-read SV calls as the truth set, and then apply Paragraph at scale to a cohort of 100 short-read sequenced samples of diverse ancestry. Our analysis shows that Paragraph has better accuracy than other existing genotypers and can be applied to population-scale studies. © 2019 The Author(s)

The intolerance to functional genetic variation of protein domains predicts the localization of pathogenic mutations within genes

Ranking human genes based on their tolerance to functional genetic variation can greatly facilitate patient genome interpretation. It is well established, however, that different parts of proteins can have different functions, suggesting that it will ultimately be more informative to focus attention on functionally distinct portions of genes. Here we evaluate the intolerance of genic sub-regions using two biological sub-region classifications. We show that the intolerance scores of these sub-regions significantly correlate with reported pathogenic mutations. This observation extends the utility of intolerance scores to indicating where pathogenic mutations are mostly likely to fall within genes

The Local Optima Level in Chemotherapy Schedule Optimisation

In this paper a multi-drug Chemotherapy Schedule Optimisation Problem (CSOP) is subject to Local Optima Network (LON) analysis. LONs capture global patterns in fitness landscapes. CSOPs have not previously been subject to fitness landscape analysis. We fill this gap: LONs are constructed and studied for meaningful structure. The CSOP formulation presents novel challenges and questions for the LON model because there are infeasible regions in the fitness landscape and an unknown global optimum; it also brings a topic from healthcare to LON analysis. Two LON Construction algorithms are proposed for sampling CSOP fitness landscapes: a Markov-Chain Construction Algorithm and a Hybrid Construction Algorithm. The results provide new insight into LONs of highly-constrained spaces, and into the proficiency of search operators on the CSOP. Iterated Local Search and Memetic Search, which are the foundations for the LON algorithms, are found to markedly out-perform a Genetic Algorithm from the literature