Identification of hot and cold spots in genome of Mycobacterium tuberculosis using Shewhart Control Charts

A New Nonparametric Multivariate Control Scheme for Simultaneous Monitoring Changes in Location and Scale

Real-time monitoring of the breast cancer index is becoming increasingly important. It can help create advances in the diagnosis and treatment of breast cancer. In today's modern medical processes, simultaneously monitoring changes in observations in terms of location and scale are convenient for the implementation of control schemes but can be challenging. In this paper, we consider a new nonparametric control scheme for monitoring location and scale parameters in multivariate processes. The proposed method is easy to implement, and the performance of the proposed control procedure is discussed. Then, we compare the proposed scheme with some competing methods. Simulation results show that the proposed scheme can efficiently detect a range of shifts. The proposed chart can trigger an alert and timely discover the change of the breast cancer index.

<p>Prediction and Monitoring Method for Breast Cancer: A Case Study for Data from the University Hospital Centre of Coimbra</p>

Breast cancer is the second most common cancer in women after skin cancer. Breast cancer can occur in both men and women, but it is far more common in women. Real-time monitoring of breast cancer indicators is becoming increasingly important. It can help create advances in the diagnosis and treatment of breast cancer. In this paper, we provide a nonparametric statistical method to predict and detect breast cancer occur. The exponentially weighted moving average (EWMA) control scheme is based on rank methods so that it is completely nonparametric. It is efficient in detecting the shifts for multivariate processes. A real example data from the University Hospital Centre of Coimbra is given to illustrate this method.

Multivariate nonparametric chart for influenza epidemic monitoring

Control chart methods have been received much attentions in biosurvillance studies. The correlation between charting statistics or regions could be considerably important in monitoring the states of multiple outcomes or regions. In addition, the process variable distribution is unknown in most situations. In this paper, we propose a new nonparametric strategy for multivariate process monitoring when the distribution of a process variable is unknown. We discuss the EWMA control chart based on rank methods for a multivariate process, and the approach is completely nonparametric. A simulation study demonstrates that the proposed method is efficient in detecting shifts for multivariate processes. A real Japanese influenza data example is given to illustrate the performance of the proposed method.

Extended insight into the Mycobacterium chelonae-abscessus complex through whole genome sequencing of Mycobacterium salmoniphilum outbreak and Mycobacterium salmoniphilum-like strains

Members of the Mycobacterium chelonae-abscessus complex (MCAC) are close to the mycobacterial ancestor and includes both human, animal and fish pathogens. We present the genomes of 14 members of this complex: the complete genomes of Mycobacterium salmoniphilum and Mycobacterium chelonae type strains, seven M. salmoniphilum isolates, and five M. salmoniphilum-like strains including strains isolated during an outbreak in an animal facility at Uppsala University. Average nucleotide identity (ANI) analysis and core gene phylogeny revealed that the M. salmoniphilum-like strains are variants of the human pathogen Mycobacterium franklinii and phylogenetically close to Mycobacterium abscessus. Our data further suggested that M. salmoniphilum separates into three branches named group I, II and III with the M. salmoniphilum type strain belonging to group II. Among predicted virulence factors, the presence of phospholipase C (plcC), which is a major virulence factor that makes M. abscessus highly cytotoxic to mouse macrophages, and that M. franklinii originally was isolated from infected humans make it plausible that the outbreak in the animal facility was caused by a M. salmoniphilum-like strain. Interestingly, M. salmoniphilum-like was isolated from tap water suggesting that it can be present in the environment. Moreover, we predicted the presence of mutational hotspots in the M. salmoniphilum isolates and 26% of these hotspots overlap with genes categorized as having roles in virulence, disease and defense. We also provide data about key genes involved in transcription and translation such as sigma factor, ribosomal protein and tRNA genes.

Extensive genomic diversity among Mycobacterium marinum strains revealed by whole genome sequencing

Mycobacterium marinum is the causative agent for the tuberculosis-like disease mycobacteriosis in fish and skin lesions in humans. Ubiquitous in its geographical distribution, M. marinum is known to occupy diverse fish as hosts. However, information about its genomic diversity is limited. Here, we provide the genome sequences for 15 M. marinum strains isolated from infected humans and fish. Comparative genomic analysis of these and four available genomes of the M. marinum strains M, E11, MB2 and Europe reveal high genomic diversity among the strains, leading to the conclusion that M. marinum should be divided into two different clusters, the "M"- and the "Aronson"-type. We suggest that these two clusters should be considered to represent two M. marinum subspecies. Our data also show that the M. marinum pan-genome for both groups is open and expanding and we provide data showing high number of mutational hotspots in M. marinum relative to other mycobacteria such as Mycobacterium tuberculosis. This high genomic diversity might be related to the ability of M. marinum to occupy different ecological niches.

Classification of pathogenic microbes using a minimal set of single nucleotide polymorphisms derived from whole genome sequences

In a context specific manner, Intra-species genomic variation plays an important role in phenotypic diversity observed among pathogenic microbes. Efficient classification of these pathogens is important for diagnosis and treatment of several infectious diseases. NGS technologies have provided access to wealth of data that can be utilized to discover important markers for pathogen classification. In this paper, we described three different approaches (Jensen-Shannon divergence, random forest and Shewhart control chart) for identification of a minimal set of SNPs that can be used for classification of organisms. These methods are generic and can be implemented for analysis of any organism. We have shown usefulness of these approaches for analysis of Mycobacterium tuberculosis and Escherichia coli isolates. We were able to identify a minimal set of 18 SNPs that can be used as molecular markers for phylogroup based classification and 8 SNPs for pathogroup based classification of E. coli.

Complex multifractal nature in Mycobacterium tuberculosis genome

The mutifractal and long range correlation (C(r)) properties of strings, such as nucleotide sequence can be a useful parameter for identification of underlying patterns and variations. In this study C(r) and multifractal singularity function f(α) have been used to study variations in the genomes of a pathogenic bacteria Mycobacterium tuberculosis. Genomic sequences of M. tuberculosis isolates displayed significant variations in C(r) and f(α) reflecting inherent differences in sequences among isolates. M. tuberculosis isolates can be categorised into different subgroups based on sensitivity to drugs, these are DS (drug sensitive isolates), MDR (multi-drug resistant isolates) and XDR (extremely drug resistant isolates). C(r) follows significantly different scaling rules in different subgroups of isolates, but all the isolates follow one parameter scaling law. The richness in complexity of each subgroup can be quantified by the measures of multifractal parameters displaying a pattern in which XDR isolates have highest value and lowest for drug sensitive isolates. Therefore C(r) and multifractal functions can be useful parameters for analysis of genomic sequences.

Use of locally weighted scatterplot smoothing (LOWESS) regression to study selection signatures in Piedmontese and Italian Brown cattle breeds

Selection is the major force affecting local levels of genetic variation in species. The availability of dense marker maps offers new opportunities for a detailed understanding of genetic diversity distribution across the animal genome. Over the last 50 years, cattle breeds have been subjected to intense artificial selection. Consequently, regions controlling traits of economic importance are expected to exhibit selection signatures. The fixation index (Fst ) is an estimate of population differentiation, based on genetic polymorphism data, and it is calculated using the relationship between inbreeding and heterozygosity. In the present study, locally weighted scatterplot smoothing (LOWESS) regression and a control chart approach were used to investigate selection signatures in two cattle breeds with different production aptitudes (dairy and beef). Fst was calculated for 42 514 SNP marker loci distributed across the genome in 749 Italian Brown and 364 Piedmontese bulls. The statistical significance of Fst values was assessed using a control chart. The LOWESS technique was efficient in removing noise from the raw data and was able to highlight selection signatures in chromosomes known to harbour genes affecting dairy and beef traits. Examples include the peaks detected for BTA2 in the region where the myostatin gene is located and for BTA6 in the region harbouring the ABCG2 locus. Moreover, several loci not previously reported in cattle studies were detected.

Genetic heterogeneity revealed by sequence analysis of Mycobacterium tuberculosis isolates from extra-pulmonary tuberculosis patients

BACKGROUND

Tuberculosis remains a major public health problem. Clinical tuberculosis manifests often as pulmonary and occasionally as extra-pulmonary tuberculosis. The emergence of drug resistant tubercle bacilli and its association with HIV is a formidable challenge to curb the spread of tuberculosis. There have been concerted efforts by whole genome sequencing and bioinformatics analysis to identify genomic patterns and to establish a relationship between the genotype of the organism and clinical manifestation of tuberculosis. Extra-pulmonary TB constitutes 15-20 percent of the total clinical cases of tuberculosis reported among immunocompetent patients, whereas among HIV patients the incidence is more than 50 percent. Genomic analysis of M. tuberculosis isolates from extra pulmonary patients has not been explored.

RESULTS

The genomic DNA of 5 extra-pulmonary clinical isolates of M. tuberculosis derived from cerebrospinal fluid, lymph node fine needle aspirates (FNAC) / biopsies, were sequenced. Next generation sequencing approach (NGS) was employed to identify Single Nucleotide Variations (SNVs) and computational methods used to predict their consequence on functional genes. Analysis of distribution of SNVs led to the finding that there are mixed genotypes in patient isolates and that many SNVs are likely to influence either gene function or their expression. Phylogenetic relationship between the isolates correlated with the origin of the isolates. In addition, insertion sites of IS elements were identified and their distribution revealed a variation in number and position of the element in the 5 extra-pulmonary isolates compared to the reference M. tuberculosis H37Rv strain.

CONCLUSIONS

The results suggest that NGS sequencing is able to identify small variations in genomes of M. tuberculosis isolates including changes in IS element insertion sites. Moreover, variations in isolates of M. tuberculosis from non-pulmonary sites were documented. The analysis of our results indicates genomic heterogeneity in the clinical isolates.