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  • 1.
    Kumar, Keshav
    Umeå universitet, Medicinska fakulteten, Institutionen för molekylärbiologi (Medicinska fakulteten).
    Discrete wavelet assisted correlation optimised warping of chromatograms: optimizing the computational time for correcting the drifts in peak positions2017Inngår i: Analytical Methods, ISSN 1759-9660, E-ISSN 1759-9679, Vol. 9, nr 13, s. 2049-2058Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Correlation optimised warping (COW) has been the most favourite chromatographic peak alignment approach in recent years. After optimization of the two parameters, slack and segment length, COW works well in aligning the chromatograms. However, one of the serious disadvantages of COW is that it is computationally time consuming. Often several segment lengths and slack parameters need to be tested to find the optimum combination for achieving the alignment that makes the whole analysis take several hours. In the present work, it has been shown that with the application of wavelet analysis prior to alignment it is possible to provide the necessary computational economy to the COW algorithm.

  • 2.
    Kumar, Keshav
    Umeå universitet, Medicinska fakulteten, Institutionen för molekylärbiologi (Medicinska fakulteten).
    Random Initialisation of the Spectral Variables: an Alternate Approach for Initiating Multivariate Curve Resolution Alternating Least Square (MCR-ALS) Analysis2017Inngår i: Journal of Fluorescence, ISSN 1053-0509, E-ISSN 1573-4994, Vol. 27, nr 6, s. 1957-1968Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Multivariate curve resolution alternating least square (MCR-ALS) analysis is the most commonly used curve resolution technique. The MCR-ALS model is fitted using the alternate least square (ALS) algorithm that needs initialisation of either contribution profiles or spectral profiles of each of the factor. The contribution profiles can be initialised using the evolve factor analysis; however, in principle, this approach requires that data must belong to the sequential process. The initialisation of the spectral profiles are usually carried out using the pure variable approach such as SIMPLISMA algorithm, this approach demands that each factor must have the pure variables in the data sets. Despite these limitations, the existing approaches have been quite a successful for initiating the MCR-ALS analysis. However, the present work proposes an alternate approach for the initialisation of the spectral variables by generating the random variables in the limits spanned by the maxima and minima of each spectral variable of the data set. The proposed approach does not require that there must be pure variables for each component of the multicomponent system or the concentration direction must follow the sequential process. The proposed approach is successfully validated using the excitation-emission matrix fluorescence data sets acquired for certain fluorophores with significant spectral overlap. The calculated contribution and spectral profiles of these fluorophores are found to correlate well with the experimental results. In summary, the present work proposes an alternate way to initiate the MCR-ALS analysis.

  • 3.
    Kumar, Keshav
    et al.
    Umeå universitet, Medicinska fakulteten, Molekylär Infektionsmedicin, Sverige (MIMS). Umeå universitet, Medicinska fakulteten, Umeå Centre for Microbial Research (UCMR).
    Cava, Felipe
    Umeå universitet, Medicinska fakulteten, Molekylär Infektionsmedicin, Sverige (MIMS). Umeå universitet, Medicinska fakulteten, Umeå Centre for Microbial Research (UCMR).
    Chromatographic analysis of peptidoglycan samples with the aid of a chemometric technique: introducing a novel analytical procedure to classify bacterial cell wall collection2019Inngår i: Analytical Methods, ISSN 1759-9660, E-ISSN 1759-9679, Vol. 11, nr 12, s. 1671-1679Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The technical development of liquid chromatography has provided the necessary sensitivity to characterise peptidoglycan samples. However, the analysis of large numbers of complex chromatographic data sets without the aid of a proper chemometric technique is a laborious task, carrying a high risk of losing important biochemical information. The present work describes the development of a simple analytical procedure using self-organising map (SOM) analysis to analyse the large number of complex chromatographic data sets from bacterial peptidoglycan samples. SOM analysis essentially maps the samples to a hexagonal sheet based on their compositional similarity, and thus provides an approach to classify the bacterial cell wall collection in an unsupervised manner. The utility of the proposed approach was successfully validated by analysing peptidoglycan samples belonging to the Alphaproteobacterium class. The classification results achieved with SOM analysis were found to correlate well with their relative similarity in peptidoglycan compositions. In summary, the SOM analysis-based analytical procedure is shown to be useful towards automatising the analyses of chromatographic data sets of peptidoglycan samples from bacterial collections.

  • 4.
    Kumar, Keshav
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för klinisk mikrobiologi.
    Cava, Felipe
    Umeå universitet, Medicinska fakulteten, Institutionen för klinisk mikrobiologi.
    Constraint randomised non-negative factor analysis (CRNNFA): an alternate chemometrics approach for analysing the biochemical data sets2017Inngår i: The Analyst, ISSN 0003-2654, E-ISSN 1364-5528, Vol. 142, nr 11, s. 1916-1928Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The present work introduces an alternate chemometrics approach constraint randomised non-negative factor analysis (CRNNFA) for analysing the bioanalytical data sets. The CRNNFA algorithm provides the outputs that are easy to interpret and correlate with the real chromatograms. The CRNNFA algorithm achieves termination when the iteration limit is reached circumventing the premature convergence. Theoretical and computational aspects of the proposed method are also described. The analytical and computational potential of CRNNFA are successfully tested by analysing the complex chromatograms of the peptidoglycan samples belonging to the Alphaproteobacterium members. The obtained results clearly show that CRNNFA can easily trace the compositional variability of the peptidoglycan samples. In summary, the proposed method in general can be a potential alternate approach for analysing the data sets obtained from different analytical and clinical fields.

  • 5.
    Kumar, Keshav
    et al.
    Umeå universitet, Medicinska fakulteten, Molekylär Infektionsmedicin, Sverige (MIMS). Umeå universitet, Medicinska fakulteten, Institutionen för molekylärbiologi (Medicinska fakulteten). Umeå universitet, Medicinska fakulteten, Umeå Centre for Microbial Research (UCMR).
    Cava, Felipe
    Umeå universitet, Medicinska fakulteten, Molekylär Infektionsmedicin, Sverige (MIMS). Umeå universitet, Medicinska fakulteten, Institutionen för molekylärbiologi (Medicinska fakulteten). Umeå universitet, Medicinska fakulteten, Umeå Centre for Microbial Research (UCMR).
    Integrating network analysis with chromatography: introducing a novel chemometry-chromatography based analytical procedure to classify the bacterial cell wall collection2018Inngår i: Analytical Methods, ISSN 1759-9660, E-ISSN 1759-9679, Vol. 10, nr 10, s. 1172-1180Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The present work integrates network analysis with chromatography and proposes a novel analytical procedure to classify the bacterial cell wall collection. The network analysis model can capture the heterogeneity present in the datasets and hence can provide unsupervised classification. The proposed approach is successfully applied for classifying the peptidoglycan samples of certain bacterial collections belonging to the class of Alphaproteobacteria. The obtained classification results are found to correlate well with their relative similarity in the peptidoglycan compositions. In summary, the proposed network analysis approach can be helpful in automatizing the bacterial cell wall analysis. The proposed approach can be useful to accelerate the research related to understanding the morphology of bacterial cell walls, host-pathogen interaction and development of effective antibiotics.

  • 6.
    Kumar, Keshav
    et al.
    Umeå universitet, Medicinska fakulteten, Molekylär Infektionsmedicin, Sverige (MIMS). Umeå universitet, Medicinska fakulteten, Institutionen för molekylärbiologi (Medicinska fakulteten). Umeå universitet, Medicinska fakulteten, Umeå Centre for Microbial Research (UCMR).
    Cava, Felipe
    Umeå universitet, Medicinska fakulteten, Molekylär Infektionsmedicin, Sverige (MIMS). Umeå universitet, Medicinska fakulteten, Institutionen för molekylärbiologi (Medicinska fakulteten). Umeå universitet, Medicinska fakulteten, Umeå Centre for Microbial Research (UCMR).
    Principal coordinate analysis assisted chromatographic analysis of bacterial cell wall collection: a robust classification approach2018Inngår i: Analytical Biochemistry, ISSN 0003-2697, E-ISSN 1096-0309, Vol. 550, s. 8-14Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In the present work, Principal coordinate analysis (PCoA) is introduced to develop a robust model to classify the chromatographic data sets of peptidoglycan sample. PcoA captures the heterogeneity present in the data sets by using the dissimilarity matrix as input. Thus, in principle, it can even capture the subtle differences in the bacterial peptidoglycan composition and can provide a more robust and fast approach for classifying the bacterial collection and identifying the novel cell wall targets for further biological and clinical studies. The utility of the proposed approach is successfully demonstrated by analysing the two different kind of bacterial collections. The first set comprised of peptidoglycan sample belonging to different subclasses of Alphaproteobacteria. Whereas, the second set that is relatively more intricate for the chemometric analysis consist of different wild type Vibrio Cholerae and its mutants having subtle differences in their peptidoglycan composition. The present work clearly proposes a useful approach that can classify the chromatographic data sets of chromatographic peptidoglycan samples having subtle differences. Furthermore, present work clearly suggest that PCoA can be a method of choice in any data analysis workflow.

  • 7.
    Kumar, Keshav
    et al.
    Umeå universitet, Medicinska fakulteten, Molekylär Infektionsmedicin, Sverige (MIMS). Umeå universitet, Medicinska fakulteten, Umeå centrum för molekylär medicin (UCMM). Umeå universitet, Medicinska fakulteten, Institutionen för molekylärbiologi (Medicinska fakulteten).
    Espaillat, Akbar
    Umeå universitet, Medicinska fakulteten, Molekylär Infektionsmedicin, Sverige (MIMS). Umeå universitet, Medicinska fakulteten, Umeå centrum för molekylär medicin (UCMM). Umeå universitet, Medicinska fakulteten, Institutionen för molekylärbiologi (Medicinska fakulteten).
    Cava, Felipe
    Umeå universitet, Medicinska fakulteten, Molekylär Infektionsmedicin, Sverige (MIMS). Umeå universitet, Medicinska fakulteten, Umeå centrum för molekylär medicin (UCMM). Umeå universitet, Medicinska fakulteten, Institutionen för molekylärbiologi (Medicinska fakulteten).
    PG-metrics: a chemometric-based approach for classifying bacterial peptidoglycan data sets and uncovering their subjacent chemical variability2017Inngår i: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 12, nr 10, artikkel-id e0186197Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Bacteria cells are protected from osmotic and environmental stresses by an exoskeleton-like polymeric structure called peptidoglycan ( PG) or murein sacculus. This structure is fundamental for bacteria's viability and thus, the mechanisms underlying cell wall assembly and how it is modulated serve as targets for many of our most successful antibiotics. Therefore, it is now more important than ever to understand the genetics and structural chemistry of the bacterial cell walls in order to find new and effective methods of blocking it for the treatment of disease. In the last decades, liquid chromatography and mass spectrometry have been demonstrated to provide the required resolution and sensitivity to characterize the fine chemical structure of PG. However, the large volume of data sets that can be produced by these instruments today are difficult to handle without a proper data analysis work-flow. Here, we present PG-metrics, a chemometric based pipeline that allows fast and easy classification of bacteria according to their muropeptide chromatographic profiles and identification of the subjacent PG chemical variability between e.g. bacterial species, growth conditions and, mutant libraries. The pipeline is successfully validated here using PG samples from different bacterial species and mutants in cell wall proteins. The obtained results clearly demonstrated that PG-metrics pipeline is a valuable bioanalytical tool that can lead us to cell wall classification and biomarker discovery.

  • 8. Tarai, Madhumita
    et al.
    Kumar, Keshav
    Umeå universitet, Medicinska fakulteten, Institutionen för molekylärbiologi (Medicinska fakulteten). Umeå universitet, Medicinska fakulteten, Molekylär Infektionsmedicin, Sverige (MIMS).
    Divya, O.
    Bairi, Partha
    Mishra, Kishor Kumar
    Mishra, Ashok Kumar
    Eigenvalue-eigenvector decomposition (EED) analysis of dissimilarity and covariance matrix obtained from total synchronous fluorescence spectral (TSFS) data sets of herbal preparations: optimizing the classification approach2017Inngår i: Spectrochimica Acta Part A - Molecular and Biomolecular Spectroscopy, ISSN 1386-1425, E-ISSN 1873-3557, Vol. 184, s. 128-133Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The present work compares the dissimilarity and covariance baseckinsupervised chemometric classification approaches by, taking the total synchronous fluorescence spectroscopy data sets acquired for the cumin and non cumin based herbal preparations. The conventional decomposition method involves eigenvalue-eigenvector analysis of the covariance of the data set and finds the factors that can explain the overall major sources of variation present in the data set. The conventional approach does this irrespective of the fact that the samples belong to intrinsically different groups and hence leads to poor class separation. The present work shows that classification of such samples can be optimized by performing the eigenvalue-eigenvector decomposition on the pair-wise dissimilarity matrix.

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