Dataset Information

Alphabet Projection of Spectra.

ABSTRACT: In the metabolomics, glycomics, and mass spectrometry of structured small molecules, the combinatoric nature of the problem renders a database impossibly large, and thus de novo analysis is necessary. De novo analysis requires an alphabet of mass difference values used to link peaks in fragmentation spectra when they are different by a mass in the alphabet divided by a charge. Often, this alphabet is not known, prohibiting de novo analysis. A method is proposed that, given fragmentation mass spectra, identifies an alphabet of m/z differences that can build large connected graphs from many intense peaks in each spectrum from a collection. We then introduce a novel approach to efficiently find recurring substructures in the de novo graph results.

SUBMITTER: Kreitzberg PA

PROVIDER: S-EPMC7520079 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Alphabet Projection of Spectra.

Kreitzberg Patrick A PA Bern Marshall M Shu Qingbo Q Yang Fuquan F Serang Oliver O

Journal of proteome research 20190729 9

In the metabolomics, glycomics, and mass spectrometry of structured small molecules, the combinatoric nature of the problem renders a database impossibly large, and thus de novo analysis is necessary. De novo analysis requires an alphabet of mass difference values used to link peaks in fragmentation spectra when they are different by a mass in the alphabet divided by a charge. Often, this alphabet is not known, prohibiting de novo analysis. A method is proposed that, given fragmentation mass spe ...[more]

PMID: 31318211

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Project description:BACKGROUND: We investigate automated and generic alphabet reduction techniques for protein structure prediction datasets. Reducing alphabet cardinality without losing key biochemical information opens the door to potentially faster machine learning, data mining and optimization applications in structural bioinformatics. Furthermore, reduced but informative alphabets often result in, e.g., more compact and human-friendly classification/clustering rules. In this paper we propose a robust and sophisticated alphabet reduction protocol based on mutual information and state-of-the-art optimization techniques. RESULTS: We applied this protocol to the prediction of two protein structural features: contact number and relative solvent accessibility. For both features we generated alphabets of two, three, four and five letters. The five-letter alphabets gave prediction accuracies statistically similar to that obtained using the full amino acid alphabet. Moreover, the automatically designed alphabets were compared against other reduced alphabets taken from the literature or human-designed, outperforming them. The differences between our alphabets and the alphabets taken from the literature were quantitatively analyzed. All the above process had been performed using a primary sequence representation of proteins. As a final experiment, we extrapolated the obtained five-letter alphabet to reduce a, much richer, protein representation based on evolutionary information for the prediction of the same two features. Again, the performance gap between the full representation and the reduced representation was small, showing that the results of our automated alphabet reduction protocol, even if they were obtained using a simple representation, are also able to capture the crucial information needed for state-of-the-art protein representations. CONCLUSION: Our automated alphabet reduction protocol generates competent reduced alphabets tailored specifically for a variety of protein datasets. This process is done without any domain knowledge, using information theory metrics instead. The reduced alphabets contain some unexpected (but sound) groups of amino acids, thus suggesting new ways of interpreting the data.

Project description:PURPOSE OF REVIEW:Despite the success of antiretroviral therapy in suppressing HIV, life-long therapy is required to avoid HIV reactivation from long-lived viral reservoirs. Currently, there is intense interest in searching for therapeutic interventions that can purge the viral reservoir to achieve complete remission in HIV patients off antiretroviral therapy. The evaluation of such interventions relies on our ability to accurately and precisely measure the true size of the viral reservoir. In this review, we assess the most commonly used HIV reservoir assays, as a clear understanding of the strengths and weaknesses of each is vital for the accurate interpretation of results and for the development of improved assays. RECENT FINDINGS:The quantification of intracellular or plasma HIV RNA or DNA levels remains the most commonly used tests for the characterization of the viral reservoir. While cost-effective and high-throughput, these assays are not able to differentiate between replication-competent or defective fractions or quantify the number of infected cells. Viral outgrowth assays provide a lower bound for the fraction of cells that can produce infectious virus, but these assays are laborious, expensive and substantially underestimate the potential reservoir of replication-competent provirus. Newer assays are now available that seek to overcome some of these problems, including full-length proviral sequencing, inducible HIV RNA assays, ultrasensitive p24 assays and murine adoptive transfer techniques. The development and evaluation of strategies for HIV remission rely upon our ability to accurately and precisely quantify the size of the remaining viral reservoir. At this time, all current HIV reservoir assays have drawbacks such that combinations of assays are generally needed to gain a more comprehensive view of the viral reservoir. The development of novel, rapid, high-throughput assays that can sensitively quantify the levels of the replication-competent HIV reservoir is still needed.

Project description:BackgroundThe Alphabet Strategy (AS) is a diabetes care checklist ensuring "important, simple things are done right all the time." Current audits of diabetes care in developed countries reveal wide variations in quality with performance of care processes frequently sub-optimal. This study had three components:• an audit to assess diabetes care quality worldwide,• a questionnaire study seeking opinions on the merits of the AS,• a pilot study to assess the practicality of implementation of the AS in a low socioeconomic setting.MethodsAudit data was collected from 52 centres across 32 countries. Data from 4537 patients were converted to Quality and Outcome Framework (QOF) scores to enable inter-centre comparison. These were compared to each country's Gross Domestic Product (GDP), and Total Health Expenditure percentage per capita (THE%). The opinions of diabetes patients and healthcare professionals from the diabetes care team at each of these centres were sought through a structured questionnaire. A retrospective audit on 100 randomly selected case notes was conducted prior to AS implementation in a diabetes outpatient clinic in India, followed by a prospective audit after four months to assess its impact on care quality.ResultsQOF scores showed wide variation across the centres (mean 49.0, range 10.2-90.1). Although there was a positive relationship between GDP and THE% to QOF scores, there were exceptions. 91% of healthcare professionals felt the AS approach was practical. Patients found the checklist to be a useful education tool. Significant improvements in several aspects of care as well as 36% improvement in QOF score were seen following implementation.ConclusionsInternational centres observed large variations in care quality, with standards frequently sub-optimal. 71% of health care professionals would consider adopting the AS in their daily practice. Implementation in a low resource country resulted in significant improvements in some aspects of diabetes care. The AS checklist for diabetes care is a freely available in the public domain encompassing patient education, care plans, and educational resources for healthcare professionals including summary guidelines. The AS may provide a unique approach in delivering high quality diabetes care in countries with limited resources.

Dataset Information

Alphabet Projection of Spectra.

Publications

Alphabet Projection of Spectra.

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