scholarly journals Peptide Pattern Recognition for high-throughput protein sequence analysis and clustering

2017 ◽  
Author(s):  
Peter Kamp Busk
Keyword(s):  
Pattern Recognition ◽  
Protein Sequence ◽  
Time Frame ◽  
Protein Sequence Analysis ◽  
Large Protein ◽  
Desktop Computer ◽  
Peptide Pattern ◽  
Large Numbers ◽  
Reasonable Time Frame ◽  
Peptide Pattern Recognition

AbstractLarge collections of protein sequences with divergent sequences are tedious to analyze for understanding their phylogenetic or structure-function relation. Peptide Pattern Recognition is an algorithm that was developed to facilitate this task but the previous version does only allow a limited number of sequences as input.I implemented Peptide Pattern Recognition as a multithread software designed to handle large numbers of sequences and perform analysis in a reasonable time frame. Benchmarking showed that the new implementation of Peptide Pattern Recognition is twenty times faster than the previous implementation on a small protein collection with 673 MAP kinase sequences. In addition, the new implementation could analyze a large protein collection with 48,570 Glycosyl Transferase family 20 sequences without reaching its upper limit on a desktop computer.Peptide Pattern Recognition is a useful software for providing comprehensive groups of related sequences from large protein sequence collections.

scholarly journals Identification of a β-glucosidase from the Mucor circinelloides genome by peptide pattern recognition

2014 ◽  
Vol 67 ◽  
pp. 47-52 ◽  
Author(s):  
Yuhong Huang ◽  
Peter Kamp Busk ◽  
Morten Nedergaard Grell ◽  
Hai Zhao ◽  
Lene Lange
Keyword(s):  
Pattern Recognition ◽  
Mucor Circinelloides ◽  
Peptide Pattern ◽  
Peptide Pattern Recognition

CFD Challenge: Application of the Commercial Finite Element Solver FIDAP in a Clinical Setting

2012 ◽  
Author(s):  
Merih Cibis ◽  
Jolanda J. Wentzel ◽  
Frank J. H. Gijsen
Keyword(s):  
Finite Element ◽  
Shear Stress ◽  
Carotid Arteries ◽  
Time Frame ◽  
Academic Hospital ◽  
Close Collaboration ◽  
Desktop Computer ◽  
Reasonable Time ◽  
Finite Element Software ◽  
Reasonable Time Frame

The Rotterdam group mainly focuses on the influence of shear stress on plaque localization and progression in human coronary and carotid arteries. Since we are in an academic hospital, we always have been working in close collaboration with cardiologists and radiologists. Since clinicians do not have the time or the sources that academic engineering groups have, we limited ourselves to perform the simulations on a standard desktop computer (Intel Xeon six core processor, 2.40 GHz CPU and 12 GB RAM) using commercial finite element software (FIDAP 8.7.4 with GAMBIT 2.4.6) within a reasonable time-frame (the weekend). The simulations were carried out by our PhD student Merih Cibis.

scholarly journals A New Functional Classification of Glucuronoyl Esterases by Peptide Pattern Recognition

2017 ◽  
Vol 08 ◽  
Author(s):  
Jane W. Agger ◽  
Peter K. Busk ◽  
Bo Pilgaard ◽  
Anne S. Meyer ◽  
Lene Lange
Keyword(s):  
Pattern Recognition ◽  
Functional Classification ◽  
Peptide Pattern ◽  
Peptide Pattern Recognition

scholarly journals Clinical correlates of anti-SARS-CoV-2 antibody profiles in Spanish COVID-19 patients from a high incidence region

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Robert Markewitz ◽  
Antje Torge ◽  
Klaus-Peter Wandinger ◽  
Daniela Pauli ◽  
Andre Franke ◽  
...  
Keyword(s):  
Case Fatality ◽  
Time Frame ◽  
Epidemic Outbreak ◽  
Sample Collection ◽  
Rt Pcr ◽  
High Incidence ◽  
Duration Of Hospitalization ◽  
Reasonable Time Frame ◽  
Support Treatment ◽  
Diagnostic And Prognostic Value

AbstractLaboratory testing for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) consists of two pillars: the detection of viral RNA via rt-PCR as the diagnostic gold standard in acute cases, and the detection of antibodies against SARS-CoV-2. However, concerning the latter, questions remain about their diagnostic and prognostic value and it is not clear whether all patients develop detectable antibodies. We examined sera from 347 Spanish COVID-19 patients, collected during the peak of the epidemic outbreak in Spain, for the presence of IgA and IgG antibodies against SARS-CoV-2 and evaluated possible associations with age, sex and disease severity (as measured by duration of hospitalization, kind of respiratory support, treatment in ICU and death). The presence and to some degree the levels of anti-SARS-CoV-2 antibodies depended mainly on the amount of time between onset of symptoms and the collection of serum. A subgroup of patients did not develop antibodies at the time of sample collection. Compared to the patients that did, no differences were found. The presence and level of antibodies was not associated with age, sex, duration of hospitalization, treatment in the ICU or death. The case-fatality rate increased exponentially with older age. Neither the presence, nor the levels of anti-SARS-CoV-2 antibodies served as prognostic markers in our cohort. This is discussed as a possible consequence of the timing of the sample collection. Age is the most important risk factor for an adverse outcome in our cohort. Some patients appear not to develop antibodies within a reasonable time frame. It is unclear, however, why that is, as these patients differ in no respect examined by us from those who developed antibodies.

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