scholarly journals The tPI (tRNA Pairing Index) a Mathematical Measure of Repetition in a (Biological) Sequence

2006 ◽  
pp. 1-2
Author(s):  
Gaston H. Gonnet
Keyword(s):  
Biological Sequence

Parallel Processing of Biological Sequence Comparison Algorithms

1988 ◽  
Author(s):  
Nolan G. Gore ◽  
Elizabeth W. Edmiston ◽  
Joel H. Saltz ◽  
Roger M. Smith
Keyword(s):  
Parallel Processing ◽  
Sequence Comparison ◽  
Biological Sequence ◽  
Biological Sequence Comparison ◽  
Comparison Algorithms

Towards Computational Models of Identifying Protein Ubiquitination Sites

2019 ◽  
Vol 20 (5) ◽  
pp. 565-578 ◽  
Author(s):  
Lidong Wang ◽  
Ruijun Zhang
Keyword(s):  
Computational Methods ◽  
Computational Models ◽  
Feature Representation ◽  
Biological Sequence ◽  
Post Translational Modification ◽  
Test Dataset ◽  
Protein Ubiquitination ◽  
Protein Functions ◽  
Independent Test Dataset ◽  
Benchmark Datasets

Ubiquitination is an important post-translational modification (PTM) process for the regulation of protein functions, which is associated with cancer, cardiovascular and other diseases. Recent initiatives have focused on the detection of potential ubiquitination sites with the aid of physicochemical test approaches in conjunction with the application of computational methods. The identification of ubiquitination sites using laboratory tests is especially susceptible to the temporality and reversibility of the ubiquitination processes, and is also costly and time-consuming. It has been demonstrated that computational methods are effective in extracting potential rules or inferences from biological sequence collections. Up to the present, the computational strategy has been one of the critical research approaches that have been applied for the identification of ubiquitination sites, and currently, there are numerous state-of-the-art computational methods that have been developed from machine learning and statistical analysis to undertake such work. In the present study, the construction of benchmark datasets is summarized, together with feature representation methods, feature selection approaches and the classifiers involved in several previous publications. In an attempt to explore pertinent development trends for the identification of ubiquitination sites, an independent test dataset was constructed and the predicting results obtained from five prediction tools are reported here, together with some related discussions.

scholarly journals Barcoding lichen-forming fungi using 454 pyrosequencing is challenged by artifactual and biological sequence variation

Genome ◽  
2016 ◽  
Vol 59 (9) ◽  
pp. 685-704 ◽  
Author(s):  
Kristiina Mark ◽  
Carolina Cornejo ◽  
Christine Keller ◽  
Daniela Flück ◽  
Christoph Scheidegger
Keyword(s):  
454 Pyrosequencing ◽  
Ecological Integrity ◽  
Common Species ◽  
Sequencing Error ◽  
Reference Database ◽  
Biological Sequence ◽  
Internal Transcribed Spacer Region ◽  
Epiphytic Lichen ◽  
Fungal Partner ◽  
A Genome

Although lichens (lichen-forming fungi) play an important role in the ecological integrity of many vulnerable landscapes, only a minority of lichen-forming fungi have been barcoded out of the currently accepted ∼18 000 species. Regular Sanger sequencing can be problematic when analyzing lichens since saprophytic, endophytic, and parasitic fungi live intimately admixed, resulting in low-quality sequencing reads. Here, high-throughput, long-read 454 pyrosequencing in a GS FLX+ System was tested to barcode the fungal partner of 100 epiphytic lichen species from Switzerland using fungal-specific primers when amplifying the full internal transcribed spacer region (ITS). The present study shows the potential of DNA barcoding using pyrosequencing, in that the expected lichen fungus was successfully sequenced for all samples except one. Alignment solutions such as BLAST were found to be largely adequate for the generated long reads. In addition, the NCBI nucleotide database—currently the most complete database for lichen-forming fungi—can be used as a reference database when identifying common species, since the majority of analyzed lichens were identified correctly to the species or at least to the genus level. However, several issues were encountered, including a high sequencing error rate, multiple ITS versions in a genome (incomplete concerted evolution), and in some samples the presence of mixed lichen-forming fungi (possible lichen chimeras).

Supercomputers and biological sequence comparison algorithms

1989 ◽  
Vol 22 (6) ◽  
pp. 497-515 ◽  
Author(s):  
Nolan G. Core ◽  
Elizabeth W. Edmiston ◽  
Joel H. Saltz ◽  
Roger M. Smith
Keyword(s):  
Sequence Comparison ◽  
Biological Sequence ◽  
Biological Sequence Comparison ◽  
Comparison Algorithms

Semiotic thoughts on biological sequence representations

2021 ◽  
Vol 24 ◽  
Author(s):  
Guillermo Restrepo
Keyword(s):  
Research Area ◽  
Biological Sequences ◽  
Biological Sequence ◽  
Mathematical Chemistry ◽  
Dna And Rna

: The deluge of biological sequences ranging from those of proteins, DNA and RNA to genomes has increased the models for their representation, which are further used to contrast those sequences. Here we present a brief bibliometric description of the research area devoted to representation of biological sequences and highlight the semiotic reaches of this process. Finally, we argue that this research area needs further research according to the evolution of mathematical chemistry and its drawbacks are required to be overcome.

Field-theoretic density estimation for biological sequence space with applications to 5′ splice site diversity and aneuploidy in cancer

2021 ◽  
Vol 118 (40) ◽  
pp. e2025782118
Author(s):  
Wei-Chia Chen ◽  
Juannan Zhou ◽  
Jason M. Sheltzer ◽  
Justin B. Kinney ◽  
David M. McCandlish
Keyword(s):  
Probability Distribution ◽  
Maximum Entropy ◽  
Density Estimation ◽  
Sequence Space ◽  
Chromosomal Abnormalities ◽  
Fundamental Problem ◽  
Probability Distributions ◽  
Biological Sequence ◽  
Point Estimates ◽  
Site Diversity

Density estimation in sequence space is a fundamental problem in machine learning that is also of great importance in computational biology. Due to the discrete nature and large dimensionality of sequence space, how best to estimate such probability distributions from a sample of observed sequences remains unclear. One common strategy for addressing this problem is to estimate the probability distribution using maximum entropy (i.e., calculating point estimates for some set of correlations based on the observed sequences and predicting the probability distribution that is as uniform as possible while still matching these point estimates). Building on recent advances in Bayesian field-theoretic density estimation, we present a generalization of this maximum entropy approach that provides greater expressivity in regions of sequence space where data are plentiful while still maintaining a conservative maximum entropy character in regions of sequence space where data are sparse or absent. In particular, we define a family of priors for probability distributions over sequence space with a single hyperparameter that controls the expected magnitude of higher-order correlations. This family of priors then results in a corresponding one-dimensional family of maximum a posteriori estimates that interpolate smoothly between the maximum entropy estimate and the observed sample frequencies. To demonstrate the power of this method, we use it to explore the high-dimensional geometry of the distribution of 5′ splice sites found in the human genome and to understand patterns of chromosomal abnormalities across human cancers.

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