Expected frequencies of DNA patterns using whittle's formula

1991 ◽  
Vol 28 (4) ◽  
pp. 886-892 ◽  
Author(s):  
Richard Cowan
Keyword(s):  
Markov Chain ◽  
Molecular Biology ◽  
Dna Sequences ◽  
Exact Method ◽  
State Transitions ◽  
Initial State ◽  
Expected Frequency

Given a realisation of a Markov chain, one can count the numbers of state transitions of each type. One can ask how many realisations are there with these transition counts and the same initial state. Whittle (1955) has answered this question, by finding an explicit though complicated formula, and has also shown that each realisation is equally likely. In the analysis of DNA sequences which comprise letters from the set {A, C, G, T}, it is often useful to count the frequency of a pattern, say ACGCT, in a long sequence and compare this with the expected frequency for all sequences having the same start letter and the same transition counts (or ‘dinucleotide counts' as they are called in the molecular biology literature). To date, no exact method exists; this paper rectifies that deficiency.

Expected frequencies of DNA patterns using whittle's formula

1991 ◽  
Vol 28 (04) ◽  
pp. 886-892 ◽  
Author(s):  
Richard Cowan
Keyword(s):  
Markov Chain ◽  
Molecular Biology ◽  
Dna Sequences ◽  
Exact Method ◽  
State Transitions ◽  
Initial State ◽  
Expected Frequency

Given a realisation of a Markov chain, one can count the numbers of state transitions of each type. One can ask how many realisations are there with these transition counts and the same initial state. Whittle (1955) has answered this question, by finding an explicit though complicated formula, and has also shown that each realisation is equally likely. In the analysis of DNA sequences which comprise letters from the set {A, C, G, T}, it is often useful to count the frequency of a pattern, say ACGCT, in a long sequence and compare this with the expected frequency for all sequences having the same start letter and the same transition counts (or ‘dinucleotide counts' as they are called in the molecular biology literature). To date, no exact method exists; this paper rectifies that deficiency.

UML-BASED MODELING AND ANALYSIS OF SECURITY THREATS

2010 ◽  
Vol 20 (06) ◽  
pp. 875-897 ◽  
Author(s):  
JUN KONG ◽  
DIANXIANG XU ◽  
XIAOQIN ZENG
Keyword(s):  
Formal Method ◽  
Graph Transformation ◽  
State Transitions ◽  
Security Threats ◽  
Sequence Diagrams ◽  
Security Threat ◽  
Initial State ◽  
Final State ◽  
Modeling And Analysis ◽  
Software Application

Poor design has been a major source of software security problems. Rigorous and designer-friendly methodologies for modeling and analyzing secure software are highly desirable. A formal method for software development, however, often suffers from a gap between the rigidity of the method and the informal nature of system requirements. To narrow this gap, this paper presents a UML-based framework for modeling and analyzing security threats (i.e. potential security attacks) rigorously and visually. We model the intended functions of a software application with UML statechart diagrams and the security threats with sequence diagrams, respectively. Statechart diagrams are automatically converted into a graph transformation system, which has a well-established theoretical foundation. Method invocations in a sequence diagram of a security threat are interpreted as a sequence of paired graph transformations. Therefore, the analysis of a security threat is conducted through simulating the state transitions from an initial state to a final state triggered by method invocations. In our approach, designers directly work with UML diagrams to visually model system behaviors and security threats while threats can still be rigorously analyzed based on graph transformation.

COMPARING VIRUS CLASSIFICATION USING GENOMIC MATERIALS ACCORDING TO DIFFERENT TAXONOMIC LEVELS

2013 ◽  
Vol 11 (06) ◽  
pp. 1343003 ◽  
Author(s):  
JING-DOO WANG
Keyword(s):  
Molecular Biology ◽  
Dna Sequences ◽  
Protein Sequences ◽  
Experimental Results ◽  
Virus Classification ◽  
Taxonomic Level ◽  
Biological Classification ◽  
Novel Approach

In this paper, three genomic materials — DNA sequences, protein sequences, and regions (domains) are used to compare methods of virus classification. Virus classes (categories) are divided by various taxonomic level of virus into three datasets for 6 order, 42 family, and 33 genera. To increase the robustness and comparability of experimental results of virus classification, the classes are selected that contain at least 10 instances, and meanwhile each instance contains at least one region name. Experimental results show that the approach using region names achieved the best accuracies — reaching 99.9%, 97.3%, and 99.0% for 6 orders, 42 families, and 33 genera, respectively. This paper not only involves exhaustive experiments that compare virus classifications using different genomic materials, but also proposes a novel approach to biological classification based on molecular biology instead of traditional morphology.

Advances in visualizing transcription factor-DNA interactions

Genome ◽  
2020 ◽  
Author(s):  
Rachel M. Price ◽  
Marek A Budzynski ◽  
Shivani Kundra ◽  
Sheila S. Teves
Keyword(s):  
Molecular Biology ◽  
Dna Sequences ◽  
Imaging Techniques ◽  
Specific Interaction ◽  
Dna Interactions ◽  
Dynamic Information ◽  
Transcription Process ◽  
Target Dna ◽  
Biology Research

At the heart of the transcription process is the specific interaction between transcription factors (TFs) and their target DNA sequences. Decades of molecular biology research have led to unprecedented insights into how TFs access the genome to regulate transcription. In the last 20 years, advances in microscopy have enabled scientists to add imaging as a powerful tool in probing two specific aspects of TF-DNA interactions: structure and dynamics. In this review, we examine how applications of diverse imaging technologies can provide structural and dynamic information that complements insights gained from molecular biology assays. As a case study, we discuss how applications of advanced imaging techniques have reshaped our understanding of TF behavior across the cell cycle, leading to a rethinking in the field of mitotic bookmarking.

scholarly journals Optical decomposition of DNA gel and modification of object mobility on micrometre scale

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Suguru Shimomura ◽  
Takahiro Nishimura ◽  
Yusuke Ogura ◽  
Jun Tanida
Keyword(s):  
Thermal Energy ◽  
Dna Sequences ◽  
Biomedical Applications ◽  
State Transitions ◽  
Dye Molecules ◽  
Radiative Relaxation ◽  
Polystyrene Beads ◽  
Object Mobility ◽  
Detection Of Biomolecules ◽  
Viscosity Changes

AbstractDNA gels can be engineered to exhibit specific properties through the choice of DNA sequences and modification with dye molecules, and can therefore be useful in biomedical applications such as the detection of biomolecules. State transitions of DNA gels on the micrometre scale can generate a viscosity gradient, which can be used to modify the mobility of micrometre-sized objects. In this paper, we propose a method for changing the viscosity of DNA gels using optical decomposition. The use of light allows for decomposition on the micrometre scale, which can be used to achieve patterned viscosity changes within DNA gels. Decomposition was induced by thermal energy released through non-radiative relaxation of excited quenchers. We demonstrated the decomposition of DNA gels in response to irradiation patterns on the micrometre scale. In addition, as a result of changes in DNA gel viscosity due to decomposition, the mobility of polystyrene beads was shown to increase. This technique could provide a new optical approach for controlling the mobility of micrometre-sized objects.

Estimating statistical significance of sequence alignments

1994 ◽  
Vol 344 (1310) ◽  
pp. 383-390 ◽  
Keyword(s):  
Molecular Biology ◽  
Large Deviations ◽  
Dna Sequences ◽  
Upper Bound ◽  
Statistical Significance ◽  
Poisson Approximation ◽  
Linear Case ◽  
Sequence Alignments ◽  
Random Sequences ◽  
Hoeffding Inequality

Algorithms that compare two proteins or DNA sequences and produce an alignment of the best matching segments are widely used in molecular biology. These algorithms produce scores that when comparing random sequences of length n grow proportional to n or to log (n) depending on the algorithm parameters. The Azuma-Hoeffding inequality gives an upper bound on the probability of large deviations of the score from its mean in the linear case. Poisson approximation can be applied in the logarithmic case.

Absorption Probabilities for Sums of Random Variables Defined on a Finite Markov Chain

1962 ◽  
Vol 58 (2) ◽  
pp. 286-298 ◽  
Author(s):  
H. D. Miller
Keyword(s):  
Markov Chain ◽  
Asymptotic Expression ◽  
Random Variables ◽  
Partial Sums ◽  
Finite Markov Chain ◽  
Initial State ◽  
Main Result Theorem ◽  
Sums Of Random Variables ◽  
Result Theorem ◽  
Image Position

SummaryThis paper is essentially a continuation of the previous one (5) and the notation established therein will be freely repeated. The sequence {ξr} of random variables is defined on a positively regular finite Markov chain {kr} as in (5) and the partial sums and are considered. Let ζn be the first positive ζr and let πjk(y), the ‘ruin’ function or absorption probability, be defined by The main result (Theorem 1) is an asymptotic expression for πjk(y) for large y in the case when , the expectation of ξ1 being computed under the unique stationary distribution for k0, the initial state of the chain, and unconditional on k1.

Towards a molecular phylogeny for the lichen family Pannariaceae (Lecanorales, Ascomycota)

2002 ◽  
Vol 80 (6) ◽  
pp. 625-634 ◽  
Author(s):  
Stefan Ekman ◽  
Per Magnus Jørgensen
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Ribosomal Dna ◽  
Dna Sequences ◽  
Sampling Procedure ◽  
Nuclear Ribosomal Dna ◽  
Dna Phylogeny ◽  
Green Algal ◽  
The Family ◽  
Optimal Trees

The phylogeny of the family Pannariaceae (Lecanorales, lichenized Ascomycota) was investigated using ITS1–5.8S–ITS2 nuclear ribosomal DNA sequences representing 21 species. Phylogenetic estimations were performed using parsimony and a Bayesian Markov chain Monte Carlo (MCMC) tree sampling procedure. Several phylogenetic null hypotheses were tested, also using MCMC. The results indicate that Pannariaceae, as currently treated, is polyphyletic and that Degelia sect. Amphiloma, Fuscopannaria subg. Micropannaria, and Moelleropsis s.str. do not belong in the family. The inclusion of Parmeliella in the Pannariaceae could not be rejected, although it falls outside the family in the optimal trees. Psoroma, Santessoniella, Protopannaria, Fuscopannaria subg. Fuscopannaria, Moelleropsis s.str., and Pannaria unequivocally belong to the family. The Pannaria sphinctrina group belongs in Pannaria despite its green-algal photobiont. Protopannaria pezizoides is not, as sometimes treated, a Pannaria, although a relationship with Psoroma could not be ruled out. In the optimal trees, Moelleropsis s.str. is nested inside Fuscopannaria subg. Fuscopannaria and Santessoniella inside Psoroma, but null hypotheses of their independence from these genera could not be rejected. Pannaria cannot be divided into two monophyletic subgenera, Pannaria and Chryopannaria. The photobiont has changed twice or three times and the ascus apex and hymenial amyloidity twice within the Pannariaceae.Key words: lichens, Pannariaceae, ITS, ribosomal DNA, phylogeny, MCMC.

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