Significance Score of Motifs in Biological Sequences

Nuel, Gregory

doi:10.5772/18448

Cited by 3 publications

(5 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We used the SPatt algorithm [ 90 ] to assess the enrichment and depletion of polyproline motifs, taking into account occurrence patterns of proline in various parts of protein structure. SPatt determines the expected occurrence of a sequence motif based on a Markov chain model of order m (model M m ), compares the observed occurrence with expected one, and calculates the p -value for the significance of a motif’s enrichment or depletion.…”

Section: Methodsmentioning

confidence: 99%

Evolutionary analysis of polyproline motifs in Escherichia coli reveals their regulatory role in translation

Motz

Jung

et al. 2018

PLoS Comput Biol

View full text Add to dashboard Cite

Translation of consecutive prolines causes ribosome stalling, which is alleviated but cannot be fully compensated by the elongation factor P. However, the presence of polyproline motifs in about one third of the E. coli proteins underlines their potential functional importance, which remains largely unexplored. We conducted an evolutionary analysis of polyproline motifs in the proteomes of 43 E. coli strains and found evidence of evolutionary selection against translational stalling, which is especially pronounced in proteins with high translational efficiency. Against the overall trend of polyproline motif loss in evolution, we observed their enrichment in the vicinity of translational start sites, in the inter-domain regions of multi-domain proteins, and downstream of transmembrane helices. Our analysis demonstrates that the time gain caused by ribosome pausing at polyproline motifs might be advantageous in protein regions bracketing domains and transmembrane helices. Polyproline motifs might therefore be crucial for co-translational folding and membrane insertion.

show abstract

Section: Methodsmentioning

confidence: 99%

Evolutionary analysis of polyproline motifs in Escherichia coli reveals their regulatory role in translation

Motz

Jung

et al. 2018

PLoS Comput Biol

View full text Add to dashboard Cite

show abstract

“…Interestingly, the value M (t) of the mgf for any real number t can be computed at a modest computational cost of O(n×|Q|×|Σ|). As pointed out in [38] it is hence…”

Section: Non-factorial Momentsmentioning

confidence: 76%

“…As explained in [37], factorial moments can be used to compute cumulants and/or non-factorial moments using various polynomial relationships. But [38] pointed out the limitations of such approach in floating-point arithmetic. In practice, high order non-factorial moments (ex: k = 5, 6, 7, .…”

Section: Non-factorial Momentsmentioning

confidence: 99%

“…In the homogeneous case, the results can be extended to establish large deviation results like in [32], but the present bound has the great interest to be valid for any finite n and without any homogeneity assumption. Moreover, as explained in [38], the first two derivatives of Λ(t) can be easily computed both allowing to speed up the numerical optimization of Eq. (4), and providing precise-type large deviation approximation using Bahadur-Rao results [2].…”

Section: Non-factorial Momentsmentioning

confidence: 99%

“…is known to have many applications in a wide range of fields (reliability, linguistic, data compression, bioinformatics, etc.) and as been extensively studied by the literature [30,11,43,23,46,38,3,21].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Moments of the Count of a Regular Expression in a Heterogeneous Random Sequence

Nuel

2019

Methodol Comput Appl Probab

View full text Add to dashboard Cite

We focus here on the distribution of the random count N of a regular expression in a multi-state random sequence generated by a heterogenous Markov source. We first briefly recall how classical Markov chain embedding techniques allow reducing the problem to the count of specific transitions in a (heterogenous) order 1 Markov chain over a deterministic finite automaton state space. From this result we derive the expression of both the mgf/pgf of N as well as the factorial and non-factorial moments of N . We then introduce the notion of evidence-based constraints in this context. Following the classical forward/backward algorithm in hidden Markov models, we provide explicit recursions allowing to compute the mgf/pgf of N under the evidence constraint. All the results presented are illustrated with a toy example.

show abstract

Sparse approaches for the exact distribution of patterns in long state sequences generated by a Markov source

Dumas

2013

Theoretical Computer Science

Self Cite

View full text Add to dashboard Cite

International audienceWe present two novel approaches for the computation of the exact distribution of a pattern in a long sequence. Both approaches take into account the sparse structure of the problem and are two-part algorithms. The first approach relies on a partial recursion after a fast computation of the second largest eigenvalue of the transition matrix of a Markov chain embedding. The second approach uses fast Taylor expansions of an exact bivariate rational reconstruction of the distribution. We illustrate the interest of both approaches on a simple toy-example and two biological applications: the transcription factors of the Human Chromosome 5 and the PROSITE signatures of functional motifs in proteins. On these example our methods demonstrate their complementarity and their hability to extend the domain of feasibility for exact computations in pattern problems to a new level

show abstract

Significance Score of Motifs in Biological Sequences

Cited by 3 publications

References 52 publications

Evolutionary analysis of polyproline motifs in Escherichia coli reveals their regulatory role in translation

Evolutionary analysis of polyproline motifs in Escherichia coli reveals their regulatory role in translation

Moments of the Count of a Regular Expression in a Heterogeneous Random Sequence

Sparse approaches for the exact distribution of patterns in long state sequences generated by a Markov source

Contact Info

Product

Resources

About