A Practical Approach to Significance Assessment in Alignment with Gaps

Chia, Nicholas; Bundschuh, Ralf

doi:10.1007/11415770_36

Cited by 4 publications

(3 citation statements)

References 37 publications

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“…Following the results of Park et al (2005) and Chia and Bundschuh (2005), this allows for a very efficient computation of for gapped alignment in the iid model.…”

Section: Sequence Alignment and Significance Assessmentmentioning

confidence: 84%

Effects of Long-Range Correlations in DNA on Sequence Alignment Score Statistics

Messer

Bundschuh

Vingron

et al. 2007

Journal of Computational Biology

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Long-range correlations in genomic base composition are a ubiquitous statistical feature among many eukaryotic genomes. In this article, these correlations are shown to substantially influence the statistics of sequence alignment scores. Using a Gaussian approximation to model the correlated score landscape, we calculate the corrections to the scale parameter of the extreme value distribution of alignment scores. Our approximate analytic results are supported by a detailed numerical study based on a simple algorithm to efficiently generate long-range correlated random sequences. We find both, mean and exponential tail of the score distribution for long-range correlated sequences to be substantially shifted compared to random sequences with independent nucleotides. The significance of measured alignment scores will therefore change upon incorporation of the correlations in the null model. We discuss the magnitude of this effect in a biological context.

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“…Following the results of Park et al (2005) and Chia and Bundschuh (2005), this allows for a very efficient computation of for gapped alignment in the iid model.…”

Section: Sequence Alignment and Significance Assessmentmentioning

confidence: 84%

Effects of Long-Range Correlations in DNA on Sequence Alignment Score Statistics

Messer

Bundschuh

Vingron

et al. 2007

Journal of Computational Biology

Self Cite

View full text Add to dashboard Cite

show abstract

“…This new model seems more accurate than other previously tested models. However, the determination of the parameters of the Gumbel distribution is a computationally expensive task9,17,31,32 although several efforts have reduced this expense by algorithm improvements,31–33 or new sample statistical procedures 34. The statistical estimation of the distribution also varies with the chosen substitution matrix and the chosen alignment algorithm 5,6,35–37.…”

Section: Discussionmentioning

confidence: 99%

Where Does the Alignment Score Distribution Shape Come from?

Ortet

Bastien

2010

Evol Bioinform Online

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Alignment algorithms are powerful tools for searching for homologous proteins in databases, providing a score for each sequence present in the database. It has been well known for 20 years that the shape of the score distribution looks like an extreme value distribution. The extremely large number of times biologists face this class of distributions raises the question of the evolutionary origin of this probability law.We investigated the possibility of deriving the main properties of sequence alignment score distributions from a basic evolutionary process: a duplication-divergence protein evolution process in a sequence space. Firstly, the distribution of sequences in this space was defined with respect to the genetic distance between sequences. Secondly, we derived a basic relation between the genetic distance and the alignment score. We obtained a novel score probability distribution which is qualitatively very similar to that of Karlin-Altschul but performing better than all other previous model.

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“…The choice for an almost constant, high λ close to the magic value log 2 for fragment mode models is based heavily on the work by Bundschuh, Milosavljević Yu, Hwa et al [39][40][41][42] that both Viterbi scores of Gumbel distribution and Forward scores of exponential function has a fixed λ = log z where z is the base of the logarithm used of the log-odd scoring system. It should be noted that, in the case of λ > log 2, the EVD is always below the respective logistic function and no switching would occur (Fig.…”

Section: 44mentioning

confidence: 99%

The Janus-Faced E-Values of Hmmer2: Extreme Value Distribution or Logistic Function?

Wong

Maurer‐Stroh

Eisenhaber

2011

J. Bioinform. Comput. Biol.

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E-value guided extrapolation of protein domain annotation from libraries such as Pfam with the HMMER suite is indispensable for hypothesizing about the function of experimentally uncharacterized protein sequences. Since the recent release of HMMER3 does not supersede all functions of HMMER2, the latter will remain relevant for ongoing research as well as for the evaluation of annotations that reside in databases and in the literature. In HMMER2, the E-value is computed from the score via a logistic function or via a domain model-specific extreme value distribution (EVD); the lower of the two is returned as E-value for the domain hit in the query sequence. We find that, for thousands of domain models, this treatment results in switching from the EVD to the statistical model with the logistic function when scores grow (for Pfam release 23, 99% in the global mode and 75% in the fragment mode). If the score corresponding to the breakpoint results in an E-value above a user-defined threshold (e.g. 0.1), a critical score region with conflicting E-values from the logistic function (below the threshold) and from EVD (above the threshold) does exist. Thus, this switch will affect E-value guided annotation decisions in an automated mode. To emphasize, switching in the fragment mode is of no practical relevance since it occurs only at E-values far below 0.1. Unfortunately, a critical score region does exist for 185 domain models in the hmmpfam and 1,748 domain models in the hmmsearch global-search mode. For 145 out the respective 185 models, the critical score region is indeed populated by actual sequences. In total, 24.4% of their hits have a logistic function-derived E-value 179 J. Bioinform. Comput. Biol. 2011.09:179-206

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A Practical Approach to Significance Assessment in Alignment with Gaps

Cited by 4 publications

References 37 publications

Effects of Long-Range Correlations in DNA on Sequence Alignment Score Statistics

Effects of Long-Range Correlations in DNA on Sequence Alignment Score Statistics

Where Does the Alignment Score Distribution Shape Come from?

The Janus-Faced E-Values of Hmmer2: Extreme Value Distribution or Logistic Function?

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