ESTIMATING THE GUMBEL SCALE PARAMETER FOR LOCAL ALIGNMENT OF RANDOM SEQUENCES BY IMPORTANCE SAMPLING WITH STOPPING TIMES

Estimating statistical significance of local protein profile-profile alignments

BACKGROUND

Alignment of sequence families described by profiles provides a sensitive means for establishing homology between proteins and is important in protein evolutionary, structural, and functional studies. In the context of a steadily growing amount of sequence data, estimating the statistical significance of alignments, including profile-profile alignments, plays a key role in alignment-based homology search algorithms. Still, it is an open question as to what and whether one type of distribution governs profile-profile alignment score, especially when profile-profile substitution scores involve such terms as secondary structure predictions.

RESULTS

This study presents a methodology for estimating the statistical significance of this type of alignments. The methodology rests on a new algorithm developed for generating random profiles such that their alignment scores are distributed similarly to those obtained for real unrelated profiles. We show that improvements in statistical accuracy and sensitivity and high-quality alignment rate result from statistically characterizing alignments by establishing the dependence of statistical parameters on various measures associated with both individual and pairwise profile characteristics. Implemented in the COMER software, the proposed methodology yielded an increase of up to 34.2% in the number of true positives and up to 61.8% in the number of high-quality alignments with respect to the previous version of the COMER method.

CONCLUSIONS

The more accurate estimation of statistical significance is implemented in the COMER method, which is now more sensitive and provides an increased rate of high-quality profile-profile alignments. The results of the present study also suggest directions for future research.

How sequence alignment scores correspond to probability models

MOTIVATION

Sequence alignment remains fundamental in bioinformatics. Pair-wise alignment is traditionally based on ad hoc scores for substitutions, insertions and deletions, but can also be based on probability models (pair hidden Markov models: PHMMs). PHMMs enable us to: fit the parameters to each kind of data, calculate the reliability of alignment parts and measure sequence similarity integrated over possible alignments.

RESULTS

This study shows how multiple models correspond to one set of scores. Scores can be converted to probabilities by partition functions with a 'temperature' parameter: for any temperature, this corresponds to some PHMM. There is a special class of models with balanced length probability, i.e. no bias toward either longer or shorter alignments. The best way to score alignments and assess their significance depends on the aim: judging whether whole sequences are related versus finding related parts. This clarifies the statistical basis of sequence alignment.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

ALP & FALP: C++ libraries for pairwise local alignment E-values

MOTIVATION

Pairwise local alignment is an indispensable tool for molecular biologists. In real time (i.e. in about 1 s), ALP (Ascending Ladder Program) calculates the E-values for protein-protein or DNA-DNA local alignments of random sequences, for arbitrary substitution score matrix, gap costs and letter abundances; and FALP (Frameshift Ascending Ladder Program) performs a similar task, although more slowly, for frameshifting DNA-protein alignments.

AVAILABILITY AND IMPLEMENTATION

To permit other C++ programmers to implement the computational efficiencies in ALP and FALP directly within their own programs, C++ source codes are available in the public domain at http://go.usa.gov/3GTSW under 'ALP' and 'FALP', along with the standalone programs ALP and FALP.

CONTACT

spouge@nih.gov

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Frameshift alignment: statistics and post-genomic applications

MOTIVATION

The alignment of DNA sequences to proteins, allowing for frameshifts, is a classic method in sequence analysis. It can help identify pseudogenes (which accumulate mutations), analyze raw DNA and RNA sequence data (which may have frameshift sequencing errors), investigate ribosomal frameshifts, etc. Often, however, only ad hoc approximations or simulations are available to provide the statistical significance of a frameshift alignment score.

RESULTS

We describe a method to estimate statistical significance of frameshift alignments, similar to classic BLAST statistics. (BLAST presently does not permit its alignments to include frameshifts.) We also illustrate the continuing usefulness of frameshift alignment with two 'post-genomic' applications: (i) when finding pseudogenes within the human genome, frameshift alignments show that most anciently conserved non-coding human elements are recent pseudogenes with conserved ancestral genes; and (ii) when analyzing metagenomic DNA reads from polluted soil, frameshift alignments show that most alignable metagenomic reads contain frameshifts, suggesting that metagenomic analysis needs to use frameshift alignment to derive accurate results.

New finite-size correction for local alignment score distributions

BACKGROUND

Local alignment programs often calculate the probability that a match occurred by chance. The calculation of this probability may require a "finite-size" correction to the lengths of the sequences, as an alignment that starts near the end of either sequence may run out of sequence before achieving a significant score.

FINDINGS

We present an improved finite-size correction that considers the distribution of sequence lengths rather than simply the corresponding means. This approach improves sensitivity and avoids substituting an ad hoc length for short sequences that can underestimate the significance of a match. We use a test set derived from ASTRAL to show improved ROC scores, especially for shorter sequences.

CONCLUSIONS

The new finite-size correction improves the calculation of probabilities for a local alignment. It is now used in the BLAST+ package and at the NCBI BLAST web site ( http://blast.ncbi.nlm.nih.gov).

Objective method for estimating asymptotic parameters, with an application to sequence alignment

Sequence alignment is an indispensable computational tool in modern molecular biology. The model underlying biological sequence alignment is of interest to physicists because it approximates the statistical mechanics of DNA and protein annealing, while bearing an intimate relationship to models of directed polymers in random media. Recent methods for determining the statistics of random sequence alignments have reduced the computation time to less than 1 s, opening up some interesting possibilities for online computation with biological search engines. Before implementation, however, the methods required an objective technique for computing regression coefficients pertinent to an asymptotic regime. Typically, physicists estimate parameters pertinent to an asymptotic regime subjectively: They eyeball their data; estimate the asymptotic regime where the regression model holds with reasonable accuracy; and then regress data only within the estimated asymptotic regime. Our publicly available computer program ARRP replaces the subjective assessment of the asymptotic regime with an objective change-point detection method, increasing confidence in the scientific objectivity of the parameter estimates. Asymptotic regression has potential applications across most of physics.

A new repeat-masking method enables specific detection of homologous sequences

Biological sequences are often analyzed by detecting homologous regions between them. Homology search is confounded by simple repeats, which give rise to strong similarities that are not homologies. Standard repeat-masking methods fail to eliminate this problem, and they are especially ill-suited to AT-rich DNA such as malaria and slime-mould genomes. We present a new repeat-masking method, tantan, which is motivated by the mechanisms that create simple repeats. This method thoroughly eliminates spurious homology predictions for DNA–DNA, protein–protein and DNA–protein comparisons. Moreover, it enables accurate homology search for non-coding DNA with extreme A + T composition.

The whole alignment and nothing but the alignment: the problem of spurious alignment flanks

Pairwise sequence alignment is a ubiquitous tool for inferring the evolution and function of DNA, RNA and protein sequences. It is therefore essential to identify alignments arising by chance alone, i.e. spurious alignments. On one hand, if an entire alignment is spurious, statistical techniques for identifying and eliminating it are well known. On the other hand, if only a part of the alignment is spurious, elimination is much more problematic. In practice, even the sizes and frequencies of spurious subalignments remain unknown. This article shows that some common scoring schemes tend to overextend alignments and generate spurious alignment flanks up to hundreds of base pairs/amino acids in length. In the UCSC genome database, e.g. spurious flanks probably comprise >18% of the human-fugu genome alignment. To evaluate the possibility that chance alone generated a particular flank on a particular pairwise alignment, we provide a simple 'overalignment' P-value. The overalignment P-value can identify spurious alignment flanks, thereby eliminating potentially misleading inferences about evolution and function. Moreover, by explicitly demonstrating the tradeoff between over- and under-alignment, our methods guide the rational choice of scoring schemes for various alignment tasks.