Exact calculation of distributions on integers, with application to sequence alignment.
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ABSTRACT:
SUBMITTER: Newberg LA
PROVIDER: S-EPMC2858568 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundAlthough the needs for analyses of secondary structures of RNAs are increasing, prediction of the secondary structures of RNAs are not always reliable. Because an RNA may have a complicated energy landscape, comprehensive representations of the whole ensemble of the secondary structures, such as the probability distributions of various features of RNA secondary structures are required.ResultsA general method to efficiently compute the distribution of any integer scalar/vector function on the secondary structure is proposed. We also show two concrete algorithms, for Hamming distance from a reference structure and for 5'-3' distance, which can be constructed by following our general method. These practical applications of this method show the effectiveness of the proposed method.ConclusionsThe proposed method provides a clear and comprehensive procedure to construct algorithms for distributions of various integer features. In addition, distributions of integer vectors, that is a combination of different integer scores, can be also described by applying our 2D expanding technique.
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Project description:The perpetually increasing rate at which viral full-genome sequences are being determined is creating a pressing demand for computational tools that will aid the objective classification of these genome sequences. Taxonomic classification approaches that are based on pairwise genetic identity measures are potentially highly automatable and are progressively gaining favour with the International Committee on Taxonomy of Viruses (ICTV). There are, however, various issues with the calculation of such measures that could potentially undermine the accuracy and consistency with which they can be applied to virus classification. Firstly, pairwise sequence identities computed based on multiple sequence alignments rather than on multiple independent pairwise alignments can lead to the deflation of identity scores with increasing dataset sizes. Also, when gap-characters need to be introduced during sequence alignments to account for insertions and deletions, methodological variations in the way that these characters are introduced and handled during pairwise genetic identity calculations can cause high degrees of inconsistency in the way that different methods classify the same sets of sequences. Here we present Sequence Demarcation Tool (SDT), a free user-friendly computer program that aims to provide a robust and highly reproducible means of objectively using pairwise genetic identity calculations to classify any set of nucleotide or amino acid sequences. SDT can produce publication quality pairwise identity plots and colour-coded distance matrices to further aid the classification of sequences according to ICTV approved taxonomic demarcation criteria. Besides a graphical interface version of the program for Windows computers, command-line versions of the program are available for a variety of different operating systems (including a parallel version for cluster computing platforms).
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Project description:We present Edlib, an open-source C/C?++?library for exact pairwise sequence alignment using edit distance. We compare Edlib to other libraries and show that it is the fastest while not lacking in functionality and can also easily handle very large sequences. Being easy to use, flexible, fast and low on memory usage, we expect it to be easily adopted as a building block for future bioinformatics tools.Source code, installation instructions and test data are freely available for download at https://github.com/Martinsos/edlib, under the MIT licence. Edlib is implemented in C/C?++?and supported on Linux, MS Windows, and Mac OS.mile.sikic@fer.hr.Supplementary data are available at Bioinformatics online.
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Project description:Computational biology is replete with high-dimensional (high-D) discrete prediction and inference problems, including sequence alignment, RNA structure prediction, phylogenetic inference, motif finding, prediction of pathways, and model selection problems in statistical genetics. Even though prediction and inference in these settings are uncertain, little attention has been focused on the development of global measures of uncertainty. Regardless of the procedure employed to produce a prediction, when a procedure delivers a single answer, that answer is a point estimate selected from the solution ensemble, the set of all possible solutions. For high-D discrete space, these ensembles are immense, and thus there is considerable uncertainty. We recommend the use of Bayesian credibility limits to describe this uncertainty, where a (1-alpha)%, 0< or =alpha< or =1, credibility limit is the minimum Hamming distance radius of a hyper-sphere containing (1-alpha)% of the posterior distribution. Because sequence alignment is arguably the most extensively used procedure in computational biology, we employ it here to make these general concepts more concrete. The maximum similarity estimator (i.e., the alignment that maximizes the likelihood) and the centroid estimator (i.e., the alignment that minimizes the mean Hamming distance from the posterior weighted ensemble of alignments) are used to demonstrate the application of Bayesian credibility limits to alignment estimators. Application of Bayesian credibility limits to the alignment of 20 human/rodent orthologous sequence pairs and 125 orthologous sequence pairs from six Shewanella species shows that credibility limits of the alignments of promoter sequences of these species vary widely, and that centroid alignments dependably have tighter credibility limits than traditional maximum similarity alignments.
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Project description:This dataset was utilized to assess the performance of a novel de novo metaproteomics pipeline, which performs sequence alignment of de novo sequences from complete metaproteomics experiments. Traditionally, metaproteomics data annotation relies on database searching that requires sample-specific databases derived from whole metagenome sequencing experiments. Creating these databases, however, is a complex, time-consuming, and error prone process, which can introduce biases affecting the outcomes and conclusions, highlighting the need for alternative methods. The evaluated approach offers rapid and orthogonal insights into metaproteomics data.
2024-10-10 | PXD050548 | Pride
Project description:BACKGROUND:cis-Regulatory modules (CRMs) of eukaryotic genes often contain multiple binding sites for transcription factors. The phenomenon that binding sites form clusters in CRMs is exploited in many algorithms to locate CRMs in a genome. This gives rise to the problem of calculating the statistical significance of the event that multiple sites, recognized by different factors, would be found simultaneously in a text of a fixed length. The main difficulty comes from overlapping occurrences of motifs. So far, no tools have been developed allowing the computation of p-values for simultaneous occurrences of different motifs which can overlap. RESULTS:We developed and implemented an algorithm computing the p-value that s different motifs occur respectively k1, ..., ks or more times, possibly overlapping, in a random text. Motifs can be represented with a majority of popular motif models, but in all cases, without indels. Zero or first order Markov chains can be adopted as a model for the random text. The computational tool was tested on the set of cis-regulatory modules involved in D. melanogaster early development, for which there exists an annotation of binding sites for transcription factors. Our test allowed us to correctly identify transcription factors cooperatively/competitively binding to DNA. METHOD:The algorithm that precisely computes the probability of simultaneous motif occurrences is inspired by the Aho-Corasick automaton and employs a prefix tree together with a transition function. The algorithm runs with the O(n|Sigma|m|H| + K|sigma|K) Piiki) time complexity, where n is the length of the text |Sigma| is the alphabet size, m is the maximal motif length, |H| is the total number of words in motifs, K is the order of Markov model, and ki is the number of occurrences of the ith motif. CONCLUSION:The primary objective of the program is to assess the likelihood that a given DNA segment is CRM regulated with a known set of regulatory factors. In addition, the program can also be used to select the appropriate threshold for PWM scanning. Another application is assessing similarity of different motifs. AVAILABILITY:Project web page, stand-alone version and documentation can be found at http://bioinform.genetika.ru/AhoPro/
| S-EPMC2174486 | biostudies-literature
Project description:BackgroundPairwise alignment of short DNA sequences with affine-gap scoring is a common processing step performed in a range of bioinformatics analyses. Dynamic programming (i.e. Smith-Waterman algorithm) is widely used for this purpose. Despite using data level parallelisation, pairwise alignment consumes much time. There are faster alignment algorithms but they suffer from the lack of accuracy.ResultsIn this paper, we present MEM-Align, a fast semi-global alignment algorithm for short DNA sequences that allows for affine-gap scoring and exploit sequence similarity. In contrast to traditional alignment method (such as Smith-Waterman) where individual symbols are aligned, MEM-Align extracts Maximal Exact Matches (MEMs) using a bit-level parallel method and then looks for a subset of MEMs that forms the alignment using a novel dynamic programming method. MEM-Align tries to mimic alignment produced by Smith-Waterman. As a result, for 99.9% of input sequence pair, the computed alignment score is identical to the alignment score computed by Smith-Waterman. Yet MEM-Align is up to 14.5 times faster than the Smith-Waterman algorithm. Fast run-time is achieved by: (a) using a bit-level parallel method to extract MEMs; (b) processing MEMs rather than individual symbols; and, (c) applying heuristics.ConclusionsMEM-Align is a potential candidate to replace other pairwise alignment algorithms used in processes such as DNA read-mapping and Variant-Calling.
| S-EPMC6528274 | biostudies-literature
Project description:MotivationThe explosive growth of next-generation sequencing datasets poses a challenge to the mapping of reads to reference genomes in terms of alignment quality and execution speed. With the continuing progress of high-throughput sequencing technologies, read length is constantly increasing and many existing aligners are becoming inefficient as generated reads grow larger.ResultsWe present CUSHAW2, a parallelized, accurate, and memory-efficient long read aligner. Our aligner is based on the seed-and-extend approach and uses maximal exact matches as seeds to find gapped alignments. We have evaluated and compared CUSHAW2 to the three other long read aligners BWA-SW, Bowtie2 and GASSST, by aligning simulated and real datasets to the human genome. The performance evaluation shows that CUSHAW2 is consistently among the highest-ranked aligners in terms of alignment quality for both single-end and paired-end alignment, while demonstrating highly competitive speed. Furthermore, our aligner shows good parallel scalability with respect to the number of CPU threads.AvailabilityCUSHAW2, written in C++, and all simulated datasets are available at http://cushaw2.sourceforge.netContactliuy@uni-mainz.de; bertil.schmidt@uni-mainz.deSupplementary informationSupplementary data are available at Bioinformatics online.
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Project description:Chemical fragment spaces exceed traditional virtual compound libraries by orders of magnitude, making them ideal search spaces for drug design projects. However, due to their immense size, they are not compatible with traditional analysis and search algorithms that rely on the enumeration of molecules. In this paper, we present SpaceProp2, an evolution of the SpaceProp algorithm, which enables the calculation of exact property distributions for chemical fragment spaces without enumerating them. We extend the original algorithm by the capabilities to compute distributions for the TPSA, the number of rotatable bonds, and the occurrence of user-defined molecular structures in the form of SMARTS patterns. Furthermore, SpaceProp2 produces example molecules for every property bin, enabling a detailed interpretation of the distributions. We demonstrate SpaceProp2 on six established make-on-demand chemical fragment spaces as well as BICLAIM, the in-house fragment space of Boehringer Ingelheim. The possibility to search multiple SMARTS patterns simultaneously as well as the produced example molecules offers previously impossible insights into the composition of these vast combinatorial molecule collections, making it an ideal tool for the analysis and design of chemical fragment spaces.
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