BACKGROUND : Wastewater-based epidemiology has been investigated as a very effective way of monitoring SARS-CoV-2 variants. This can be achieved through accurate lineage deconvolution of wastewater sequencing data. Variants Ratios from Pooled Sequencing (VaRaPS) is a Python package designed for this purpose, utilizing pooled sequencing data and lineage mutation profiles to estimate their proportions. RESULTS : VaRaPS re-implements core algorithms from the literature, achieving significant improvements in computational speed and efficiency. Comparative analyzes with simulated and synthetic data sets demonstrate its superior performance in lineage prevalence estimation, underscored by its user-oriented design for broader accessibility. CONCLUSIONS : By improving speed and accuracy in SARS-CoV-2 variant analysis, VaRaPS offers valuable insights into viral evolution, supporting ongoing surveillance efforts in the post-pandemic landscape.

We measure the influence of individual observations on the sequence of the hidden states of the Hidden Markov Model (HMM) by means of the Kullback–Leibler distance (KLD). Namely, we consider the KLD between the conditional distribution of the hidden states' chain given the complete sequence of observations and the conditional distribution of the hidden chain given all the observations but the one under consideration. We introduce a linear complexity algorithm for computing the influence of all the observations. As an illustration, we investigate the application of our algorithm to the problem of detecting meaningful observations} in HMM data series.

Interest in Conotoxin has been rapidly growing over the past number of years due to its potential for effective use in the design of drugs to treat a myriad of conditions including, neuromuscular disorders, chronic pain and schizophrenia. As a result it is necessary to develop powerful and efficient techniques which can accurately classify conotoxin super families. In this paper, we propose a novel technique which makes use of support vector machines for classification. The method which considers suboptimal alignments of words with restricted length and computes local alignment partition functions to produce free scores for the alignments plays the key role in the feature extraction step of support vector machine classification. In the classification of conotoxin proteins, the proposed approach, SVM-Freescore, demonstrates its potential use by yielding an improved sensitivity and specificity of approximately 5.864% and 3.76%, respectively.

BACKGROUND: Conotoxin has been proven to be effective in drug design and could be used to treat various disorders such as schizophrenia, neuromuscular disorders and chronic pain. With the rapidly growing interest in conotoxin, accurate conotoxin superfamily classification tools are desirable to systematize the increasing number of newly discovered sequences and structures. However, despite the significance and extensive experimental investigations on conotoxin, those tools have not been intensively explored. RESULTS: In this paper, we propose to consider suboptimal alignments of words with restricted length. We developed a scoring system based on local alignment partition functions, called free score. The scoring system plays the key role in the feature extraction step of support vector machine classification. In the classification of conotoxin proteins, our method, SVM-Freescore, features an improved sensitivity and specificity by approximately 5.864% and 3.76%, respectively, over previously reported methods. For the generalization purpose, SVM-Freescore was also applied to classify superfamilies from curated and high quality database such as ConoServer. The average computed sensitivity and specificity for the superfamily classification were found to be 0.9742 and 0.9917, respectively. CONCLUSIONS: The SVM-Freescore method is shown to be a useful sequence-based analysis tool for functional and structural characterization of conotoxin proteins. The datasets and the software are available at http://faculty.uaeu.ac.ae/nzaki/SVM-Freescore.htm.

BACKGROUND: Protein loops encompass 50% of protein residues in available three-dimensional structures. These regions are often involved in protein functions, e.g. binding site, catalytic pocket... However, the description of protein loops with conventional tools is an uneasy task. Regular secondary structures, helices and strands, have been widely studied whereas loops, because they are highly variable in terms of sequence and structure, are difficult to analyze. Due to data sparsity, long loops have rarely been systematically studied. RESULTS: We developed a simple and accurate method that allows the description and analysis of the structures of short and long loops using structural motifs without restriction on loop length. This method is based on the structural alphabet HMM-SA. HMM-SA allows the simplification of a three-dimensional protein structure into a one-dimensional string of states, where each state is a four-residue prototype fragment, called structural letter. The difficult task of the structural grouping of huge data sets is thus easily accomplished by handling structural letter strings as in conventional protein sequence analysis. We systematically extracted all seven-residue fragments in a bank of 93000 protein loops and grouped them according to the structural-letter sequence, named structural word. This approach permits a systematic analysis of loops of all sizes since we consider the structural motifs of seven residues rather than complete loops. We focused the analysis on highly recurrent words of loops (observed more than 30 times). Our study reveals that 73% of loop-lengths are covered by only 3310 highly recurrent structural words out of 28274 observed words). These structural words have low structural variability (mean RMSd of 0.85 A). As expected, half of these motifs display a flanking-region preference but interestingly, two thirds are shared by short (less than 12 residues) and long loops. Moreover, half of recurrent motifs exhibit a significant level of amino-acid conservation with at least four significant positions and 87% of long loops contain at least one such word. We complement our analysis with the detection of statistically over-represented patterns of structural letters as in conventional DNA sequence analysis. About 30% (930) of structural words are over-represented, and cover about 40% of loop lengths. Interestingly, these words exhibit lower structural variability and higher sequential specificity, suggesting structural or functional constraints. CONCLUSIONS: We developed a method to systematically decompose and study protein loops using recurrent structural motifs. This method is based on the structural alphabet HMM-SA and not on structural alignment and geometrical parameters. We extracted meaningful structural motifs that are found in both short and long loops. To our knowledge, it is the first time that pattern mining helps to increase the signal-to-noise ratio in protein loops. This finding helps to better describe protein loops and might permit to decrease the complexity of long-loop analysis. Detailed results are available at http://www.mti.univ-paris-diderot.fr/publication/supplementary/2009/ACCLoop/.

BACKGROUND: The use of current high-throughput genetic, genomic and post-genomic data leads to the simultaneous evaluation of a large number of statistical hypothesis and, at the same time, to the multiple-testing problem. As an alternative to the too conservative Family-Wise Error-Rate (FWER), the False Discovery Rate (FDR) has appeared for the last ten years as more appropriate to handle this problem. However one drawback of FDR is related to a given rejection region for the considered statistics, attributing the same value to those that are close to the boundary and those that are not. As a result, the local FDR has been recently proposed to quantify the specific probability for a given null hypothesis to be true. RESULTS: In this context we present a semi-parametric approach based on kernel estimators which is applied to different high-throughput biological data such as patterns in DNA sequences, genes expression and genome-wide association studies. CONCLUSION: The proposed method has the practical advantages, over existing approaches, to consider complex heterogeneities in the alternative hypothesis, to take into account prior information (from an expert judgment or previous studies) by allowing a semi-supervised mode, and to deal with truncated distributions such as those obtained in Monte-Carlo simulations. This method has been implemented and is available through the R package kerfdr via the CRAN or at (http://stat.genopole.cnrs.fr/software/kerfdr).

SUMMARY: The seq++ package offers a reference set of programs and an extensible library to biologists and developers working on sequence statistics. Its generality arises from the ability to handle sequences described with any alphabet (nucleotides, amino acids, codons and others). seq++ enables sequence modelling with various types of Markov models, including variable length Markov models and the newly developed parsimonious Markov models, all of them potentially phased. Simulation modules are supplied for Monte Carlo methods. Hence, this toolbox allows the study of any biological process which can be described by a series of states taken from a finite set.

Summary: S-SPatt allows the counting of patterns occurrences in text files and, assuming these texts are generated from a random Markovian source, the computation of the P-value of a given observation using a simple binomial approximation. Availability: S-SPatt is available at: http://stat.genopole.cnrs.fr/spatt Contact:[email protected]