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The recent advent of long-read sequencing technologies, such as Pacific Biosciences (PacBio) and Oxford Nanopore technology (ONT), has led to substantial accuracy and computational cost improvements. However, de novo whole-genome assembly still presents significant challenges related to the computational cost and the quality of the results. Accordingly, sequencing accuracy and throughput continue to improve, and many tools are constantly emerging. Therefore, selecting the correct sequencing platform, the proper sequencing depth and the assembly tools are necessary to perform high-quality assembly. This paper evaluates the primary assembly reconstruction from recent hybrid and non-hybrid pipelines on different genomes. We find that using PacBio high-fidelity long-read (HiFi) plays an essential role in haplotype construction with respect to ONT reads. However, we observe a substantial improvement in the correctness of the assembly from high-fidelity ONT datasets and combining it with HiFi or short-reads., Competing Interests: Declaration of Competing Interest No conflict of interest exists., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Motivation: The explosive growth of the biological sequences databases stimulated by genome projects has modified the framework of several applications in the biological sequence analysis area. In most cases, this new scenario is characterized by studies on large sets of sequences, suggesting the need for effective and automatic methods for their clustering. A more effective clustering of the database could be followed by the application of common family analysis schemes to the groups so formed., Results: In this work, we present a new strategy to reduce the computational cost associated with the clustering of large sets of sequences which are expected to contain several families. The strategy is based on the grouping of the sequences into families by using a dynamic threshold on a pairwise sequence similarity criterion. Routine clustering of large data sets can now be done very efficiently. The method developed here achieves a computational space reduction of about an order of magnitude over more traditional ones of all-versus-all comparisons. The outcome of this approach produces family groupings that reproduce closely already accepted biological results. Our work includes a parallel implementation for distributed memory multiprocessors with a dynamic scheduling strategy for performance optimization., Availability: By anonymous ftp at ftp.ac.uma.es (/pub/ots/pCluster directory), or from our Web site http://www.cnb. uam.es/www/software/software_index.html, Contact: ots@ac.uma.es

We present an approach to the study of the relationships between biological sequences and structures applying image analysis methods to dotplots. We introduce a set of analytical tools based on different types of digital image-processing filters that are new within the context of dotplots. We have reformulated some of the usual approaches in dotplot analysis as mathematical operations on images within the framework of mathematical morphology. An X-Window-based implementation of this new approach has been developed and is available by anonymous FTP.

The authors describe a new algorithm for the fast Hough transform (FHT) that satisfactorily solves the problems other fast algorithms propose in the literature-erroneous solutions, point redundance, scaling, and detection of straight lines of different sizes-and needs less storage space. By using the information generated by the algorithm for the detection of straight lines, they manage to detect the segments of the image without appreciable computational overhead. They also discuss the performance and the parallelization of the algorithm and show its efficiency with some examples.

We present a new parallel computing approach to the case of exhaustive sequential sequence comparison algorithms on message-passing architectures. In this context a modification of guided self-scheduling as well as efficient buffering strategies are presented. We discuss two specific implementations, one on the Paramid parallel computer, and the other on a cluster of workstations running PVM. In both cases the parallel performance is higher than with any other method presented so far. The code is public domain and can be obtained by anonymous ftp at ftp.cnb.uam.es.

In this work we present results of the mapping on hypercube computers of some of the key steps involved in the procedure for 3D structural determination from transmission electron microscopy images. The goal is the introduction of parallel processing tools in the field of electron microscopy image processing. We show how the rich topology of the hypercube, combined with an efficient programming strategy, allows for order-of-magnitude increase in computational capacity for such time-consuming tasks as calculation of multidimensional FFT's, cross-correlation coefficients, fuzzy partitioning functionals and the filtered back-projection 3D reconstruction method.

Pattern recognition methods based on the theory of fuzzy sets are tested for their ability to classify electron microscopy images of biological specimens. The concept of fuzzy sets was chosen for its ability to represent classes of objects that are vaguely described from the measured data. A number of partitional clustering algorithms and an extensive set of cluster-validity functionals (some already reported and some newly developed) have been applied to a test-data set and to two real-data sets of images. One of the real-data sets corresponded to images of the Escherichia coli 50S ribosomal subunits depleted of proteins L7/L12 and the other set to images of the E. coli 70S monosome in the range of overlap views. These two latter sets had been previously studied by another clustering methodology. The new results obtained by the application of fuzzy clustering techniques will be compared to those previously obtained and some conclusions about the consistency of these classifications will be drawn from this comparison.

This paper presents new methods and procedures for studying collective behaviour in rats. The animals are assumed to be indistinguishable one from another and the behaviour of the group is represented analysed and interpreted in terms of the temporal evolution of a finite state probabilistic automaton. The automaton states are defined by measures on the clustering degree considered as a social response variable. The electronic system developed to carry out the cluster analysis and the automatic control of the social behaviour in the experimental environment includes a multimicroprocessor interacting with a 'social box' in which, together with classical sensors and effectors, a phototransistor based position sensor is included. Preliminary experiments show the discriminative power of the cluster automaton concerning sexual differences and emotivity, as well as the extensive of a basic mechanism of clustering as a collective response to stress. Pharmacologically, the new experimental medium proposed in this paper may be used to detect a new range of products affecting social but not individual behaviour. Also, well-known products, which in normal doses produce no detectable modification of individual behaviour, might have detectable effects on the collective level. Be this as it may, the experimental environment described constitutes a further experimental facility for the analysis and control of animal behaviour.