MOTIVATION: The presence or absence of some genes in a genome can influence whether other genes are likely to be present or absent. Understanding these gene co-occurrence and avoidance patterns reveals fundamental principles of genome organization, with applications ranging from evolutionary reconstruction to rational design of synthetic genomes. RESULTS: PanForest, presented here, uses random forest classifiers to predict the presence and absence of genes in genomes from the set of other genes present. Performance statistics output by PanForest reveal how predictable each gene's presence or absence is, based on the presence or absence of other genes in the genome. Further, PanForest produces statistics indicating the importance of each gene in predicting the presence or absence of each other gene. The PanForest software can run serially or in parallel, thereby facilitating the analysis of pangenomes at Network of Life scale.A pangenome of 12 741 accessory genes in 1000 Escherichia coli genomes was analysed in around 5 h using eight processors. To demonstrate PanForest's utility, we present a case study and show that certain genes associated with resistance to antimicrobial drugs reliably predict the presence or absence of other genes associated with resistance to the same drug. Further, we highlight several associations between those genes and others not known to be associated with antimicrobial resistance (AMR), or associated with resistance to other drugs. We envisage PanForest's use in studies from multiple disciplines concerning the dynamics of gene distributions in pangenomes ranging from biomedical science and synthetic biology to molecular ecology. AVAILABILITY AND IMPLEMENTATION: The software if freely available with a full manual and can be found with at www.github.com/alanbeavan/PanForest DOI: https://doi.org/10.5281/zenodo.17865482.

Abstract Summary: TOPD/FMTS has been developed to evaluate similarities and differences between phylogenetic trees. The software implements several new algorithms (including the Disagree method that returns the taxa, that disagree between two trees and the Nodal method that compares two trees using nodal information) and several previously described methods (such as the Partition method, Triplets or Quartets) to compare phylogenetic trees. One of the novelties of this software is that the FMTS (From Multiple to Single) program allows the comparison of trees that contain both orthologs and paralogs. Each option is also complemented with a randomization analysis to test the null hypothesis that the similarity between two trees is not better than chance expectation. Availability: The Perl source code of TOPD/FMTS is available at http://genomes.urv.es/topd. Contact: [email protected] Supplementary information: A complete tutorial and several examples of how to use the software have been included on the home page of the application.

In the past number of years the demand for high performance computing has greatly increased in the area of bioinformatics. The huge increase in size of many genomic databases has meant that many common tasks in bioinformatics are not possible to complete in a reasonable amount of time on a single processor. Recently distributed computing has emerged as an inexpensive alternative to dedicated parallel computing. We have developed a general-purpose distributed computing platform that is capable of using semi-idle computing resources to simulate a dedicated computing cluster. We have identified the suitability of a number of bioinformatics tasks to distributed computing. We briefly outline and evaluate two distributed bioinformatics programs, DSEARCH and DPRml, which have been developed for our system.

UNLABELLED: Clann has been developed in order to provide methods of investigating phylogenetic information through the application of supertrees. AVAILABILITY: Clann has been precompiled for Linux, Apple Macintosh and Windows operating systems and is available from http://bioinf.may.ie/software/clann. Source code is available on request from the authors. SUPPLEMENTARY INFORMATION: Clann has been written in the C programming language. Source code is available on request.

MOTIVATION: In recent years there has been increased interest in producing large and accurate phylogenetic trees using statistical approaches. However for a large number of taxa, it is not feasible to construct large and accurate trees using only a single processor. A number of specialized parallel programs have been produced in an attempt to address the huge computational requirements of maximum likelihood. We express a number of concerns about the current set of parallel phylogenetic programs which are currently severely limiting the widespread availability and use of parallel computing in maximum likelihood-based phylogenetic analysis. RESULTS: We have identified the suitability of phylogenetic analysis to large-scale heterogeneous distributed computing. We have completed a distributed and fully cross-platform phylogenetic tree building program called distributed phylogeny reconstruction by maximum likelihood. It uses an already proven maximum likelihood-based tree building algorithm and a popular phylogenetic analysis library for all its likelihood calculations. It offers one of the most extensive sets of DNA substitution models currently available. We are the first, to our knowledge, to report the completion of a distributed phylogenetic tree building program that can achieve near-linear speedup while only using the idle clock cycles of machines. For those in an academic or corporate environment with hundreds of idle desktop machines, we have shown how distributed computing can deliver a 'free' ML supercomputer.

BACKGROUND: Many current gene prediction methods use only one model to represent protein-coding regions in a genome, and so are less likely to predict the location of genes that have an atypical sequence composition. It is likely that future improvements in gene finding will involve the development of methods that can adequately deal with intra-genomic compositional variation. RESULTS: This work explores a new approach to gene-prediction, based on the Self-Organizing Map, which has the ability to automatically identify multiple gene models within a genome. The current implementation, named RescueNet, uses relative synonymous codon usage as the indicator of protein-coding potential. CONCLUSIONS: While its raw accuracy rate can be less than other methods, RescueNet consistently identifies some genes that other methods do not, and should therefore be of interest to gene-prediction software developers and genome annotation teams alike. RescueNet is recommended for use in conjunction with, or as a complement to, other gene prediction methods.

Abstract Summary: A software program CRANN has been developed in order to detect adaptive evolution in protein-coding DNA sequences. Availability: CRANN is available from http://bioinf.may.ie/crann/ Supplementary Information: CRANN has been written in the C programming language. Source code is available on request.

UNLABELLED: The program General Codon Usage Analysis (GCUA) has been developed for analysing codon and amino acid usage patterns. AVAILABILITY: ftp://ftp.nhm.ac.uk/pub/gcua. Freely available for academic use, commercial users should contact the author. CONTACT: [email protected]