MOTIVATION: The combination of long-read sequencing technologies like Oxford Nanopore with single-cell RNA sequencing (scRNAseq) assays enables the detailed exploration of transcriptomic complexity, including isoform detection and quantification, by capturing full-length cDNAs. However, challenges remain, including the lack of advanced simulation tools that can effectively mimic the unique complexities of scRNAseq long-read datasets. Such tools are essential for the evaluation and optimization of isoform detection methods dedicated to single-cell long-read studies. RESULTS: We developed AsaruSim, a workflow that simulates synthetic single-cell long-read Nanopore datasets, closely mimicking real experimental data. AsaruSim employs a multi-step process that includes the creation of a synthetic count matrix, generation of perfect reads, optional PCR amplification, introduction of sequencing errors, and comprehensive quality control reporting. Applied to a dataset of human peripheral blood mononuclear cells, AsaruSim accurately reproduced experimental read characteristics. AVAILABILITY AND IMPLEMENTATION: The source code and full documentation are available at https://github.com/GenomiqueENS/AsaruSim.

UNLABELLED: The preparation of an appropriate sequence dataset is the starting point of all genomic analyses. We present retrieve-ensembl-seq, an application that considerably eases the retrieval of sequences from the Ensembl database, via our user-friendly web site or web services. The user provides Ensembl identifiers or gene names, and the program returns corresponding upstream, downstream, intronic, exonic, UTR or whole gene sequences. retrieve-ensembl-seq also offers a multiple organism mode to retrieve sequences from homologous genes at any taxonomical level. And we introduce various original filters such as the masking of coding fragments and the avoidance of sequence redundancy for genes with multiple transcripts. retrieve-ensembl-seq is included in the software suite regulatory sequence analysis tools (RSAT), allowing instant submission of retrieved sequences to further analysis tools. AVAILABILITY: retrieve-ensembl-seq is integrated in the RSAT suite: http://rsat.ulb.ac.be/rsat. Web site: http://rsat.ulb.ac.be/rsat/retrieve-ensembl-seq_form.cgi. Web services: http://rsat.ulb.ac.be/rsat/web_services/RSATWS.wsdl. Stand-alone distribution: freely available under an academic licence to download from the RSAT web site. The complete manual, a convenient tutorial and demos are available from the RSAT website. Additional help can be found on the RSAT public forum.

BACKGROUND: Correct identification of individual Hox proteins is an essential basis for their study in diverse research fields. Common methods to classify Hox proteins focus on the homeodomain that characterise homeobox transcription factors. Classification is hampered by the high conservation of this short domain. Phylogenetic tree reconstruction is a widely used but time-consuming classification method. RESULTS: We have developed an automated procedure, HoxPred, that classifies Hox proteins in their groups of homology. The method relies on a discriminant analysis that classifies Hox proteins according to their scores for a combination of protein generalised profiles. 54 generalised profiles dedicated to each Hox homology group were produced de novo from a curated dataset of vertebrate Hox proteins. Several classification methods were investigated to select the most accurate discriminant functions. These functions were then incorporated into the HoxPred program. CONCLUSION: HoxPred shows a mean accuracy of 97%. Predictions on the recently-sequenced stickleback fish proteome identified 44 Hox proteins, including HoxC1a only found so far in zebrafish. Using the Uniprot databank, we demonstrate that HoxPred can efficiently contribute to large-scale automatic annotation of Hox proteins into their paralogous groups. As orthologous group predictions show a higher risk of misclassification, they should be corroborated by additional supporting evidence. HoxPred is accessible via SOAP and Web interface http://cege.vub.ac.be/hoxpred/. Complete datasets, results and source code are available at the same site.