SUMMARY: "tidk" (short for telomere identification toolkit) uses a simple, fast algorithm to scan long DNA reads for the presence of short tandemly repeated DNA in runs, and to aggregate them based on canonical DNA string representation. These are telomeric repeat candidates. Our algorithm is shown to be accurate in genomes for which the telomeric repeat unit is known and is tested across a wide variety of newly assembled genomes to uncover new telomeric repeat units. Tools are provided to identify telomeric repeats de novo, scan genomes for known telomeric repeats, and to visualize telomeric repeats on the assembly. "tidk" is implemented in Rust and is available as a command line tool which can be compiled using the Rust toolchain or downloaded as a binary from bioconda. AVAILABILITY AND IMPLEMENTATION: The "tidk" Rust crate is freely available under the MIT license (https://crates.io/crates/tidk), and the source code is available at https://github.com/tolkit/telomeric-identifier.

BACKGROUND: PacBio high fidelity (HiFi) sequencing reads are both long (15-20 kb) and highly accurate (> Q20). Because of these properties, they have revolutionised genome assembly leading to more accurate and contiguous genomes. In eukaryotes the mitochondrial genome is sequenced alongside the nuclear genome often at very high coverage. A dedicated tool for mitochondrial genome assembly using HiFi reads is still missing. RESULTS: MitoHiFi was developed within the Darwin Tree of Life Project to assemble mitochondrial genomes from the HiFi reads generated for target species. The input for MitoHiFi is either the raw reads or the assembled contigs, and the tool outputs a mitochondrial genome sequence fasta file along with annotation of protein and RNA genes. Variants arising from heteroplasmy are assembled independently, and nuclear insertions of mitochondrial sequences are identified and not used in organellar genome assembly. MitoHiFi has been used to assemble 374 mitochondrial genomes (368 Metazoa and 6 Fungi species) for the Darwin Tree of Life Project, the Vertebrate Genomes Project and the Aquatic Symbiosis Genome Project. Inspection of 60 mitochondrial genomes assembled with MitoHiFi for species that already have reference sequences in public databases showed the widespread presence of previously unreported repeats. CONCLUSIONS: MitoHiFi is able to assemble mitochondrial genomes from a wide phylogenetic range of taxa from Pacbio HiFi data. MitoHiFi is written in python and is freely available on GitHub ( https://github.com/marcelauliano/MitoHiFi ). MitoHiFi is available with its dependencies as a Docker container on GitHub (ghcr.io/marcelauliano/mitohifi:master).

MOTIVATION: High-throughput RNA sequencing (RNA-seq) is now the standard method to determine differential gene expression. Identifying differentially expressed genes crucially depends on estimates of read-count variability. These estimates are typically based on statistical models such as the negative binomial distribution, which is employed by the tools edgeR, DESeq and cuffdiff. Until now, the validity of these models has usually been tested on either low-replicate RNA-seq data or simulations. RESULTS: A 48-replicate RNA-seq experiment in yeast was performed and data tested against theoretical models. The observed gene read counts were consistent with both log-normal and negative binomial distributions, while the mean-variance relation followed the line of constant dispersion parameter of ∼0.01. The high-replicate data also allowed for strict quality control and screening of 'bad' replicates, which can drastically affect the gene read-count distribution. AVAILABILITY AND IMPLEMENTATION: RNA-seq data have been submitted to ENA archive with project ID PRJEB5348. CONTACT: [email protected].

MOTIVATION: The Oxford Nanopore MinION device represents a unique sequencing technology. As a mobile sequencing device powered by the USB port of a laptop, the MinION has huge potential applications. To enable these applications, the bioinformatics community will need to design and build a suite of tools specifically for MinION data. RESULTS: Here we present poRe, a package for R that enables users to manipulate, organize, summarize and visualize MinION nanopore sequencing data. As a package for R, poRe has been tested on Windows, Linux and MacOSX. Crucially, the Windows version allows users to analyse MinION data on the Windows laptop attached to the device. AVAILABILITY AND IMPLEMENTATION: poRe is released as a package for R at http://sourceforge.net/projects/rpore/. A tutorial and further information are available at https://sourceforge.net/p/rpore/wiki/Home/.

The reproducibility of experiments is key to the scientific process, and particularly necessary for accurate reporting of analyses in data-rich fields such as phylogenomics. We present ReproPhylo, a phylogenomic analysis environment developed to ensure experimental reproducibility, to facilitate the handling of large-scale data, and to assist methodological experimentation. Reproducibility, and instantaneous repeatability, is built in to the ReproPhylo system and does not require user intervention or configuration because it stores the experimental workflow as a single, serialized Python object containing explicit provenance and environment information. This 'single file' approach ensures the persistence of provenance across iterations of the analysis, with changes automatically managed by the version control program Git. This file, along with a Git repository, are the primary reproducibility outputs of the program. In addition, ReproPhylo produces an extensive human-readable report and generates a comprehensive experimental archive file, both of which are suitable for submission with publications. The system facilitates thorough experimental exploration of both parameters and data. ReproPhylo is a platform independent CC0 Python module and is easily installed as a Docker image or a WinPython self-sufficient package, with a Jupyter Notebook GUI, or as a slimmer version in a Galaxy distribution.

SUMMARY: High-quality draft genomes are now easy to generate, as sequencing and assembly costs have dropped dramatically. However, building a user-friendly searchable Web site and database for a newly annotated genome is not straightforward. Here we present Badger, a lightweight and easy-to-install genome exploration environment designed for next generation non-model organism genomes. AVAILABILITY: Badger is released under the GPL and is available at http://badger.bio.ed.ac.uk/. We show two working examples: (i) a test dataset included with the source code, and (ii) a collection of four filarial nematode genomes. CONTACT: [email protected].

BACKGROUND: Next-generation DNA sequencing technologies have made it possible to generate transcriptome data for novel organisms quickly and cheaply, to the extent that the effort required to annotate and publish a new transcriptome is greater than the effort required to sequence it. Often, following publication, details of the annotation effort are only available in summary form, hindering subsequent exploitation of the data. To promote best-practice in annotation and to ensure that data remain accessible, we have written afterParty, a web application that allows users to assemble, annotate and publish novel transcriptomes using only a web browser. RESULTS: afterParty is a robust web application that implements best-practice transcriptome assembly, annotation, browsing, searching, and visualization. Users can turn a collection of reads (from Roche 454 chemistry) or assembled contigs (from any sequencing chemistry, including Illumina Solexa RNA-Seq) into a searchable, browsable transcriptome resource and quickly make it publicly available. Contigs are functionally annotated based on similarity to known sequences and protein domains. Once assembled and annotated, transcriptomes derived from multiple species or libraries can be compared and searched. afterParty datasets can either be created using the existing afterParty server, or using local instances that can be built easily using a virtual machine. afterParty includes powerful visualization tools for transcriptome dataset exploration and uses a flexible annotation architecture which will allow additional types of annotation to be added in the future. CONCLUSIONS: afterParty's main use case scenario is one in which a working biologist has generated a large volume of transcribed sequence data and wishes to turn it into a useful resource that has some durability. By reducing the effort, bioinformatics skills, and computational resources needed to annotate and publish a transcriptome, afterParty will facilitate the annotation and sharing of sequence data that would otherwise remain unavailable. A typical metazoan transcriptome containing several tens of thousands of contigs can be annotated in a few minutes of interactive time and a few days of computational time.

BACKGROUND: The increasing availability of molecular sequence data means that the accuracy of future phylogenetic studies is likely to by limited by systematic bias and taxon choice rather than by data. In order to take advantage of increasing datasets, user-friendly tools are required to facilitate phylogenetic analyses and to reduce duplication of dataset assembly efforts. Current phylogenetic pipelines are dependency-heavy and have significant technical barriers to use. RESULTS: Here we present iPhy, a web application that lets non-technical users assemble, share and analyse DNA sequence datasets for multigene phylogenetic investigations. Built on a simple client-server architecture, iPhy eases the collection of gene sets for analysis, facilitates alignment and reliably generates phylogenetic analysis-ready data files. Phylogenetic trees generated in external programs can be imported and stored, and iPhy integrates with iTol to allow trees to be displayed with rich data annotation. The datasets collated in iPhy can be shared through the client interface. We show how systematic biases can be addressed by using explicit criteria when selecting sequences for analysis from a large dataset. A representative instance of iPhy can be accessed at iphy.bio.ed.ac.uk, but the toolkit can also be deployed on a local server for advanced users. CONCLUSIONS: iPhy provides an easy-to-use environment for the assembly, analysis and sharing of large phylogenetic datasets, while encouraging best practices in terms of phylogenetic analysis and taxon selection.

BACKGROUND: The expressed sequence tag (EST) methodology is an attractive option for the generation of sequence data for species for which no completely sequenced genome is available. The annotation and comparative analysis of such datasets poses a formidable challenge for research groups that do not have the bioinformatics infrastructure of major genome sequencing centres. Therefore, there is a need for user-friendly tools to facilitate the annotation of non-model species EST datasets with well-defined ontologies that enable meaningful cross-species comparisons. To address this, we have developed annot8r, a platform for the rapid annotation of EST datasets with GO-terms, EC-numbers and KEGG-pathways. RESULTS: annot8r automatically downloads all files relevant for the annotation process and generates a reference database that stores UniProt entries, their associated Gene Ontology (GO), Enzyme Commission (EC) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) annotation and additional relevant data. For each of GO, EC and KEGG, annot8r extracts a specific sequence subset from the UniProt dataset based on the information stored in the reference database. These three subsets are then formatted for BLAST searches. The user provides the protein or nucleotide sequences to be annotated and annot8r runs BLAST searches against these three subsets. The BLAST results are parsed and the corresponding annotations retrieved from the reference database. The annotations are saved both as flat files and also in a relational postgreSQL results database to facilitate more advanced searches within the results. annot8r is integrated with the PartiGene suite of EST analysis tools. CONCLUSION: annot8r is a tool that assigns GO, EC and KEGG annotations for data sets resulting from EST sequencing projects both rapidly and efficiently. The benefits of an underlying relational database, flexibility and the ease of use of the program make it ideally suited for non-model species EST-sequencing projects.

BACKGROUND: Phylogenetic analysis of large, multiple-gene datasets, assembled from public sequence databases, is rapidly becoming a popular way to approach difficult phylogenetic problems. Supermatrices (concatenated multiple sequence alignments of multiple genes) can yield more phylogenetic signal than individual genes. However, manually assembling such datasets for a large taxonomic group is time-consuming and error-prone. Additionally, sequence curation, alignment and assessment of the results of phylogenetic analysis are made particularly difficult by the potential for a given gene in a given species to be unrepresented, or to be represented by multiple or partial sequences. We have developed a software package, TaxMan, that largely automates the processes of sequence acquisition, consensus building, alignment and taxon selection to facilitate this type of phylogenetic study. RESULTS: TaxMan uses freely available tools to allow rapid assembly, storage and analysis of large, aligned DNA and protein sequence datasets for user-defined sets of species and genes. The user provides GenBank format files and a list of gene names and synonyms for the loci to analyse. Sequences are extracted from the GenBank files on the basis of annotation and sequence similarity. Consensus sequences are built automatically. Alignment is carried out (where possible, at the protein level) and aligned sequences are stored in a database. TaxMan can automatically determine the best subset of taxa to examine phylogeny at a given taxonomic level. By using the stored aligned sequences, large concatenated multiple sequence alignments can be generated rapidly for a subset and output in analysis-ready file formats. Trees resulting from phylogenetic analysis can be stored and compared with a reference taxonomy. CONCLUSION: TaxMan allows rapid automated assembly of a multigene datasets of aligned sequences for large taxonomic groups. By extracting sequences on the basis of both annotation and BLAST similarity, it ensures that all available sequence data can be brought to bear on a phylogenetic problem, but remains fast enough to cope with many thousands of records. By automatically assisting in the selection of the best subset of taxa to address a particular phylogenetic problem, TaxMan greatly speeds up the process of generating multiple sequence alignments for phylogenetic analysis. Our results indicate that an automated phylogenetic workbench can be a useful tool when correctly guided by user knowledge.

UNLABELLED: Expressed sequence tags (ESTs) offer a low-cost approach to gene discovery and are being used by an increasing number of laboratories to obtain sequence information for a wide variety of organisms. The challenge lies in processing and organizing this data within a genomic context to facilitate large scale analyses. Here we present PartiGene, an integrated sequence analysis suite that uses freely available public domain software to (1) process raw trace chromatograms into sequence objects suitable for submission to dbEST; (2) place these sequences within a genomic context; (3) perform customizable first-pass annotation of the data; and (4) present the data as HTML tables and an SQL database resource. PartiGene has been used to create a number of non-model organism database resources including NEMBASE (http://www.nematodes.org) and LumbriBase (http://www.earthworms.org/). The packages are readily portable, freely available and can be run on simple Linux-based workstations. AVAILABILITY: PartiGene is available from http://www.nematodes.org/PartiGene and also forms part of the EST analysis software, associated with the Natural Environmental Research Council (UK) Bio-Linux project (http://envgen.nox.ac.uk/biolinux.html).

BACKGROUND: The genomes of an increasing number of species are being investigated through generation of expressed sequence tags (ESTs). However, ESTs are prone to sequencing errors and typically define incomplete transcripts, making downstream annotation difficult. Annotation would be greatly improved with robust polypeptide translations. Many current solutions for EST translation require a large number of full-length gene sequences for training purposes, a resource that is not available for the majority of EST projects. RESULTS: As part of our ongoing EST programs investigating these "neglected" genomes, we have developed a polypeptide prediction pipeline, prot4EST. It incorporates freely available software to produce final translations that are more accurate than those derived from any single method. We show that this integrated approach goes a long way to overcoming the deficit in training data. CONCLUSIONS: prot4EST provides a portable EST translation solution and can be usefully applied to >95% of EST projects to improve downstream annotation. It is freely available from http://www.nematodes.org/PartiGene.

Global sequence comparisons between large datasets, such as those arising from genome projects, can be problematic to display and analyze. We have developed SimiTri, a Java/Perl-based application, which allows simultaneous display and analysis of relative similarity relationships of the dataset of interest to three different databases. We illustrate its utility in identifying Caenorhabditis elegans genes that have distinct patterns of phylogenetic affinity suggestive of horizontal gene transfer. SimiTri is freely downloadable from http://www.nematodes.org/SimiTri/ and the source code is freely available from the authors.

BACKGROUND: Expressed sequence tags (ESTs) are single pass reads from randomly selected cDNA clones. They provide a highly cost-effective method to access and identify expressed genes. However, they are often prone to sequencing errors and typically define incomplete transcripts. To increase the amount of information obtainable from ESTs and reduce sequencing errors, it is necessary to cluster ESTs into groups sharing significant sequence similarity. RESULTS: As part of our ongoing EST programs investigating 'orphan' genomes, we have developed a clustering algorithm, CLOBB (Cluster on the basis of BLAST similarity) to identify and cluster ESTs. CLOBB may be used incrementally, preserving original cluster designations. It tracks cluster-specific events such as merging, identifies 'superclusters' of related clusters and avoids the expansion of chimeric clusters. Based on the Perl scripting language, CLOBB is highly portable relying only on a local installation of NCBI's freely available BLAST executable and can be usefully applied to > 95 % of the current EST datasets. Analysis of the Danio rerio EST dataset demonstrates that CLOBB compares favourably with two less portable systems, UniGene and TIGR Gene Indices. CONCLUSIONS: CLOBB provides a highly portable EST clustering solution and is freely downloaded from: http://www.nematodes.org/CLOBB