Microbiome studies aim to answer the following questions: which organisms are in the sample and what is their impact on the patient or the environment? To answer these questions, investigators have to perform comparative analyses on their classified sequences based on the collected metadata, such as treatment, condition of the patient, or the environment. The integrity of sequences, classifications, and metadata is paramount for the success of such studies. Still, the area of data management for the preliminary study results appears to be neglected.

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Large-scale selection analyses of protein-coding sequences and phylogenetic tree reconstructions require suitable trees in Newick format. We developed the NewickTreeModifier, a simple web-based tool to trim and modify Newick trees for such analyses. The users can choose provided master trees or upload a tree to prune it to selected species provided in FASTA, NEXUS, or PHYLIP sequence format with an internal converter, a simple species list, or directly determined from a checklist interface of the master trees. Plant, insect, and vertebrate master trees comprise the maximum number of species in an up-to-date phylogenetic order directly transferable to the pruned Newick outfile. NTM is available at https://retrogenomics.uni-muenster.de/tools/ntm.

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paPAML simplifies, amplifies, and accelerates selection analyses via parallel processing, including detection of negatively selected sites. paPAML compiles results of site, branch, and branch-site models and detects site-specific negative selection with the output of a codon list labelling significance values. The tool simplifies selection analyses for casual and inexperienced users and accelerates computing speeds up to the number of allocated computer threads

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Upstream open reading frames (uORFs) are initiated by AUG or near-cognate start codons and have been identified in the transcript leader sequences of the majority of eukaryotic transcripts. Functionally, uORFs are implicated in downstream translational regulation of the main protein coding sequence and may serve as a source of non-canonical peptides. Genetic defects in uORF sequences have been linked to the development of various diseases, including cancer. To simplify uORF-related research, the initial release of uORFdb in 2014 provided a comprehensive and manually curated collection of uORF- elated literature. Here, we present an updated sequence-based version of uORFdb, accessible at https://www.bioinformatics.uni-muenster.de/tools/uorfdb. The new uORFdb enables users to directly access sequence information, graphical displays, and genetic variation data for over 2.4 million human uORFs. It also includes sequence data of >4.2 million uORFs in 12 additional species. Multiple uORFs can be displayed in transcript- and reading-frame-specific models to visualize the translational context. A variety of filters, sequence-related information, and links to external resources (UCSC Genome Browser, dbSNP, ClinVar) facilitate immediate in-depth analysis of individual uORFs. The database also contains uORF-related somatic variation data obtained from whole-genome sequencing (WGS) analyses of 677 cancer samples collected by the TCGA consortium.

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Evolution is change over time. Although neutral changes promoted by drift effects are most reliable for phylogenetic reconstructions, selection-relevant changes are of only limited use to reconstruct phylogenies. On the other hand, comparative analyses of neutral and selected changes of protein-coding DNA sequences (CDS) retrospectively tell us about episodic constrained, relaxed, and adaptive incidences. The ratio of sites with nonsynonymous (amino acid altering) versus synonymous (not altering) mutations directly measures selection pressure and can be analysed by using the Phylogenetic Analysis by Maximum Likelihood (PAML) software package. We developed a CDS extractor for compiling protein-coding sequences (CDS-extractor) and parallel PAML (paPAML) to simplify, amplify, and accelerate selection analyses via parallel processing, including detection of negatively selected sites. paPAML compiles results of site, branch-site, and branch models and detects site-specific negative selection with the output of a codon list labelling significance values. The tool simplifies selection analyses for casual and inexperienced users and accelerates computing speeds up to the number of allocated computer threads. We then applied paPAML to examine the evolutionary impact on a new GINS Complex Subunit 3 exon, and neutrophil-associated as well as lysin and apolipoprotein genes. Compared with codeml (PAML version 4.9j) and HyPhy (HyPhy FEL version 2.5.26), all paPAML test runs performed with 10 computing threads led to identical selection pressure results, whereas the total selection analysis via paPAML, including all model comparisons, was about 3 to 5 times faster than the longest running codeml model and about 7 to 15 times faster than the entire processing time of these codeml runs.

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To effectively analyze the increasing amounts of available genomic data, improved comparative analytical tools that are accessible to and applicable by a broad scientific community are essential. We built the "2-n-way" software suite to provide a fundamental and innovative processing framework for revealing and comparing inserted elements among various genomes. The suite is comprised of two user-friendly web-based modules. The 2-way module generates pairwise whole-genome alignments of target and query species. The resulting genome coordinates of blocks (matching sequences) and gaps (missing sequences) from multiple 2-ways are then transferred to the n-way module and sorted into projects, where user-defined coordinates from reference species are projected to the block/gap coordinates of orthologous loci in query species to provide comparative information about presence (blocks) or absence (gaps) patterns of targeted elements over many entire genomes and phylogroups. Thus, the "2-n-way" software suite is ideal for performing multi-directional, non-ascertainment-biased screenings to extract all possible presence/absence data of user-relevant elements in orthologous sequences. To highlight its applicability and versatility, we used 2-n-way to expose ~100 lost introns in vertebrates, analyzed thousands of potential phylogenetically informative bat and whale retrotransposons, and novel human exons as well as thousands of human polymorphic retrotransposons.

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Identifying single organisms in environmental samples is one of the key tasks of metagenomics. During the last few years, third generation sequencing technologies have enabled researchers to sequence much longer molecules, but at the expense of sequencing accuracy. Thus, new algorithms needed to be developed to cope with this new type of data. With this in mind, we developed a tool called MetaG. An intuitive web interface makes the software accessible to a vast range of users, including those without extensive bioinformatic expertise. Evaluation of MetaG’s performance showed that it makes nearly perfect classifications of viral isolates using simulated short and long reads. MetaG also outperformed current state-of-the-art algorithms on data from targeted sequencing of the 16S and 28S rRNA genes. Since MetaG’s output is also supplemented with information about hosts and antibiotic resistances of pathogens, we expect it to be especially useful to the healthcare sector. Moreover, the outstanding accuracy of the taxonomic assignments will make MetaG a serious alternative for anyone working with metagenomic sequences.

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Freely available NanoPipe software allows effortless and reliable analysis of MinION sequencing data for experienced bioinformaticians, as well for wet-lab biologists with minimum bioinformatics knowledge. Moreover, for the latter group, we describe the basic algorithm necessary for MinION sequencing analysis from the first to last step.

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Our understanding of genome-wide and comparative sequence information has been broadened considerably by the databases available from the University of California Santa Cruz (UCSC) Genome Bioinformatics Department. In partic- ular, the identification and visualization of genomic sequences, present in some species but absent in others, led to fundamental insights into gene and genome evolution. However, the UCSC tools currently enable one to visualize orthologous genomic loci for a range of species in only a single locus. For large-scale comparative analyses of such presence/absence patterns a multilocus view would be more desirable. Such a tool would enable us to compare thou- sands of relevant loci simultaneously and to resolve many different questions about, for example, phylogeny, specific aspects of genome and gene evolution, such as the gain or loss of exons and introns, the emergence of novel transposed elements, nonprotein-coding RNAs, and viral genomic particles. Here, we present the first tool to facilitate the parallel analysis of thousands of genomic loci for cross-species presence/absence patterns based on multiway genome alignments. This genome presence/absence compiler uses annotated or other compilations of coordinates of genomic locations and compiles all presence/absence patterns in a flexible, color-coded table linked to the individual UCSC Genome Browser alignments. We provide examples of the versatile information content of such a screening system especially for 7SL-derived transposed elements, nuclear mitochondrial DNA, DNA transposons, and miRNAs in primates

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Origin of the 1918 pandemic H1N1 influenza A virus as studied by codon usage patterns and phylogenetic analysis DARISUREN ANHLAN, 1 NORBERT GRUNDMANN, 2 WOJCIECH MAKALOWSKI,2 STEPHAN LUDWIG, 1 and CHRISTOPH SCHOLTISSEK 3 1 Institute of Molecular Virology (IMV), Centre of Molecular Biology of Inflammation (ZMBE), University of Mu¨nster, 48149 Mu¨ nster, Germany 2 Institute of Bioinformatics, University of Mu¨nster, 48149 Mu¨nster, Germany 3 St. Jude Children’s Research Hospital, Memphis, Tennessee 38105, USA ABSTRACT The pandemic of 1918 was caused by an H1N1 influenza A virus, which is a negative strand RNA virus; however, little is known about the nature of its direct ancestral strains. Here we applied a broad genetic and phylogenetic analysis of a wide range of influenza virus genes, in particular the PB1 gene, to gain information about the phylogenetic relatedness of the 1918 H1N1 virus. We compared the RNA genome of the 1918 strain to many other influenza strains of different origin by several means, including relative synonymous codon usage (RSCU), effective number of codons (ENC), and phylogenetic relationship. We found that the PB1 gene of the 1918 pandemic virus had ENC values similar to the H1N1 classical swine and human viruses, but different ENC values from avian as well as H2N2 and H3N2 human viruses. Also, according to the RSCU of the PB1 gene, the 1918 virus grouped with all human isolates and ‘‘classical’’ swine H1N1 viruses. The phylogenetic studies of all eight RNA gene segments of influenza A viruses may indicate that the 1918 pandemic strain originated from a H1N1 swine virus, which itself might be derived from a H1N1 avian precursor, which was separated from the bulk of other avian viruses in toto a long time ago. The high stability of the RSCU pattern of the PB1 gene indicated that the integrity of RNA structure is more important for influenza virus evolution than previously thought.

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DNA sequences afford access to the evolutionary pathways of life. Particularly mobile elements that constantly co-evolve in genomes encrypt recent and ancient information of their host’s history. In mammals there is an extraordinarily abundant activity of mobile elements that occurs in a dynamic succession of active families, subfamilies, types, and subtypes of retroposed elements. The high frequency of retroposons in mammals implies that, by chance, such elements also insert into each other. While inactive elements are no longer able to retropose, active elements retropose by chance into other active and inactive elements. Thousands of such directional, element-in-element insertions are found in present-day genomes. To help analyze these events, we developed a computational algorithm (Transpositions in Transpositions, or TinT) that examines the different frequencies of nested transpositions and reconstructs the chronological order of retroposon activitie

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The large number of sequenced genomes required the development of software that reconstructs the consensus sequences of transposons and other repetitive elements. However, the available tools usually focus on the accurate identification of raw repeats and provide no information about the taxonomic position of the reconstructed consensi. TEclass is a tool to classify unknown transposable elements into their four main functional categories, which reflect their mode of transposition: DNA transposons, long terminal repeats (LTRs), long interspersed nuclear elements (LINEs) and short interspersed nuclear elements (SINEs). TEclass uses machine learning support vector machine (SVM) for classification based on oligomer frequencies. It achieves 90–97% accuracy in the classification of novel DNA and LTR repeats, and 75% for LINEs and SINEs.

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