SUMMARY: The ongoing pandemic caused by SARS-CoV-2 emphasizes the importance of genomic surveillance to understand the evolution of the virus, to monitor the viral population, and plan epidemiological responses. Detailed analysis, easy visualization and intuitive filtering of the latest viral sequences are powerful for this purpose. We present CovRadar, a tool for genomic surveillance of the SARS-CoV-2 Spike protein. CovRadar consists of an analytical pipeline and a web application that enable the analysis and visualization of hundreds of thousand sequences. First, CovRadar extracts the regions of interest using local alignment, then builds a multiple sequence alignment, infers variants and consensus and finally presents the results in an interactive app, making accessing and reporting simple, flexible and fast. AVAILABILITY AND IMPLEMENTATION: CovRadar is freely accessible at https://covradar.net, its open-source code is available at https://gitlab.com/dacs-hpi/covradar. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: ASES is a versatile tool for assessing the impact of alternative splicing (AS), initiation and termination of transcription on protein diversity in evolution. It identifies exon and transcript orthogroups from a set of input genes/species for comparative transcriptomics analyses. It computes an evolutionary splicing graph, where the nodes are exon orthogroups, allowing for a direct evaluation of AS conservation. It also reconstructs a transcripts' phylogenetic forest to date the appearance of specific transcripts and explore the events that have shaped them. ASES web server features a highly interactive interface enabling the synchronous selection of events, exons or transcripts in the different outputs, and the visualization and retrieval of the corresponding amino acid sequences, for subsequent 3D structure prediction. AVAILABILITY AND IMPLEMENTATION: http://www.lcqb.upmc.fr/Ases. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: The understanding of the ever-increasing number of metagenomic sequences accumulating in our databases demands for approaches that rapidly 'explore' the content of multiple and/or large metagenomic datasets with respect to specific domain targets, avoiding full domain annotation and full assembly. RESULTS: S3A is a fast and accurate domain-targeted assembler designed for a rapid functional profiling. It is based on a novel construction and a fast traversal of the Overlap-Layout-Consensus graph, designed to reconstruct coding regions from domain annotated metagenomic sequence reads. S3A relies on high-quality domain annotation to efficiently assemble metagenomic sequences and on the design of a new confidence measure for a fast evaluation of overlapping reads. Its implementation is highly generic and can be applied to any arbitrary type of annotation. On simulated data, S3A achieves a level of accuracy similar to that of classical metagenomics assembly tools while permitting to conduct a faster and sensitive profiling on domains of interest. When studying a few dozens of functional domains-a typical scenario-S3A is up to an order of magnitude faster than general purpose metagenomic assemblers, thus enabling the analysis of a larger number of datasets in the same amount of time. S3A opens new avenues to the fast exploration of the rapidly increasing number of metagenomic datasets displaying an ever-increasing size. AVAILABILITY AND IMPLEMENTATION: S3A is available at http://www.lcqb.upmc.fr/S3A_ASSEMBLER/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Discovering over-represented approximate motifs in DNA sequences is an essential part of bioinformatics. This topic has been studied extensively because of the increasing number of potential applications. However, it remains a difficult challenge, especially with the huge quantity of data generated by high throughput sequencing technologies. To overcome this problem, existing tools use greedy algorithms and probabilistic approaches to find motifs in reasonable time. Nevertheless these approaches lack sensitivity and have difficulties coping with rare and subtle motifs. RESULTS: We developed DiNAMO (for DNA MOtif), a new software based on an exhaustive and efficient algorithm for IUPAC motif discovery. We evaluated DiNAMO on synthetic and real datasets with two different applications, namely ChIP-seq peaks and Systematic Sequencing Error analysis. DiNAMO proves to compare favorably with other existing methods and is robust to noise. CONCLUSIONS: We shown that DiNAMO software can serve as a tool to search for degenerate motifs in an exact manner using IUPAC models. DiNAMO can be used in scanning mode with sliding windows or in fixed position mode, which makes it suitable for numerous potential applications. AVAILABILITY: https://github.com/bonsai-team/DiNAMO .

We present a new educational initiative called Meet-U that aims to train students for collaborative work in computational biology and to bridge the gap between education and research. Meet-U mimics the setup of collaborative research projects and takes advantage of the most popular tools for collaborative work and of cloud computing. Students are grouped in teams of 4-5 people and have to realize a project from A to Z that answers a challenging question in biology. Meet-U promotes "coopetition," as the students collaborate within and across the teams and are also in competition with each other to develop the best final product. Meet-U fosters interactions between different actors of education and research through the organization of a meeting day, open to everyone, where the students present their work to a jury of researchers and jury members give research seminars. This very unique combination of education and research is strongly motivating for the students and provides a formidable opportunity for a scientific community to unite and increase its visibility. We report on our experience with Meet-U in two French universities with master's students in bioinformatics and modeling, with protein-protein docking as the subject of the course. Meet-U is easy to implement and can be straightforwardly transferred to other fields and/or universities. All the information and data are available at www.meet-u.org.

MOTIVATION: The detection of structural variations (SVs) in short-range Paired-End (PE) libraries remains challenging because SV breakpoints can involve large dispersed repeated sequences, or carry inherent complexity, hardly resolvable with classical PE sequencing data. In contrast, large insert-size sequencing libraries (Mate-Pair libraries) provide higher physical coverage of the genome and give access to repeat-containing regions. They can thus theoretically overcome previous limitations as they are becoming routinely accessible. Nevertheless, broad insert size distributions and high rates of chimerical sequences are usually associated to this type of libraries, which makes the accurate annotation of SV challenging. RESULTS: Here, we present Ulysses, a tool that achieves drastically higher detection accuracy than existing tools, both on simulated and real mate-pair sequencing datasets from the 1000 Human Genome project. Ulysses achieves high specificity over the complete spectrum of variants by assessing, in a principled manner, the statistical significance of each possible variant (duplications, deletions, translocations, insertions and inversions) against an explicit model for the generation of experimental noise. This statistical model proves particularly useful for the detection of low frequency variants. SV detection performed on a large insert Mate-Pair library from a breast cancer sample revealed a high level of somatic duplications in the tumor and, to a lesser extent, in the blood sample as well. Altogether, these results show that Ulysses is a valuable tool for the characterization of somatic mosaicism in human tissues and in cancer genomes.

MOTIVATION: The most common RNA-Seq strategy consists of random shearing, amplification and high-throughput sequencing of the RNA fraction. Methods to analyze transcription level variations along the genome from the read count profiles generated by the RNA-Seq protocol are needed. RESULTS: We developed a statistical approach to estimate the local transcription levels and to identify transcript borders. This transcriptional landscape reconstruction relies on a state-space model to describe transcription level variations in terms of abrupt shifts and more progressive drifts. A new emission model is introduced to capture not only the read count variance inside a transcript but also its short-range autocorrelation and the fraction of positions with zero counts. The estimation relies on a particle Gibbs algorithm whose running time makes it more suited to microbial genomes. The approach outperformed read-overlapping strategies on synthetic and real microbial datasets. AVAILABILITY: A program named Parseq is available at: http://www.lgm.upmc.fr/parseq/. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Automatic error correction of high-throughput sequencing data can have a dramatic impact on the amount of usable base pairs and their quality. It has been shown that the performance of tasks such as de novo genome assembly and SNP calling can be dramatically improved after read error correction. While a large number of methods specialized for correcting substitution errors as found in Illumina data exist, few methods for the correction of indel errors, common to technologies like 454 or Ion Torrent, have been proposed. RESULTS: We present Fiona, a new stand-alone read error-correction method. Fiona provides a new statistical approach for sequencing error detection and optimal error correction and estimates its parameters automatically. Fiona is able to correct substitution, insertion and deletion errors and can be applied to any sequencing technology. It uses an efficient implementation of the partial suffix array to detect read overlaps with different seed lengths in parallel. We tested Fiona on several real datasets from a variety of organisms with different read lengths and compared its performance with state-of-the-art methods. Fiona shows a constantly higher correction accuracy over a broad range of datasets from 454 and Ion Torrent sequencers, without compromise in speed. CONCLUSION: Fiona is an accurate parameter-free read error-correction method that can be run on inexpensive hardware and can make use of multicore parallelization whenever available. Fiona was implemented using the SeqAn library for sequence analysis and is publicly available for download at http://www.seqan.de/projects/fiona. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: The seq++ package offers a reference set of programs and an extensible library to biologists and developers working on sequence statistics. Its generality arises from the ability to handle sequences described with any alphabet (nucleotides, amino acids, codons and others). seq++ enables sequence modelling with various types of Markov models, including variable length Markov models and the newly developed parsimonious Markov models, all of them potentially phased. Simulation modules are supplied for Monte Carlo methods. Hence, this toolbox allows the study of any biological process which can be described by a series of states taken from a finite set.