Fascin-1 expression is associated with neuroendocrine prostate cancer and directly suppressed by androgen receptor.

BACKGROUND: Neuroendocrine prostate cancer (NEPC) is an aggressive form of prostate cancer, arising from resistance to androgen-deprivation therapies. However, the molecular mechanisms associated with NEPC development and invasiveness are still poorly understood. Here we investigated the expression and functional significance of Fascin-1 (FSCN1), a pro-metastasis actin-bundling protein associated with poor prognosis of several cancers, in neuroendocrine differentiation of prostate cancer. METHODS: Differential expression analyses using Genome Expression Omnibus (GEO) database, clinical samples and cell lines were performed. Androgen or antagonist's cellular treatments and knockdown experiments were used to detect changes in cell morphology, molecular markers, migration properties and in vivo tumour growth. Chromatin immunoprecipitation-sequencing (ChIP-Seq) data and ChIP assays were analysed to decipher androgen receptor (AR) binding. RESULTS: We demonstrated that FSCN1 is upregulated during neuroendocrine differentiation of prostate cancer in vitro, leading to phenotypic changes and NEPC marker expression. In human prostate cancer samples, FSCN1 expression is restricted to NEPC tumours. We showed that the androgen-activated AR downregulates FSCN1 expression and works as a transcriptional repressor to directly suppress FSCN1 expression. AR antagonists alleviate this repression. In addition, FSCN1 silencing further impairs in vivo tumour growth. CONCLUSION: Collectively, our findings identify FSCN1 as an AR-repressed gene. Particularly, it is involved in NEPC aggressiveness. Our results provide the rationale for the future clinical development of FSCN1 inhibitors in NEPC patients.

Porechop_ABI: discovering unknown adapters in Oxford Nanopore Technology sequencing reads for downstream trimming.

Motivation: Oxford Nanopore Technologies (ONT) sequencing has become very popular over the past few years and offers a cost-effective solution for many genomic and transcriptomic projects. One distinctive feature of the technology is that the protocol includes the ligation of adapters to both ends of each fragment. Those adapters should then be removed before downstream analyses, either during the basecalling step or by explicit trimming. This basic task may be tricky when the definition of the adapter sequence is not well documented. Results: We have developed a new method to scan a set of ONT reads to see if it contains adapters, without any prior knowledge on the sequence of the potential adapters, and then trim out those adapters. The algorithm is based on approximate k-mers and is able to discover adapter sequences based on their frequency alone. The method was successfully tested on a variety of ONT datasets with different flowcells, sequencing kits and basecallers. Availability and implementation: The resulting software, named Porechop_ABI, is open-source and is available at https://github.com/bonsai-team/Porechop_ABI. Supplementary information: Supplementary data are available at Bioinformatics advances online.

Detecting MicroRNAs in Plant Genomes with miRkwood.

We present miRkwood, a comprehensive software tool developed to identify microRNAs and their precursor in plant genomes, with or without small-RNA-seq sequencing data. We describe how to install the software, how to set up and run it, and how to explore and analyse the results: genomic annotations, secondary structure of the precursor, alignments, reads distribution.

Conservation of the glycogen metabolism pathway underlines a pivotal function of storage polysaccharides in Chlamydiae.

The order Chlamydiales includes obligate intracellular pathogens capable of infecting mammals, fishes and amoeba. Unlike other intracellular bacteria for which intracellular adaptation led to the loss of glycogen metabolism pathway, all chlamydial families maintained the nucleotide-sugar dependent glycogen metabolism pathway i.e. the GlgC-pathway with the notable exception of both Criblamydiaceae and Waddliaceae families. Through detailed genome analysis and biochemical investigations, we have shown that genome rearrangement events have resulted in a defective GlgC-pathway and more importantly we have evidenced a distinct trehalose-dependent GlgE-pathway in both Criblamydiaceae and Waddliaceae families. Altogether, this study strongly indicates that the glycogen metabolism is retained in all Chlamydiales without exception, highlighting the pivotal function of storage polysaccharides, which has been underestimated to date. We propose that glycogen degradation is a mandatory process for fueling essential metabolic pathways that ensure the survival and virulence of extracellular forms i.e. elementary bodies of Chlamydiales.

TgAP2IX-5 is a key transcriptional regulator of the asexual cell cycle division in Toxoplasma gondii.

Apicomplexan parasites have evolved efficient and distinctive strategies for intracellular replication where the timing of emergence of the daughter cells (budding) is a decisive element. However, the molecular mechanisms that provide the proper timing of parasite budding remain unknown. Using Toxoplasma gondii as a model Apicomplexan, we identified a master regulator that controls the timing of the budding process. We show that an ApiAP2 transcription factor, TgAP2IX-5, controls cell cycle events downstream of centrosome duplication. TgAP2IX-5 binds to the promoter of hundreds of genes and controls the activation of the budding-specific cell cycle expression program. TgAP2IX-5 regulates the expression of specific transcription factors that are necessary for the completion of the budding cycle. Moreover, TgAP2IX-5 acts as a limiting factor that ensures that asexual proliferation continues by promoting the inhibition of the differentiation pathway. Therefore, TgAP2IX-5 is a master regulator that controls both cell cycle and developmental pathways.

Genetic basis for virulence differences of various Cryptosporidium parvum carcinogenic isolates.

Cryptosporidium parvum is known to cause life-threatening diarrhea in immunocompromised hosts and was also reported to be capable of inducing digestive adenocarcinoma in a rodent model. Interestingly, three carcinogenic isolates of C. parvum, called DID, TUM1 and CHR, obtained from fecal samples of naturally infected animals or humans, showed higher virulence than the commercially available C. parvum IOWA isolate in our animal model in terms of clinical manifestations, mortality rate and time of onset of neoplastic lesions. In order to discover the potential genetic basis of the differential virulence observed between C. parvum isolates and to contribute to the understanding of Cryptosporidium virulence, entire genomes of the isolates DID, TUM1 and CHR were sequenced then compared to the C. parvum IOWA reference genome. 125 common SNVs corresponding to 90 CDSs were found in the C. parvum genome that could explain this differential virulence. In particular variants in several membrane and secreted proteins were identified. Besides the genes already known to be involved in parasite virulence, this study identified potential new virulence factors whose functional characterization can be achieved through CRISPR/Cas9 technology applied to this parasite.

Comparative assessment of long-read error correction software applied to Nanopore RNA-sequencing data.

MOTIVATION: Nanopore long-read sequencing technology offers promising alternatives to high-throughput short read sequencing, especially in the context of RNA-sequencing. However this technology is currently hindered by high error rates in the output data that affect analyses such as the identification of isoforms, exon boundaries, open reading frames and creation of gene catalogues. Due to the novelty of such data, computational methods are still actively being developed and options for the error correction of Nanopore RNA-sequencing long reads remain limited. RESULTS: In this article, we evaluate the extent to which existing long-read DNA error correction methods are capable of correcting cDNA Nanopore reads. We provide an automatic and extensive benchmark tool that not only reports classical error correction metrics but also the effect of correction on gene families, isoform diversity, bias toward the major isoform and splice site detection. We find that long read error correction tools that were originally developed for DNA are also suitable for the correction of Nanopore RNA-sequencing data, especially in terms of increasing base pair accuracy. Yet investigators should be warned that the correction process perturbs gene family sizes and isoform diversity. This work provides guidelines on which (or whether) error correction tools should be used, depending on the application type. BENCHMARKING SOFTWARE: https://gitlab.com/leoisl/LR_EC_analyser.

Use of whole-genome sequencing in the molecular investigation of care-associated HCoV-OC43 infections in a hematopoietic stem cell transplant unit.

BACKGROUND: While respiratory viral infections are recognized as a frequent cause of illness in hematopoietic stem cell transplantation (HSCT) recipients, HCoV-OC43 infections have rarely been investigated as healthcare-associated infections in this population. OBJECTIVES: In this report, HCoV-OC43 isolates collected from HSCT patients were retrospectively characterized to identify potential clusters of infection that may stand for a hospital transmission. STUDY DESIGN: Whole-genome and S gene sequences were obtained from nasal swabs using next-generation sequencing and phylogenetic trees were constructed. Similar identity matrix and determination of the most common ancestor were used to compare clusters of patient's sequences. Amino acids substitutions were analysed. RESULTS: Genotypes B, E, F and G were identified. Two clusters of patients were defined from chronological data and phylogenetic trees. Analyses of amino acids substitutions of the S protein sequences identified substitutions specific for genotype F strains circulating among European people. CONCLUSIONS: HCoV-OC43 may be implicated in healthcare-associated infections.

miRkwood: a tool for the reliable identification of microRNAs in plant genomes.

BACKGROUND: MicroRNAs (miRNAs) play crucial roles in post-transcriptional regulation of eukaryotic gene expression and are involved in many aspects of plant development. Although several prediction tools are available for metazoan genomes, the number of tools dedicated to plants is relatively limited. RESULTS: Here, we present miRkwood, a user-friendly tool for the identification of miRNAs in plant genomes using small RNA sequencing data. Deep-sequencing data of Argonaute associated small RNAs showed that miRkwood is able to identify a large diversity of plant miRNAs and limits false positive predictions. Moreover, it outperforms current tools such as ShortStack and contrary to ShortStack, miRkwood provides a quality score allowing users to rank miRNA predictions. CONCLUSION: miRkwood is a very efficient tool for the annotation of miRNAs in plant genomes. It is available as a web server, as a standalone version, as a docker image and as a Galaxy tool: http://bioinfo.cristal.univ-lille.fr/mirkwood.

A complete protocol for whole-genome sequencing of virus from clinical samples: Application to coronavirus OC43.

Genome sequencing of virus has become a useful tool for better understanding of virus pathogenicity and epidemiological surveillance. Obtaining virus genome sequence directly from clinical samples is still a challenging task due to the low load of virus genetic material compared to the host DNA, and to the difficulty to get an accurate genome assembly. Here we introduce a complete sequencing and analyzing protocol called V-ASAP for Virus Amplicon Sequencing Assembly Pipeline. Our protocol is able to generate the viral dominant genome sequence starting from clinical samples. It is based on a multiplex PCR amplicon sequencing coupled with a reference-free analytical pipeline. This protocol was applied to 11 clinical samples infected with coronavirus OC43 (HcoV-OC43), and led to seven complete and two nearly complete genome assemblies. The protocol introduced here is shown to be robust, to produce a reliable sequence, and could be applied to other virus.

DiNAMO: highly sensitive DNA motif discovery in high-throughput sequencing data.

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 .

MATAM: reconstruction of phylogenetic marker genes from short sequencing reads in metagenomes.

Motivation: Advances in the sequencing of uncultured environmental samples, dubbed metagenomics, raise a growing need for accurate taxonomic assignment. Accurate identification of organisms present within a community is essential to understanding even the most elementary ecosystems. However, current high-throughput sequencing technologies generate short reads which partially cover full-length marker genes and this poses difficult bioinformatic challenges for taxonomy identification at high resolution. Results: We designed MATAM, a software dedicated to the fast and accurate targeted assembly of short reads sequenced from a genomic marker of interest. The method implements a stepwise process based on construction and analysis of a read overlap graph. It is applied to the assembly of 16S rRNA markers and is validated on simulated, synthetic and genuine metagenomes. We show that MATAM outperforms other available methods in terms of low error rates and recovered fractions and is suitable to provide improved assemblies for precise taxonomic assignments. Availability and implementation: https://github.com/bonsai-team/matam. Contact: pierre.pericard@gmail.com or helene.touzet@univ-lille1.fr. Supplementary information: Supplementary data are available at Bioinformatics online.

Efficient approximations of RNA kinetics landscape using non-redundant sampling.

MOTIVATION: Kinetics is key to understand many phenomena involving RNAs, such as co-transcriptional folding and riboswitches. Exact out-of-equilibrium studies induce extreme computational demands, leading state-of-the-art methods to rely on approximated kinetics landscapes, obtained using sampling strategies that strive to generate the key landmarks of the landscape topology. However, such methods are impeded by a large level of redundancy within sampled sets. Such a redundancy is uninformative, and obfuscates important intermediate states, leading to an incomplete vision of RNA dynamics. RESULTS: We introduce RNANR, a new set of algorithms for the exploration of RNA kinetics landscapes at the secondary structure level. RNANR considers locally optimal structures, a reduced set of RNA conformations, in order to focus its sampling on basins in the kinetic landscape. Along with an exhaustive enumeration, RNANR implements a novel non-redundant stochastic sampling, and offers a rich array of structural parameters. Our tests on both real and random RNAs reveal that RNANR allows to generate more unique structures in a given time than its competitors, and allows a deeper exploration of kinetics landscapes. AVAILABILITY AND IMPLEMENTATION: RNANR is freely available at https://project.inria.fr/rnalands/rnanr . CONTACT: yann.ponty@lix.polytechnique.fr.

Assessment of Common and Emerging Bioinformatics Pipelines for Targeted Metagenomics.

Targeted metagenomics, also known as metagenetics, is a high-throughput sequencing application focusing on a nucleotide target in a microbiome to describe its taxonomic content. A wide range of bioinformatics pipelines are available to analyze sequencing outputs, and the choice of an appropriate tool is crucial and not trivial. No standard evaluation method exists for estimating the accuracy of a pipeline for targeted metagenomics analyses. This article proposes an evaluation protocol containing real and simulated targeted metagenomics datasets, and adequate metrics allowing us to study the impact of different variables on the biological interpretation of results. This protocol was used to compare six different bioinformatics pipelines in the basic user context: Three common ones (mothur, QIIME and BMP) based on a clustering-first approach and three emerging ones (Kraken, CLARK and One Codex) using an assignment-first approach. This study surprisingly reveals that the effect of sequencing errors has a bigger impact on the results that choosing different amplified regions. Moreover, increasing sequencing throughput increases richness overestimation, even more so for microbiota of high complexity. Finally, the choice of the reference database has a bigger impact on richness estimation for clustering-first pipelines, and on correct taxa identification for assignment-first pipelines. Using emerging assignment-first pipelines is a valid approach for targeted metagenomics analyses, with a quality of results comparable to popular clustering-first pipelines, even with an error-prone sequencing technology like Ion Torrent. However, those pipelines are highly sensitive to the quality of databases and their annotations, which makes clustering-first pipelines still the only reliable approach for studying microbiomes that are not well described.

Modeling alternate RNA structures in genomic sequences.

We introduce the concept of RNA multistructures, which is a formal grammar-based framework specifically designed to model a set of alternate RNA secondary structures. Such alternate structures can either be a set of suboptimal foldings, or distinct stable folding states, or variants within an RNA family. We provide several such examples and propose an efficient algorithm to search for RNA multistructures within a genomic sequence.

Deciphering metatranscriptomic data.

Metatranscriptomic data contributes another piece of the puzzle to understanding the phylogenetic structure and function of a community of organisms. High-quality total RNA is a bountiful mixture of ribosomal, transfer, messenger and other noncoding RNAs, where each family of RNA is vital to answering questions concerning the hidden microbial world. Software tools designed for deciphering metatranscriptomic data fall under two main categories: the first is to reassemble millions of short nucleotide fragments produced by high-throughput sequencing technologies into the original full-length transcriptomes for all organisms within a sample, and the second is to taxonomically classify the organisms and determine their individual functional roles within a community. Species identification is mainly established using the ribosomal RNA genes, whereas the behavior and functionality of a community is revealed by the messenger RNA of the expressed genes. Numerous chemical and computational methods exist to separate families of RNA prior to conducting further downstream analyses, primarily suitable for isolating mRNA or rRNA from a total RNA sample. In this chapter, we demonstrate a computational technique for filtering rRNA from total RNA using the software SortMeRNA. Additionally, we propose a post-processing pipeline using the latest software tools to conduct further studies on the filtered data, including the reconstruction of mRNA transcripts for functional analyses and phylogenetic classification of a community using the ribosomal RNA.

SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data.

MOTIVATION: The application of next-generation sequencing (NGS) technologies to RNAs directly extracted from a community of organisms yields a mixture of fragments characterizing both coding and non-coding types of RNAs. The task to distinguish among these and to further categorize the families of messenger RNAs and ribosomal RNAs (rRNAs) is an important step for examining gene expression patterns of an interactive environment and the phylogenetic classification of the constituting species. RESULTS: We present SortMeRNA, a new software designed to rapidly filter rRNA fragments from metatranscriptomic data. It is capable of handling large sets of reads and sorting out all fragments matching to the rRNA database with high sensitivity and low running time.

RNA locally optimal secondary structures.

RNA locally optimal secondary structures provide a concise and exhaustive description of all possible secondary structures of a given RNA sequence, and hence a very good representation of the RNA folding space. In this paper, we present an efficient algorithm that computes all locally optimal secondary structures for any folding model that takes into account the stability of helical regions. This algorithm is implemented in a software called regliss that runs on a publicly accessible web server.

BRASERO: A Resource for Benchmarking RNA Secondary Structure Comparison Algorithms.

The pairwise comparison of RNA secondary structures is a fundamental problem, with direct application in mining databases for annotating putative noncoding RNA candidates in newly sequenced genomes. An increasing number of software tools are available for comparing RNA secondary structures, based on different models (such as ordered trees or forests, arc annotated sequences, and multilevel trees) and computational principles (edit distance, alignment). We describe here the website BRASERO that offers tools for evaluating such software tools on real and synthetic datasets.

RNAspace.org: An integrated environment for the prediction, annotation, and analysis of ncRNA.

The annotation of noncoding RNA genes remains a major bottleneck in genome sequencing projects. Most genome sequences released today still come with sets of tRNAs and rRNAs as the only annotated RNA elements, ignoring hundreds of other RNA families. We have developed a web environment that is dedicated to noncoding RNA (ncRNA) prediction, annotation, and analysis and allows users to run a variety of tools in an integrated and flexible manner. This environment offers complementary ncRNA gene finders and a set of tools for the comparison, visualization, editing, and export of ncRNA candidates. Predictions can be filtered according to a large set of characteristics. Based on this environment, we created a public website located at http://RNAspace.org. It accepts genomic sequences up to 5 Mb, which permits for an online annotation of a complete bacterial genome or a small eukaryotic chromosome. The project is hosted as a Source Forge project (http://rnaspace.sourceforge.net/).