Sequence Annotation Research Articles

Novel plasmids in multidrug-resistant Shigella flexneri serotypes from Pakistan

Shigellosis is the main cause of food and waterborne diarrhea and is an emerging threat to human health. The current study characterized the indigenous multidrug-resistant Shigella flexneri serotypes for their plasmid profiles and genetic diversity, to characterize the plasmid evolutionary patterns and distribution. In total, 199 identified S. flexneri isolates belonging to six different serotypes were analyzed for plasmid profiling, followed by an analysis of whole genome sequencing. All isolates of S. flexneri resistant to antibiotics harbored multiple copies of plasmids with sizes ranging from 1.25 kbp to 9.4kbp. These isolates were clustered into 22 distinct plasmid patterns, labeled as p1-p22. Among these, p1 (24%) and p10 (13%) were the predominant plasmid profiles. All S. flexneri strains were grouped into 12 clades with a 75% similarity level. Also, a significant association was observed among the plasmid patterns, p23 and p17 with the drug-resistant patterns AMC, SXT, C (19.5%) and OFX, AMC, NA, CIP (13.5%), respectively. Moreover, the most widespread plasmid patterns p4, p10, and p1 showed a significant association with the serotypes 1b (29.16%), 2b (36%), and 7a (100%), respectively. After plasmid sequence assembly and annotation analysis, a variety of small plasmids that vary in size from 973 to 6200bp were discovered. Many of these plasmids displayed high homology and coverage with plasmids from non-S. flexneri. Several novel plasmids of small size were discovered in multidrug-resistant S. flexneri. The data also showed that plasmid profile analysis is more consistent than antibiotic susceptibility pattern analysis for identifying epidemic strains of S. flexneri isolated in Pakistan.

Precision Oncology Core Data Model to Support Clinical Genomics Decision Making.

Precision oncology mandates developing standardized common data models (CDMs) to facilitate analyses and enable clinical decision making. Expert-opinion-based precision oncology initiatives are epitomized in Molecular Tumor Boards (MTBs), which process large volumes of clinical-genomic data to match genotypes with molecularly guided therapies. We used the Johns Hopkins University MTB as a use case and developed a precision oncology core data model (Precision-DM) to capture key clinical-genomic data elements. We leveraged existing CDMs, building upon the Minimal Common Oncology Data Elements model (mCODE). Our model was defined as a set of profiles with multiple data elements, focusing on next-generation sequencing and variant annotations. Most elements were mapped to terminologies or code sets and the Fast Healthcare Interoperability Resources (FHIR). We subsequently compared our Precision-DM with existing CDMs, including the National Cancer Institute's Genomic Data Commons (NCI GDC), mCODE, OSIRIS, the clinical Genome Data Model (cGDM), and the genomic CDM (gCDM). Precision-DM contained 16 profiles and 355 data elements. 39% of the elements derived values from selected terminologies or code sets, and 61% were mapped to FHIR. Although we used most elements contained in mCODE, we significantly expanded the profiles to include genomic annotations, resulting in a partial overlap of 50.7% between our core model and mCODE. Limited overlap was noted between Precision-DM and OSIRIS (33.2%), NCI GDC (21.4%), cGDM (9.3%), and gCDM (7.9%). Precision-DM covered most of the mCODE elements (87.7%), with less coverage for OSIRIS (35.8%), NCI GDC (11%), cGDM (26%) and gCDM (33.3%). Precision-DM supports clinical-genomic data standardization to support the MTB use case and may allow for harmonized data pulls across health care systems, academic institutions, and community medical centers.

Revisiting and corrections to the annotated SRSF3 (SRp20) gene structure and RefSeq sequences from the human and mouse genomes.

SRSF3 (SRp20) is the smallest member of the serine/arginine (SR)-rich protein family. We found the annotated human SRSF3 and mouse Srsf3 RefSeq sequences are much larger than the detected SRSF3/Srsf3 RNA size by Northern blot. Mapping of RNA-seq reads from various human and mouse cell lines to the annotated SRSF3/Srsf3 gene illustrated only a partial coverage of its terminal exon 7. By 5' RACE and 3' RACE, we determined that SRSF3 gene spanning over 8422 bases and Srsf3 gene spanning over 9423 bases. SRSF3/Srsf3 gene has seven exons with exon 7 bearing two alternative polyadenylation signals (PAS). Through alternative PAS selection and exon 4 exclusion/inclusion by alternative RNA splicing, SRSF3/Srsf3 gene expresses four RNA isoforms. The major SRSF3 mRNA isoform with exon 4 exclusion by using a favorable distal PAS to encode a full-length protein is 1411nt long (not annotated 4228nt) and the same major mouse Srsf3 mRNA isoform is only 1295nt (not annotated 2585nt). The difference from the redefined RNA size of SRSF3/Srsf3 to the corresponding RefSeq sequence is at the 3' UTR region. Collectively, the redefined SRSF3/Srsf3 gene structure and expression will allow better understanding of SRSF3 functions and its regulations in health and diseases.

Poincaré maps for visualization of large protein families.

In the era of constantly increasing amounts of the available protein data, a relevant and interpretable visualization becomes crucial, especially for tasks requiring human expertise. Poincaré disk projection has previously demonstrated its important efficiency for visualization of biological data such as single-cell RNAseq data. Here, we develop a new method PoincaréMSA for visual representation of complex relationships between protein sequences based on Poincaré maps embedding. We demonstrate its efficiency and potential for visualization of protein family topology as well as evolutionary and functional annotation of uncharacterized sequences. PoincaréMSA is implemented in open source Python code with available interactive Google Colab notebooks as described at https://www.dsimb.inserm.fr/POINCARE_MSA.

Isolation of bacteriophages infecting Xanthomonas oryzae pv. oryzae and genomic characterization of novel phage vB_XooS_NR08 for biocontrol of bacterial leaf blight of rice.

Bacterial leaf blight (BLB) disease of rice caused by Xanthomonas oryzae pv. oryzae (Xoo) is one of the most destructive diseases worldwide in rice-growing regions. The Ineffectiveness of chemicals in disease management has increased the interest in phage therapy. In this study, we isolated 19 bacteriophages, infecting Xoo, from a rice field, which belonged to phage families Siphoviridae, Myoviridae, and Podoviridae on the basis of electron microscopy. Among 19 phages, Phage vB_XooS_NR08, a member of the Siphoviridae family, expressed antibacterial activity against all Xoo strains tested and did not lyse X. campestris and other unrelated bacterial hosts. Phage NR08 showed more than 80% viability at a temperature range of 4°C-40°C, pH range of 5-9, and direct exposure to sunlight for 2 h, whereas UV light and chemical agents were highly detrimental. In a one-step growth curve, NR08 has a 40-min latent period, followed by a 30-min burst period with a burst size of 250 particle/bacterium. The genome of NR08 is double-stranded DNA, linear having a size of 98,812 bp with a G + C content of 52.9%. Annotation of the whole-genome sequence indicated that NR08 encodes 142 putative open reading frames (ORFs), including one ORF for tRNA, namely, trna1-GlnTTG. Comparative genome analysis of NR08 showed that it shares maximum similarity with Pseudomonas phage PaMx42 (40% query coverage, 95.39% identity, and acc. Length 43,225) and Xanthomonas phage Samson (40% query coverage, 96.68% identity, and acc. Length 43,314). The average alignment percentage (AP) of NR08 with other Xoophages was only 0.32 to 1.25 since the genome of NR08 (98.8 kb) is almost double of most of the previously reported Xoophages (43-47 kb), thus indicating NR08 a novel Xoophage. In in vitro bacterial challenge assay, NR08 showed bacteriostasis up to 24 h and a 99.95% reduction in bacterial growth in 48 h. In rice pot efficacy trials, single-dose treatment of NR08 showed a significant reduction in disease up to 90.23% and 79.27% on 7 and 21 dpi, respectively. However, treatment using 2% skim milk-supplemented phage preparation was significantly less effective as compared to the neat phage preparation. In summary, this study characterized a novel Xoophage having the potential as a biocontrol agent in the mitigation of BLB in rice.

Multi-Feature Fusion Method for Chinese Pesticide Named Entity Recognition

Chinese pesticide named-entity recognition (NER) aims to identify named entities related to pesticide properties from unstructured Chinese pesticide information texts. In view of the characteristics of massive, fragmented, professional, and complex semantic relationships of pesticide information data, a deep learning method based on multi-feature fusion was applied to improve the accuracy of pesticide NER. In this study, the pesticide data set is manually annotated by the begin inside outside (BIO) sequence annotation scheme. Bi-directional long short-term memory (BiLSTM) and iterated dilated convolutional neural networks (IDCNN) combined with conditional random field (CRF) form the model BiLSTM-IDCNN-CRF, and it is applied to implement named-entity recognition in Chinese pesticide data sets. IDCNN is introduced to enhance the semantic representation ability and local feature capture ability of the text. BiLSTM network and IDCNN network are combined to obtain the long-distance dependence relationship and context features of different granularity of pesticide data text. Finally, CRF is used to implement the sequence labeling task. According to the experiment results, the accuracy rate, recall rate, and F1 score of the BiLSTM-IDCNN-CRF model in the Chinese pesticide data set were 78.59%, 68.71%, and 73.32%, respectively, which are significantly better than other compared models. Experiments show that the BiLSTM-IDCNN-CRF model can effectively identify and extract entities from Chinese pesticide information text data, which is helpful in constructing the pesticide information knowledge graph and intelligent question-answering.

QSP: An open sequence database for quorum sensing related gene analysis with an automatic annotation pipeline

Quorum sensing (QS) has attracted great attention due to its important role in the bacterial interactions and its relevance to water management. With the development of high-throughput sequencing technology, a specific database for QS-related sequence annotation is urgently needed. Here, Hidden Markov Model (HMM) profiles for 38 types of QS-related proteins were built using a total of 4024 collected seed sequences. Based on both homolog search and keywords confirmation against the non-redundant database, we established a QS-related protein (QSP) database, that includes 809,721 protein sequences and 186,133 nucleotide sequences, downloaded available at: https://github.com/chunxiao-dcx/QSP. The entries were classified into 38 types and 315 subtypes among 91 bacterial phyla. Furthermore, an automatic annotation pipeline, named QSAP, was developed for rapid annotation, classification and abundance quantification of QSP-like sequences from sequencing data. This pipeline provided the two homolog alignment strategies offered by Diamond (Blastp) or HMMER (Hmmscan), as well as a data cleansing function for a subset or union set of the hits. The pipeline was tested using 14 metagenomic samples from various water environments, including activated sludge, deep-sea sediments, estuary water, and reservoir water. The QSAP pipeline is freely available for academic use in the code repository at: https://github.com/chunxiao-dcx/QSAP. The establishment of this database and pipeline, provides a useful tool for QS-related sequence annotation in a wide range of projects, and will increase our understanding of QS communication in aquatic environments.

Machine learning for the identification of respiratory viral attachment machinery from sequences data.

At the outset of an emergent viral respiratory pandemic, sequence data is among the first molecular information available. As viral attachment machinery is a key target for therapeutic and prophylactic interventions, rapid identification of viral "spike" proteins from sequence can significantly accelerate the development of medical countermeasures. For six families of respiratory viruses, covering the vast majority of airborne and droplet-transmitted diseases, host cell entry is mediated by the binding of viral surface glycoproteins that interact with a host cell receptor. In this report it is shown that sequence data for an unknown virus belonging to one of the six families above provides sufficient information to identify the protein(s) responsible for viral attachment. Random forest models that take as input a set of respiratory viral sequences can classify the protein as "spike" vs. non-spike based on predicted secondary structure elements alone (with 97.3% correctly classified) or in combination with N-glycosylation related features (with 97.0% correctly classified). Models were validated through 10-fold cross-validation, bootstrapping on a class-balanced set, and an out-of-sample extra-familial validation set. Surprisingly, we showed that secondary structural elements and N-glycosylation features were sufficient for model generation. The ability to rapidly identify viral attachment machinery directly from sequence data holds the potential to accelerate the design of medical countermeasures for future pandemics. Furthermore, this approach may be extendable for the identification of other potential viral targets and for viral sequence annotation in general in the future.

Chloroplast genomic characterization and phylogenetic analysis of Castanopsis hystrix

The structure and size of the chloroplast genome of Castanopsis hystrix was determined by Illumina HiSeq 2500 sequencing platform to understand the difference between C. hystrix and the chloroplast genome of the same genus, and the evolutionary position of C. hystrix in the genus, so as to facilitate species identification, genetic diversity analysis and resource conservation of the genus. Bioinformatics analysis was used to perform sequence assembly, annotation and characteristic analysis. R, Python, MISA, CodonW and MEGA 6 bioinformatics software were used to analyze the genome structure and number, codon bias, sequence repeats, simple sequence repeat (SSR) loci and phylogeny. The genome size of C. hystrix chloroplast was 153 754 bp, showing tetrad structure. A total of 130 genes were identified, including 85 coding genes, 37 tRNA genes and 8 rRNA genes. According to codon bias analysis, the average number of effective codons was 55.5, indicating that the codons were highly random and low in bias. Forty-five repeats and 111 SSR loci were detected by SSR and long repeat fragment analysis. Compared with the related species, chloroplast genome sequences were highly conserved, especially the protein coding sequences. Phylogenetic analysis showed that C. hystrix is closely related to the Hainanese cone. In summary, we obtained the basic information and phylogenetic position of the chloroplast genome of red cone, which will provide a preliminary basis for species identification, genetic diversity of natural populations and functional genomics research of C. hystrix.

Plasmid pMO1 from Marinitoga okinawensis, first non-cryptic plasmid reported within Thermotogota

Mobile genetic elements (MGEs), such as viruses and plasmids, drive the evolution and adaptation of their cellular hosts from all three domains of life. This includes microorganisms thriving in the most extreme environments, like deep-sea hydrothermal vents. However, our knowledge about MGEs still remains relatively sparse in these abyssal ecosystems. Here we report the isolation, sequencing, assembly, and functional annotation of pMO1, a 28.2 kbp plasmid associated with the reference strain Marinitoga okinawensis. Carrying restriction/modification and chemotaxis protein-encoding genes, pMO1 likely affects its host's phenotype and represents the first non-cryptic plasmid described among the phylum Thermotogota.

Large-scale analysis of the genome of the rare alkaline-halophilic Stachybotrys microspora reveals 46 cellulase genes.

Fungi are of great importance in biotechnology, for example in the production of enzymes and metabolites. The main goal of this study was to obtain a high-coverage draft of the Stachybotrys microspora genome and to annotate and analyze the genome sequence data. The rare fungus S.microspora N1 strain is distinguished by its ability to grow in an alkaline halophilic environment and to efficiently secrete cellulolytic enzymes. Here we report the draft genome sequence composed of 3715 contigs, a genome size of 35 343 854 bp, with a GC content of 53.31% and a coverage around 20.5×. The identification of cellulolytic genes and of their corresponding functions was carried out through analysis and annotation of the whole genome sequence. Forty-six cellulases were identified using the fungicompanion bioinformatic tool. Interestingly, an S.microspora endoglucanase selected from those with a low isoelectric point was predicted to have a halophilic profile and share significant homology with a well-known bacterial halophilic cellulase. These results confirm previous biochemical studies revealing a halophilic character, which is a very rare feature among fungal cellulases. All these properties suggest that cellulases of S.microspora may have potential for use in the biofuel, textile, and detergent industries.

A new approach of integrating industry prior knowledge for HAZOP interaction

Accidents often occur in the petrochemical industry, which have a negative impact on society and the environment. Learning Process Safety Knowledge (PSK) from accident cases is essential to prevent accidents and improve safety level. Hazard and Operability Analysis (HAZOP) is a popular hazard risk analysis method. Its report contains large-scale PSK, which can provide safety analysis and decision support for the industry. Subject to the characteristics of PSK, existing researches mine them in the form of sequence labeling. However, there are two intractable problems that cause the PSK mined by the model to be inaccurate. (1) PSK in HAZOP is domain specific, which is rare or even absent in general-domain texts. (2) The entity boundaries are ambiguous. Most domain-specific entities for HAZOP lack boundary characters. Inaccurate security knowledge is not acceptable from the perspective of process safety engineering. To solve the problems, we present a PSK mining architecture with External Lexicon Prior knowledge called EDPMA, EDPMA is prior knowledge-based multi-task HAZOP knowledge mining model. Specifically, EDPMA consists of prior knowledge constructor and sequence labeling model. The prior knowledge constructor expresses prior knowledge in the form of word embedding by three steps. For the sequence annotation model, we improve its embedding and decoding layers. The former incorporated the word vectors generated by the prior knowledge constructor, and the latter added the task of entity boundary prediction. We conduct multiple evaluation experiments on HAZOP datasets. The experimental results show that the accuracy, recall and F1-score of the EDPMA model are 92.92%, 91.85% and 92.38% respectively, which is better than the existing research. Our study represents a meaningful attempt to introduce prior knowledge in HAZOP knowledge mining and makes an important contribution to intelligence the field of process safety.

BnaSD.C3 is a novel major quantitative trait locus affecting semi-dwarf architecture in Brassica napus

Plant height is a key plant architectural trait that affects the seed yield, harvest index and lodging resistance in Brassica napus L., although the genetic mechanisms affecting plant height remain unclear. Here, a semi-dwarf mutant, df34, was obtained by ethyl methanesulphonate-induced mutagenesis. Genetic analysis showed that the semi-dwarf phenotype is controlled by one semi-dominant gene, which was located on chromosome C03 using a bulked segregant analysis coupled with whole-genome sequencing, and this gene was named BnaSD.C3. Then BnaSD.C3 was fine-mapped to a 297.35-kb segment of the “Darmor-bzh” genome, but there was no potential candidate gene for the semi-dwarf trait underlying this interval. Furthermore, the interval was aligned to the Zhongshuang 11 reference genome. Finally, combining structural variation analysis, transcriptome sequencing, phytohormone analyses and gene annotation information, BnaC03G0466900ZS and BnaC03G0478900ZS were determined to be the most likely candidate genes affecting the plant height of df34. This study provides a novel major locus for breeding and new insights into the genetic architecture of plant height in B. napus.

Comparative Genomic Analysis of a Thermophilic Protease-Producing Strain Geobacillus stearothermophilus H6.

The genus Geobacillus comprises thermophilic gram-positive bacteria which are widely distributed, and their ability to withstand high temperatures makes them suitable for various applications in biotechnology and industrial production. Geobacillus stearothermophilus H6 is an extremely thermophilic Geobacillus strain isolated from hyperthermophilic compost at 80 °C. Through whole-genome sequencing and genome annotation analysis of the strain, the gene functions of G. stearothermophilus H6 were predicted and the thermophilic enzyme in the strain was mined. The G. stearothermophilus H6 draft genome consisted of 3,054,993 bp, with a genome GC content of 51.66%, and it was predicted to contain 3750 coding genes. The analysis showed that strain H6 contained a variety of enzyme-coding genes, including protease, glycoside hydrolase, xylanase, amylase and lipase genes. A skimmed milk plate experiment showed that G. stearothermophilus H6 could produce extracellular protease that functioned at 60 °C, and the genome predictions included 18 secreted proteases with signal peptides. By analyzing the sequence of the strain genome, a protease gene gs-sp1 was successfully screened. The gene sequence was analyzed and heterologously expressed, and the protease was successfully expressed in Escherichia coli. These results could provide a theoretical basis for the development and application of industrial strains.

Study on the Cellular Anti-Inflammatory Effect of Torularhodin Produced by Sporidiobolus pararoseus ZQHL Isolated from Vinegar Fungus.

The red stretcher bacterium Sporidiobolus pararoseus is a high producer of carotenoids such as torularhodin, but its presence in vinegar has not been detected. Moreover, torularhodin has several biological activities, but its effect on the LPS-induced RAW 264.7 inflammatory cell model has also yet to be elucidated. In this study, S. pararoseus was identified in different vinegar samples from China by ITS sequencing. Meanwhile, one of the strains was deeply resolved by whole genome sequencing and functional annotation and named S. pararoseus ZQHL. Subsequently, the antioxidant effect of the fungal carotenoid torularhodin was investigated using in vitro DPPH, ABTS, and cellular models. Finally, LPS-induced RAW 264.7 cells were used as an inflammation model to assess torularhodin's protective effect on inflammatory cells and to determine whether the TLR4 pathway is associated with this process. The results indicate that torularhodin has good free radical scavenging ability in vitro and can contribute to cell viability. More importantly, torularhodin alleviated LPS-induced cellular inflammatory damage and reduced the expression of inflammatory factors such as TLR4, MyD88, and TNF-a. The mechanism may attenuate the cellular inflammatory response by inhibiting the TLR4 inflammatory pathway. In conclusion, torularhodin produced by S. pararoseus fungi in vinegar samples significantly scavenged free radicals in vitro and alleviated RAW 264.7 cellular inflammation by modulating the TLR4 pathway.

Co-learning of appearance and shape for precise ejection fraction estimation from echocardiographic sequences.

Accurate estimation of ejection fraction (EF) from echocardiography is of great importance for evaluation of cardiac function. It is usually obtained by the Simpson's bi-plane method based on the segmentation of the left ventricle (LV) in two keyframes. However, obtaining accurate EF estimation from echocardiography is challenging due to (1) noisy appearance in ultrasound images, (2) temporal dynamic movement of myocardium, (3) sparse annotation of the full sequence, and (4) potential quality degradation during scanning. In this paper, we propose a multi-task semi-supervised framework, which is denoted as MCLAS, for precise EF estimation from echocardiographic sequences of two cardiac views. Specifically, we first propose a co-learning mechanism to explore the mutual benefits of cardiac segmentation and myocardium tracking iteratively on appearance level and shape level, therefore alleviating the noisy appearance and enforcing the temporal consistency of the segmentation results. This temporal consistency, as shown in our work, is critical for precise EF estimation. Then we propose two auxiliary tasks for the encoder, (1) view classification to help extract the discriminative features of each view, and automatize the whole pipeline of EF estimation in clinical practice, and (2) EF regression to help regularize the spatiotemporal embedding of the echocardiographic sequence. Both two auxiliary tasks can improve the segmentation-based EF prediction, especially for sequences of poor quality. Our method is capable of automating the whole pipeline of EF estimation, from view identification, cardiac structures segmentation to EF calculation. The effectiveness of our method is validated in aspects of segmentation, tracking, consistency analysis, and clinical parameters estimation. When compared with existing methods, our method shows obvious superiority for LV volumes on ED and ES phases, and EF estimation, with Pearson correlation of 0.975, 0.983 and 0.946, respectively. This is a significant improvement for echocardiography-based EF estimation and improves the potential of automated EF estimation in clinical practice. Besides, our method can obtain accurate and temporal-consistent segmentation for the in-between frames, which enables it for cardiac dynamic function evaluation.

PlantTribes2: Tools for comparative gene family analysis in plant genomics.

Plant genome-scale resources are being generated at an increasing rate as sequencing technologies continue to improve and raw data costs continue to fall; however, the cost of downstream analyses remains large. This has resulted in a considerable range of genome assembly and annotation qualities across plant genomes due to their varying sizes, complexity, and the technology used for the assembly and annotation. To effectively work across genomes, researchers increasingly rely on comparative genomic approaches that integrate across plant community resources and data types. Such efforts have aided the genome annotation process and yielded novel insights into the evolutionary history of genomes and gene families, including complex non-model organisms. The essential tools to achieve these insights rely on gene family analysis at a genome-scale, but they are not well integrated for rapid analysis of new data, and the learning curve can be steep. Here we present PlantTribes2, a scalable, easily accessible, highly customizable, and broadly applicable gene family analysis framework with multiple entry points including user provided data. It uses objective classifications of annotated protein sequences from existing, high-quality plant genomes for comparative and evolutionary studies. PlantTribes2 can improve transcript models and then sort them, either genome-scale annotations or individual gene coding sequences, into pre-computed orthologous gene family clusters with rich functional annotation information. Then, for gene families of interest, PlantTribes2 performs downstream analyses and customizable visualizations including, (1) multiple sequence alignment, (2) gene family phylogeny, (3) estimation of synonymous and non-synonymous substitution rates among homologous sequences, and (4) inference of large-scale duplication events. We give examples of PlantTribes2 applications in functional genomic studies of economically important plant families, namely transcriptomics in the weedy Orobanchaceae and a core orthogroup analysis (CROG) in Rosaceae. PlantTribes2 is freely available for use within the main public Galaxy instance and can be downloaded from GitHub or Bioconda. Importantly, PlantTribes2 can be readily adapted for use with genomic and transcriptomic data from any kind of organism.

The Metagenomic Analysis of Viral Diversity in Colorado Potato Beetle Public NGS Data.

The Colorado potato beetle (CPB) is one of the most serious insect pests due to its high ecological plasticity and ability to rapidly develop resistance to insecticides. The use of biological insecticides based on viruses is a promising approach to control insect pests, but the information on viruses which infect leaf feeding beetles is scarce. We performed a metagenomic analysis of 297 CPB genomic and transcriptomic samples from the public National Center for Biotechnology Information Sequence Read Archive (NCBI SRA) database. The reads that were not aligned to the reference genome were assembled with metaSPAdes, and 13314 selected contigs were analyzed with BLAST tools. The contigs and non-aligned reads were also analyzed with Kraken2 software. A total of 3137 virus-positive contigs were attributed to different viruses belonging to 6 types, 17 orders, and 32 families, matching over 97 viral species. The annotated sequences can be divided into several groups: those that are homologous to genetic sequences of insect viruses (Adintoviridae, Ascoviridae, Baculoviridae, Dicistroviridae, Chuviridae, Hytrosaviridae, Iflaviridae, Iridoviridae, Nimaviridae, Nudiviridae, Phasmaviridae, Picornaviridae, Polydnaviriformidae, Xinmoviridae etc.), plant viruses (Betaflexiviridae, Bromoviridae, Kitaviridae, Potyviridae), and endogenous retroviral elements (Retroviridae, Metaviridae). Additionally, the full-length genomes and near-full length genome sequences of several viruses were assembled. We also found sequences belonging to Bracoviriform viruses and, for the first time, experimentally validated the presence of bracoviral genetic fragments in the CPB genome. Our work represents the first attempt to discover the viral genetic material in CPB samples, and we hope that further studies will help to identify new viruses to extend the arsenal of biopesticides against CPB.

PrIntMap-R: An Online Application for Intraprotein Intensity and Peptide Visualization from Bottom-Up Proteomics.

Bottom-up proteomics (BUP) produces rich data, but visualization and analysis are time-consuming and often require programming skills. Many tools analyze these data at the proteome-level, but fewer options exist for individual proteins. Sequence coverage maps are common, but do not proportion peptide intensity. Abundance-based visualization of sequence coverage facilitates detection of protein isoforms, domains, potential truncation sites, peptide "hot-spots", and localization of post-translational modifications (PTMs). Redundant stacked-sequence coverage is an important tool in designing hydrogen-deuterium exchange (HDX) experiments. Visualization tools often lack graphical and tabular-export of processed data which complicates publication of results. Quantitative peptide abundance across amino acid sequences is an essential and missing tool in proteomics toolkits. Here we created PrIntMap-R, an online application that only requires peptide files from a database search and FASTA protein sequences. PrIntMap-R produces a variety of plots for quantitative visualization of coverage; annotation of specific sequences, PTM's, and comparisons of one or many samples overlaid with calculated fold-change or several intensity metrics. We show use-cases including protein phosphorylation, identification of glycosylation, and the optimization of digestion conditions for HDX experiments. PrIntMap-R is freely available, open source, and can run online with no installation, or locally by downloading source code from GitHub.

The Clostridium-infecting filamentous phage CAK1 genome analysis allows to define a new potential clade of Tubulavirales.

What we know about Tubulavirales, i.e. filamentous phages, essentially comes from Gram-negative-infecting Inoviridae. However, metagenomics recently suggests filamentous phages are much more widespread and diverse. Here, we report the complete sequence and functional annotation of CAK1, a 6.6kb filamentous phage that was shown to chronically infect Clostridium beijerinckii 30 years ago and only represents the second filamentous phage cultivated on a Gram-positive bacterium. CAK1 has a typical filamentous phage modular genome with no homologs in databases and we were interested to compare it with a pig gut filamentous phage metagenomics dataset that we previously assembled and for which many filamentous phages were predicted to infect Clostridium species by bioinformatics means. CAK1 is distantly related to nine of these sequences, two of which having been predicted as Clostridium-associated. In itself, this small cluster of CAK1-connected sequences sheds light on the diversity of filamentous phages that putatively infect Clostridium species, and probably many other Gram-positive genera.