Sequence Databases Research Articles

Discovery of a cryptic founder variant in MYBPC3 associated with hypertrophic cardiomyopathy by promoting the most frequent natural missplicing events

Abstract Introduction MYBPC3 splicing errors are a common cause of hypertrophic cardiomyopathy (HCM). Most known actionable intronic variants are located near exons. However, genetic variants in deep intronic regions are also emerging as casuals factors of HCM. The previously undescribed variant MYBPC3 (NM_000256.3):c.2308+227G>A is not reported in gnomAD or dbSNP but is overrepresented in a cohort of probands with HCM from the same geographical area. This led us to suspect its clinical relevance despite the very low probability of being splice-altering as assigned by SpliceAI (Δ score = 0.03). Purpose To conduct a phenotypic and clinical description of the cohort, ascertain the pathogenicity of the variant, and investigate the existence of a common ancestor. Methods (1) Observational analysis of clinical data and familial evaluation results, (2) Functional study involving mRNA expression analysis in peripheral blood, along with in silico analysis of the splicing disruption mechanism, and (3) Haplotype analysis to evaluate the founder effect and assess the age of the mutation. Results MYBPC3:c.2308+227G>A was identified in 26 probands with HCM (69% male; mean age at diagnosis 53±10 years). Eighty relatives from 20 families were screened resulting in 17 cases diagnosed (mean age 49±19 years; 53% males), 19 positive genotype/negative phenotype and 38 non-carriers. The greatest cosegregation involved three cases carrying the variant, confirmed in four different families. Males were diagnosed at a younger age but without significant differences in clinical features or events during follow-up (table). RT-PCR expression analysis in patients' blood revealed that this variant disrupts pre-mRNA splicing by promoting the use of cryptic donor and acceptor splice sites, located downstream and upstream of the variant, respectively, which are already present in the intron 23 reference sequence. Strikingly, these cryptic splice sites represent one of the most frequent natural unannotated missplicing events in MYBPC3, as evidenced by SpliceVault analysis of high-throughput RNA sequencing databases. The direct primary event induced by the variant is expected to disrupt a regulatory RNA-protein binding site, thereby unblocking the use of preexisting cryptic splice sites and amplifying natural exonification missplicing events within intron 23 (figure). Finally, the haplotype analysis, based on the copy number of nine STRs covering a total of 29.2 Mb surrounding MYBPC3:c.2308+227G>A, revealed that 17 unrelated carriers shared a DNA segment of 10 Mb (10,067,150 bp). The origin of this mutation is estimated to be at least 82 generations ago and roughly 1649 years in the past (IC95%:1180-2300). Conclusions MYBPC3:c.2308+227G>A is an elusive novel deep intronic variant causing HCM by a molecular mechanism not previously described in this context. The founder effect of this variant is confirmed in our geographical area.Phenotypic and clinical characteristics

Strategy to develop and validate digital droplet PCR methods for global antimicrobial resistance wastewater surveillance.

According to World Health Organization (WHO), antimicrobial resistance (AMR) is currently one of the world's top 10 health threats, causing infections to become difficult or impossible to treat, increasing the risk of disease spread, severe illness, disability, and death. Accurate surveillance is a key component in the fight against AMR. Wastewater is progressively becoming a new player in AMR surveillance, with the promise of a cost-effective real-time tracking of global AMR profiles in specific regions. One of the most useful analytical methods for wastewater surveillance is currently based on real-time PCR (qPCR) and digital droplet PCR (ddPCR) technologies. As stated in the EU Wastewater Treatment Directive proposal, methodological standardization, including a workflow for method development and validation, will play a crucial role in global monitoring of AMR in wastewater. However, according to our knowledge, there are currently no qPCR and ddPCR methods for AMR surveillance available that have been validated according to international standard performance criteria. Therefore, this study proposes a workflow for the development and validation of PCR-based methods for a harmonized and global AMR surveillance, including the construction of specific sequence databases and microbial collections for an efficient method development and method specificity evaluation. Following this strategy, we have developed and validated four duplex ddPCR methods responding to international standard performance criteria, focusing on seven AMR genes (ARG's), including extended spectrum beta-lactam (blaCTX-M), carbapenem (blaKPC-2/3), tetracycline (tet(M)), erythromycin (erm(B)), vancomycin (vanA), sulfonamide (sul2), and aminoglycoside (aac(3)-IV), as well as one indicator of antibiotic (multi-) resistance and horizontal gene transfer, named the class I integron (intl1). The performance of these ddPCR methods was successfully assessed for their specificity, as no false-positive and false-negative results were observed. These ddPCR methods were also considered to be highly sensitive as showing a limit of detection below 25 copies of the targets. In addition, their applicability was confirmed using 14 wastewater samples collected from two Belgian water resource recovery facilities. The proposed study represents therefore a step forward to reinforce method harmonization in the context of the global AMR surveillance in wastewater. PRACTITIONER POINTS: In the context of wastewater surveillance, no PCR-based methods for global AMR monitoring are currently validated according to international standards. Consequently, we propose a workflow to develop and validate PCR-based methods for a harmonized and global AMR surveillance. This workflow resulted here in four duplex ddPCR methods targeting seven ARGs and one general indicator for mobilizable resistance genes. The applicability of these validated ddPCR methods was confirmed on 14 wastewater samples from two Belgian water resource recovery facilities.

A highly sensitive and specific real-time quantitative polymerase chain reaction assay for Perkinsus marinus detection in oysters

Oyster aquaculture is one of the fastest-growing food production industries worldwide; however, it faces a significant challenge from the protist Perkinsus marinus, particularly in the USA. Although several quantitative molecular diagnostic methodologies are available for identifying diseases caused by P. marinus, the primer pairs used therein led to non-specific identification of other Perkinsus spp. Hence, a quantitative real-time PCR (Pm-qPCR) assay specific for P. marinus was developed using a TaqMan-based probe with the internal quencher in this study. A primer pair and probe specific to P. marinus were designed from a hypothetical protein of P. marinus collected from the whole-genome shotgun sequence database of the National Center for Biotechnology Information (NCBI). In silico analysis using homologous sequences of P. olseni and P. chesapeaki confirmed the high specificity of primers designed in this study. The Pm-qPCR assay was performed using seven different strains of P. marinus, P. olseni, and P. chesapeaki, revealing high specificity and sensitivity for detecting only P. marinus strains. In conclusion, it was demonstrated that Pm-qPCR can effectively and accurately diagnose P. marinus with high specificity and sensitivity. This assay is promising for monitoring oyster health and disease management in ecosystems and aquaculture.

Ruminant microbiome data are skewed and unFAIR, undermining their usefulness for sustainable production improvement

The ruminant microbiome plays a key role in the health, feed utilization and environmental impact of ruminant production systems. Microbiome research provides insights to reduce the environmental footprint and improve meat and milk production from ruminants. However, the microbiome composition depends on the ruminant species, habitat and diet, highlighting the importance of having a good representation of ruminant microbiomes in their local environment to translate research findings into beneficial approaches. This information is currently lacking. In this study, we examined the metadata of farmed ruminant microbiome studies to determine global representativeness and summarized information by ruminant species, geographic location, body site, and host information. We accessed data from the International Nucleotide Sequence Database Collaboration via the National Center for Biotechnology Information database. We retrieved 47,628 sample metadata, with cattle accounting for more than two-thirds of the samples. In contrast, goats, which have a similar global population to cattle, were underrepresented with less than 4% of the total samples. Most samples originated in Western Europe, North America, Australasia and China but countries with large ruminant populations in South America, Africa, Asia, and Eastern Europe were underrepresented. Microbiomes from the gastrointestinal tract were the most frequently studied, comprising about 87% of all samples. Additionally, the number of samples from other body sites such as the respiratory tract, milk, skin, reproductive tract, and fetal tissue, has markedly increased over the past decade. More than 40% of the samples lacked basic information and many were retrieved from generic taxonomic classifications where the ruminant species was manually recovered. The lack of basic information such as age, breed or sex can limit the reusability of the data for further analysis and follow-up studies. This requires correct taxonomic assignment of the ruminant host and basic metadata information using accepted ontologies adapted to host-associated microbiomes. Repositories should require this information as a condition of acceptance. The results of this survey highlight the need to encourage studies of the ruminant microbiome from underrepresented ruminant species and countries worldwide. This shortfall in information poses a challenge for the development of microbiome-based strategies to meet sustainability requirements, particularly in areas with expanding livestock production systems.

Construction of Protein Sequence Databases for Metaproteomics: A Review of the Current Tools and Databases.

In metaproteomics studies, constructing a reference protein sequence database that is both comprehensive and not overly large is critical for the peptide identification step. Therefore, the availability of well-curated reference databases and tools for custom database construction is essential to enhance the performance of metaproteomics analyses. In this review, we first provide an overview of metaproteomics by presenting a concise historical background, outlining a typical experimental and bioinformatics workflow, emphasizing the crucial step of constructing a protein sequence database for metaproteomics. We then delve into the current tools available for building such databases, highlighting their individual approaches, utility, and advantages and limitations. Next, we examine existing protein sequence databases, detailing their scope and relevance in metaproteomics research. Then, we provide practical recommendations for constructing protein sequence databases for metaproteomics, along with an overview of the current challenges in this area. We conclude with a discussion of anticipated advancements, emerging trends, and future directions in the construction of protein sequence databases for metaproteomics.

A systematic approach for identifying unique genomic sequences for Fusarium oxysporum f. sp. lactucae race 1 and development of molecular diagnostic tools

Fusarium oxysporum f. sp. lactucae (FOLac) is a soil- and seedborne fungal pathogen that causes Fusarium wilt of lettuce, an important disease threatening global lettuce production. Based on pathogenicity on differential lettuce cultivars, four races (1-4) have been identified, with race 1 the only race detected in the United States, and the closely related, emerging race 4 known only in Europe. The development of race-specific diagnostic tools is hindered by insufficient genomic data to distinguish between the two races and FOLac from other F. oxysporum formae speciales and nonpathogenic isolates. Here, we describe a systematic approach for developing diagnostic markers for FOLac race 1 that utilized a comprehensive sequence database of F. oxysporum to identify 15 unique genomic sequences. Marker specificity was validated through an exhaustive screening process against genomic data from 797 F. oxysporum isolates representing 64 formae speciales and various plants and non-plant substrates. One of the unique sequences was used to develop a TaqMan quantitative polymerase chain reaction assay and a recombinase polymerase amplification assay, both exhibiting 100% sensitivity and specificity when tested against purified DNA from 171 F. oxysporum isolates and 69 lettuce samples. The relationship between qPCR Ct values and colony forming units (CFU)/g values was also determined. This study not only introduces a new marker for FOLac race 1 diagnostics and soil quantitation, but also underscores the value of an extensive genomic database and screening software pipeline for developing molecular diagnostics for F. oxysporum formae speciales and other fungal taxa.

Shedding light on some dark branches: The under‐appreciated diversity of gymnosome pteropods and their coiled thecosome prey in the Neotropics

AbstractHoloplanktonic organisms are thought to have enormous populations sizes, expansive geographic ranges, and low species diversity. Previous work suggests that pelagic gastropods are no exception to this generalization. However, most of these data are derived from temperate species, whereas the bulk of pteropod diversity occurs in the tropics. Here, we present DNA barcode data for COI focused on the under‐studied limacinoid, gymnosome, and pseudothecosome pteropods collected from the coastal waters of Panama. We applied four molecular species delimitation approaches to determine the number of operational taxonomic units (OTUs) identifiable from our sequences and all data available from Barcode of Life Database (BOLD) and GenBank and compared these to the Barcode Index Number generated by the BOLD. Assemble Species by Automatic Partitioning (ASAP), Poisson Tree Processes (PTP), Bayesian Poisson Tree Processes (bPTP), and Generalized Mixed Yule Coalescent (GMYC) methods generally gave congruent results and suggest that pteropod diversity in the tropics is significantly underrepresented both in DNA sequence databases and in the number of named OTUs. Of 42 specimens collected and sequenced, we recovered 16 OTUs, only one of which belonged to an OTU already represented in sequence databases. Neighbor‐joining trees including the previously published sequences show that in all three groups morphospecies can include genetically divergent lineages. Many very divergent (>15% from nearest neighbor) taxa are also still represented by only a single sequence, suggesting that there is a large amount of cryptic or pseudo‐cryptic diversity still to be described. To aid this future endeavor, we include some preliminary 16S data derived from new pteropod‐specific primers.

The Ribosomal Operon Database: A Full-Length rDNA Operon Database Derived From Genome Assemblies.

Current rDNA reference sequence databases are tailored towards shorter DNA markers, such as parts of the 16/18S marker or the internally transcribed spacer (ITS) region. However, due to advances in long-read DNA sequencing technologies, longer stretches of the rDNA operon are increasingly used in environmental sequencing studies to increase the phylogenetic resolution. There is, therefore, a growing need for longer rDNA reference sequences. Here, we present the ribosomal operon database (ROD), which includes eukaryotic full-length rDNA operons fished from publicly available genome assemblies. Full-length operons were detected in 34.1% of the 34,701 examined eukaryotic genome assemblies from NCBI. In most cases (53.1%), more than one operon variant was detected, which can be due to intragenomic operon copy variability, allelic variation in non-haploid genomes, or technical errors from the sequencing and assembly process. The highest copy number found was 5947 in Zea mays. In total, 453,697 unique operons were detected, with 69,480 operon variant clusters remaining after intragenomic clustering at 99% sequence identity. The operon length varied extensively across eukaryotes, ranging from 4136 to 16,463 bp, which will lead to considerable polymerase chain reaction (PCR) bias during amplification of the entire operon. Clustering the full-length operons revealed that the different parts (i.e., 18S, 28S, and the hypervariable regions V4 and V9 of 18S) provide divergent taxonomic resolution, with 18S, the V4 and V9 regions being the most conserved. The ROD will be updated regularly to provide an increasing number of full-length rDNA operons to the scientific community.

Taxonomy Identifiers (TaxId) for Biodiversity Genomics: a guide to getting TaxId for submission of data to public databases

Biodiversity genomics critically depends on correct taxonomic identification of the sample from which data are derived. Tracking of that taxonomic information through systems that archive data and report on genome sequencing efforts. For submission of data to the International Nucleotide Sequence Database Collaboration (INSDC) databases (DNA DataBank of Japan [DDBJ], European Nucleotide Archive [ENA] and National Center for Biotechnology Information [NCBI]), samples and data derived from them must be assigned a species-level NCBI Taxonomy taxonomic identifier (TaxId, sometimes referred to as taxId or txid). We thus need to be able to identify the TaxId for a target species efficiently. Because the NCBI Taxonomy does not include all known species and cannot preemptively represent unknown taxa, we also need an efficient process for generating new TaxIds for species not yet listed. This document provides workflows for different kinds of TaxId acquisition scenarios and was created to guide users in these processes. Although developed for European projects such as Darwin Tree of Life and the European Reference Genome Atlas, the workflows are universally applicable and describe the use of ENA in resolving taxonomic issues. Too Long: Didn't Read (TL;DR): Use the ENA REST API programmatically to retrieve TaxIds for target species and confirm that sequence data can be submitted to those TaxIds. Use the NCBI Web interface to NCBI Taxonomy to identify potential homotypic synonyms. Request a new TaxId from ENA for a species not yet in NCBI Taxonomy, and for species-like entries for which the full Linnaean binomen is not determined (see https://ena-docs.readthedocs.io/en/latest/faq/taxonomy_requests.html#creating-taxon-requests). Discuss directly with the NCBI Taxonomy curators or the curators at ENA and NCBI whenever you think there is an opportunity to improve their database.

Best Practice for Publishing Environmental DNA (eDNA) Data According to FAIR Principles

Reversing global biodiversity loss will require transformational human actions and robust measurements of their effectiveness. Diversity assessment using environmental DNA (eDNA) has emerged as a cutting-edge technique with the potential to address the challenges of measuring biodiversity. Vast amounts of eDNA sequences and eDNA-based species detections are generated in scientific studies. These datasets are typically stored in a variety of different repositories in multiple formats, hindering their reuse (Berry et al. 2020). Ensuring the publication of eDNA data following the FAIR (Findable, Accessible, Interoperable, Reusable) principles (Wilkinson et al. 2016) would revolutionise environmental assessment, including monitoring of biodiversity, individual species, and interactions across extensive spatial and temporal scales, and generate critical knowledge for evidence-based management. Archiving FAIR eDNA data requires standardising data formats and vocabularies, cyberinfrastructures, guidelines, data sharing policy, and collaboration among scientists and institutions. Some of these requirements are addressed by existing data standards and infrastructures, including Darwin Core (DwC) (Wieczorek et al. 2012), Minimum Information about any (x) Sequence (MIxS) (Yilmaz et al. 2011), the Global Biodiversity Information Facility (GBIF) network, and International Nucleotide Sequence Database Collaboration (INSDC) partners (Arita et al. 2020). However, multiple challenges remain and FAIR data practices have yet to be established among the eDNA community. This is partly because critical attributes unique to eDNA data are not adequately accommodated by existing standards. For example, monitoring contamination and excluding non-target taxa, and the parameters used for quality filtering and species detection vary greatly between studies, depending on the study scopes and the associated financial and ecological costs of incorrectly inferring presence or absence. Making such information FAIR is needed for future studies reusing data and requiring high confidence levels in species detection and taxonomic assignment. Furthermore, the procedures of targeted-taxon detection approach (e.g., interpretations of quantitative polymerase chain reaction (qPCR) results in detecting the presence of DNA from individual taxa) have not yet been fully captured by existing standards. Increasing efforts have been made to establish minimum reporting requirements to validate eDNA study methods and data (Klymus et al. 2020, Thalinger et al. 2021). These requirements need further development to be translated into data standards and formats to enhance machine readability and reusability, and to support and guide the eDNA community, so that they are effectively utilised. In this talk, we share our best practice guide for formatting and publishing eDNA data, developed by an international multidisciplinary working-group comprising eDNA researchers, journal editors, and biodiversity and omics data scientists. We identified required data types, formats and metadata checklists through reviewing and integrating existing data standards, devising subject-specific vocabularies, and introducing additional terms to accommodate the distinctive properties of eDNA data. Implementing the FAIR eDNA data best practice guide, offers a pivotal step towards standardising and enhancing the publication and re-use of eDNA data.

Texture-Image-Oriented Coverless Data Hiding Based on Two-Dimensional Fractional Brownian Motion

In an AI-immersing age, scholars look for new possibilities of employing AI technology to their fields, and how to strengthen security and protect privacy is no exception. In a coverless data hiding domain, the embedding capacity of an image generally depends on the size of a chosen database. Therefore, choosing a suitable database is a critical issue in coverless data hiding. A novel coverless data hiding approach is proposed by applying deep learning models to generate texture-like cover images or code images. These code images are then used to construct steganographic images to transmit covert messages. Effective mapping tables between code images in the database and hash sequences are established during the process. The cover images generated by a two-dimensional fractional Brownian motion (2D FBM) are simply called fractional Brownian images (FBIs). The only parameter, the Hurst exponent, of the 2D FBM determines the patterns of these cover images, and the seeds of a random number generator determine the various appearances of a pattern. Through the 2D FBM, we can easily generate as many FBIs of multifarious sizes, patterns, and appearances as possible whenever and wherever. In the paper, a deep learning model is treated as a secret key selecting qualified FBIs as code images to encode corresponding hash sequences. Both different seeds and different deep learning models can pick out diverse qualified FBIs. The proposed coverless data hiding scheme is effective when the amount of secret data is limited. The experimental results show that our proposed approach is more reliable, efficient, and of higher embedding capacity, compared to other coverless data hiding methods.

NovoLign: metaproteomics by sequence alignment

Abstract Tremendous advances in mass spectrometric and bioinformatic approaches have expanded proteomics into the field of microbial ecology. The commonly used spectral annotation method for metaproteomics data relies on database searching, which requires sample-specific databases obtained from whole metagenome sequencing experiments. However, creating these databases is complex, time-consuming, and prone to errors, potentially biasing experimental outcomes and conclusions. This asks for alternative approaches that can provide rapid and orthogonal insights into metaproteomics data. Here we present NovoLign, a de novo metaproteomics pipeline that performs sequence alignment of de novo sequences from complete metaproteomics experiments. The pipeline enables rapid taxonomic profiling of complex communities and evaluates the taxonomic coverage of metaproteomics outcomes obtained from database searches. Furthermore, the NovoLign pipeline supports the creation of reference sequence databases for database searching to ensure comprehensive coverage. We assessed the NovoLign pipeline for taxonomic coverage and false positive annotations using a wide range of in silico and experimental data, including pure reference strains, laboratory enrichment cultures, synthetic communities, and environmental microbial communities. In summary, we present NovoLign, a de novo metaproteomics pipeline that employs large-scale sequence alignment to enable rapid taxonomic profiling, evaluation of database searching outcomes, and the creation of reference sequence databases. The NovoLign pipeline is publicly available via: https://github.com/hbckleikamp/NovoLign.

A Reference Database of Reptile Images

While there are millions of reptile images available online, they are not well organized and not easily findable, accessible, interoperable, or reproducible (FAIR). More importantly, they are not standardized and thus hardly comparable. Here we present a reference database of more than 14,000 standardized images of 1045 reptile species (969 lizard and 76 snake species) that are based on preserved specimens in 20 different collections, including 533 type species of genera and type specimens of 72 species. All images were taken with standardized views, including dorsal and ventral body shots as well as dorsal, ventral, and lateral views of the heads and other body parts. Although only 11 out of the 20 collections are cross-referenced in VertNet, some others are indexed in GBIF, and this fraction will certainly grow in the near future. The utility of this and similar image collections will further grow with additional material and further cross-referencing, e.g., to DNA sequence databases or citizen science projects. The images are searchable and freely available on Morphobank (Project 5121) and on Figshare.

EVE-X: Software to Identify Novel Viral Insertions in Wild-Caught Arthropod Hosts From Next-Generation Short Read Data.

Eukaryotic genomes harbour sequences derived from non-retroviral RNA viruses, known as endogenous viral elements (EVEs) or non-retroviral integrated RNA virus sequences (NIRVS). These sequences represent a record of past infections and have been implicated in host anti-viral response. We have created a program to identify viral sequences integrated in a host genome. It begins with a specimen BAM file and outputs candidate NIRVS, along with putative host insertion sites and overlapping genomic features of the host genome in XML and visual formats, with minimal intermediary intervention. We ran through this software short-read data derived from the genomes of 222 wild-caught A. aegypti mosquitoes, from a dozen geographical regions, and located putative NIRVS from seven virus families. This program is as accurate as currently available software for NIRVS detection, and represents a significant improvement in adaptability and user-friendliness. Furthermore, the flexibility of this pipeline allows the user to search for sequence integrations across the genome of any organism, as long as a query sequence database and a reference genome is provided. Potential extended applications include identification of integrated transgenic sequences used for research or vector control strategies.

DDBJ update in 2024: the DDBJ Group Cloud service for sharing pre-publication data.

The Bioinformation and DNA Data Bank of Japan Center (DDBJ Center, https://www.ddbj.nig.ac.jp) provides public databases that cover a wide range of fields in life sciences. As a founding member of the International Nucleotide Sequence Database Collaboration (INSDC), the DDBJ Center accepts and distributes nucleotide sequence data ranging from raw reads to assembled and annotated sequences with the National Center for Biotechnology Information and the European Bioinformatics Institute. Besides INSDC databases, the DDBJ Center provides databases for functional genomics (Genomic Expression Archive), metabolomics (MetaboBank), human genetic variations (TogoVar-repository) and human genetic and phenotypic data (Japanese Genotype-phenotype Archive). These database systems have been built on the National Institute of Genetics supercomputer, which is also a platform for the DDBJ Group Cloud (DGC) services for sharing and analysis of pre-publication data among research groups. This paper reports recent updates on the databases and the services of the DDBJ Center, highlighting the DGC service.

Combining handcrafted and learned features using deep learning to improve protein-protein interaction prediction performance

ABSTRACT Understanding protein-protein interactions (PPIs) is essential because knowledge regarding PPIs helps in determining the biochemical functions of organisms. Advances made in machine learning techniques, for example, DeepFE-PPI, GcForest-PPI, and DeepPPI, have been applied to enhance PPI prediction performance. However, most methods use either handcrafted or learned features. Improving protein representation quality can significantly impact PPI model performance. This study aims to propose a deep neural network-based model, in which both handcrafted and learned features are fused to improve PPI prediction. In our method, the handcrafted features are computed by sequence descriptors, including Amino Acid Composition (AAC), Local Descriptors, Pseudo-AAC, Amphiphilic Pseudo-AAC, and Quasi-Sequence-Order. The learned features are generated by Word2vec after being trained on a large database of protein sequences. Standard evaluation methods and benchmark datasets are used to assess the proposed model. The results show that our model achieves 95.8% and 99.3% accuracy on the Saccharomyces cerevisiae and Human datasets, respectively. Additionally, the generalizability of our model is evaluated through independent tests, and our findings indicate that the proposed feature combination method correctly classifies experimentally validated interactions in eight datasets, including cross-species and PPI networks. The source code and data for this work are included at https://github.com/npxquynhdhsp/ComFeatPPI.git.

AnnoDUF: A Web-Based Tool for Annotating Functions of Proteins Having Domains of Unknown Function.

The rapid expansion of biological sequence databases due to high-throughput genomic and proteomic sequencing methods has left a considerable number of identified protein sequences with unclear or incomplete functional annotations. Domains of unknown function (DUFs) are protein domains that lack functional annotations but are present in numerous proteins. To address the challenge of finding functional annotations for DUFs, we have developed a computational method that efficiently identifies and annotates these enigmatic protein domains by utilizing the position-specific iterative basic local alignment search tool (PSI-BLAST) and data mining techniques. Our pipeline identifies putative potential functionalities of DUFs, thereby decreasing the gap between known sequences and functions. The tool can also take user input sequences to annotate. We executed our pipeline on 5111 unique DUF sequences obtained from Pfam, resulting in putative annotations for 2007 of these. These annotations were subsequently incorporated into a comprehensive database and interfaced with a web-based server named "AnnoDUF". AnnoDUF is freely accessible to both academic and industrial users, via the World Wide Web at the link http://bts.ibab.ac.in/annoduf.php. All scripts used in this study are uploaded to the GitHub repository, and these can be accessed from https://github.com/BioToolSuite/AnnoDUF.

Detection of host cell microprotein impurities in antibody drug products

Chinese hamster ovary (CHO) cells are used to produce almost 90% of therapeutic monoclonal antibodies (mAbs) and antibody fusion proteins (Fc-fusion). The annotation of non-canonical translation events in these cellular factories remains incomplete, limiting our ability to study CHO cell biology and detect host cell protein (HCP) impurities in the final antibody drug product. We utilised ribosome footprint profiling (Ribo-seq) to identify novel open reading frames (ORFs) including N-terminal extensions and thousands of short ORFs (sORFs) predicted to encode microproteins. Mass spectrometry-based HCP analysis of eight commercial antibody drug products (7 mAbs and 1 Fc-fusion protein) using the extended protein sequence database revealed the presence of microprotein impurities. We present evidence that microprotein abundance varies with growth phase and can be affected by the cell culture environment. In addition, our work provides a vital resource to facilitate future studies of non-canonical translation and the regulation of protein synthesis in CHO cell lines.

Association of ATM and ARID1A in gastric carcinoma

BackgroundThe ataxia telangiectasia mutated (ATM) gene is involved in the repair of double-stranded DNA breaks and a component of the DNA damage repair pathway. Tumors with mutations or low expression of both ARID1A and ATM exhibit increased numbers of tumor-infiltrating lymphocytes and a favorable prognosis. However, the relationship between ATM and ARID1A in gastric carcinoma (GC) is unclear. MethodsWe used the mRNA expression data from the Asian Cancer Research Group to construct tissue microarrays (N = 249). Next-generation sequencing (NGS) databases of Samsung Medical Center (SMC) (N = 813) were used to compare genetic alterations. Tissue microarrays were used for ATM and ARID1A immunohistochemistry, and expressions were categorized as “low” and “high.” NGS data from TCGA-STAD (N = 431) were used as independent cohorts for genetic alterations validation. ResultsIn GCs, 32.1% (80/249) of the cases showed low ATM protein expression (ATMlow) and 20.9% (52/249) showed low ARID1A expression (ARID1Alow). ATMlow was significantly associated with older age (P <.01), gross type of tumor (P =.02), histology (P <. 01), lower incidence of perineural invasion (P =.04), lower disease stage (P <.01), microsatellite instability-high (P <.01), and ARID1Alow (P <.01). Furthermore, GCs in the SMC NGS database showed that ATM mutations were significantly correlated with ARID1A mutations (P <.01), and this finding remained significant in TCGA-STAD validation cohort (P <.01). ConclusionATMlow in GCs shows a characteristic clinicopathological feature that correlates strongly with ARID1Alow. ATM mutation was also associated with ARID1A mutations, highlighting the interactions between ATM and ARID1A in GC and suggesting a potential therapeutic target.

Molecular Detection of Microsporum Canis Infection in Cats in Baghdad Governorate

Microsporum canis is a keratinized skin fungus that causes diseases in all animals, especially in cats. It is able to infected human causes Tinea corporis and Tinea capitis. In this study,the aim was toward detection and confirmation of Microsporum canis infection in cats in Baghdad governorate using polymerase chain reaction (PCR).The diagnosis depend on sequencing of the PCR products obtained by using a universal primer to amplification of the primer DppV which was 895 bp.A total of two hundred and eighty three hair and skin scraping samples were collected from infected cats with and without skin lesions. Preliminarily examined of samples with 10% KOH preparation, and cultured for fungal identification . For the culture; Saboruad dextrose agar was used . Culture results showed that four isolates were positive for M. canis , the best growth of these isolates was confirmed by PCR and DPPV gene then sequencing of this gene. PCR technique showed that one isolate from the total of 283 isolates were assured for the DPPV gene and yielded a band at 895 bp, this finding is regarded as distinctive for the genus Microsporum canis ,which was dumped in the GenBank nucleotide sequence database under accession number OR126262. Results sequencing of this gene in Iraq revealed compatibility with global results in USA and Belgium.