Biology subjects

Nookaew, I.

Publications and source records attributed to Nookaew, I..

16 recordsLinked to original sources

bioRxiv2026

A transcriptomic-driven segmentation and cell simulation framework for high-resolution spatial transcriptomics and cell-cell communication

The Visium HD spatial transcriptomics platform enables transcriptome-wide profiling at near-single-cell resolution. However, accurate segmentation of cells to define spatial boundaries relies heavily on histological images. Previous approaches struggle to define cells when the tissues have high cell density, are inflamed, or are mineralized, leading to transcriptomic bleed-through and inaccurate clustering. To address this, we developed TENGU (Transcript-signal Enrichment and Grouping Unit), a comprehensive end-to-end bioinformatic software package. Unlike existing tools, TENGU employs a transcript-first segmentation approach, prioritizing transcript-signal density as the primary modality and utilizing histological images only as a secondary supplement in unresolved regions. These initial boundaries are further optimized through a novel transcriptomic-driven cell simulation algorithm. Iterative refinement of boundaries based on localized gene expression probabilities effectively minimizes spatial scattering and preserves biologically distinct molecular signatures. The pipeline seamlessly integrates tissue segmentation, high-resolution cell-type annotation, and basic spatially aware cell-cell communication (CCC) analysis. We rigorously benchmarked TENGU against the 10X Genomics and Bin2cell pipelines for cell segmentation across diverse and technically challenging microenvironments. TENGU demonstrated superior transcriptomic distinctness in the murine brain, successfully captured matrix-embedded osteocytes, and localized critical osteoimmune CCC networks (Tgfb and Il1a) in a murine model of osteomyelitis. TENGU also resolved species-specific, pro-tumorigenic signaling hubs (MDK-SDC4) within a highly compacted human colorectal cancer xenograft. By mitigating the constraints of traditional image-dependent segmentation, TENGU provides a highly adaptable and robust computational framework that empowers researchers to accurately decode the complex functional micro-anatomy of both healthy and pathological tissues.

bioinformatics↗

bioRxiv2026

Alignment-Free Guided Design of a Pan-Orthoflavivirus RT-qPCR Assay

The co-circulation and rapid expansion of the genus Orthoflavivirus, including dengue virus (DENV), Zika (ZIKV), and Japanese encephalitis virus (JEV), pose significant global health challenges. Developing inclusive pan-genus molecular diagnostics is hindered by high nucleotide divergence (>25%-30%) and the computational limitations of traditional multiple sequence alignment in detecting conserved motifs across large datasets. To overcome these limitations, we developed a systematic alignment-free design pipeline that uses rigorous k-mer analysis and compacted De Bruijn graphs. We analyzed 11,846 RefSeq viral genomes to identify phylogenetically conserved, functionally relevant signatures within the Orthoflavivirus genus as a case study. The pipeline identified a conserved 600-bp region within the non-structural protein 5 gene, facilitating the design of a broad-spectrum TaqMan RT-qPCR assay. Analytical validation against standard reference strains demonstrated a limit of detection of 1-10 copies/{micro}L for DENV1-4, ZIKV, and JEV, with no cross-reactivity against non-target pathogens. In a clinical evaluation of archived samples, the assay achieved 97.33% overall accuracy. It demonstrated 100% sensitivity and specificity for DENV serotypes, yielding significantly earlier cycle threshold (Ct) values compared to a standard commercial kit, while ZIKV detection showed 100% specificity with 71.43% sensitivity. This study validates an alignment-free, k-mer guided approach for uncovering conserved diagnostic targets in highly variable viral genera. The resulting assay offers a robust tool for frontline surveillance, and the computational framework provides a scalable solution for future pandemic preparedness.

microbiology↗

bioRxiv2025

An Enhancement of Extrachromosomal Circular DNA Enrichment and Amplification to Address the Extreme Low Overlap Between Replicates

Extrachromosomal circular DNA (eccDNA) of chromosomal origin is present in all eukaryotic organisms and tissues that have been tested. Populations of eccDNA exhibit immense diversity and a characteristically low degree of overlap between samples, suggesting low inheritance of eccDNA between cells or a deficiency in the methods by which eccDNA is detected. This study revisits the Circle-seq approach for enrichment of eccDNA to address these limitations, hypothesizing that experimental procedures significantly contribute to the observed low eccDNA overlap. We optimized the protocol by reducing the time needed to complete the procedure. Linear DNA is digested by increasing Exonuclease V activity. We employed CRISPR-Cas9 for mitochondrial linearization, which proved superior to using restriction enzymes. A key finding is the critical role of random hexamer primer concentration and genomic DNA input in Rolling circle amplification (RCA) for generating high-quality long amplicons from eccDNA (concatemeric tandem copy [CTC]), essential for confident de novo eccDNA construction from long-read sequencing data. Lower primer concentrations substantially increased the percentage of CTC-derived eccDNA and improved the overlap of identified eccDNAs in technical replicates. Applying this revised approach to human myeloma and breast cancer cell lines, as well as xenograft models, demonstrated >50% overlap in detected eccDNA, a substantial improvement over the <1% overlap observed in previous studies. Additionally, the oncogenic signature of eccDNAs can be identified across all replicates. These findings provide guidelines for developing standardized procedures for eccDNA profiling, advancing our understanding of eccDNA biology, and its potential clinical applications.

genomics↗

bioRxiv2025

Mechanisms of mitochondrial reactive oxygen species action in bone mesenchymal cells

Mitochondrial reactive oxygen species (mtROS), insufficient NAD+, and cellular senescence all contribute to the decrease in bone formation with aging. ROS can cause senescence and decrease NAD+, but it remains unknown whether these mechanisms mediate the effects of ROS in vivo. Here, we generated mice with deletion of the mitochondrial antioxidant enzyme Sod2 in Osx1-Cre (Sp7-tTA, tetO-EGFP/cre) targeted cells designated Sod2{Delta}Osx1 mice. We showed that Sod2 deletion caused low bone mass. Osteoblastic cells from these mice had impaired mitochondrial respiration and attenuated NAD+ levels. Administration of an NAD+ precursor improved mitochondrial function in vitro but failed to rescue the low bone mass of Sod2{Delta}Osx1 mice. Single-cell RNA-sequencing of bone mesenchymal cells indicated that ROS had no significant effects on markers of senescence but disrupted parathyroid hormone signaling, iron metabolism, and proteostasis. Our data support the rationale that treatment combinations aimed at decreasing mtROS and senescent cells and increasing NAD+ should confer additive effects in delaying age-associated osteoporosis.

cell biology↗

bioRxiv2024

CAIM: Coverage-based Analysis for Identification of Microbiome

Accurate taxonomic profiling of microbial taxa in a metagenomic sample is vital to gain insights into microbial ecology. Recent advancements in sequencing technologies have contributed tremendously toward understanding these microbes at species resolution through a whole shotgun metagenomic (WMS) approach. In this study, we developed a new bioinformatics tool, CAIM, for accurate taxonomic classification and quantification within both long- and short-read metagenomic samples using an alignment-based method. CAIM depends on two different containment techniques to identify species in metagenomic samples using their genome coverage information to filter out false positives rather than the traditional approach of relative abundance. In addition, we propose a nucleotide-count based abundance estimation, which yield lesser root mean square error than the traditional read-count approach. We evaluated the performance of CAIM on 28 metagenomic mock communities and 2 synthetic datasets by comparing it with other top-performing tools. CAIM maintained a consitently good performance across datasets in identifying microbial taxa and in estimating relative abundances than other tools. CAIM was then applied to a real dataset sequenced on both Nanopore (with and without amplification) and Illumina sequencing platforms and found high similality of taxonomic profiles between the sequencing platforms. Lastly, CAIM was applied to fecal shotgun metagenomic datasets of 232 colorectal cancer patients and 229 controls obtained from 4 different countries and primary 44 liver cancer patients and 76 controls. The predictive performance of models using the genome-coverage cutoff was better than those using the relative-abundance cutoffs in discriminating colorectal cancer and primary liver cancer patients from healthy controls with a highly confident species markers. Key Points- Metagenomic coverage is an important index to obtain highly accurate species identification by reducing false positives from whole shotgun metagenomic data. - Comparative analyses of CAIM and other bioinformatics tools for species identification on many mock community whole shotgun metagenomic datasets generated by short-read and long-read sequencing and synthetic datasets were performed, showing that CAIM has a very good performance compared with the other tools. - Using the metagenomic coverage approach through CAIM improves the predictive power of species biomarkers identified from in stool samples of colorectal cancer and primary liver datasets.

bioinformatics↗

bioRxiv2023

A framework for defining mesenchymal cell types associated with murine periosteal and endosteal bone

Single-cell RNA sequencing has led to numerous novel designations for mesenchymal cell types associated with bone. Consequently, there are now multiple designations for what appear to be the same cell type. In addition, existing datasets contain relatively small numbers of mature osteoblasts and osteocytes and there has been no comparison of periosteal bone cells to those at the endosteum and trabecular bone. The main goals of this study were to increase the amount of single cell RNA sequence data for osteoblasts and osteocytes, to compare cells from the periosteum to those inside bone, and to clarify the major categories of cell types associated with murine bone. To do this, we created an atlas of murine bone-associated cells by harmonizing published datasets with in-house data from cells targeted by Osx1-Cre and Dmp1-Cre driver strains. Cells from periosteal bone were analyzed separately from those isolated from the endosteum and trabecular bone. Over 100,000 mesenchymal cells were mapped to reveal 11 major clusters designated fibro-1, fibro-2, chondrocytes, articular chondrocytes, tenocytes, adipo-CAR, osteo-CAR, pre-osteoblasts, osteoblasts, osteocytes, and osteo-X, the latter defined in part by Postn expression. Osteo-X, osteo-CAR, and pre-osteoblasts were closely associated with osteoblasts at the trabecular bone surface. Wnt16 was expressed in multiple cell types from the periosteum but not in any cells from endocortical or cancellous bone. Fibro-2 cells, which express markers of skeletal stem cells, localized to the periosteum but not trabecular bone in adult mice. Suppressing bone remodeling eliminated osteoblasts and altered gene expression in pre-osteoblasts but did not change the abundance or location of osteo-X or osteo-CAR cells. These results provide a framework for identifying bone cell types in murine single cell RNA sequencing datasets and suggest that osteoblast progenitors reside near the surface of remodeling bone. Author SummaryThe skeleton of vertebrate animals is produced by bone forming cells known as osteoblasts. New osteoblasts are continually produced throughout life to maintain the skeleton. However, the identity of the cells from which new osteoblasts are derived is not clear. Single cell RNA sequencing provides a gene expression profile of individual cells and allows them to be grouped into clusters of cells with similar profiles. In many cases, distinct clusters represent distinct cell types. Recent single cell RNA sequencing studies of mouse bones by several different laboratories have identified many novel cell types, some of which may be osteoblast progenitors. In many cases, different laboratories have created different names for what appear to be the same cell type. To help clarify this situation, we performed single cell RNA sequencing studies and combined our results with those from several published studies to create a harmonized map of the cell types associated with mouse bones. We also identified gene signatures for each cell type that can be used to improve the consistency of cell type designation in future single cell RNA sequencing studies of mouse bone.

genomics↗

bioRxiv2022

Genomic Distance-based Rapid Uncovering of Microbial Population Structures (GRUMPS): a reference free genomic data cleaning methodology

Accurate datasets are crucial for rigorous large-scale sequence-based analyses such as those performed in phylogenomics and pangenomics. As the volume of available sequence data grows and the quality of these sequences varies, there is a pressing need for reliable methods to swiftly identify and eliminate low-quality and misidentified genomes from datasets prior to analysis. Here we introduce a robust, controlled, computationally efficient method for deriving species-level population structures of bacterial species, regardless of the dataset size. Additionally, our pipeline can classify genomes into their respective species at the genus level. By leveraging this methodology, researchers can rapidly clean datasets encompassing entire bacterial species and examine the sub-species population structures within the provided genomes. These cleaned datasets can subsequently undergo further refinement using a variety of methods to yield sequence sets with varying levels of diversity that faithfully represent entire species. Increasing the efficiency and accuracy of curation of species-level datasets not only enhances the reliability of downstream analyses, but also facilitates a deeper understanding of bacterial population dynamics and evolution.

bioinformatics↗

bioRxiv2022

Single Cell Cortical Bone Transcriptomics Defines Novel Osteolineage Gene Sets Altered in Chronic Kidney Disease

Due to a lack of spatial-temporal resolution at the single cell level, the etiologies of the bone dysfunction caused by diseases such as normal aging, osteoporosis, and the metabolic bone disease associated with chronic kidney disease (CKD) remain largely unknown. To this end, flow cytometry and scRNAseq were performed on long bone cells from Sost-cre/Ai9+ mice, and pure osteolineage transcriptomes were identified, including novel osteocyte-specific gene sets. Clustering analysis isolated osteoblast precursors that expressed Tnc, Mmp13, and Spp1, and a mature osteoblast population defined by Smpd3, Col1a1, and Col11a1. Osteocytes were demarcated by Cd109, Ptprz1, Ramp1, Bambi, Adamts14, Spns2, Bmp2, WasI, and Phex. We validated our in vivo scRNAseq using integrative in vitro promoter occupancy via ATACseq coupled with transcriptomic analyses of a conditional, temporally differentiated MSC cell line. Further, trajectory analyses predicted osteoblast-to-osteocyte transitions via defined pathways associated with a distinct metabolic shift as determined by single-cell flux estimation analysis (scFEA). Using the adenine mouse model of CKD, at a time point prior to major skeletal alterations, we found that gene expression within all stages of the osteolineage was disturbed. In sum, distinct populations of osteoblasts/osteocytes were defined at the single cell level. Using this roadmap of gene assembly, we demonstrated unrealized molecular defects across multiple bone cell populations in a mouse model of CKD, and our collective results suggest a potentially earlier and more broad bone pathology in this disease than previously recognized.

cell biology↗

bioRxiv2022

Accurate Identification of Extrachromosomal Circular DNA from Long-read Sequences.

Extrachromosomal circular DNA (eccDNA) of chromosomal origin is found in a range of eukaryotic species and cell type including cancer where eccDNA with oncogenes appear to drive tumorigenesis. Most studies of eccDNA employ short-read sequencing to identify for their identification. However, short-read sequencing cannot resolve the complexity of genomic repeats, which can lead to missing eccDNA identification. An alternative is the long-read sequencing technologies that can potentially be used to construct complete eccDNA. We present a software suite, Construction-based Rolling-circle amplification for eccDNA Sequence Identification and Location (CReSIL) 2.0, to identify and characterize eccDNA from long-read sequences. CReSILs performance in the identification of eccDNA, with a minimum F1 score of 0.98, is superior to the other bioinformatic tools based on simulated data. CReSIL provides many useful features for genomic annotation, which can used to infer eccDNA function and Circos visualization for eccDNA architecture investigation. We demonstrated CReSILs capability in many of the long-read sequencing datasets. This includes datasets enriched for eccDNA as well as whole genome datasets from many cells that contained large eccDNA. CReSIL suite software will be a versatile tool to deeply investigate eccDNA in biological samples.

bioinformatics↗

bioRxiv2021

Enterotoxigenic Escherichia coli display a distinct growth phase before entry into stationary phase with shifts in tryptophan- fucose- and putrescine metabolism and degradation of neurotransmitter precursors

Enterotoxigenic Escherichia coli (ETEC) is a major cause of diarrhea in children and adults in endemic areas. Gene regulation of ETEC during growth in vitro and in vivo needs to be further evaluated, and here we describe the full transcriptome and metabolome of ETEC during growth from mid-logarithmic growth to stationary phase in rich medium (LB medium). We identified specific genes and pathways subjected to rapid transient alterations in gene expression and metabolite production during the transition between logarithmic to stationary growth. The transient phase during late exponential growth is different from the subsequent induction of stationary phase-induced genes, including stress and survival responses as described earlier. The transient phase was characterized by the repression of genes and metabolites involved in organic substance transport. Genes involved in fucose and putrescine metabolism were upregulated, and genes involved in iron transport were repressed. Expression of toxins and colonization factors were not changed, suggesting retained virulence. Metabolomic analyses showed that the transient phase was characterized by a drop of intracellular amino acids, e.g., L-tyrosine, L-tryptophan, L-phenylalanine, L-leucine, and L-glutamic acid, followed by increased levels at induction of stationary phase. A pathway enrichment analysis of the entire transcriptome and metabolome showed activation of pathways involved in the degradation of neurotransmitters aminobutyrate (GABA) and precursors of 5-hydroxytryptamine (serotonin). This work provides a comprehensive framework for further studies on transcriptional and metabolic regulation in pathogenic E. coli. ImportanceWe show that E. coli, exemplified by the pathogenic subspecies enterotoxigenic E. coli (ETEC), undergoes a stepwise transcriptional and metabolic transition into the stationary phase. At a specific entry point, E. coli induces activation and repression of specific pathways. This leads to a rapid decrease of intracellular levels of L-tyrosine, L-tryptophan, L-phenylalanine, L-leucine, and L-glutamic acid due to metabolism into secondary compounds. The resulting metabolic activity leads to an intense but short peak of indole production, suggesting that this is the previously described "indole peak," rapid decrease of intermediate molecules of bacterial neurotransmitters, increased putrescine and fucose uptake, increased glutathione levels, and decreased iron uptake. This specific transient shift in gene expression and metabolomics is short-lived and disappears when bacteria enter the stationary phase. We suggest it mainly prepares bacteria for ceased growth, but the pathways involved suggest that this transient phase substantially influences survival and virulence.

microbiology↗

bioRxiv2021

Mmp-13 deletion in cells of the mesenchymal lineage increases bone mass, decreases endocortical osteoclast number, and attenuates the cortical bone loss caused by estrogen deficiency in mice

The protective effect of estrogens against cortical bone loss is mediated via direct actions on mesenchymal lineage cells, but functional evidence for the precise molecular mechanism(s) and the mediators of these effects has only recently began to emerge. We report that the matrix metalloproteinase 13 (MMP-13) is the highest up-regulated gene in calvaria or bone marrow cells from mice lacking the estrogen receptor (ER) alpha in osteoprogenitors. We, therefore, generated mice with conditional Mmp-13 deletion in Prrx1 expressing cells (Mmp-13{Delta}Prrx1) and compared the effect of estrogen deficiency on their bone phenotype to that of control littermates (Mmp-13f/f). Femur and tibia length was decreased in sham-operated Mmp-13{Delta}Prrx1 mice as compared to Mmp-13f/f. Cortical thickness and trabecular bone volume in the femur and tibia were increased and osteoclast number at the endocortical surfaces was decreased in the sham-operated female Mmp-13{Delta}Prrx1 mice; whereas bone formation rate was unaffected. Ovariectomy (OVX) caused a decrease of cortical thickness in the femur and tibia of Mmp-13f/f control mice. This effect was attenuated in the Mmp-13{Delta}Prrx1 mice; but the decrease of trabecular bone caused by OVX was not affected. These results reveal that mesenchymal cell-derived MMP-13 regulates osteoclast number, bone resorption, and bone mass. We have recently reported that the loss of cortical, but not trabecular bone, caused by OVX is also attenuated in Cxcl12{Delta}Prrx1 mice. Together with the present report, this functional genetic evidence provides proof of principle that increased production of mesenchymal cell-derived factors, such as CXCL12 and MMP-13, are important mediators of the adverse effect of estrogen deficiency on cortical, but not trabecular, bone. Therefore, the mechanisms responsible for the protective effect of estrogens on these two major bone compartments are different.

molecular biology↗

bioRxiv2020

A chromosome-level assembly of the black tiger shrimp (Penaeus monodon) genome facilitates the identification of novel growth-associated genes

The black tiger shrimp (Penaeus monodon) is one of the most prominent farmed crustacean species with an average annual global production of 0.5 million tons in the last decade. To ensure sustainable and profitable production through genetic selective breeding programs, several research groups have attempted to generate a reference genome using short-read sequencing technology. However, the currently available assemblies lack the contiguity and completeness required for accurate genome annotation due to the highly repetitive nature of the genome and technical difficulty in extracting high-quality, high-molecular weight DNA in this species. Here, we report the first chromosome-level whole-genome assembly of P. monodon. The combination of long-read Pacific Biosciences (PacBio) and long-range Chicago and Hi-C technologies enabled a successful assembly of this first high-quality genome sequence. The final assembly covered 2.39 Gb (92.3% of the estimated genome size) and contained 44 pseudomolecules, corresponding to the haploid chromosome number. Repetitive elements occupied a substantial portion of the assembly (62.5%), highest of the figures reported among crustacean species. The availability of this high-quality genome assembly enabled the identification of novel genes associated with rapid growth in the black tiger shrimp through the comparison of hepatopancreas transcriptome of slow-growing and fast-growing shrimps. The results highlighted several gene groups involved in nutrient metabolism pathways and revealed 67 newly identified growth-associated genes. Our high-quality genome assembly provides an invaluable resource for accelerating the development of improved shrimp strain in breeding programs and future studies on gene regulations and comparative genomics.

genomics↗

bioRxiv2018

Decoding the Epitranscriptional Landscape from Native RNA Sequences

Sequencing of native RNA and corresponding cDNA was performed using Oxford Nanopore Technology. The % Error of Specific Bases (%ESB) was higher for native RNA than for cDNA, which enabled detection of ribonucleotide modification sites. Based on %ESB differences of the two templates, a bioinformatic tool ELIGOS was developed and applied to rRNAs of E. coli, yeast and human cells. ELIGOS captured 91%, 95%, [~]75%, respectively, of the known variety of RNA methylation sites in these rRNAs. Yeast transcriptomes from different growth conditions were also compared, which identified an association between metabolic adaptation and inferred RNA modifications. ELIGOS was further applied to human transcriptome datasets, which identified the well-known DRACH motif containing N6-methyadenine being located close to 3-untranslated regions of mRNA. Moreover, the RNA G-quadruplex motif was uncovered by ELIGOS. In summary, we have developed an experimental method coupled with bioinformatic software to uncover native RNA modifications and secondary-structures within transcripts.

genetics↗

bioRxiv2018

Rapid Sequencing of Multiple RNA Viruses in their Native Form

Long-read nanopore sequencing by a MinION device offers the unique possibility to directly sequence native RNA. We combined an enzymatic poly-A tailing reaction with the native RNA sequencing to (i) sequence complex population of single-stranded (ss)RNA viruses in parallel, (ii) detect genome, subgenomic mRNA/mRNA simultaneously, (iii) detect a complex transcriptomic architecture without the need for assembly, (iv) enable real-time detection. Using this protocol, positive-ssRNA, negative-ssRNA, with/without a poly(A)-tail, segmented/non-segmented genomes were mixed and sequenced in parallel. Mapping of the generated sequences on the reference genomes showed 100% length recovery with up to 97 % identity. This work provides a proof of principle and the validity of this strategy, opening up a wide range of applications to study RNA viruses.

genomics↗

bioRxiv2018

Memote: A community-driven effort towards a standardized genome-scale metabolic model test suite

Several studies have shown that neither the formal representation nor the functional requirements of genome-scale metabolic models (GEMs) are precisely defined. Without a consistent standard, comparability, reproducibility, and interoperability of models across groups and software tools cannot be guaranteed.\n\nHere, we present memote (https://github.com/opencobra/memote) an open-source software containing a community-maintained, standardized set of metabolic model tests. The tests cover a range of aspects from annotations to conceptual integrity and can be extended to include experimental datasets for automatic model validation. In addition to testing a model once, memote can be configured to do so automatically, i.e., while building a GEM. A comprehensive report displays the models performance parameters, which supports informed model development and facilitates error detection.\n\nMemote provides a measure for model quality that is consistent across reconstruction platforms and analysis software and simplifies collaboration within the community by establishing workflows for publicly hosted and version controlled models.

systems biology↗

bioRxiv2017

dBBQs : dataBase of Bacterial Quality scores

BackgroundIt is well-known that genome sequencing technologies are becoming significantly cheaper and faster. As a result of this, the exponential growth in sequencing data in public databases allows us to explore ever growing large collections of genome sequences. However, it is less known that the majority of available sequenced genome sequences in public databases are not complete, drafts of varying qualities. We have calculated quality scores for around 100,000 bacterial genomes from all major genome repositories and put them in a fast and easy-to-use database.\n\nResultsProkaryotic genomic data from all sources were collected and combined to make a non-redundant set of bacterial genomes. The genome quality score for each was calculated by four different measurements: assembly quality, number of rRNA and tRNA genes, and the occurrence of conserved functional domains. The dataBase of Bacterial Quality scores (dBBQs) was designed to store and retrieve quality scores. It offers fast searching and download features which the result can be used for further analysis. In addition, the search results are shown in interactive JavaScript chart framework using DC.js. The analysis of quality scores across major public genome databases find that around 68% of the genomes are of acceptable quality for many uses.\n\nConclusionsdBBQs (available at http://arc-gem.uams.edu/dbbqs) provides genome quality scores for all available prokaryotic genome sequences with a user-friendly Web-interface. These scores can be used as cut-offs to get a high-quality set of genomes for testing bioinformatics tools or improving the analysis. Moreover, all data of the four measurements that were combined to make the quality score for each genome, which can potentially be used for further analysis. dBBQs will be updated regularly and is freely use for non-commercial purpose.

bioinformatics↗