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At least 19 recordsLinked to original sources
PubMed2026

VirBinn improves viral genome binning from metagenomic Hi-C through graph diffusion.

MOTIVATION: Metagenomic Hi-C provides in situ proximity signals that can improve genome binning and enable virus-host-association analysis. However, viral genome recovery remains difficult because virus-virus Hi-C contact matrices are extremely sparse. Viral genomes are small, often low-abundance, and frequently assemble into short contigs, leaving many true within-genome links unobserved and causing viral bins to fragment. RESULTS: We present VirBinn, a graph-diffusion framework for viral binning from metagenomic Hi-C. VirBinn enhances virus-virus connectivity through two complementary mechanisms: random-walk-with-restart enhancement on the sparse virus-virus contact graph and host-guided diffusion that propagates viral seeds through the host network to infer indirect virus-virus associations. The enhanced views are integrated and clustered using Leiden community detection to produce viral metagenome-assembled genomes (vMAGs). On dataset-specific simulation benchmarks with ground truth, VirBinn consistently recovers more high-quality vMAGs than Hi-C-based and shotgun-based baselines and substantially increases the number of near-complete genomes. On four real metagenomic Hi-C datasets spanning human gut, pig gut, sheep gut (long-read assembly), and wastewater, VirBinn yields more high-completeness vMAGs under CheckV and produces bins with strong within-cluster contact support. Finally, host linkage analysis using reconstructed host MAGs reveals habitat-specific host-association patterns and plausible host taxonomic profiles. AVAILABILITY AND IMPLEMENTATION: VirBinn is available at https://github.com/dyxstat/VirBinn. The scripts to reproduce the results and figures in this article are available at https://github.com/dyxstat/Reproduce_VirBinn.

Genome, Viral↗
PubMed2025

A Graph Contrastive Learning Method for Enhancing Genome Recovery in Complex Microbial Communities.

Accurate genome binning is essential for resolving microbial community structure and functional potential from metagenomic data. However, existing approaches-primarily reliant on tetranucleotide frequency (TNF) and abundance profiles-often perform sub-optimally in the face of complex community compositions, low-abundance taxa, and long-read sequencing datasets. To address these limitations, we present MBGCCA, a novel metagenomic binning framework that synergistically integrates graph neural networks (GNNs), contrastive learning, and information-theoretic regularization to enhance binning accuracy, robustness, and biological coherence. MBGCCA operates in two stages: (1) multimodal information integration, where TNF and abundance profiles are fused via a deep neural network trained using a multi-view contrastive loss, and (2) self-supervised graph representation learning, which leverages assembly graph topology to refine contig embeddings. The contrastive learning objective follows the InfoMax principle by maximizing mutual information across augmented views and modalities, encouraging the model to extract globally consistent and high-information representations. By aligning perturbed graph views while preserving topological structure, MBGCCA effectively captures both global genomic characteristics and local contig relationships. Comprehensive evaluations using both synthetic and real-world datasets-including wastewater and soil microbiomes-demonstrate that MBGCCA consistently outperforms state-of-the-art binning methods, particularly in challenging scenarios marked by sparse data and high community complexity. These results highlight the value of entropy-aware, topology-preserving learning for advancing metagenomic genome reconstruction.

canonical correlation analysis↗
PubMed2006

Meta-analysis of genome-wide linkage studies for bone mineral density.

Genome-wide linkage studies have shown several chromosome loci that may harbor genes that regulate bone mineral density (BMD), but results have been inconsistent. A meta-analysis was performed to assess evidence for linkage of BMD across whole genome scan studies. Eleven whole-genome scans of BMD or osteoporosis containing 3,097 families with 12,685 individuals were included in this genome scan meta-analysis (GSMA). For each study, 120 genomic bins of approximately 30 cM were defined and ranked according to maximum evidence for linkage within each bin. Bin ranks were weighted and summed across all studies. The summed rank for each bin was assessed empirically for significance using permutation methods. A total of seven bins lie above the 95% confidence level (P=0.05) and one bin was above the 99% confidence level (P=0.01) in the GSMA of eleven linkage studies: bins 16.1 (16pter-16p12.3, Psumrnk <0.01), 3.3 (3p22.2-3p14.1), 1.1 (1pter-1p36.22), 18.2 (18p11.23-18q12.2), 6.3 (6p21.1-6q15), 20.1 (20pter-20p12.3), and 18.1 (18pter-18p11.23). GSMA was performed with seven studies with linkage scores of LOD >1-1.85 for sensitivity test, confirming the linkage on chromosome 16p and 3p and revealing evidence of new linkage in bins 10.2 (10p14-10q11.21) and 22.2 (22q12.3-22pter). In conclusion, the meta-analysis of whole-genome linkage studies of BMD has shown chromosome 16pter-16p12.3 to have the greatest evidence of linkage as well as revealing evidence of linkage in chromosomes 1p, 3p, 6, 10, 18, 20p, and 22q across studies. This data may provide a basis with which to carry out targeted linkage and candidate gene studies particularly in these regions.

Bone Density↗
PubMed2005

Meta-analysis of genome scans of age-related macular degeneration.

A genetic contribution to the development of age-related macular degeneration (AMD) is well established. Several genome-wide linkage studies have identified a number of putative susceptibility loci for AMD but only a few of these regions have been replicated in independent studies. Here, we perform a meta-analysis of six AMD genome screens using the genome-scan meta-analysis method, which allows linkage results from several studies to be combined, providing greater power to identify regions that show only weak evidence for linkage in individual studies. Results from non-parametric analysis for a broad AMD clinical phenotype (including two studies with quantitative traits) were extracted. For each study, 120 genomic bins of approximately 30 cM were defined and ranked according to maximum evidence for linkage within each bin. Bin ranks were weighted according to study size and summed across all studies; the summed rank (SR) for each bin was assessed empirically for significance using permutation methods. A high SR indicates a region with consistent evidence for linkage across studies. The strongest evidence for an AMD susceptibility locus was found on chromosome 10q26 where genome-wide significant linkage was observed (P=0.00025). Several other regions met the empirical significance criteria for bins likely to contain linked loci including adjacent pairs of bins on chromosomes 1q, 2p, 3p and 16. Several of the regions identified here showed only weak evidence for linkage in the individual studies. These results will help prioritize regions for future positional and functional candidate gene studies in AMD.

Aging↗
PubMed2003

Meta-analysis of four rheumatoid arthritis genome-wide linkage studies: confirmation of a susceptibility locus on chromosome 16.

OBJECTIVE: Susceptibility to rheumatoid arthritis (RA) is likely to involve several genes of weak effect, and consequently, individual studies may have insufficient power to detect linkage. Four major RA genome-wide linkage studies have been carried out, but apart from the well-established HLA susceptibility locus, none of the reported significant regions of linkage has been replicated. We applied a genome-search meta-analysis to 4 RA genome searches to assess linkage across studies, using published results. METHODS: For each study, 120 genomic bins of approximately 30 cM were defined and ranked according to the maximum evidence for linkage within each bin. Ranks were summed across studies and each bin was assessed empirically by the magnitude of summed rank, using a permutation test. A high summed rank indicated a region in which evidence for linkage was consistent across several studies. RESULTS: In addition to the HLA locus (P < 0.00002), the strongest evidence for an RA susceptibility locus was found on chromosome 16 (P = 0.004). This locus was not identified as statistically significant in any of the 4 individual RA genome searches. In total, 12 regions achieved a significant (P < 0.05) summed rank, compared with the 6 bins expected by random chance. Four of these regions (on chromosomes 6p, 16cen, 6q, and 12p) reached a significance value of P < 0.01, suggesting that a subset of these regions contains RA susceptibility loci. CONCLUSION: Using a meta-analysis approach, we have identified existing and novel putative RA susceptibility loci. These results can provide a basis for further positional and functional candidate-gene studies, and may prove useful in other complex rheumatic diseases.

Arthritis, Rheumatoid↗
PubMed2007

Meta-analysis of genome-wide scans provides evidence for sex- and site-specific regulation of bone mass.

UNLABELLED: Several genome-wide scans have been performed to detect loci that regulate BMD, but these have yielded inconsistent results, with limited replication of linkage peaks in different studies. In an effort to improve statistical power for detection of these loci, we performed a meta-analysis of genome-wide scans in which spine or hip BMD were studied. Evidence was gained to suggest that several chromosomal loci regulate BMD in a site-specific and sex-specific manner. INTRODUCTION: BMD is a heritable trait and an important predictor of osteoporotic fracture risk. Several genome-wide scans have been performed in an attempt to detect loci that regulate BMD, but there has been limited replication of linkage peaks between studies. In an attempt to resolve these inconsistencies, we conducted a collaborative meta-analysis of genome-wide linkage scans in which femoral neck BMD (FN-BMD) or lumbar spine BMD (LS-BMD) had been studied. MATERIALS AND METHODS: Data were accumulated from nine genome-wide scans involving 11,842 subjects. Data were analyzed separately for LS-BMD and FN-BMD and by sex. For each study, genomic bins of 30 cM were defined and ranked according to the maximum LOD score they contained. While various densitometers were used in different studies, the ranking approach that we used means that the results are not confounded by the fact that different measurement devices were used. Significance for high average rank and heterogeneity was obtained through Monte Carlo testing. RESULTS: For LS-BMD, the quantitative trait locus (QTL) with greatest significance was on chromosome 1p13.3-q23.3 (p = 0.004), but this exhibited high heterogeneity and the effect was specific for women. Other significant LS-BMD QTLs were on chromosomes 12q24.31-qter, 3p25.3-p22.1, 11p12-q13.3, and 1q32-q42.3, including one on 18p11-q12.3 that had not been detected by individual studies. For FN-BMD, the strongest QTL was on chromosome 9q31.1-q33.3 (p = 0.002). Other significant QTLs were identified on chromosomes 17p12-q21.33, 14q13.1-q24.1, 9q21.32-q31.1, and 5q14.3-q23.2. There was no correlation in average ranks of bins between men and women and the loci that regulated BMD in men and women and at different sites were largely distinct. CONCLUSIONS: This large-scale meta-analysis provided evidence for replication of several QTLs identified in previous studies and also identified a QTL on chromosome 18p11-q12.3, which had not been detected by individual studies. However, despite the large sample size, none of the individual loci identified reached genome-wide significance.

Bone Density↗
PubMed2005

Genome scan meta-analysis of rheumatoid arthritis.

OBJECTIVE: Genome scans for rheumatoid arthritis (RA) have yielded inconsistent results. The absence of replication of linkage might be due to lack of power of individual studies. We performed a genome scan meta-analysis of published data to increase statistical power and to assess evidence for linkage of RA across genome scan studies. METHODS: Four RA whole-genome scans containing 767 families with 964 sibling pairs were included for the genome scan meta-analysis (GSMA). The GSMA method was applied to pool the results obtained from four genome scans. For each study, 120 genomic bins of approximately 30 centimorgans were defined and ranked according to maximum evidence for linkage within each bin. Bin ranks were weighted and summed across all studies. The summed rank for each bin was assessed empirically for significance using permutation methods. RESULTS: A total of nine bins lay above the 95% confidence level (P=0.05) and four bins were above the 99% confidence level (P=0.01) in the RA GSMA, suggesting that these bins contain RA-linked loci: bins 6.2, 6.4, 8.1, 18.3, 12.3, 12.2, 1.5, 6.3 and 16.2. The strongest evidence for linkage occurred on chromosome 6p22.3-6p21.1 (bin 6.2), containing the HLA region (P(sumrnk)=0.0000008). CONCLUSION: This RA GSMA confirmed the evidence for HLA loci as the greatest susceptibility factor to RA and showed evidence for linkage at non-HLA loci, such as chromosomes 1p, 6, 8p, 12, 16 and 18q, across studies. These data may provide a basis to carry out targeted linkage and candidate gene studies, particularly in the regions.

Arthritis, Rheumatoid↗
PubMed2025

Soffritto: a deep learning model for predicting high-resolution replication timing.

MOTIVATION: Replication timing (RT) refers to the order in which DNA loci are replicated during S phase. RT is cell-type specific and implicated in cellular processes including transcription, differentiation, and disease. RT is typically quantified genome-wide using two-fraction assays (e.g. Repli-Seq) which sort cells into early and late S phase fractions followed by DNA sequencing, yielding a ratio as the RT signal. While two-fraction RT data are widely available in multiple cell lines, it is limited in its ability to capture high-resolution RT features. To address this, high-resolution Repli-Seq, which quantifies RT across 16 fractions, was developed, but it is costly and technically challenging with very limited data generated to date. RESULTS: Here, we developed Soffritto, a deep learning model that predicts high-resolution RT data using two-fraction RT data, histone ChIP-seq data, GC content, and gene density as input. Soffritto is composed of a Long Short-Term Memory (LSTM) module and a prediction module. The LSTM module learns long- and short-range interactions between genomic bins, while the prediction module is composed of a fully connected layer that outputs a 16-fraction probability vector for each bin using the LSTM module's embeddings as input. By performing both within cell line and cross-cell line training and testing for five human and mouse cell lines, we show that Soffritto is able to capture experimental 16-fraction RT signals with high accuracy, and the predicted signals allow detection of high-resolution RT patterns. AVAILABILITY AND IMPLEMENTATION: Soffritto is available at https://github.com/ay-lab/Soffritto.

Deep Learning↗
PubMed2025

CDACHIE: chromatin domain annotation by integrating chromatin interaction and epigenomic data with contrastive learning.

MOTIVATION: Chromatin domain annotation identifies functional genomic regions, such as active and inactive zones, based on epigenomic features like histone modifications, DNA methylation, and chromatin accessibility. While recent methods have utilized both chromatin interaction data (e.g. Hi-C) and epigenomic data, they often overlook the direct relationship between these data types. RESULTS: In this study, we introduce Chromatin Domain Annotation using Contrastive Learning for Hi-C and Epigenomic Data (CDACHIE), a method for identifying chromatin domains from Hi-C and epigenomic data. Our approach leverages contrastive learning to generate aligned representative vectors for both data types at each genomic bin. The concatenated vectors are then clustered using K-means to classify distinct chromatin domain types. CDACHIE achieves superior performance in Variance Explained, evaluated across gene expression, replication timing, and ChIA-PET data. This highlights its robust ability to integrate semantic associations between Hi-C and epigenomic features within the embedding space. AVAILABILITY AND IMPLEMENTATION: The source code is available at GitHub: https://github.com/maruyama-lab-design/CDACHIE. An archival snapshot of the code used in this study is available on Zenodo: https://doi.org/10.5281/zenodo.15751780.

Chromatin↗
PubMed2025

Enhancing genome recovery across metagenomic samples using MAGmax.

SUMMARY: The number of metagenome-assembled genomes (MAGs) is rapidly increasing with the growing scale of metagenomic studies, driving fast progress in microbiome research. Sample-wise assembly has become the standard due to its computational efficiency and strain-level resolution. It requires dereplication, the removal of near-identical genomes assembled in different metagenomic samples. We present MAGmax, an efficient dereplication tool that enhances both the quantity and quality of MAGs through a strategy of bin merging and reassembly. Unlike dRep, which selects a single representative bin per genome cluster, MAGmax merges multiple bins within a cluster and reassembles them to increase coverage. MAGmax produces more dereplicated, higher-quality MAGs than dRep at 1.6&#xd7; its speed and using three times less memory. AVAILABILITY AND IMPLEMENTATION: The MAGmax open source software, implemented in Rust, is available under the GPLv3 license at https://github.com/soedinglab/MAGmax.

Metagenomics↗
PubMed2026

Cleanifier: contamination removal from microbial sequences using spaced seeds of a human pangenome index.

MOTIVATION: The first step when working with DNA data of human-derived microbiomes is to remove human contamination for two reasons. First, many countries have strict privacy and data protection guidelines for human sequence data, so microbiome data containing partly human data cannot be easily further processed or published. Second, human contamination may cause problems in downstream analysis, such as metagenomic binning or genome assembly. For large-scale metagenomics projects, fast and accurate removal of human contamination is therefore critical. RESULTS: We introduce Cleanifier, a fast and memory frugal alignment-free tool for detecting and removing human contamination based on gapped k-mers, or spaced seeds. Cleanifier uses a pangenome index of known human gapped k-mers, and the creation and use of alternative references is also possible. Reads are classified and filtered according to their gapped k-mer content. Cleanifier supports two filtering modes: one that queries all gapped k-mers and one that queries only a sample of them. A comparison of Cleanifier with other state-of-the-art tools shows that the sampling mode makes Cleanifier the fastest method with comparable accuracy. When using a probabilistic Cuckoo filter to store the complete k-mer set, Cleanifier has similar memory requirements to methods that use a sampled minimizer index. At the same time, Cleanifier is more flexible, because it can use different sampling methods on the same index. AVAILABILITY AND IMPLEMENTATION: Cleanifier is available via gitlab (https://gitlab.com/rahmannlab/cleanifier), PyPi (https://pypi.org/project/cleanifier/), and Bioconda (https://anaconda.org/bioconda/cleanifier). The pre-computed human pangenome index is available at Zenodo (https://doi.org/10.5281/zenodo.15639519).

Humans↗
PubMed2006

Testing for genetic heterogeneity in the genome search meta-analysis method.

The Genome Search Meta-Analysis (GSMA) method is widely used to detect linkage by pooling results of previously published genome-wide linkage studies. The GSMA uses a non-parametric summed rank statistic in 30 cM bins of the genome. Zintzaras and Ioannidis ([2005] Genet. Epidemiol. 28:123-137) developed a method of testing for heterogeneity of evidence for linkage in the GSMA, with three heterogeneity statistics (Q, Ha, B). They implement two testing procedures, restricted versus unrestricted for the summed rank within the bin. We show here that the rank-unrestricted test provides a conservative test for high heterogeneity and liberal test for low heterogeneity in linked regions. The rank-restricted test should therefore be used, despite the extensive simulations needed. In a simulation study, we show that the power to detect heterogeneity is low. For 20 studies of affected sib pairs, simulated assuming linkage in all studies to a gene with sibling relative risk of 1.3, the power to detect low heterogeneity using the Q statistic was 14%. With linkage present in 50% of the studies (to a gene with sibling relative risk of 1.4), the Q heterogeneity statistic had power of 29% to detect high heterogeneity. The power to detect linkage using the summed rank was high in both of these situations, at 98% and 79%, respectively. Although testing for heterogeneity in the GSMA is of interest, the currently available method provides little additional information to that provided by the summed rank statistic.

Genetic Heterogeneity↗
PubMed2006

Unigene derived microsatellite markers for the cereal genomes.

Unigene derived microsatellite (UGMS) markers have the advantage of assaying variation in the expressed component of the genome with unique identity and positions. We characterized the microsatellite motifs present in the unigenes of five cereal species namely, rice, wheat, maize, Sorghum and barley and compared with those in Arabidopsis. The overall UGMS frequency in the five cereal species was 1/7.6 kb. The maximum UGMS frequency was in rice (1/3.6 kb) and the lowest in wheat (1/10.6 kb). GC-rich trinucleotide repeat motifs coding for alanine followed by arginine and the dinucleotide repeat motif GA were found to be abundant UGMS classes across all the five cereal species. Primers could be designed for 95% (wheat and barley) to 97% (rice) of the identified microsatellites. The proportion and frequency of occurrence of long hypervariable class I (> or = 20 nucleotides) and potentially variable class II (12-20 nucleotides) UGMS across five cereal species were characterized. The class I UGMS markers were physically mapped in silico on to the finished rice genome and bin-mapped in wheat. Comparative mapping based on class I UGMS markers in rice and wheat revealed syntenic relationships between the two genomes. High degree of conservation and cross-transferability of the class I UGMS markers were evident among the five cereal species, which was validated experimentally. The class I UGMS-conserved orthologous set (COS) markers identified in this study would be useful for understanding the evolution of genes and genomes in cereals.

Arabidopsis↗
PubMed2005

Mapping with a few plants: using selective mapping for microsatellite saturation of the Prunus reference map.

The concept of selective (or bin) mapping is used here for the first time, using as an example the Prunus reference map constructed with an almond x peach F2 population. On the basis of this map, a set of six plants that jointly defined 65 possible different genotypes for the codominant markers mapped on it was selected. Sixty-three of these joint genotypes corresponded to a single chromosomal region (a bin) of the Prunus genome, and the two remaining corresponded to two bins each. The 67 bins defined by these six plants had a 7.8-cM average length and a maximum individual length of 24.7 cM. Using a unit of analysis composed of these six plants, their F1 hybrid parent, and one of the parents of the hybrid, we mapped 264 microsatellite (or simple-sequence repeat, SSR) markers from 401 different microsatellite primer pairs. Bin mapping proved to be a fast and economic strategy that could be used for further map saturation, the addition of valuable markers (such as those based on microsatellites or ESTs), and giving a wider scope to, and a more efficient use of, reference mapping populations.

Chromosome Mapping↗
PubMed2006

Genome-wide similarity search for transcription factors and their binding sites in a metal-reducing prokaryote Geobacter sulfurreducens.

The knowledge obtained from understanding individual elements involved in gene regulation is important for reconstructing gene regulatory networks, a key for understanding cellular behavior. To study gene regulatory interactions in a model microorganism, Geobacter sulfurreducens, which participates in metal reduction and energy harvesting, we investigated the presence of 59 known Escherichia coli transcription factors and predicted transcription regulatory sites in its genome. The supplementary material, available at http://www.geobacter.org/research/genomescan/, provides the results of similarity comparisons that identified regulatory proteins of G. sulfurreducens and the genome locations of the predicted regulatory sites, including the list of putative regulatory elements in the upstream regions of every predicted operon and singleton open reading frame. Regulatory sequence elements, predicted using genome similarity searches to matrices of established transcription regulatory elements from E. coli, provide an initial insight into regulation of genes and operons in G. sulfurreducens. The predicted regulatory elements were predominantly located in the upstream regions of operons and singleton open reading frames. The validity of the predictions was examined using a permutation approach. Sequence similarity searches indicate that E. coli transcription factors ArgR, CytR, DeoR, FlhCD (both FlhC and FlhD subunits), FruR, GalR, GlpR, H-NS, LacI, MetJ, PurR, TrpR, and Tus are likely missing from G. sulfurreducens. Phylogenetic analysis suggests that one HU subunit is present in G. sulfurreducens as compared to two subunits in E. coli, while each of the two E. coli IHF subunits, HimA and HimD, have two homologs in G. sulfurreducens. The closest homolog of E. coli RpoE in G. sulfurreducens may be more similar to FecI than to RpoE. These findings represent the first step in the understanding of the regulatory relationships in G. sulfurreducens on the genome scale.

Bacterial Proteins↗
PubMed2026

Exploring the hypothetical role of Bacteroides species in depression progression: insights from metagenomic analysis.

Depression, a psychiatric disorder with significant morbidity and mortality, has a complex etiology. Recent advances in microbiome research have highlighted the potential role of fecal microbiota in depression pathogenesis. This study utilized shotgun metagenomic sequencing to compare the fecal microbiota of 28 depression patients and 26 healthy individuals. Significant differences in fecal microbiota composition were observed between the two groups. We generated 350 non-redundant high-quality metagenome-assembled genomes (MAGs) by binning and conducted comparisons between the depression and control groups. Notably, we found that the MAGs enriched in people with depression mostly belonged to Bacteroides, indicating a close link between Bacteroides abundance and the development of depression, suggesting that Bacteroides might be a potential culprit for depression. In the depression group, we found that the module of nitric oxide synthesis was remarkably enriched, and all Bacteroides MAGs contained genes annotated as nitric oxide synthase, suggesting that increased levels of Bacteroides may contribute to elevated nitric oxide synthesis. A distinct microbial signature consisting of Arthrobacter sp._U41, Bacillus cereus, Campylobacter rectus, and Pasteurella dagmatis accurately discriminates between depressed individuals and healthy controls, achieving an average area under the receiver operating characteristic curve of 0.950. This research sheds light on the potential role of fecal microbiota in depression and highlights specific metabolic pathways and microbial markers for further investigation.IMPORTANCEThis research highlighted significant differences in the composition and function of fecal microbiota between individuals with depression and healthy individuals, particularly the enrichment of Bacteroides metagenome-assembled genomes (MAGs) in depression patients. The upregulation of the nitric oxide synthesis pathway associated with these MAGs belonging to Bacteroides in the gut of depression patients had also been observed. The selected bacterial biomarkers reliably differentiate depression cases from healthy controls with high diagnostic accuracy (mean area under the receiver operating characteristic curve = 0.950). Our results suggest the importance of exploring microbial markers as potential diagnostic and therapeutic targets in managing depression.

Humans↗
PubMed2005

Differentiation of xanthomonads causing the bacterial leaf spot of poinsettia in China from the pathotype strain of Xanthomonas axonopodis pv. poinsettiicola.

In October 2003, a new bacterial disease with symptoms similar to those caused by Xanthomonas axonopodis pv. poinsettiicola was observed on poinsettia leaves at a flower nursery in Zhejiang Province of China. Three Xanthomonas strains were isolated from infected plants and classified as X. axonopodis. They were differentiated from the pathotype strain LMG849 of X. axonopodis pv. poinsettiicola causing bacterial leaf spot of poinsettia by comparison of pathogenicity, substrate utilization and BOX-PCR genomic fingerprints.

Cell Differentiation↗