Genome Database Integration

Author(s):  
Andrew Robinson ◽  
Wenny Rahayu
2013 ◽  
Vol 42 (D1) ◽  
pp. D810-D817 ◽  
Author(s):  
Judith A. Blake ◽  
Carol J. Bult ◽  
Janan T. Eppig ◽  
James A. Kadin ◽  
Joel E. Richardson ◽  
...  

1999 ◽  
Author(s):  
Peter Buneman ◽  
S. Davidson ◽  
V. Tannen

2021 ◽  
Vol 22 (4) ◽  
pp. 1622
Author(s):  
Yanyan Wang ◽  
Zefeng Zhai ◽  
Yueting Sun ◽  
Chen Feng ◽  
Xiang Peng ◽  
...  

B-BOX proteins are zinc finger transcription factors that play important roles in plant growth, development, and abiotic stress responses. In this study, we identified 15 PavBBX genes in the genome database of sweet cherry. We systematically analyzed the gene structures, clustering characteristics, and expression patterns of these genes during fruit development and in response to light and various hormones. The PavBBX genes were divided into five subgroups. The promoter regions of the PavBBX genes contain cis-acting elements related to plant development, hormones, and stress. qRT-PCR revealed five upregulated and eight downregulated PavBBX genes during fruit development. In addition, PavBBX6, PavBBX9, and PavBBX11 were upregulated in response to light induction. We also found that ABA, BR, and GA3 contents significantly increased in response to light induction. Furthermore, the expression of several PavBBX genes was highly correlated with the expression of anthocyanin biosynthesis genes, light-responsive genes, and genes that function in multiple hormone signaling pathways. Some PavBBX genes were strongly induced by ABA, GA, and BR treatment. Notably, PavBBX6 and PavBBX9 responded to all three hormones. Taken together, BBX proteins likely play major roles in regulating anthocyanin biosynthesis in sweet cherry fruit by integrating light, ABA, GA, and BR signaling pathways.


Author(s):  
Jianglin Feng ◽  
Nathan C Sheffield

Abstract Summary Databases of large-scale genome projects now contain thousands of genomic interval datasets. These data are a critical resource for understanding the function of DNA. However, our ability to examine and integrate interval data of this scale is limited. Here, we introduce the integrated genome database (IGD), a method and tool for searching genome interval datasets more than three orders of magnitude faster than existing approaches, while using only one hundredth of the memory. IGD uses a novel linear binning method that allows us to scale analysis to billions of genomic regions. Availability https://github.com/databio/IGD


Genetics ◽  
2003 ◽  
Vol 163 (4) ◽  
pp. 1299-1313
Author(s):  
Zheng Xu ◽  
Britton Lance ◽  
Claudia Vargas ◽  
Budak Arpinar ◽  
Suchendra Bhandarkar ◽  
...  

Abstract A bioinformatics tool called ODS3 has been created for mapping by sequencing. The tool allows the creation of integrated genomic maps from genetic, physical mapping, and sequencing data and permits an integrated genome map to be stored, retrieved, viewed, and queried in a stand-alone capacity, in a client/server relationship with the Fungal Genome Database (FGDB), and as a web-browsing tool for the FGDB. In that ODS3 is programmed in Java, the tool promotes platform independence and supports export of integrated genome-mapping data in the extensible markup language (XML) for data interchange with other genome information systems. The tool ODS3 is used to create an initial integrated genome map of the AIDS-related fungal pathogen, Pneumocystis carinii. Contig dynamics would indicate that this physical map is ∼50% complete with ∼200 contigs. A total of 10 putative multigene families were found. Two of these putative families were previously characterized in P. carinii, namely the major surface glycoproteins (MSGs) and HSP70 proteins; three of these putative families (not previously characterized in P. carinii) were found to be similar to families encoding the HSP60 in Schizosaccharomyces pombe, the heat-shock Ψ protein in S. pombe, and the RNA synthetase family (i.e., MES1) in Saccharomyces cerevisiae. Physical mapping data are consistent with the 16S, 5.8S, and 26S rDNA genes being single copy in P. carinii. No other fungus outside this genus is known to have the rDNA genes in single copy.


2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A711-A711
Author(s):  
Matthew Robinson ◽  
Kevin Vervier ◽  
Simon Harris ◽  
David Adams ◽  
Doreen Milne ◽  
...  

BackgroundThe gut microbiome of cancer patients appears to be associated with response to Immune Checkpoint Inhibitor (ICIs) treatment.1–4 However, the bacteria linked to response differ between published studies.MethodsLongitudinal stool samples were collected from 69 patients with advanced melanoma receiving approved ICIs in the Cambridge (UK) MELRESIST study. Pretreatment samples were analysed by Microbiotica, using shotgun metagenomic sequencing. Microbiotica’s sequencing platform comprises the world’s leading Reference Genome Database and advanced Microbiome Bioinformatics to give the most comprehensive and precise mapping of the gut microbiome. This has enabled us to identify gut bacteria associated with ICI response missed using public reference genomes. Published microbiome studies in advanced melanoma,1–3renal cell carcinoma (RCC) and non-small cell lung cancer (NSCLC)4 were reanalysed with the same platform.ResultsAnalysis of the MELRESIST samples showed an overall change in the microbiome composition between advanced melanoma patients and a panel of healthy donor samples, but not between patients who subsequently responded or did not respond to ICIs. However, we did identify a discrete microbiome signature which correlated with response. This signature predicted response with an accuracy of 93% in the MELRESIST cohort, but was less predictive in the published melanoma cohorts.1–3 Therefore, we developed a bioinformatic analytical model, incorporating an interactive random forest model and the MELRESIST dataset, to identify a microbiome signature which was consistent across all published melanoma studies. This model was validated three times by accurately predicting the outcome of an independent cohort. A final microbiome signature was defined using the validated model on MELRESIST and the three published melanoma cohorts. This was very accurate at predicting response in all four studies combined (91%), or individually (82–100%). This signature was also predictive of response in a NSCLC study and to a lesser extent in RCC. The core of this signature is nine bacteria significantly increased in abundance in responders.ConclusionsAnalysis of the MELRESIST study samples, precision microbiome profiling by the Microbiotica Platform and a validated bioinformatic analysis, have enabled us to identify a unique microbiome signature predictive of response to ICI therapy in four independent melanoma studies. This removes the challenge to the field of different bacteria apparently being associated with response in different studies, and could represent a new microbiome biomarker with clinical application. Nine core bacteria may be driving response and hold potential for co-therapy with ICIs.Ethics ApprovalThe study was approved by Newcastle & North Tyneside 2 Research Ethics Committee, approval number 11/NE/0312.ReferencesMatson V, Fessler J, Bao R, et al. The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients. Science 2018;359(6371):104–108.Gopalakrishnan V, Spencer CN, Nezi L, et al. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 2018;359(6371):97–103.Frankel AE, Coughlin LA, Kim J, et al. Metagenomic shotgun sequencing and unbiased metabolomic profiling identify specific human gut microbiota and metabolites associated with immune checkpoint therapy efficacy in melanoma patients. Neoplasia 2017;19(10):848–855.Routy B, Le Chatelier E, Derosa L, et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science 2018;359(6371):91–97.


Children ◽  
2021 ◽  
Vol 8 (7) ◽  
pp. 601
Author(s):  
Kyung-Sun Park

In this study, two different approaches were applied in the analysis of the GAA gene. One was analyzed based on patients with Pompe disease, and the other was analyzed based on GAA genomic data from unaffected carriers in a general population genetic database. For this, GAA variants in Korean and Japanese patients reported in previous studies and in patients reported in the Pompe disease GAA variant database were analyzed as a model. In addition, GAA variants in the Korean Reference Genome Database (KRGDB), the Japanese Multi Omics Reference Panel (jMorp), and the Genome Aggregation Database (gnomAD) were analyzed. Overall, approximately 50% of the pathogenic or likely pathogenic variants (PLPVs) found in unaffected carriers were also found in real patients with Pompe disease (Koreans, 57.1%; Japanese, 46.2%). In addition, there was a moderate positive correlation (Spearman’s correlation coefficient of 0.45–0.69) between the proportion of certain PLPVs in patients and the minor allele frequency of their variants in a general population database. Based on the analysis of general population databases, the total carrier frequency for Pompe disease in Koreans and Japanese was estimated to be 1.7% and 0.7%, respectively, and the predicted genetic prevalence was 1:13,657 and 1:78,013, respectively.


Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Tao Liu ◽  
Yutong Cui ◽  
Xuli Jia ◽  
Jing Zhang ◽  
Ruoran Li ◽  
...  

Abstract Algae are the oldest taxa on Earth, with an evolutionary relationship that spans prokaryotes (Cyanobacteria) and eukaryotes. A long evolutionary history has led to high algal diversity. Their organelle DNAs are characterized by uniparental inheritance and a compact genome structure compared with nuclear genomes; thus, they are efficient molecular tools for the analysis of gene structure, genome structure, organelle function and evolution. However, an integrated organelle genome database for algae, which could enable users to both examine and use relevant data, has not previously been developed. Therefore, to provide an organelle genome platform for algae, we have developed a user-friendly database named Organelle Genome Database for Algae (OGDA, http://ogda.ytu.edu.cn/). OGDA contains organelle genome data either retrieved from several public databases or sequenced in our laboratory (Laboratory of Genetics and Breeding of Marine Organism [MOGBL]), which are continuously updated. The first release of OGDA contains 1055 plastid genomes and 755 mitochondrial genomes. Additionally, a variety of applications have been integrated into this platform to analyze the structural characteristics, collinearity and phylogeny of organellar genomes for algae. This database represents a useful tool for users, enabling the rapid retrieval and analysis of information related to organellar genomes for biological discovery.


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