The GeneAround GO viewer

Tanoue, Junko; Yoshikawa, Masatoshi; Uemura, Shunsuke

doi:10.1093/bioinformatics/18.12.1705

Cited by 10 publications

(5 citation statements)

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“…Using embedded Javascript, virtually unlimited text information can be associated with each graphic element and a user can control the amount of information displayed at one time. These features make SVG well suited for visualizing genomic data, as demonstrated in several bioinformatics applications [10-14]. …”

Section: Methodsmentioning

confidence: 99%

eQTL Viewer: visualizing how sequence variation affects genome-wide transcription

2007

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Background: Expression Quantitative Trait Locus (eQTL) mapping methods have been used to identify the genetic basis of gene expression variations. To map eQTL, thousands of expression profiles are related with sequence polymorphisms across the genome through their correlated variations. These eQTL distribute in many chromosomal regions, each of which can include many genes. The large number of mapping results produced makes it difficult to consider simultaneously the relationships between multiple genomic regions and multiple expressional profiles. There is a need for informative bioinformatics tools to assist the visualization and interpretation of these mapping results.

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Section: Methodsmentioning

confidence: 99%

eQTL Viewer: visualizing how sequence variation affects genome-wide transcription

2007

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“…Answers from models independently trained on different class-terms may violate the True Path Rule, which states that if a gene g is annotated to a term t, it must be also annotated to any ancestor of t in the GO term hierarchy [69]. To get likelihood values consistent with this rule, we apply two post-processing steps to each "raw" likelihood value p(g, t) given by the models.…”

Section: Prediction Likelihoodmentioning

confidence: 99%

Gene function finding through cross-organism ensemble learning

Moro

Masseroli

2021

BioData Mining

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Background Structured biological information about genes and proteins is a valuable resource to improve discovery and understanding of complex biological processes via machine learning algorithms. Gene Ontology (GO) controlled annotations describe, in a structured form, features and functions of genes and proteins of many organisms. However, such valuable annotations are not always reliable and sometimes are incomplete, especially for rarely studied organisms. Here, we present GeFF (Gene Function Finder), a novel cross-organism ensemble learning method able to reliably predict new GO annotations of a target organism from GO annotations of another source organism evolutionarily related and better studied. Results Using a supervised method, GeFF predicts unknown annotations from random perturbations of existing annotations. The perturbation consists in randomly deleting a fraction of known annotations in order to produce a reduced annotation set. The key idea is to train a supervised machine learning algorithm with the reduced annotation set to predict, namely to rebuild, the original annotations. The resulting prediction model, in addition to accurately rebuilding the original known annotations for an organism from their perturbed version, also effectively predicts new unknown annotations for the organism. Moreover, the prediction model is also able to discover new unknown annotations in different target organisms without retraining.We combined our novel method with different ensemble learning approaches and compared them to each other and to an equivalent single model technique. We tested the method with five different organisms using their GO annotations: Homo sapiens, Mus musculus, Bos taurus, Gallus gallus and Dictyostelium discoideum. The outcomes demonstrate the effectiveness of the cross-organism ensemble approach, which can be customized with a trade-off between the desired number of predicted new annotations and their precision.A Web application to browse both input annotations used and predicted ones, choosing the ensemble prediction method to use, is publicly available at http://tiny.cc/geff/. Conclusions Our novel cross-organism ensemble learning method provides reliable predicted novel gene annotations, i.e., functions, ranked according to an associated likelihood value. They are very valuable both to speed the annotation curation, focusing it on the prioritized new annotations predicted, and to complement known annotations available.

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“…The amounts of heavy metals increased in the beginning and decreased afterward, except for Mn that showed the opposite trend. An increase in the metal amount is attributed to different factors such as contamination by sediment particles, passive sorption onto recalcitrant organic fractions, and active accumulation by microbial colonizers (Breteler et al, 1981;Gadd, 1993;Zawislanski et al, 2001;Kovacova and Sturdik, 2002). As litter decomposition proceeds, organic acids forming during the humification could create stable complexes with metals and, therefore, affect the metal solubility and speciation (Fox and Comerford, 1990).…”

Section: Dynamics Of Heavy Metals In Different Litter Speciesmentioning

confidence: 99%

Dynamics and characteristics of biogenic silica and macro- and microelements in decomposing litter in the Min River estuary, southeast China

Zhai

Qiu

Gao

et al. 2021

Elementa: Science of the Anthropocene

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Tidal marshes are important recycling areas for biogenic silica (BSi) and macro- and microelements at the land–sea interface and are key locations for examining the decomposition process of wetland plant litter. In this study, in situ decomposition experiments were conducted with Phragmites australis, Cyperus malaccensis, and Spartina alterniflora in the Min River estuary wetland. Litterbags of 0.2-mm mesh size were used to evaluate the litter decomposition process and residual values of BSi and macro- and microelements, including C, N, Cr, Cu, Cd, Zn, Pb, Al, Mn, and Fe over 520 days. The litter decomposition rate significantly differed among species in the following order: C. malaccensis (0.005 d–1) > S. alterniflora (0.004 d–1) > P. australis (0.003 d–1) with BSi release rates of 98.64%, 96.75%, and 97.23%, respectively. Although there were net releases of BSi, C, and N from the three litter species, continuous decrease in the BSi/(C, N) ratio indicated that BSi was removed from the litter much faster than C and N. The accumulation index results showed that Cu, Pb, Al, and Fe were net-accumulated in the litter, whereas Cd, Mn, Cr, and Zn were predominantly released during litter decay. Pearson’s correlation analysis results showed that the amounts of N, Cu, Cd, Pb, Al, and Fe in the litter restrained BSi release with a significant negative correlation. These findings in the Min River estuary have important implications for geochemical cycles within wetland systems and the transport processes of potential nutrients out of the system.

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The GeneAround GO viewer

Abstract: We have developed a system for visualizing the Gene Ontology((TM)) hierarchy. The graphical browser interactively displays diagrams of the inheritance relationship for each term to help understand the meanings of terms when handling gene annotation data.

Cited by 10 publications

References 0 publications

eQTL Viewer: visualizing how sequence variation affects genome-wide transcription

eQTL Viewer: visualizing how sequence variation affects genome-wide transcription

Gene function finding through cross-organism ensemble learning

Dynamics and characteristics of biogenic silica and macro- and microelements in decomposing litter in the Min River estuary, southeast China

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