In-silico Gene Annotation Prediction Using the Co-expression Network Structure

Romero, Miguel; Finke, Jorge; Quimbaya, Mauricio; Rocha, Camilo

doi:10.1007/978-3-030-36683-4_64

“…It uses any existing body of knowledge about gene annotations for a given genome, and the topological properties of its gene co-expression network, to train a supervised machine learning model that is designed to discover unknown annotations. These results, sumarized in Table 1, revealed that the topological properties derived from co-expression networks improve our predictions for annotating genes (Romero et al, 2020).…”

Section: Epigenetic and Genetic Characterization Of Cropsmentioning

confidence: 59%

“…The 'Max FP' column summarizes the number of times (out of a total of 50) such an annotation is suggested for a gene, while the 'FP' column identifies the number of genes that are consistently given such an annotation. From (Romero et al, 2020).…”

Section: Epigenetic and Genetic Characterization Of Cropsmentioning

confidence: 99%

“…Our main result so far is the discovery of a major QTL for resistance to the virus in two Colombian cultivars, FD 50 and FD 2000, as well as two QTLs that control the damages caused by the insect vector (Romero et al, 2014). This QTL for RHBV resistance, renamed as qHBV4.1, controls RHBV incidence, which is simply the percentage of plants that show symptoms of viral infection, no matter the extent or severity of the symptoms.…”

Section: Tolerance To Rice Hoja Blanca Virusmentioning

confidence: 99%

See 1 more Smart Citation

The ÓMICAS alliance, an international research program on multi-omics for crop breeding optimization

Jaramillo-Botero¹,

Colorado²,

Quimbaya³

et al. 2022

Self Cite

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The OMICAS alliance is part of the Colombian government’s Scientific Ecosystem, established between 2017-2018 to promote world-class research, technological advancement and improved competency of higher education across the nation. Since the program’s kick-off, OMICAS has focused on consolidating and validating a multi-scale, multi-institutional, multi-disciplinary strategy and infrastructure to advance discoveries in plant science and the development of new technological solutions for improving agricultural productivity and sustainability. The strategy and methods described in this article, involve the characterization of different crop models, using high-throughput, real-time phenotyping technologies as well as experimental tissue characterization at different levels of the omics hierarchy and under contrasting conditions, to elucidate epigenome-, genome-, proteome- and metabolome-phenome relationships. The massive data sets are used to derive in-silico models, methods and tools to discover complex underlying structure-function associations, which are then carried over to the production of new germplasm with improved agricultural traits. Here, we describe OMICAS’ R&D trans-disciplinary multi-project architecture, explain the overall strategy and methods for crop-breeding, recent progress and results, and the overarching challenges that lay ahead in the field.

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“…In particular, over the last twenty years, the interests of research have focused on complex networks, namely networks whose structure is irregular, complex and dynamically evolving in time [26,42,67]. Complex networks naturally model many real-world scenarios, such as social interactions [31,55], biological [40,41,62] and economical [37,70] systems, Internet [36], and the World Wide Web [63], just to name a few examples. Traditionally, these networks are described using graphs, where nodes represent elements of the network, and edges represent relationships between some pairs of elements.…”

Section: Introductionmentioning

confidence: 99%

Analyzing, Exploring, and Visualizing Complex Networks via Hypergraphs using SimpleHypergraphs.jl

Spagnuolo¹,

Cordasco²,

Szufel³

et al. 2020

Internet Mathematics

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Real-world complex networks are usually being modeled as graphs. The concept of graphs assumes that the relations within the network are binary (for instance, between pairs of nodes); however, this is not always true for many real-life scenarios, such as peer-to-peer communication schemes, paper co-authorship, or social network interactions. For such scenarios, it is often the case that the underlying network is better and more naturally modeled by hypergraphs. A hypergraph is a generalization of a graph in which a single (hyper)edge can connect any number of vertices. Hypergraphs allow modelers to have a complete representation of multi-relational (many-to-many) networks; hence, they are extremely suitable for analyzing and discovering more subtle dependencies in such data structures. Working with hypergraphs requires new software libraries that make it possible to perform operations on them, from basic algorithms (such as searching or traversing the network) to computing significant hypergraph measures, to including more challenging algorithms (such as community detection). In this paper, we present a new software library, SimpleHypergraphs.jl, written in the Julia language and designed for high-performance computing on hypergraphs and propose two new algorithms for analyzing their properties: s-betweenness and modified label propagation. We also present various approaches for hypergraph visualization integrated into our tool. In order to demonstrate how to exploit the library in practice, we discuss two case studies based on the 2019 Yelp Challenge dataset and the collaboration network built upon the Game of Thrones TV series. The results are promising and they confirm the ability of hypergraphs to provide more insight than standard graph-based approaches.

show abstract

“…In particular, over the last twenty years, the interests of research have focused on complex networks, namely networks whose structure is irregular, complex and dynamically evolving in time [26,42,67]. Complex networks naturally model many real-world scenarios, such as social interactions [31,55], biological [40,41,62] and economical [37,70] systems, Internet [36], and the World Wide Web [63], just to name a few examples. Traditionally, these networks are described using graphs, where nodes represent elements of the network, and edges represent relationships between some pairs of elements.…”

Section: Introductionmentioning

confidence: 99%

Analyzing, Exploring, and Visualizing Complex Networks via Hypergraphs using SimpleHypergraphs.jl

Antelmi,

Cordasco,

Kamiński

et al. 2020

Preprint

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Real-world complex networks are usually being modeled as graphs. The concept of graphs assumes that the relations within the network are binary (for instance, between pairs of nodes); however, this is not always true for many real-life scenarios, such as peer-to-peer communication schemes, paper co-authorship, or social network interactions. For such scenarios, it is often the case that the underlying network is better and more naturally modeled by hypergraphs. A hypergraph is a generalization of a graph in which a single (hyper)edge can connect any number of vertices. Hypergraphs allow modelers to have a complete representation of multi-relational (many-to-many) networks; hence, they are extremely suitable for analyzing and discovering more subtle dependencies in such data structures. Working with hypergraphs requires new software libraries that make it possible to perform operations on them, from basic algorithms (such as searching or traversing the network) to computing significant hypergraph measures, to including more challenging algorithms (such as community detection). In this paper, we present a new software library, SimpleHypergraphs.jl, written in the Julia language and designed for high-performance computing on hypergraphs and propose two new algorithms for analyzing their properties: s-betweenness and modified label propagation. We also present various approaches for hypergraph visualization integrated into our tool. In order to demonstrate how to exploit the library in practice, we discuss two case studies based on the 2019 Yelp Challenge dataset and the collaboration network built upon the Game of Thrones TV series. The results are promising and they confirm the ability of hypergraphs to provide more insight than standard graph-based approaches.

show abstract

In-silico Gene Annotation Prediction Using the Co-expression Network Structure

Cited by 6 publications

References 25 publications

The ÓMICAS alliance, an international research program on multi-omics for crop breeding optimization

The ÓMICAS alliance, an international research program on multi-omics for crop breeding optimization

Analyzing, Exploring, and Visualizing Complex Networks via Hypergraphs using SimpleHypergraphs.jl

Analyzing, Exploring, and Visualizing Complex Networks via Hypergraphs using SimpleHypergraphs.jl

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