Chaos game representation and its applications in bioinformatics

Löchel, Hannah F; Heider, Dominik

doi:10.1016/j.csbj.2021.11.008

Cited by 49 publications

(20 citation statements)

References 65 publications

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“…In areas where identification is important, such as identifying the presence of disease vectors, an elevated false-negative or positive rate is unacceptable. We believe that the approach presented here and others (such as one-shot and few-shot learning) have significant potential for being used as a basis for classifying reads from environmental samples since genomic signatures and deepneural networks can capture global patterns within genomes that other approaches may miss 29,34,61 . For example, FCGRs can be seen as more than just k-mer counts since visual information about the overall structure of sequence, along with k-mer counts, is encoded into the representation 29,34,62 .…”

Section: Accessionmentioning

confidence: 99%

“…Following this, each CR sequence was transformed into a FCGR with a depth of 6 29 . Previous work has shown that this representation has the ability to efficiently summarize the unique characteristics of each genome and can be used for clustering and supervised learning 26,[34][35][36] . The FCGRs for each CR, along with the class labels, were then used as inputs into a semi-supervised deep learning model 27 .…”

Section: Validation Against Non-culicidae Dipteran Mitogenomesmentioning

confidence: 99%

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In silico identification of multiple conserved motifs within the control region of Culicidae mitogenomes

Harrison

Rudar

Ogden

et al. 2022

Sci Rep

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Mosquitoes are important vectors for human and animal diseases. Genetic markers, like the mitochondrial COI gene, can facilitate the taxonomic classification of disease vectors, vector-borne disease surveillance, and prevention. Within the control region (CR) of the mitochondrial genome, there exists a highly variable and poorly studied non-coding AT-rich area that contains the origin of replication. Although the CR hypervariable region has been used for species differentiation of some animals, few studies have investigated the mosquito CR. In this study, we analyze the mosquito mitogenome CR sequences from 125 species and 17 genera. We discovered four conserved motifs located 80 to 230 bp upstream of the 12S rRNA gene. Two of these motifs were found within all 392 Anopheles (An.) CR sequences while the other two motifs were identified in all 37 Culex (Cx.) CR sequences. However, only 3 of the 304 non-Culicidae Dipteran mitogenome CR sequences contained these motifs. Interestingly, the short motif found in all 37 Culex sequences had poly-A and poly-T stretch of similar length that is predicted to form a stable hairpin. We show that supervised learning using the frequency chaos game representation of the CR can be used to differentiate mosquito genera from their dipteran relatives.

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Section: Accessionmentioning

confidence: 99%

Section: Validation Against Non-culicidae Dipteran Mitogenomesmentioning

confidence: 99%

In silico identification of multiple conserved motifs within the control region of Culicidae mitogenomes

Harrison

Rudar

Ogden

et al. 2022

Sci Rep

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“…The DNA classification methodology was implemented in this research to generate significant development using comparative processing algorithms with image matrices due to the progress of computational platforms (Hoang et al, 2016). This methodology includes sequencing applications using fractal selfsimilarity theories and chaos game representation (CGR), reporting progress in genetics and protein field applications (Löchel and Heider, 2021;Jones et al, 2011). Spatial activities sequencing data will be mapped in the habitat and the characterization and classification analysis through the typical fractal image matrices of the deterministic spatial behavior of an animal will be implemented using algorithms with low computational cost compared to other methodologies and focused on cattle welfare projection and the rational management of ecosystems (Herlin et al, 2021;Hoang et al, 2016;Wang et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Behavioral fractal method associated with GPS tracking to spatial activity sequences of grazing cattle

Rodríguez-Márquez

Díaz

Delgado

2023

Sci. agric. (Piracicaba, Braz.)

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Global positioning and geographic information systems are essential for studying foraging animal behavior. This study aims to implement a fractal self-similarity and chaos game computational efficient methodology to determine the behavior-associated fractal using GPS data of activity sequences in spatial ranges of livestock movement trajectories in interaction with habitat factors. Six cows were randomly selected with an average weight of 480 kg, maintained under the same conditions, and a GPS-equipped collar was installed, programmed at intervals of 1 min and an average of 9 h daylight. Roughly 192810 registries and an average of 32135 signals per cow from trajectory tracking in spatial activity sequencing were used as a variable of interest in the fractal characterization methodology. Spatial patterns were evaluated using the Morán's spatial autocorrelation indices, cluster, and non-parametric statistics, evaluating deterministic spatial patterns of preferential activities associated to spatial ranges of less than 7.1 m (resting 42 %, grazing 38 %). GPS information was refined through spatial ranges and changes in activities under resting, eating, traveling, and complementary schemes associated to the fractal displacement behavior of grazing cattle. This information was processed and mapped using fractal self-similarity rules in the Sierpinski triangle to determine the typical fractal of spatial activities per animal in the habitat. The particular fractal record of each bovine as a function of trajectory sequences was mapped for binary image matrices, registering a good classification (83 %) of the animals by breed and climatological cycle, using information from the sequencing of spatial activities associated to the preferred behavior in the habitat.

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“…In this case, the 2-dimensional FCGR representation of the sequences must be flattened and cannot be fully exploited. For an extensive review on CGR and its applications in bioinformatics, we refer the reader to [21].…”

Section: Introductionmentioning

confidence: 99%

Accurate and Fast Clade Assignment via Deep Learning and Frequency Chaos Game Representation

Cartes

Anand

Ciccolella

et al. 2022

Preprint

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Background: Since the beginning of the COVID-19 pandemic there has been an explosion of sequencing of the SARS-CoV-2 virus, making it the most widely sequenced virus in the history. Several databases and tools have been created to keep track of genome sequences and variants of the virus, most notably the GISAID platform hosts millions of complete genome sequences, and it is continuously expanding every day. A challenging task is the development of fast and accurate tools that are able to distinguish between the different SARS-CoV-2 variants and assign them to a clade. Results: In this paper, we leverage the Frequency Chaos Game Representation (FCGR) and Convolutional Neural Networks (CNNs) to develop an original method that learns how to classify genome sequences that we implement into CouGaR-g, a tool for the clade assignment problem on SARS-CoV-2 sequences. On a testing subset of the GISAID, CouGaR-g achieves an 96.29% overall accuracy, while a similar tool, Covidex, obtained a 77,12% overall accuracy. As far as we know, our method is the first using Deep Learning and FCGR for intra-species classification. Furthermore, by using some feature importance methods CouGaR-g allows to identify k-mers that matches SARS-CoV-2 marker variants. Conclusions: By combining FCGR and CNNs, we develop a method that achieves a better accuracy than Covidex (which is based on Random Forest) for clade assignment of SARS-CoV-2 genome sequences, also thanks to our training on a much larger dataset, with comparable running times. Our method implemented in CouGaR-g is able to detect k-mers that capture relevant biological information that distinguishes the clades, known as marker variants. Availability: The trained models can be tested online providing a FASTA file (with one or multiple sequences) at https://huggingface.co/spaces/BIASLab/sars-cov-2-classification-fcgr. CouGaR-g is also available at https://github.com/AlgoLab/CouGaR-g under the GPL.

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Chaos game representation and its applications in bioinformatics

Abstract: Graphical abstract

Cited by 49 publications

References 65 publications

In silico identification of multiple conserved motifs within the control region of Culicidae mitogenomes

In silico identification of multiple conserved motifs within the control region of Culicidae mitogenomes

Behavioral fractal method associated with GPS tracking to spatial activity sequences of grazing cattle

Accurate and Fast Clade Assignment via Deep Learning and Frequency Chaos Game Representation

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