ToPS: A Framework to Manipulate Probabilistic Models of Sequence Data

Kashiwabara, André Yoshiaki; Bonadio, Ígor; Onuchic, Vitor; Amado, Felipe; Mathias, Rafael; Durham, Alan Mitchell

doi:10.1371/journal.pcbi.1003234

Cited by 10 publications

(20 citation statements)

References 30 publications

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“…CodAn uses two different architectures for analysing transcripts, one for full and one for partial transcripts (Figure 5. Both architectures are described using Generalized Hidden Markov Models implemented using the ToPS probabilistic framewok [12]. Of note, we partition our probabilistic model in GG content specific sub-models [28].…”

Section: Algorithm Implementationmentioning

confidence: 99%

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CodAn: predictive models for the characterization of mRNA transcripts in Eukaryotes

Nachtigall

Kashiwabara

Durham

2019

Preprint

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Characterization of the coding sequences (CDSs) is an essential step on transcriptome annotation. Incorrect characterization of CDSs can lead to the prediction of non-existent proteins that can eventually compromise knowledge if databases are populated with similar incorrect predictions made in different genomes. Even though some recent methods have succeeded in correctly prediction of the stop codon position in strand-specific sequences, prediction of the complete CDS is still far from a gold standard. More importantly, prediction in strand-blind sequences and in partial sequences is deficient, presenting very low accuracy. Here, we present CodAn, a new computational approach to predict CDS and UTR, that significantly pushes the boundaries of CDS prediction in strand-blind and in partial sequences, increases strandspecific full-CDS predictions and matches or surpasses gold-standard results in strand-specific stop codon predictions. CodAn is freely available for download at https://github.com/pedronachtigall/CodAn.

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Section: Algorithm Implementationmentioning

confidence: 99%

“…CodAn uses ToPS [12] to implement the Generalized Hidden Markov Model architectures, Python (v.3.6.8) and Perl (v5.26.1) scripts to prepare and process data for the ToPS probabilistic framework.…”

Section: Algorithm Implementationmentioning

confidence: 99%

CodAn: predictive models for the characterization of mRNA transcripts in Eukaryotes

Nachtigall

Kashiwabara

Durham

2019

Preprint

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“…Afirma-se que, se for possível modelar sequências de dados categóricos com precisão, então é possível fazer boas previsões e planejamento ótimo nos processos de decisão. Kashiwabara et. al.…”

Section: Trabalhos Relacionados E Contextualizaçãounclassified

Aplicação da Árvore Probabilística de Sufixo na Predição de Resultados do Processo de Extração de Café Solúvel

Silva¹,

Lima²,

Lopes³

et al. 2017

Anais Do XLIV Seminário Integrado De Software E Hardware (SEMISH 2017)

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A extração de café solúvel é um processo industrial que gera grande quantidade de dados em tempo real, como rendimento, pH, temperaturas, concentração, percentual de sólidos solúveis, dentre outros. No entanto, essa grande quantidade de dados é pouco aproveitada na melhoria do processo. Este trabalho apresenta uma metodologia capaz de sumarizar resultados do extrator de café por meio deárvores probabilísticas de sufixo, nas quais o histórico de observações dos resultados é utilizado na estimação de probabilidades de ocorrência de cada classe, indicando se o extrator está operando adequadamente. A metodologia está em estudo na Cia Iguaçu de Café Solúvel e poderá ser estendida para outras aplicações no futuro.

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“…Essa separação é possível, pois toda a construção e composição dos modelos probabilísticos é feita no ToPS (Toolkit of Probabilistic Models of Sequences). ToPS (Kashiwabara et al, 2013) é um arcabouço para manipulação de modelos probabilísticos que representam sequências de símbolos.…”

Section: Sumáriounclassified

“…publicação do ToPS(Kashiwabara et al, 2013) disponibilizou diversos modelos probabilísticos, entre os quais destacam-se para esse trabalho as Cadeias de Markov, Cadeias Ocultas de Markov (HMM) e a Cadeias Ocultas de Markov Generalizadas (GHMM). Após a publicação, o arcabouço continuou sendo base para novos projetos de pesquisa que adicionaram novos modelos.…”

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Predição de genes ab initio combinada com informações de alinhamento

Lopes¹

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Palavras-chave: modelos probabilísticos, predição de genes, cadeia de markov oculta generalizada, aprendizado de máquina, bioinformática.iii iv Abstract Lopes, Bruno Tenório da Silveira Ab initio gene prediction combined with alignment information. 2019. 70 p. DissertationIn Bioinformatics, the field of computational prediction of protein-coding genes is one of the most challenging and did not have many advances in the last decade. There are two main groups of methods for predicting genes: ab initio methods and extrinsic methods. The ab initio or intrinsic group includes the programs that perform the prediction using only the target sequence as input.This group focuses on the search for gene structures based on biological signals and preserved portions of the sequence. The other group, called extrinsic, consists of the programs that require other (reference) sequences in addition to the target sequence to perform the prediction by aligning the target sequence against reference sequences. There are also prediction approaches that attempt to join the two prediction methods, called the hybrid, incorporating alignments to increase the precision of the ab initio predictors. In this dissertation we developed an extension of the ToPS computational framework to implement two hybrid prediction techniques and assess their benefits and relative merits. The results obtained show a clear benefit from including genome alignments in the prediction and the pros and cons of using transcript mapping. Additionally, we have devised a generic model to include probabilistic extraneous information into a gene predictor. This model is implemented in ToPS and can be used to further develop gene prediction strategies.

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ToPS: A Framework to Manipulate Probabilistic Models of Sequence Data

Cited by 10 publications

References 30 publications

CodAn: predictive models for the characterization of mRNA transcripts in Eukaryotes

CodAn: predictive models for the characterization of mRNA transcripts in Eukaryotes

Aplicação da Árvore Probabilística de Sufixo na Predição de Resultados do Processo de Extração de Café Solúvel

Predição de genes ab initio combinada com informações de alinhamento

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