BlockClust: efficient clustering and classification of non-coding RNAs from short read RNA-seq profiles

Videm, Pavankumar; Rose, Dominic; Costa, Fabrizio; Backofen, Rolf

doi:10.1093/bioinformatics/btu270

Cited by 19 publications

(20 citation statements)

References 36 publications

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“…That is, for each generated structure, we track the frequency of each SSC and compute the empirical probability of the SSC triplet over the randomly generated samples using a binomial model. Similar features have been used in classifying non-coding RNAs such as microRNAs [ 14 , 15 , 32 ]. If a given SSC triplet does not appear in samples, the corresponding probability is set to 0.…”

Section: Methodsmentioning

confidence: 99%

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Prediction of bacterial small RNAs in the RsmA (CsrA) and ToxT pathways: a machine learning approach

et al. 2017

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BackgroundSmall RNAs (sRNAs) constitute an important class of post-transcriptional regulators that control critical cellular processes in bacteria. Recent research using high-throughput transcriptomic approaches has led to a dramatic increase in the discovery of bacterial sRNAs. However, it is generally believed that the currently identified sRNAs constitute a limited subset of the bacterial sRNA repertoire. In several cases, sRNAs belonging to a specific class are already known and the challenge is to identify additional sRNAs belonging to the same class. In such cases, machine-learning approaches can be used to predict novel sRNAs in a given class.MethodsIn this work, we develop novel bioinformatics approaches that integrate sequence and structure-based features to train machine-learning models for the discovery of bacterial sRNAs. We show that features derived from recurrent structural motifs in the ensemble of low energy secondary structures can distinguish the RNA classes with high accuracy.ResultsWe apply this approach to predict new members in two broad classes of bacterial small RNAs: 1) sRNAs that bind to the RNA-binding protein RsmA/CsrA in diverse bacterial species and 2) sRNAs regulated by the master regulator of virulence, ToxT, in Vibrio cholerae.ConclusionThe involvement of sRNAs in bacterial adaptation to changing environments is an increasingly recurring theme in current research in microbiology. It is likely that future research, combining experimental and computational approaches, will discover many more examples of sRNAs as components of critical regulatory pathways in bacteria. We have developed a novel approach for prediction of small RNA regulators in important bacterial pathways. This approach can be applied to specific classes of sRNAs for which several members have been identified and the challenge is to identify additional sRNAs.

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Section: Methodsmentioning

confidence: 99%

“…Several computational methods and bioinformatics tools have been developed to enable genome-wide predictions for sRNAs [ 11 – 15 ]. Some approaches are based on comparative sequences and the conservation of sRNAs across genomes.…”

Section: Introductionmentioning

confidence: 99%

Prediction of bacterial small RNAs in the RsmA (CsrA) and ToxT pathways: a machine learning approach

et al. 2017

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“…We assessed the accuracy of SeRPeNT by performing a comparison against BlockClust (26), an unsupervised method that also predicts known small non-coding RNA families from small RNA sequencing (sncRNA-seq) data. We evaluated the accuracy to detect known miRNAs, tRNAs, and snoRNAs from the Gencode annotation (27) using the same procedure and dataset used by Videm et al (26) (Supplementary Materials and Methods). SeRPeNT shows overall similar precision for miRNAs (0.858) and tRNAs (0.855), and a dramatic improvement of the precision for snoRNAs (0.922) ( Supplementary Table S1A).…”

Section: Fast and Accurate Discovery Of Small Non-coding Rnasmentioning

confidence: 99%

The discovery potential of RNA processing profiles

Pagès

Dotú

Guigó

et al. 2016

Preprint

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Small non-coding RNAs (sncRNAs) are highly abundant molecules that regulate essential cellular processes and are classified according to sequence and structure. Here we argue that read profiles from size-selected RNA sequencing capture the posttranscriptional processing specific to each RNA family, thereby providing functional information independently of sequence and structure. We developed SeRPeNT, a new computational method that exploits reproducibility across replicates and uses dynamic time-warping and density-based clustering algorithms to identify, characterize and compare sncRNAs by harnessing the power of read profiles. We applied SeRPeNT to: (i) generate an extended human annotation with 671 new sncRNAs from known classes and 131 from new potential classes, (ii) show pervasive differential processing of sncRNAs between cell compartments and (iii) predict new molecules with miRNA-like behaviour from snoRNA, tRNA and long non-coding RNA precursors, potentially dependent on the miRNA biogenesis pathway. Furthermore, we validated experimentally four predicted novel non-coding RNAs: a miRNA, a snoRNA-derived miRNA, a processed tRNA and a new uncharacterized sncRNA. SeRPeNT facilitates fast and accurate discovery and characterization of sncRNAs at an unprecedented scale. SeRPeNT code is available under the MIT license at https://github.com/comprna/SeRPeNT.

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“…Por outro lado, métodos de aprendizado tais como os usados no snoReport têm sido amplamente usados na identificação e classificação de diferentes classes de ncRNAs [34,95,94,86,63]. Muitos desses métodos são baseados em aprendizado supervisionado, onde alguns atributos previamente conhecidos, chamados features, são coletados de uma sequência de RNA, de suas estruturas primária e secundária e, então, usados em um classificador.…”

Section: Introductionunclassified

“…Machine learning methods such as used on snoReport have been widely used on identification and classification of different families of ncRNAs [34,95,94,86,63]. Many of these methods are based on supervised learning, where some previous known attributes, called features, are collected from a sequence and then used in a classifier.…”

Section: Introductionmentioning

confidence: 99%

Identificação de snoRNAs usando aprendizagem de máquina

Oliveira¹

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Dedico este trabalho a todos que me ajudaram e me apoiaram nestes 7 anos de UnB. Primeiramente a Deus, pois nunca me deixou na mão, principalmente nestes tempos de mestrado, onde sempre senti sua companhia em todos os momentos. Também dedico a minha família, por todo incentivo a continuar estudando e, por fim, a minha namorada, amigos e professores, especialmente a professora Maria Emília, por sempre acreditar em meu potencial. "Comece fazendo o que é necessário, depois o que é possível, e em breve estarás fazendo o impossível." São Francisco de Assis iv Agradecimentos Agradeço as instituições de ensino que me deram a chance de fazer esta dissertação de mestrado: as universidades de Brasília, de Leipzig e de Freiburg, das quais me deram a chance de ver o mundo com outros olhos, melhorando o meu eu cientista e o meu eu humano. Também agradeço ao grupo de bioinformática da UnB e da Universidade de Freiburg, pela amizade e apoio nestes tempos de pesquisa e muito estudo. Por fim agradeço as pessoas que com pequenos gestos e atitudes me ajudaram a seguir em frente, em especial o Dr. Christian Schulz-Huotari e os professores Fabrizio Costa, Rolf Backofen e Jana Hertel. Obrigado!! v Resumo Métodos de aprendizagem de máquina vêm sendo amplamente usados na identificação e classificação de diferentes famílias de RNAs não-codificadores (ncRNAs). Muitos desses métodos são baseados na aprendizagem supervisionada, onde atributos anteriormente conhecidos, chamados features, são extraídos de uma sequência e usados em um classificador. Nesta dissertação, apresentamos dois métodos para a identificação das duas classes principais de snoRNAs, C/D box e H/ACA box snoRNAs: snoReport 2.0, uma melhoria significativa da primeira versão do snoReport; e o snoRNA-EDeN, um novo método baseado no EDeN, que é um kernel decomposicional de grafos. O snoReport 2.0 é um método que, usando features extraídas de sequências candidatas em genomas, combina predição de estrutura secundária de ncRNAs com Máquina de Vetores de Suporte (Support Vector Machine-SVM), para identificar C/D box e H/ACA box snoRNAs. Seu classificador de H/ACA box snoRNA mostrou um F-score de 93% (uma melhoria de 10% em relação à primeira versão do snoReport), enquanto o classificador de C/D box snoRNA obteve F-score de 94% (melhoria de 14%). Alem disso, ambos os classificadores tiveram todas as medidas de performances acima de 90%. Na fase de validação, o snoReport 2.0 identificou 67,43% dos snoRNAs de vertebrados de ambas as classes. Em Nematóides, o snoReport 2.0 identificou 29,6% dos C/D box snoRNAs e 69% dos H/ACA box snoR-NAs. Para as Drosofilídeas, foram identificados 3,2% dos C/D box snoRNAs e 76,7% dos H/ACA box snoRNAs. Esses resultados mostram que o snoReport 2.0 é eficiente na identificação de snoRNAs em organismos vertebrados, e também para H/ACA box snoRNAs de organismos invertebrados. Por outro lado, em vez de usar features de uma sequência (em geral, difíceis de identificar), uma abordagem recente de aprendizagem de máquina é descrita a seguir. Dada uma região de ...

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BlockClust: efficient clustering and classification of non-coding RNAs from short read RNA-seq profiles

Cited by 19 publications

References 36 publications

Prediction of bacterial small RNAs in the RsmA (CsrA) and ToxT pathways: a machine learning approach

Prediction of bacterial small RNAs in the RsmA (CsrA) and ToxT pathways: a machine learning approach

The discovery potential of RNA processing profiles

Identificação de snoRNAs usando aprendizagem de máquina

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