ArticleBioinformatics (Oxford, England)2014
BlockClust: efficient clustering and classification of non-coding RNAs from short read RNA-seq profiles.
Article in Bioinformatics (Oxford, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 21 citations in OpenAlex.
- The zebrafish (NAR genomics and bioinformatics · 2025Article
- Comparison and benchmark of deep learning methods for non-coding RNA classification.PLoS computational biology · 2024Article
- Article
- The RNA workbench 2.0: next generation RNA data analysis.Nucleic acids research · 2019Article
- aliFreeFold: an alignment-free approach to predict secondary structure from homologous RNA sequences.Bioinformatics (Oxford, England) · 2018Article
- The discovery potential of RNA processing profiles.Nucleic acids research · 2018Article
- Prediction of bacterial small RNAs in the RsmA (CsrA) and ToxT pathways: a machine learning approach.BMC genomics · 2017Article
- RNAscClust: clustering RNA sequences using structure conservation and graph based motifs.Bioinformatics (Oxford, England) · 2017Article
- A Review on Recent Computational Methods for Predicting Noncoding RNAs.BioMed research international · 2017Review
- SHARAKU: an algorithm for aligning and clustering read mapping profiles of deep sequencing in non-coding RNA processing.Bioinformatics (Oxford, England) · 2016Article
- Nonlinear analysis of tRNAs nucleotide sequences by random walks: randomness and order in the primitive informational polymers.Journal of molecular evolution · 2015Article
- Emerging applications of read profiles towards the functional annotation of the genome.Frontiers in genetics · 2015Review
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
summaryNon-coding RNAs (ncRNAs) play a vital role in many cellular processes such as RNA splicing, translation, gene regulation. However the vast majority of ncRNAs still have no functional annotation. One prominent approach for putative function assignment is clustering of transcripts according to sequence and secondary structure. However sequence information is changed by post-transcriptional modifications, and secondary structure is only a proxy for the true 3D conformation of the RNA polymer. A different type of information that does not suffer from these issues and that can be used for the detection of RNA classes, is the pattern of processing and its traces in small RNA-seq reads data. Here we introduce BlockClust, an efficient approach to detect transcripts with similar processing patterns. We propose a novel way to encode expression profiles in compact discrete structures, which can then be processed using fast graph-kernel techniques. We perform both unsupervised clustering and develop family specific discriminative models; finally we show how the proposed approach is scalable, accurate and robust across different organisms, tissues and cell lines. AVAILABILITY: The whole BlockClust galaxy workflow including all tool dependencies is available at http://toolshed.g2.bx.psu.edu/view/rnateam/blockclust_workflow.
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