ArticleBioinformatics (Oxford, England)2010
Classification of ncRNAs using position and size information in deep sequencing data.
Article in Bioinformatics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 23 citations in OpenAlex.
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- Editorial focus: understanding off-target effects as the key to successful RNAi therapy.Cellular & molecular biology letters · 2019Review
- Editorial focus: entering into the non-coding RNA era.Cellular & molecular biology letters · 2018Review
- RNomic identification and evaluation of npcTB_6715, a non-protein-coding RNA gene as a potential biomarker for the detection of Mycobacterium tuberculosis.Journal of cellular and molecular medicine · 2017Article
- SHARAKU: an algorithm for aligning and clustering read mapping profiles of deep sequencing in non-coding RNA processing.Bioinformatics (Oxford, England) · 2016Article
- Differential and coherent processing patterns from small RNAs.Scientific reports · 2015Article
- Emerging applications of read profiles towards the functional annotation of the genome.Frontiers in genetics · 2015Review
- CoRAL: predicting non-coding RNAs from small RNA-sequencing data.Nucleic acids research · 2013Article
- PARma: identification of microRNA target sites in AGO-PAR-CLIP data.Genome biology · 2013Article
- MicroRNA discovery by similarity search to a database of RNA-seq profiles.Frontiers in genetics · 2013Article
- Hybridization-based reconstruction of small non-coding RNA transcripts from deep sequencing data.Nucleic acids research · 2012Article
- deepBlockAlign: a tool for aligning RNA-seq profiles of read block patterns.Bioinformatics (Oxford, England) · 2012Article
- Sequencing technologies and genome sequencing.Journal of applied genetics · 2011Review
- A concise review on epigenetic regulation: insight into molecular mechanisms.International journal of molecular sciences · 2011Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationSmall non-coding RNAs (ncRNAs) play important roles in various cellular functions in all clades of life. With next-generation sequencing techniques, it has become possible to study ncRNAs in a high-throughput manner and by using specialized algorithms ncRNA classes such as miRNAs can be detected in deep sequencing data. Typically, such methods are targeted to a certain class of ncRNA. Many methods rely on RNA secondary structure prediction, which is not always accurate and not all ncRNA classes are characterized by a common secondary structure. Unbiased classification methods for ncRNAs could be important to improve accuracy and to detect new ncRNA classes in sequencing data.
resultsHere, we present a scoring system called ALPS (alignment of pattern matrices score) that only uses primary information from a deep sequencing experiment, i.e. the relative positions and lengths of reads, to classify ncRNAs. ALPS makes no further assumptions, e.g. about common structural properties in the ncRNA class and is nevertheless able to identify ncRNA classes with high accuracy. Since ALPS is not designed to recognize a certain class of ncRNA, it can be used to detect novel ncRNA classes, as long as these unknown ncRNAs have a characteristic pattern of deep sequencing read lengths and positions. We evaluate our scoring system on publicly available deep sequencing data and show that it is able to classify known ncRNAs with high sensitivity and specificity. AVAILABILITY: Calculated pattern matrices of the datasets hESC and EB are available at the project web site http://www.bio.ifi.lmu.de/ALPS. An implementation of the described method is available upon request from the authors.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.