DSAP: deep-sequencing small RNA analysis pipeline

Huang, Po-Jung; Liu, Yi‐Chung; Lee, Chi-Ching; Lin, Wei Chen; Gan, Richie Ruei Chi; Lyu, Ping; Tang, Petrus

doi:10.1093/nar/gkq392

Cited by 86 publications

(62 citation statements)

References 31 publications

(26 reference statements)

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“…The rapid identification of novel miRNAs has been also accomplished by the coupling of NGS to computational methods. Among the most commonly used computational tools, we recall miRDeep (Bar et al 2008;Friedlander et al 2008;Oulas et al 2009), miRanalyzer (Hackenberg et al 2011), miRExpress (Wang et al 2009), deepBase (Yang et al 2010), miRTRAP (Hendrix et al 2010), mirTools (Zhu et al 2010), SSCprofiler (Oulas and Poirazi 2011), mirExplorer (Guan et al 2011), MIReNA (Mathelier and Carbone 2010), DSAP (Huang et al 2010), UEA sRNA workbench (Stocks et al 2012), and miRNAkey (Ronen et al 2010). Generally, these programs share the same two basic principles: (1) mapping of the reads to the reference genome and (2) checking for the presence of hairpin structures.…”

Section: Introductionmentioning

confidence: 99%

Rat mir-155 generated from the lncRNA Bic is ‘hidden’ in the alternate genomic assembly and reveals the existence of novel mammalian miRNAs and clusters

Uva

Sacco

Cornò

et al. 2013

RNA

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MicroRNAs (miRNAs) are a class of small noncoding RNAs acting as post-transcriptional gene expression regulators in many physiological and pathological conditions. During the last few years, many novel mammalian miRNAs have been predicted experimentally with bioinformatics approaches and validated by next-generation sequencing. Although these strategies have prompted the discovery of several miRNAs, the total number of these genes still seems larger. Here, by exploiting the species conservation of human, mouse, and rat hairpin miRNAs, we discovered a novel rat microRNA, mir-155. We found that mature miR-155 is overexpressed in rat spleen myeloid cells treated with LPS, similarly to humans and mice. Rat mir-155 is annotated only on the alternate genome, suggesting the presence of other "hidden" miRNAs on this assembly. Therefore, we comprehensively extended the homology search also to mice and humans, finally validating 34 novel mammalian miRNAs (two in humans, five in mice, and up to 27 in rats). Surprisingly, 15 of these novel miRNAs (one for mice and 14 for rats) were found only on the alternate and not on the reference genomic assembly. To date, our findings indicate that the choice of genomic assembly, when mapping small RNA reads, is an important option that should be carefully considered, at least for these animal models. Finally, the discovery of these novel mammalian miRNA genes may contribute to a better understanding of already acquired experimental data, thereby paving the way to still unexplored investigations and to unraveling the function of miRNAs in disease models.

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Section: Introductionmentioning

confidence: 99%

Rat mir-155 generated from the lncRNA Bic is ‘hidden’ in the alternate genomic assembly and reveals the existence of novel mammalian miRNAs and clusters

Uva

Sacco

Cornò

et al. 2013

RNA

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“…Further, as most of the miRNA-detecting methods focus exclusively on the mature miRNAs, a drawback is the failure to collect and quantify information on the precursors, which could result in limitations in elucidating the miRNA transcriptome. [43] animal sequence similarity web-based DSAP [44] animal sequence similarity web-based mirTools [45] animal sequence similarity & miRDeep web-based miRNAKey animal sequence similarity stand-alone miRNEST animal & plant sequence similarity web-based * Only the miRCat component is for detecting new miRNAs; ** Updated miRanalyzer could also predict new miRNAs in plants, and it has a new stand-alone version.…”

Section: Available Tools For Analyzing Mirnas From Deep Sequencing Datamentioning

confidence: 99%

Genomic Databases for Crop Improvement

Lai

Lorenc

Edwards

2014

The Role of Bioinformatics in Agriculture

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MicroRNAs (miRNAs) are 20-to 24-nucleotide endogenous small RNA molecules emerging as an important class of sequence-specific, trans-acting regulators for modulating gene expression at the post-transcription level. There has been a surge of interest in the past decade in identifying miRNAs and profiling their expression pattern using various experimental approaches. In particular, ultra-deep sampling of specifically prepared low-molecular-weight RNA libraries based on next-generation sequencing technologies has been used successfully in diverse species. The challenge now is to effectively deconvolute the complex sequencing data to provide comprehensive and reliable information on the miRNAs, miRNA precursors, and expression profile of miRNA genes. Here we review the recently developed computational tools and their applications in profiling the miRNA transcriptomes, with an emphasis on the model plant Arabidopsis thaliana. Highlighted is also progress and insight into miRNA biology derived from analyzing available deep sequencing data.

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“…Predicts new miRNA with high accuracy using a machine learning approach based on random forests after filtering out sequences from the first two steps to reduce the numbers of false positives. DSAP [31] is a popular tool for analyzing deep-sequencing miRNA data generated by Solexa. DSAP doesn't require a target genome and instead clusters the reads into groups that are mapped against existing RNA/miRNA databases.…”

Section: Mirna Deep Sequencing Identification Toolsmentioning

confidence: 99%

Computational Tools for Genome-Wide miRNA Prediction and Study

Malas¹,

Ravasi²

2012

TOBIOJ

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MicroRNAs (miRNAs) are single-stranded non-coding RNA susually of 22 nucleotidesin length that play an important post-transcriptional regulation role in many organisms. MicroRNAs bind a seed sequence to the 3´-untranslated region (UTR) region of the target messenger RNA (mRNA), inducing degradation or inhibition of translation and resulting in a reduction in the protein level. This regulatory mechanism is central to many biological processes and perturbation could lead to diseases such as cancer. Given the biological importance, of miRNAs, there is a great need to identify and study their targets and functions. However, miRNAs are very difficult to clone in the lab and this has hindered the identification of novel miRNAs. Next-generation sequencing coupled with new computational tools has recently evolved to help researchers efficiently identify large numbers of novel miRNAs. In this review, we describe recent miRNA prediction tools and discuss their priorities, advantages and disadvantages.

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DSAP: deep-sequencing small RNA analysis pipeline

Cited by 86 publications

References 31 publications

Rat mir-155 generated from the lncRNA Bic is ‘hidden’ in the alternate genomic assembly and reveals the existence of novel mammalian miRNAs and clusters

Rat mir-155 generated from the lncRNA Bic is ‘hidden’ in the alternate genomic assembly and reveals the existence of novel mammalian miRNAs and clusters

Genomic Databases for Crop Improvement

Computational Tools for Genome-Wide miRNA Prediction and Study

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