Some resources and tools related to noncoding RNAs

In ‘Meet some code-breakers of noncoding RNAs,’ the technology feature in the February issue of Nature Methods, we speak with a few scientists about the path ahead in methods for characterize the noncoding RNAs.

With their input, we compiled a list of some of resources and tools in this field.

We can gladly include additional resources. Please comment on this page. You can also tweet us: @naturemethods or @metricausa

Some resources and tools related to noncoding RNAs:

 

Resource Description Publication
DASHR Database of small human noncoding RNAs

Leung, Y.Y et al DASHR:database of small human noncoding RNAs. Nucleic Acids Res. 44:D216-22. (2016)

FANTOM CAT Functional Annotation of the mammalian genome (FANTOM) is an international consortium.

This resource is an atlas of human long noncoding RNAs with accurate 5’ ends

 

 

Chung-Chau, H. et al Annotation of noncoding transcripts for example to find functional lncRNAs that show an effect on global expression after knockout/knockdown Nature 543,  199–204  (2017).

Okazaki, Y. et al.Analysis of the mouse transcriptome based on functional annotation of 60,770 full-length cDNAs.
420(6915):563-73 (2002).

Gencode Resource about human and mouse noncoding RNAs, drawing on data generated by the Encyclopedia of DNA Elements (ENCODE) consortium.Information about the noncoding RNA species and their annotations are here Harrow J, et al. GENCODE: The reference human genome annotation for The ENCODE ProjectGenome Research doi: 10.1101/gr.135350.111. (2012)
LNCipedia Database of annotations of  functional long noncoding RNAs manually curated from the scientific literature Clark MB, et al. lncRNAdb: a reference database for long noncoding RNAs. Nucleic Acids Res 39: D146-151 (2011).
 lncRNAdb  Database of annotations of  functional long noncoding RNAs manually curated from the scientific literature Amaral, P.P et al lncRNAdb: a reference database for long noncoding RNAs. Nucleic Acids Res 39: D146-151.(2011).
lncRNAWiki A Wiki to encourage community-based curation of human long noncoding RNAs. Ma, L et al. LncRNAWiki: harnessing community knowledge in collaborative curation of human long non-coding RNAs Nucleic Acids Research43, D1, p. Pages D187–D192, (2015).

 

lncRNAtor A portal for long noncoding RNA with information such as expression profiles and coding potential. Data sources include TCGA, GEO, ENCODE and modENCODE. Park, C. et al. lncRNAtor: a comprehensive resource for functional investigation of long non-coding RNAs. Bioinformatics. 30(17):2480-5. (2014).
MINTbase Database of tRNA fragments from 11,000 people and 32 cancer types Pliatsika, V.et al. Nucleic Acids Res. 46, D1, D152–D159 (2018).
miRBase Database of published miRNA sequences and annotations Griffiths-Jones S. et al. Nucleic Acids Res. 36, D154-158 (2008).
miRDip A resource with human data; for finding microRNAs that target a gene; or genes targeted by a microRNA Tokar, T. et al mirDIP 4.1- integrative database of human microRNA target predictions, Nucleic Acids Res. 46(D1):D360-D370. (2018).
miRGeneDB A database of validated and anotated human microRNA genes Fromm, B. et al et al. MirGene DB2.0: the curated microRNA GeneDatabase, manuscript in bioarXiv. doi: https://doi.org/10.1101/258749
Noncode A noncoding RNA database with information from 17 species especially long noncoding RNAs. The information is mined from the scientific literature and data resources such as lncRNAdb, and lncipedia.

It includes links to literature about tools such as ncFANs for functional annotation of lncRNAs,

Liu C, et al. NONCODE: an integrated knowledge database of non-coding RNAs. Nucleic Acids Research, 2005, 33 (Database issue):D112-D115. (2005)
Regulome resources and data  Resources and data from the Center for Personal Dynamic Regulomes, including the ATAQ-Seq protocol and transcriptional landscape data from 13 cell types from healthy people and 3 cell types from people afflicted by leukemia. Corces MR, et al. Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution. Nature Genetics  48(10):1193-203 (2016).
RNA central Resource hosted at the European Bioinformatics Institute that draws on a number of other database resources, such as

LncBase

This resource includes, for example, a database of experimentally supported miRNA:gene interactions and analysis tools and pipelines such as for miRNA pathway analysis

snOPY

snoRNA orthological gene database with information abut snoRNAs, snoRNA gene loci and target RNAs.

TarBase

Manually curated experimentally validated miRNA-gene interactions

 

 Tools 
miRDeep
miRDeep2
Tools for miRNA identification from RNA-seq data An, J et al miRDeep*: an integrated application tool for miRNA identification from RNA sequencing data.Nucleic Acids Res.41(2):727-37 (2013).

Friedländer MR et al. miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res. 40(1):37-52. (2012)

 MiRNA prediction tool   miRNA prediction Miranda, KC et al. A pattern-based method for the identification of MicroRNA binding sites and their corresponding heteroduplexes Cell126, 1203-1217, (2006).
 OASIS  Small non-coding RNA detection and expression analysis tool Capece, V. et al. Oasis: online analysis of small RNA deep sequencing data. Bioinformatics 31, 2205–2207 (2015).
Datasets
Analysis of 13 cell types; expression of primate and tissue-specific microRNAs Human miRNAs, their targets, and visualization of the loci on the human genome browser Londin, E, et al. Analysis of 13 cell types reveals evidence for the expression of numerous novel primate- and tissue-specific microRNAs Proc. Natl. Acad. Sci.U.S.A. 112(10):E1106-15. (2015).

Sources: H. Chang, Stanford University School of Medicine; Rory Johnson, University of Bern, E. Marshall, BC Cancer Agency; M. Turner, Babraham Institute; U. Ohler, Max Delbrück Center for Molecular Medicine; I. Rigoutsos, Philadelphia University + Thomas Jefferson University; Nature Research.

 

 

Method of the Year 2016

As is our tradition every year we have chosen a method, or in this case a set of methods, that have experienced rapid growth in the last years. This year’s choice of epitranscriptome analysis does not comprise a single technique but is based on advances in detecting, enriching and profiling base modifications on all RNA species.

Some of these modifications are abundant and have known functions, others are rare and their role is still obscure. We believe recent methodological advances, as detailed in a Review by Chengqi Yi and colleagues, lay the groundwork for a comprehensive profiling of some of these marks that will shed light on their role in the cell.

Our selection of methods to watch highlights areas we think will experience growth in the coming year and be influential in biological research: from global metabolomics, to RNA-targeting CRISPR, to elucidating single cell function and faster brain imaging.  We do not claim to provide a comprehensive list and our choices may be biased by our fields of interest. We do hope you enjoy reading this feature and if you disagree with us, or if you think we have overlooked an important area, please let us know.

The ethics of self-organizing tissue

It becomes increasingly clear that stem cells are able to form remarkably complex structures in vitro, if they are handled right. In this month’s issue, two pieces raise the question of whether recent developments in methods for patterning embryonic stem cells in vitro raise potential ethical, regulatory or public perception concerns, or if they may do so in the future.

You can find the commentary from leading stem cell and developmental biologists here [https://www.nature.com/nmeth/journal/v12/n10/full/nmeth.3586.html] and the editorial here  [https://www.nature.com/nmeth/journal/v12/n10/full/nmeth.3618.html].

We note that some of these matters were also brought up in a paper published at the end of last year (Cells Tissues Organs 2014;199(4):221-7).

 

Receptor dimerization revisited

Last December we published an Article by Simon Davis and colleagues challenging the conventional methodology being used in BRET studies that detect dimerization of G protein coupled receptors. The conclusions were contested in a Correspondence by Michel Bouvier and colleagues. In a new Correspondence, a former member of the Bouvier lab further argues against the results and conclusions of the Davis study with experimental results using the ‘Type 2’ BRET assay of Davis and colleagues.

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Assessing receptor dimerization by BRET

Using a new quantitative framework for evaluation of receptor dimerization using bioluminescence energy transfer (BRET), Simon Davis and colleagues at the University of Oxford present evidence that the signals arising from BRET measurements, and interpreted as evidence of dimerization, are the result of random interactions and not evidence of stable complexes.

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