Literature DB >> 34415926

Finding differentially expressed sRNA-Seq regions with srnadiff.

Matthias Zytnicki1, Ignacio González1.   

Abstract

Small RNAs (sRNAs) encompass a great variety of molecules of different kinds, such as microRNAs, small interfering RNAs, Piwi-associated RNA, among others. These sRNAs have a wide range of activities, which include gene regulation, protection against virus, transposable element silencing, and have been identified as a key actor in determining the development of the cell. Small RNA sequencing is thus routinely used to assess the expression of the diversity of sRNAs, usually in the context of differentially expression, where two conditions are compared. Tools that detect differentially expressed microRNAs are numerous, because microRNAs are well documented, and the associated genes are well defined. However, tools are lacking to detect other types of sRNAs, which are less studied, and whose precursor RNA is not well characterized. We present here a new method, called srnadiff, which finds all kinds of differentially expressed sRNAs. To the extent of our knowledge, srnadiff is the first tool that detects differentially expressed sRNAs without the use of external information, such as genomic annotation or additional sequences of sRNAs.

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Year:  2021        PMID: 34415926      PMCID: PMC8378736          DOI: 10.1371/journal.pone.0256196

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


  47 in total

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Review 2.  Biogenesis of small RNAs in animals.

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3.  RazerS 3: faster, fully sensitive read mapping.

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Review 4.  Classification and comparison of small RNAs from plants.

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Journal:  Annu Rev Plant Biol       Date:  2013-01-16       Impact factor: 26.379

5.  The Arabidopsis information resource: Making and mining the "gold standard" annotated reference plant genome.

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Journal:  Genesis       Date:  2015-08-04       Impact factor: 2.487

6.  The sequence read archive.

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Journal:  Nucleic Acids Res       Date:  2010-11-09       Impact factor: 16.971

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

Authors:  Pavankumar Videm; Dominic Rose; Fabrizio Costa; Rolf Backofen
Journal:  Bioinformatics       Date:  2014-06-15       Impact factor: 6.937

8.  deepTools: a flexible platform for exploring deep-sequencing data.

Authors:  Fidel Ramírez; Friederike Dündar; Sarah Diehl; Björn A Grüning; Thomas Manke
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9.  Ensembl 2020.

Authors:  Andrew D Yates; Premanand Achuthan; Wasiu Akanni; James Allen; Jamie Allen; Jorge Alvarez-Jarreta; M Ridwan Amode; Irina M Armean; Andrey G Azov; Ruth Bennett; Jyothish Bhai; Konstantinos Billis; Sanjay Boddu; José Carlos Marugán; Carla Cummins; Claire Davidson; Kamalkumar Dodiya; Reham Fatima; Astrid Gall; Carlos Garcia Giron; Laurent Gil; Tiago Grego; Leanne Haggerty; Erin Haskell; Thibaut Hourlier; Osagie G Izuogu; Sophie H Janacek; Thomas Juettemann; Mike Kay; Ilias Lavidas; Tuan Le; Diana Lemos; Jose Gonzalez Martinez; Thomas Maurel; Mark McDowall; Aoife McMahon; Shamika Mohanan; Benjamin Moore; Michael Nuhn; Denye N Oheh; Anne Parker; Andrew Parton; Mateus Patricio; Manoj Pandian Sakthivel; Ahamed Imran Abdul Salam; Bianca M Schmitt; Helen Schuilenburg; Dan Sheppard; Mira Sycheva; Marek Szuba; Kieron Taylor; Anja Thormann; Glen Threadgold; Alessandro Vullo; Brandon Walts; Andrea Winterbottom; Amonida Zadissa; Marc Chakiachvili; Bethany Flint; Adam Frankish; Sarah E Hunt; Garth IIsley; Myrto Kostadima; Nick Langridge; Jane E Loveland; Fergal J Martin; Joannella Morales; Jonathan M Mudge; Matthieu Muffato; Emily Perry; Magali Ruffier; Stephen J Trevanion; Fiona Cunningham; Kevin L Howe; Daniel R Zerbino; Paul Flicek
Journal:  Nucleic Acids Res       Date:  2020-01-08       Impact factor: 16.971

10.  Analysis of Nearly One Thousand Mammalian Mirtrons Reveals Novel Features of Dicer Substrates.

Authors:  Jiayu Wen; Erik Ladewig; Sol Shenker; Jaaved Mohammed; Eric C Lai
Journal:  PLoS Comput Biol       Date:  2015-09-01       Impact factor: 4.475

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