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Differential Expression of miR-145 in Children with Kawasaki Disease

Shimizu, Chisato ; Kim, Jihoon ; Stepanowsky, Petra ; [et al.]

Public Library of Science (PLoS) ; 2013

In: PLoS ONE Vol. 8, No. 3 ( 2013-3-6), p. e58159-

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In: PLoS ONE, Public Library of Science (PLoS), Vol. 8, No. 3 ( 2013-3-6), p. e58159-

Type of Medium: Online Resource

ISSN: 1932-6203

URL: Article

DOI: 10.1371/journal.pone.0058159

DOI: 10.1371/journal.pone.0058159.g001

DOI: 10.1371/journal.pone.0058159.g002

DOI: 10.1371/journal.pone.0058159.g003

DOI: 10.1371/journal.pone.0058159.g004

DOI: 10.1371/journal.pone.0058159.g005

DOI: 10.1371/journal.pone.0058159.g006

DOI: 10.1371/journal.pone.0058159.g007

DOI: 10.1371/journal.pone.0058159.t001

DOI: 10.1371/journal.pone.0058159.t002

DOI: 10.1371/journal.pone.0058159.t003

DOI: 10.1371/journal.pone.0058159.s001

DOI: 10.1371/journal.pone.0058159.s002

DOI: 10.1371/journal.pone.0058159.s003

DOI: 10.1371/journal.pone.0058159.s004

DOI: 10.1371/journal.pone.0058159.s005

DOI: 10.1371/journal.pone.0058159.s006

DOI: 10.1371/journal.pone.0058159.s007

DOI: 10.1371/journal.pone.0058159.s008

DOI: 10.1371/journal.pone.0058159.s009

Language: English

Publisher: Public Library of Science (PLoS)

Publication Date: 2013

detail.hit.zdb_id: 2267670-3

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Prediction of MicroRNA Precursors Using Parsimonious Feature Sets

Stepanowsky, Petra ; Levy, Eric ; Kim, Jihoon ; [et al.]

SAGE Publications ; 2014

In: Cancer Informatics Vol. 13s1 ( 2014-01), p. CIN.S13877-

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In: Cancer Informatics, SAGE Publications, Vol. 13s1 ( 2014-01), p. CIN.S13877-

Abstract: MicroRNAs (miRNAs) are a class of short noncoding RNAs that regulate gene expression through base pairing with messenger RNAs. Due to the interest in studying miRNA dysregulation in disease and limits of validated miRNA references, identification of novel miRNAs is a critical task. The performance of different models to predict novel miRNAs varies with the features chosen as predictors. However, no study has systematically compared published feature sets. We constructed a comprehensive feature set using the minimum free energy of the secondary structure of precursor miRNAs, a set of nucleotide-structure triplets, and additional extracted sequence and structure characteristics. We then compared the predictive value of our comprehensive feature set to those from three previously published studies, using logistic regression and random forest classifiers. We found that classifiers containing as few as seven highly predictive features are able to predict novel precursor miRNAs as well as classifiers that use larger feature sets. In a real data set, our method correctly identified the holdout miRNAs relevant to renal cancer.

Type of Medium: Online Resource

ISSN: 1176-9351 , 1176-9351

URL: Article

DOI: 10.4137/CIN.S13877

Language: English

Publisher: SAGE Publications

Publication Date: 2014

detail.hit.zdb_id: 2202739-7

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MAGI: a Node.js web service for fast microRNA-Seq analysis in a GPU infrastructure

Kim, Jihoon ; Levy, Eric ; Ferbrache, Alex ; [et al.]

Oxford University Press (OUP) ; 2014

In: Bioinformatics Vol. 30, No. 19 ( 2014-10-01), p. 2826-2827

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In: Bioinformatics, Oxford University Press (OUP), Vol. 30, No. 19 ( 2014-10-01), p. 2826-2827

Abstract: Summary: MAGI is a web service for fast MicroRNA-Seq data analysis in a graphics processing unit (GPU) infrastructure. Using just a browser, users have access to results as web reports in just a few hours— & gt;600% end-to-end performance improvement over state of the art. MAGI’s salient features are (i) transfer of large input files in native FASTA with Qualities (FASTQ) format through drag-and-drop operations, (ii) rapid prediction of microRNA target genes leveraging parallel computing with GPU devices, (iii) all-in-one analytics with novel feature extraction, statistical test for differential expression and diagnostic plot generation for quality control and (iv) interactive visualization and exploration of results in web reports that are readily available for publication. Availability and implementation: MAGI relies on the Node.js JavaScript framework, along with NVIDIA CUDA C, PHP: Hypertext Preprocessor (PHP), Perl and R. It is freely available at http://magi.ucsd.edu . Contact: j5kim@ucsd.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Type of Medium: Online Resource

ISSN: 1367-4811 , 1367-4803

URL: Article

DOI: 10.1093/bioinformatics/btu377

Language: English

Publisher: Oxford University Press (OUP)

Publication Date: 2014

detail.hit.zdb_id: 1468345-3

SSG: 12

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