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Please use this identifier to cite or link to this item:
http://krishi.icar.gov.in/jspui/handle/123456789/42773
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | M.S. Farooqi | en_US |
dc.contributor.author | Dwijesh Chandra Mishra | en_US |
dc.contributor.author | K.K. Chaturvedi | en_US |
dc.contributor.author | Anil Rai | en_US |
dc.contributor.author | S.B. Lal | en_US |
dc.contributor.author | S. Kumar | en_US |
dc.contributor.author | J. Bhati | en_US |
dc.contributor.author | A. Sharma | en_US |
dc.date.accessioned | 2020-11-27T06:55:57Z | - |
dc.date.available | 2020-11-27T06:55:57Z | - |
dc.date.issued | 2019-01-01 | - |
dc.identifier.citation | Not Available | en_US |
dc.identifier.issn | Not Available | - |
dc.identifier.uri | http://krishi.icar.gov.in/jspui/handle/123456789/42773 | - |
dc.description | Not Available | en_US |
dc.description.abstract | The next generation sequencing technology, RNA-sequencing (RNA-seq), has got growing acceptance in transcriptome analyses. Statistical methods used for gene expression analyses with RNAseq provide meaningful inferences of gene expression using counts of reads. There are various statistical models with its pros and cons available for RNA-seq data analysis. There is a need for consistent statistical methods to explore the information from the developing sequencing technologies. The current article gives a review of the statistical methods with their limitations that can be useful for the RNA-seq analysis. The main emphasis is given to the parametric, nonparametric and hybrid models for identifying the genes with differential expression. | en_US |
dc.description.sponsorship | Not Available | en_US |
dc.language.iso | English | en_US |
dc.publisher | Not Available | en_US |
dc.relation.ispartofseries | Not Available; | - |
dc.subject | RNA-seq | en_US |
dc.subject | Statistical models | en_US |
dc.subject | R packages | en_US |
dc.subject | edgeR | en_US |
dc.subject | DEseq | en_US |
dc.title | A Review on Recent Statistical Models for RNA-Seq Data | en_US |
dc.title.alternative | Not Available | en_US |
dc.type | Research Paper | en_US |
dc.publication.projectcode | Not Available | en_US |
dc.publication.journalname | Journal of Applied Bioinformatics and Computational Biology | en_US |
dc.publication.volumeno | 8(1) | en_US |
dc.publication.pagenumber | Not Available | en_US |
dc.publication.divisionUnit | Not Available | en_US |
dc.publication.sourceUrl | DOI: 10.4172/2329-9533.1000162 | en_US |
dc.publication.sourceUrl | https://www.scitechnol.com/peer-review/a-review-on-recent-statistical-models-for-rnaseq-data-Y4Zg.php?article_id=9296 | en_US |
dc.publication.authorAffiliation | ICAR::Indian Agricultural Statistics Research Institute | en_US |
dc.ICARdataUseLicence | http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf | en_US |
dc.publication.naasrating | Not Available | - |
Appears in Collections: | AEdu-IASRI-Publication |
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