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RNA-Seq

RNA-Seq (RNA sequencing), also called whole transcriptome shotgun sequencing (WTSS), uses next-generation sequencing (NGS) to reveal the presence and quantity of RNA in a biological sample at a given moment in time. RNA-Seq is used to analyze the continually changing cellular transcriptome. Specifically, RNA-Seq facilitates the ability to look at alternative gene spliced transcripts, post-transcriptional modifications, gene fusion, mutations/SNPs and changes in gene expression. [Source: Wikipedia]

plantDARIO

Submitted by ChenLiang on Thu, 04/06/2017 - 18:49

High-throughput sequencing techniques have made it possible to assay an organism's entire repertoire of small non-coding RNAs (ncRNAs) in an efficient and cost-effective manner. The moderate size of small RNA-seq datasets makes it feasible to provide free web services to the research community that provide many basic features of a small RNA-seq analysis, including quality control, read normalization, ncRNA quantification, and the prediction of putative novel ncRNAs. DARIO is one such system that so far has been focussed on animals.

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DsTRD

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

Salvia miltiorrhiza has been comprehensively studied as a medicinal model plant. However, research progress on this species is significantly hindered by its unavailable genome sequences and limited number of expressed sequence tags in the National Center for Biotechnology Information database. Thus, a transcript database must be developed to assist researchers to browse, search, and align sequences for gene cloning and functional analysis in S. miltiorrhiza.

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SePIA

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

Large-scale sequencing experiments are complex and require a wide spectrum of computational tools to extract and interpret relevant biological information. This is especially true in projects where individual processing and integrated analysis of both small RNA and complementary RNA data is needed. Such studies would benefit from a computational workflow that is easy to implement and standardizes the processing and analysis of both sequenced data types.

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BBBomics

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

Abstract is not available.[1]

 

 

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GeneFriends

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

Co-expression networks have proven effective at assigning putative functions to genes based on the functional annotation of their co-expressed partners, in candidate gene prioritization studies and in improving our understanding of regulatory networks. The growing number of genome resequencing efforts and genome-wide association studies often identify loci containing novel genes and there is a need to infer their functions and interaction partners.

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