You are here

Windows

SubmiRine

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

MicroRNAs (miRNAs) regulate gene expression by binding to partially complementary sequences on target mRNA transcripts, thereby causing their degradation, deadenylation, or inhibiting their translation. Genomic variants can alter miRNA regulation by modifying miRNA target sites, and multiple human disease phenotypes have been linked to such miRNA target site variants (miR-TSVs).

Rating: 
Average: 5 (1 vote)

MIRNA-DISTILLER

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

MicroRNAs (miRNA) are small non-coding RNA molecules of ~22 nucleotides which regulate large numbers of genes by binding to seed sequences at the 3'-untranslated region of target gene transcripts. The target mRNA is then usually degraded or translation is inhibited, although thus resulting in posttranscriptional down regulation of gene expression at the mRNA and/or protein level.

Rating: 
Average: 5 (1 vote)

miRVaS

Submitted by ChenLiang on Mon, 10/24/2016 - 23:12

Genetic variants in or near miRNA genes can have profound effects on miRNA expression and targeting. As user-friendly software for the impact prediction of miRNA variants on a large scale is still lacking, we created a tool called miRVaS. miRVaS automates this prediction by annotating the location of the variant relative to functional regions within the miRNA hairpin (seed, mature, loop, hairpin arm, flanks) and by annotating all predicted structural changes within the miRNA due to the variant.

Rating: 
5
Average: 5 (2 votes)

nRC

Submitted by ChenLiang on Tue, 01/09/2018 - 17:25

Non-coding RNA (ncRNA) are small non-coding sequences involved in gene expression regulation of many biological processes and diseases. The recent discovery of a large set of different ncRNAs with biologically relevant roles has opened the way to develop methods able to discriminate between the different ncRNA classes.

Rating: 
Average: 5 (1 vote)

SMiRK

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

Micro RNAs (miRNAs), important regulators of cell function, can be interrogated by high-throughput sequencing in a rapid and cost-effective manner. However, the tremendous amount of data generated by such methods is not easily analyzed. In order to extract meaningful information and draw biological conclusions from miRNA data, many challenges in quality control, alignment, normalization, and analysis must be overcome. Typically, these would only be possible with the dedicated efforts of a specialized computational biologist for a sustained period of time.

Rating: 
Average: 5 (1 vote)

miARma-Seq

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

Large-scale RNAseq has substantially changed the transcriptomics field, as it enables an unprecedented amount of high resolution data to be acquired. However, the analysis of these data still poses a challenge to the research community. Many tools have been developed to overcome this problem, and to facilitate the study of miRNA expression profiles and those of their target genes. While a few of these enable both kinds of analysis to be performed, they also present certain limitations in terms of their requirements and/or the restrictions on data uploading.

Rating: 
Average: 5 (1 vote)

icTAIR

Submitted by ChenLiang on Thu, 04/06/2017 - 17:57

Gene expression regulators, such as transcription factors (TFs) and microRNAs (miRNAs), have varying regulatory targets based on the tissue and physiological state (context) within which they are expressed. While the emergence of regulator-characterizing experiments has inferred the target genes of many regulators across many contexts, methods for transferring regulator target genes across contexts are lacking. Further, regulator target gene lists frequently are not curated or have permissive inclusion criteria, impairing their use.

Rating: 
Average: 5 (1 vote)

iDeep

Submitted by ChenLiang on Sun, 09/10/2017 - 17:07

RNAs play key roles in cells through the interactions with proteins known as the RNA-binding proteins (RBP) and their binding motifs enable crucial understanding of the post-transcriptional regulation of RNAs. How the RBPs correctly recognize the target RNAs and why they bind specific positions is still far from clear. Machine learning-based algorithms are widely acknowledged to be capable of speeding up this process. Although many automatic tools have been developed to predict the RNA-protein binding sites from the rapidly growing multi-resource data, e.g.

Rating: 
Average: 5 (1 vote)

microRNAome

Submitted by ChenLiang on Tue, 01/09/2018 - 17:31

MicroRNAs are short RNAs that serve as regulators of gene expression and are essential components of normal development as well as modulators of disease. MicroRNAs generally act cell-autonomously, and thus their localization to specific cell types is needed to guide our understanding of microRNA activity. Current tissue-level data have caused considerable confusion, and comprehensive cell-level data do not yet exist. Here, we establish the landscape of human cell-specific microRNA expression.

Rating: 
Average: 5 (1 vote)

miRvial

Submitted by ChenLiang on Tue, 01/09/2018 - 19:26

MicroRNAs form an essential class of post-transcriptional gene regulator of eukaryotic species, and play critical parts in development and disease and stress responses. MicroRNAs may originate from various genomic loci, have structural characteristics, and appear in canonical or modified forms, making them subtle to detect and analyze. We present miRvial, a robust computational method and companion software package that supports parameter adjustment and visual inspection of candidate microRNAs.

Rating: 
4
Average: 3.5 (2 votes)

Pages

Subscribe to Windows