Long non-coding RNAs (lncRNAs, pseudogenes and circRNAs) have recently come into light as powerful players in cancer pathogenesis and it More »
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from Bioresearch Online by C. Rajan, contributing writer Researchers at the Indiana University School of Medicine have just discovered a More »
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Long non-coding RNAs (lncRNAs) are non-protein coding transcripts longer than 200 nucleotides. The post-transcriptional regulation is influenced by these lncRNAs by interfering with the microRNA pathways, involving in diverse cellular processes. The regulation of gene expression by lncRNAs at the epigenetic level, transcriptional and post-transcriptional level have been well known and widely studied. Recent recognition that lncRNAs make effects in many biological and pathological processes such as stem cell pluripotency, neurogenesis, oncogenesis and etc. This review focuses on the functional roles of lncRNAs in epigenetics and related research progress are summarized.
- Cao J. (2014) The functional role of long non-coding RNAs and epigenetics. Biol Proced Online 16:11. [article]
|Tuesday, October 14, 2014|
|University of Virginia School of Medicine, Charlottesville, EUA|
SEMINAR | 14 OCTOBER | 12:00 | MAIN AUDITORIUM
Small and long noncoding RNAs in control of cell proliferation and differentiation
Anindya Dutta, University of Virginia School of Medicine, Charlottesville, EUA
Long non-coding RNAs (lncRNAs, pseudogenes and circRNAs) have recently come into light as powerful players in cancer pathogenesis and it is becoming increasingly clear that they have the potential of greatly contributing to the spread and success of personalized cancer medicine. In this concise review, the authors briefly:
- Introduce these three classes of long non-coding RNAs.
- Discuss their applications as diagnostic and prognostic biomarkers.
- Describe their appeal as targets and as drugs,
- Point out the limitations that still lie ahead of their definitive entry into clinical practice.
- Vitiello M, Tuccoli A, Poliseno L. (2014) Long non-coding RNAs in cancer: implications for personalized therapy. Cell Oncol (Dordr) [Epub ahead of print]. [abstract]
With the rise of emerging economies around the world and a concomitant upgrade of health care systems, the global population has been rapidly expanding. As a consequence, worldwide demand for agricultural products is also growing.
Crops now provide food and the other important resources for seven billion humans.
Food supplies are primarily based on such crops as wheat, maize, rice and vegetables. But as the area of arable land and of cultivated land continues to decline, the future ability to meet the world’s food security needs has come under a cloud of uncertainty.
Meanwhile, the use of pesticides and fertilizers has triggered long-term adverse effects on the environment, and has presented a serious threat to human health.
PLEK: a tool for predicting long non-coding RNAs and messenger RNAs based on an improved k-mer scheme
High-throughput transcriptome sequencing (RNA-seq) technology promises to discover novel protein-coding and non-coding transcripts, particularly the identification of long non-coding RNAs (lncRNAs) from de novo sequencing data. This requires tools that are not restricted by prior gene annotations, genomic sequences and high-quality sequencing.
A team led by researchers at the Xidian University, present an alignment-free tool called PLEK (predictor of long non-coding RNAs and messenger RNAs based on an improved k-mer scheme), which uses a computational pipeline based on an improved k-mer scheme and a support vector machine (SVM) algorithm to distinguish lncRNAs from messenger RNAs (mRNAs), in the absence of genomic sequences or annotations. The performance of PLEK was evaluated on well-annotated mRNA and lncRNA transcripts. 10-fold cross-validation tests on human RefSeq mRNAs and GENCODE lncRNAs indicated that our tool could achieve accuracy of up to 95.6%. The researchers demonstrated the utility of PLEK on transcripts from other vertebrates using the model built from human datasets. PLEK attained >90% accuracy on most of these datasets. PLEK also performed well using a simulated dataset and two real de novo assembled transcriptome datasets (sequenced by PacBio and 454 platforms) with relatively high indel sequencing errors. In addition, PLEK is approximately eightfold faster than a newly developed alignment-free tool, named Coding-Non-Coding Index (CNCI), and 244 times faster than the most popular alignment-based tool, Coding Potential Calculator (CPC), in a single-threading running manner.
Availability – PLEK is open-source software and can be freely downloaded from https://sourceforge.net/projects/plek/files/
- Li A, Zhang J, Zhou Z. (2014) PLEK: a tool for predicting long non-coding RNAs and messenger RNAs based on an improved k-mer scheme. BMC Bioinformatics 15(1):311. [article]