Drosophila poly suggests a novel role for the Elongator complex in insulin receptor-target of rapamycin signalling.

Multi-cellular organisms need to successfully link cell growth and metabolism to environmental cues during development. Insulin receptor-target of rapamycin (InR-TOR) signalling is a highly conserved pathway that mediates this link. Herein, we describe poly, an essential gene in Drosophila that mediates InR-TOR signalling.

Accurate microRNA target prediction correlates with protein repression levels.

Background: MicroRNAs are small endogenously expressed non-coding RNA molecules that regulate target gene expression through translation repression or messenger RNA degradation. MicroRNA regulation is performed through pairing of the microRNA to sites in the messenger RNA of protein coding genes. Since experimental identification of miRNA target genes poses difficulties, computational microRNA target prediction is one of the key means in deciphering the role of microRNAs in development and disease.

The database of experimentally supported targets: a functional update of TarBase.

TarBase5.0 is a database which houses a manually curated collection of experimentally supported microRNA (miRNA) targets in several animal species of central scientific interest, plants and viruses. MiRNAs are small non-coding RNA molecules that exhibit an inhibitory effect on gene expression, interfering with the stability and translational efficiency of the targeted mature messenger RNAs.

Genomic and epigenetic alterations deregulate microRNA expression in human epithelial ovarian cancer.

MicroRNAs (miRNAs) are an abundant class of small noncoding RNAs that function as negative gene regulators. miRNA deregulation is involved in the initiation and progression of human cancer; however, the underlying mechanism and its contributions to genome-wide transcriptional changes in cancer are still largely unknown. We studied miRNA deregulation in human epithelial ovarian cancer by integrative genomic approach, including miRNA microarray (n = 106), array-based comparative genomic hybridization (n = 109), cDNA microarray (n = 76), and tissue array (n = 504).

An overview of multiple sequence alignment.

Multiple sequence alignment is perhaps the most commonly applied bioinformatics technique. It often leads to fundamental biological insight into sequence-structure-function relationships of nucleotide or protein sequence families. In this unit, an overview of multiple sequence alignment techniques is presented, covering a history of nearly 30 years from the early pioneering methods to the current state-of-the-art techniques.

A simple and fast secondary structure prediction method using hidden neural networks.

Motivation: In this paper, we present a secondary structure prediction method YASPIN that unlike the current state-of-the-art methods utilizes a single neural network for predicting the secondary structure elements in a 7-state local structure scheme and then optimizes the output using a hidden Markov model, which results in providing more information for the prediction.

Results: YASPIN was compared with the current top-performing secondary structure prediction methods, such as PHDpsi, PROFsec, SSPro2, JNET and PSIPRED. The overall prediction accuracy on the independent EVA5 sequence set is comparable with that of the top performers, according to the Q3, SOV and Matthew's correlations accuracy measures.