MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression via recognition of cognate sequences and interference of transcriptional, translational or epigenetic processes. Bioinformatics tools developed for miRNA study include those for miRNA prediction and discovery, structure, analysis and target prediction. We manually curated 95 review papers and ∼1000 miRNA bioinformatics tools published since 2003. We classified and ranked them based on citation number or PageRank score, and then performed network analysis and text mining (TM) to study the miRNA tools development trends. Five key trends were observed: (1) miRNA identification and target prediction have been hot spots in the past decade; (2) manual curation and TM are the main methods for collecting miRNA knowledge from literature; (3) most early tools are well maintained and widely used; (4) classic machine learning methods retain their utility; however, novel ones have begun to emerge; (5) disease-associated miRNA tools are emerging. Our analysis yields significant insight into the past development and future directions of miRNA tools.

Overexpression of human a-synuclein in model systems, including cultured neurons, drosophila and mice, leads to biochemical and pathological changes that mimic synucleopathies including Parkinson's disease. We have overexpressed both wild-type (WT) and mutant alanine53 fi threonine (A53T) human a-synuclein by transgenic injection into Caenorhabditis elegans. Motor deficits were observed when either WT or A53T a-synuclein was overexpressed with a pan-neuronal or motor neuron promoter. Neuronal and dendritic loss were accelerated in all three sets of C. elegans dopaminergic neurons when human a-synuclein was overexpressed under the control of a dopaminergic neuron or panneuronal promoter, but not with a motor neuron promoter.There were no significant differences in neuronal loss between overexpressed WT and A53T forms or between worms of different ages (4 days, 10 days or 2 weeks). These results demonstrate neuronal and behavioral perturbations elicited by human a-synuclein in C. elegans that are dependent upon expression in specific neuron subtypes. This transgenic model in C. elegans, an invertebrate organism with excellent experimental resources for further genetic manipulation, may help facilitate dissection of pathophysiologic mechanisms of various synucleopathies. Keywords: a-synuclein, model organism, motor neuron, neurodegeneration, worm transgenic. Synucleopathies represent a large range of neuropathologically defined conditions that include Parkinson's disease (PD), dementia with Lewy bodies, Pick's disease and multiple system atrophy ( Spillantini et al. 1997Spillantini et al. , 1998Baba et al. 1998;Takeda et al. 1998). PD is a neurodegenerative disorder that affects 1% of the population over the age of 50 years. PD neurodegeneration is found predominantly in dopaminergic neurons of the substantia nigra where the pathological hallmark is the appearance of intracellular inclusions termed Lewy bodies. These bodies consist of protein complexes that include neurofilaments, ubiquitin and a-synuclein (Forno 1996 2 ; Spillantini et al. Abbreviations used: A53T, mutant alanine53 fi threonine; ADE, anterior deirid; CEP, cephalic neurons; GFP, green fluorescent protein; PD, Parkinson's disease; PDE, posterior deirid; UCHL1, ubiquitin carboxyl-terminal hydrolase L1; TBSB, Tris-buffered saline with 0.5% bovine serum albumin; WT, wild type.

DNA microarray technologies together with rapidly increasing genomic sequence information is leading to an explosion in available gene expression data. Currently there is a great need for efficient methods to analyze and visualize these massive data sets. A self-organizing map (SOM) is an unsupervised neural network learning algorithm which has been successfully used for the analysis and organization of large data files. We have here applied the SOM algorithm to analyze published data of yeast gene expression and show that SOM is an excellent tool for the analysis and visualization of gene expression profiles.z 1999 Federation of European Biochemical Societies.

Circular RNAs (circRNAs) are a unique class of RNA molecule identified more than 40 years ago which are produced by a covalent linkage via back-splicing of linear RNA. Recent advances in sequencing technologies and bioinformatics tools have led directly to an ever-expanding field of types and biological functions of circRNAs. In parallel with technological developments, practical applications of circRNAs have arisen including their utilization as biomarkers of human disease. Currently, circRNA-associated bioinformatics tools can support projects including circRNA annotation, circRNA identification and network analysis of competing endogenous RNA (ceRNA). In this review, we collected about 100 circRNA-associated bioinformatics tools and summarized their current attributes and capabilities. We also performed network analysis and text mining on circRNA tool publications in order to reveal trends in their ongoing development.

We identified pathways and candidate genes associated with the rupture of human sIA wall. Our results may provide clues to the molecular mechanism in sIA wall rupture and insight for novel therapeutic strategies to prevent rupture.