Feature selection is an important pre-processing task in the analysis of complex data. Selecting an appropriate subset of features can improve classification or clustering and lead to better understanding of the data. An important example is that of finding an informative group of genes out of thousands that appear in gene-expression analysis. Numerous supervised methods have been suggested but only a few unsupervised ones exist.
MicroRNAs (miRNAs) are endogenous ~23 nucleotides (nt) RNAs, regulating gene expression by pairing to the mRNAs of protein-coding genes to direct their post-transcriptional repression. Both in normal and aberrant activities, miRNAs contribute to a recurring paradigm of cellular behaviors in pathological settings, especially in gerontology. Autophagy, a multi-step lysosomal degradation process with function to degrade long-lived proteins and damaged organelles, has significant impact on gerontology.
We describe miTRATA, the first web-based tool for microRNA Truncation and Tailing Analysis--the analysis of 3' modifications of microRNAs including the loss or gain of nucleotides relative to the canonical sequence. miTRATA is implemented in Python (version 3) and employs parallel processing modules to enhance its scalability when analyzing multiple small RNA (sRNA) sequencing datasets. It utilizes miRBase, currently version 21, as a source of known microRNAs for analysis. miTRATA notifies user(s) via email to download as well as visualize the results online.
We have developed Pharmacogenomics And Cell database (PACdb), a results database that makes available relationships between single nucleotide polymorphisms, gene expression, and cellular sensitivity to various drugs in cell-based models to help determine genetic variants associated with drug response. The current version also supports summary analysis on differentially expressed genes between the HapMap samples of European and African ancestry, as well as queries for summary information of correlations between gene expression and pharmacological phenotypes.
The double helix is a conformation that genomic DNA usually assumes; under certain conditions, however, guanine-rich DNA sequences can form a four-stranded structure, G-quadruplex, which is found to play a role in regulating gene expression. Indeed, it has been demonstrated that the G-quadruplex formed in the c-MYC promoter suppresses its transcriptional activity. Recent studies suggest that G-quadruplex motifs (GQMs) are enriched in human gene promoters.
MicroRNA (miRNA) family is a group of miRNAs that derive from the common ancestor. Normally, members from the same miRNA family have similar physiological functions; however, they are not always conserved in primary sequence or secondary structure. Proper family prediction from primary sequence will be helpful for accurate identification and further functional annotation of novel miRNA.
The recognition of microRNA (miRNA)-binding residues in proteins is helpful to understand how miRNAs silence their target genes. It is difficult to use existing computational method to predict miRNA-binding residues in proteins due to the lack of training examples. To address this issue, unlabeled data may be exploited to help construct a computational model. Semisupervised learning deals with methods for exploiting unlabeled data in addition to labeled data automatically to improve learning performance, where no human intervention is assumed.
Amphioxus has been an important model for understanding the evolution of chordates and origin of vertebrates. Comparative transcriptome analysis can facilitate delineation of gene expression patterns of amphioxus at different developmental stages. So far, however, few such analyses have been performed. Here we have systematically compared amphioxus ESTs from five developmental stages. For the egg, gastrula, neurula, larva and adult stages, amphioxus ESTs were assembled, respectively, into 3364, 3230, 10,299, 4052 and 3866 contigs, and 193, 183, 933, 178 and 151 singlets.
MicroRNAs are crucial regulators of gene expression at post-transcriptional level. Understanding origin and evolution of miRNAs and their functions. Transposable elements (TEs) provide a natural mechanism for the origin of new miRNAs derived from TEs (MDTEs) were collected to contruct a database named MDTE database (MDTE DB) for storing, searching and analyzing MDTEs. The database proveds a convenient source for studying the origin and evolution of miRNAs.[1]
Identification of disease-associated miRNAs (disease miRNAs) is critical for understanding disease etiology and pathogenesis. Since miRNAs exert their functions by regulating the expression of their target mRNAs, several methods based on the target genes were proposed to predict disease miRNA candidates. They achieved only limited success as they all suffered from the high false-positive rate of target prediction results.
Our database aim to make it easy for researchers to find the right tools or data source for their own specific study in miRNA field. Now we have collect >1000 tools. This database will update when every new 100 tools add in. Last update time 22nd April, 2018 (v1.2).
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