Allows the screening of non-coding RNAs recorded into the Rfam database. GORAP proposes a pipeline enabling both de novo predictions and the reconstruction of phylogenic trees leaning on ncRNA. The application includes multiple filters and can be customized with the addition of users' defined tools, family or constrains.
Permits high-quality sequence annotation without specific knowledge of the field. Unitas allows subsequent analysis of non-template 3′-nucleotides. It enables researches to elucidate possible functions of these cryptic RNAs by first of all spotting them in sncRNA transcriptomes. This tool is suitable for the detection of particularly low abundance phasiRNAs and their source loci. Its sensitivity depends far less on the amount of background reads.
Discovers genes involved in a biological process. RNAiCut employs the connectivity of subgraphs of protein-protein interaction (PPI) networks to find score thresholds from functional genomic data. It allows hit-list gene selection with orthogonal datasets. This tool calculates the edge count of induced subgraph and determines the P-value of finding a PPI subgraph. It is useful for functional genomics research.
Permits users to detect ping-pong cycle activity in small RNA-Seq data. PingPongPro is an application that locates sites of piRNA-mediated cleavage and identifies transposons suppressed through the ping-pong cycle. This method takes additional covariates into account and auto-tunes its parameters to the data. It is built on the next-generation sequencing (NGS) analysis library SeqAn.
Predicts the pathogenicity of single nucleotide substitutions in human mitochondrial tRNA (mt-tRNA) variations. PON-mt-tRNA arranges the variations with the maximum likehood (ML) method and has a 2-fold importance for diagnosing the pathogenicity of mt-tRNA variations. It classifies variations into five classes: neutral, likely neutral, pathogenic, likely pathogenic and variants of uncertain significance.
Detects biologically relevant microRNAs in a real data analysis of human hepatocellular carcinoma (HCC) data. GS is a statistical method and a clustering algorithm that aims to detect identified differentially variable (DV) genomic probes. It is useful for the violation of the normality assumption. This model is based on a mixture of multivariate normal distributions.
Checks and traces small RNA sequencing data (sRNA-Seq). miRTrace is divided in two main modes: (i) a quality control mode, which generates a six-parts report, encompassing information such as read length and phred score distributions, statistics, or contamination; (ii) a trace mode, allowing users to focus on the data related with the sample organism origin by using the composition of clade-specific miRNAs.