Alternative polyadenylation identification software tools | RNA sequencing data analysis
Alternative polyadenylation (APA) is a pervasive mechanism in the regulation of most human genes, and its implication in diseases including cancer is only beginning to be appreciated. Since conventional APA profiling has not been widely adopted, global cancer APA studies are very limited.
Performs de novo identification and quantification of dynamic APA events between two conditions, regardless of any prior APA annotation. DaPars identifies a distal polyA site based on RNA-seq data, uses a regression model to infer the exact location of the proximal APA site after correcting the potential RNA-seq non-uniformity bias along gene body, detects statistically significant dynamic APAs and has the potential to detect >2 dynamic APA events.
Characterizes 3’ untranslated regions (UTRs) in assembled RNA-seq data through direct observation of poly(A) tails. KLEAT employes several evidence types within RNA-seq reads to analyze the structures of assembled transcripts for poly(A) tails, filters 3’ UTR cleavage site (CS) candidates. It returns metrics that can be used in downstream post-processing. This tool compares putative cleavage sites to annotation and EST databases.
Infers sequence motifs that are associated with the processing of poly(A) sites in specific samples. KAPAC is an approach that deduces position-dependent activities of sequence motifs on 3′ end processing from changes in poly(A) site usage between conditions. The software analysis of TCGA data reveals pyrimidine-rich elements associated with the use of poly(A) sites in cancer and implicates the polypyrimidine tract-binding protein 1 (PTBP1) in the regulation of 3′ end processing in glioblastoma.
Infers relative poly(A) site used in terminal exons from RNA sequencing data and KAPAC. PAQR is composed of three modules: (1) a script to deduce transcript integrity values, (2) a script to create the coverage profiles for all considered terminal exons, and (3) a script to obtain the relative usage together with the estimated expression of poly(A) sites with sufficient evidence of usage. The software enables evaluation of 3′ end processing in data sets such as those from The Cancer Genome Atlas (TCGA).
Deduces alternative polyadenylation (APA) from conventional RNA-seq data. QAPA uses conventional RNA-seq data to infer poly(A) site selection and alternative 3′ UTR usage. The software employs estimation of alternative 3′ untranslated region (UTR) expression in combination with an expanded resource of annotated poly(A) sites to demarcate UTR sequences that are specifically affected by APA. It facilitates the systematic discovery and characterization of APA across diverse physiologically normal and disease conditions.
Identifies preferential usage of alternative polyadenylation (APA) sites, comparing two biological conditions, starting from known alternative sites and alignments obtained from standard RNA-seq experiments. roar approach is based on Fisher test to detect disequilibriums in the number of reads falling over the 3’UTRs when comparing two biological conditions. Counts and fragments lengths are used to calculate the prevalence of the short isoform over the long one in both conditions, therefore the ratio of these ratios represents the relative “shortening” (or lengthening) in one condition with respect to the other.
Estimates 3’ untranslated region (UTR) landscape from RNA-seq. GETUTR has three steps: (1) preprocessing for the extraction of all reads in RNA-seq data, (2) smoothing via algorithms and (3) normalization applied for all genes. Three smoothing algorithms that were tested on their average lengths of 3’ UTR and on the prediction of polyadenylation cleavage site (PCS) are available through this software.