Translated open reading frame detection software tools | Ribosome profiling data analysis
Ribosome profiling via high-throughput sequencing (ribo-seq) is a promising new technique for characterizing the occupancy of ribosomes on messenger RNA (mRNA) at base-pair resolution. The ribosome is responsible for translating mRNA into proteins, so information about its occupancy offers a detailed view of ribosome density and position which could be used to discover new translated open reading frames (ORFs), among other things.
Represents an unsupervised Bayesian approach. Rp-Bp allows users to identify translated open reading frames (ORFs) based on ribosome profiles. It detects all translated ORFs which exhibit this pattern. This tool consists of two steps: (1) it constructs a profile for each ORF from ribo-seq reads, (2) it involves the prediction of ORF translation from the profiles using a different variant of the two component mixture model.
A rigorous statistical approach that identifies translated regions on the basis of the characteristic three-nucleotide periodicity of Ribo-seq data. We used RiboTaper with deep Ribo-seq data from HEK293 cells to derive an extensive map of translation that covered open reading frame (ORF) annotations for more than 11,000 protein-coding genes. We also found distinct ribosomal signatures for several hundred upstream ORFs and ORFs in annotated noncoding genes (ncORFs). Mass spectrometry data confirmed that RiboTaper achieved excellent coverage of the cellular proteome. Although dozens of novel peptide products were validated in this manner, few of the currently annotated long noncoding RNAs appeared to encode stable polypeptides. RiboTaper is a powerful method for comprehensive de novo identification of actively used ORFs from Ribo-seq data.
Allows users to identify and quantify translation from protein-coding DNA sequences (CDSs) regardless of start codon. ORF-RATER makes the assumption that translated ORFs display a pattern of ribosome occupancy that mimics that of annotated genes. This tool is based on linear regression, which naturally integrates multiple lines of evidence simultaneously. Also, it enables each open reading frame (ORF) to be evaluated in the context of nearby and overlapping ORFs.
Measures the magnitude of disagreement between these two distributions taking into account lower scores reflecting higher similarity. FLOSS is able to identify effect of the small number of lncRNAs yielding substantial non-ribosome-associated fragments. It can analyze and distinguish individual annotated coding sequences and non-coding transcripts. This method permits users to predict the results of ribosome affinity purification, which separate true footprints from background RNA by physical rather than computational means.
Allows users to identify translated coding sequences (CDSs) by leveraging both the total abundance and the codon periodicity structure in ribosome-protected RNA fragments (RPFs). riboHMM utilizes data about ribosome footprint to deduct translated sequences. This method is particularly useful to identify CDSs in the transcriptome of human lymphoblastoid cell lines (LCLs), or to detect high-confidence translated CDS.
Allows users to analyze ribosomal profiling data and identify translated open reading frames (ORFs). RibORF is useful for the detection of ORFs that combines alignment of ribosomal A-sites, 3-nt periodicity, and uniformity across codons. It is able to distinguish in-frame ORFs from overlapping off-frame ORFs, and it recognizes reads arising from RNAs that are not associated with ribosomes.
Allows users to detect actively translated small open reading frames (smORFs). ORFscore quantifies the biased distribution of ribosome-protected fragments (RPFs) toward the first frame of a given conserved coding sequence (CDS). ORFscore is able to quantify the number of RPFs in each frame and determines whether RPFs were uniformly distributed or preferentially accumulated in one frame.