Isoform-level ribosome occupancy quantification software tools | Ribo-seq data analysis
Ribosome profiling is a useful technique for studying translational dynamics and quantifying protein synthesis. Applications of this technique have shown that ribosomes are not uniformly distributed along mRNA transcripts. Understanding how each transcript-specific distribution arises is important for unraveling the translation mechanism.
Quantifies isoform-level ribosome profiles. Ribomap is a conceptual framework and software that produces accurate isoform specific ribosome profiles by accounting for multi-mapping sequence reads using RNA-seq estimates of isoform abundance. The software works in three stages: (i) Transcript abundance estimation, (ii) Mapping ribo-seq reads to the reference transcriptome and (iii) Ribosome profile estimation. It can serve as a useful first step for downstream analysis of translational regulation from ribo-seq data.
Recovers the A-site positions from ribosome profiling data. RiboAsiteDeblur enables comparisons of ribosome profiling data from different replicates, conditions, and laboratories. It does not assume any specific prior distribution of RNase digestion patterns. This tool is able to preserve the subcodon resolution in the estimated A-site positions. It recovers ribosome profiles with a clear frame preference.
Models translation using statistical and computational methods. TASEP is based on an efficient initiation rate approximation scheme combined with a novel Monte Carlo simulation strategy inside an optimization algorithm. It can capture the high-level physical interaction between ribosomes and transcripts by describing the ribosomes as travelling on the mRNAs. This tool requires specification of hundred gene translation initiation rates prior to simulation in case of large models.
Predicts ribosome footprint profile shapes from transcript sequences. Riboshape is a suite of algorithms. It applies kernel smoothing to codon sequences to build predictive features, and uses these features to builds a sparse regression model to predict the ribosome footprint profile shapes. It also proposes a wide range of applications, including inferring isoform-specific ribosome footprints, designing transcripts with fast translation speeds and discovering unknown modulation during translation.
Enables users to process Ribosome Profiling (Ribo-seq) and RNA sequencing (RNA-seq) datasets. RiboPip computes a splice-aware alignment to a reference database along with a read summarization and data quality assessments. It is composed of four steps: (1) filtering and preparing the raw sequence files, (2) removing unwanted RNAs, (3) computing a splice-aware alignment to a given reference database, (3) extracting data subsets with desired properties for further analysis.
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