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Computational methods for ribosome profiling data analysis

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Abstract Since the introduction of the ribosome profiling technique in 2009 its popularity has greatly increased. It is widely used for the comprehensive assessment of gene expression and for studying the mechanisms of regulation at the translational level. As the number of ribosome profiling datasets being produced continues to grow, so too does the need for reliable software that can provide answers to the biological questions it can address. This review describes the computational methods and tools that have been developed to analyze ribosome profiling data at the different stages of the process. It starts with initial routine processing of raw data and follows with more specific tasks such as the identification of translated open reading frames, differential gene expression analysis, or evaluation of local or global codon decoding rates. The review pinpoints challenges associated with each step and explains the ways in which they are currently addressed. In addition it provides a comprehensive, albeit incomplete, list of publicly available software applicable to each step, which may be a beneficial starting point to those unexposed to ribosome profiling analysis. The outline of current challenges in ribosome profiling data analysis may inspire computational biologists to search for novel, potentially superior, solutions that will improve and expand the bioinformatician's toolbox for ribosome profiling data analysis. This article is characterized under: Translation > Ribosome Structure/Function RNA Evolution and Genomics > Computational Analyses of RNA Translation > Translation Mechanisms Translation > Translation Regulation
The principle of ribosome profiling. (a) The ribosome protects mRNA from nuclease digestion. The sequences of the protected fragments (footprints) constitute ribosome profiling data. (b) A schematic example of a ribosome footprints density plot (ribosome profile). It shows positions of ribosome decoding centers (brown columns) inferred from sequences of ribosome footprints along an RNA transcript (green bar). The height of the columns reflects the number of footprints matching the corresponding mRNA position. The density suggests the efficient translation of an upstream open reading frame (uORF) overlapping the annotated protein coding region (CDS) and the presence of a ribosome pause site in the CDS
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The RiboSeq.Org web portal serves as an entry point to GWIPS‐Viz, Trips‐Viz, and RiboGalaxy. GWIPS‐Viz provides visualizations of publicly available ribosome footprints mapped to several genomes. Trips‐Viz offers rich functionality for the analysis of public and user generated data aligned to transcriptomes. RiboGalaxy provides a cross‐platform graphical interface for the tools initially written as command line software
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RUST metafootprint profiles that can be used for the assessment of sequencing biases that are manifested by high relative entropy (measured as Kullback–Leibler divergence) at the ends of footprints. The decoding center of the ribosome (A‐site) is denoted by the vertical red line. The blue line represents Kullback–Leibler divergence at an individual codon level. The green line represents Kullback–Leibler divergence for adjacent codons. In the absence of sequencing biases the Kullback–Leibler divergence is expected to be the highest at the decoding center. (a) A dataset with low sequencing bias. (b) A dataset with high sequencing bias at the 5′ ends of footprints. For the data sources see the text
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Assessment of ribosome profiling data quality. (a, b) Triplet periodicity plots that show the number of footprints aligning to one of the three subcodon positions (differentially colored) for each subcodon position. (a) An example of good quality data showing strong periodicity and desirable read length distribution. (b) An example of data showing no triplet periodicity and an unexpected read length distribution. (c, d) Subcodon ribosome profile of an ENSEMBL transcript expressed from the human B2M locus visualized with Trips‐Viz. The ORF plot at the bottom shows three reading frames (differentially colored) with white dashes for AUG codons and black dashes for stops. The annotated CDS is demarked by the vertical black lines in the main plot and corresponds to the second reading frame. The footprint density is shown separately depending on the subcodon phase of the aligned reads as curves that are colored to match the color of the supported reading frames. The reading frame detection is possible in (c), but not in (d) which correspond to (a) and (b) respectively. In addition, in (c) the vast majority of reads map entirely within the CDS, while in (d) there are reads which map to the 3′ trailer region that are unlikely to be derived from translating ribosomes. For the source of the data see text
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Examples of metagene profiles. (a) The profile was created by aggregating Ribo‐Seq counts from a region surrounding the annotated start codon (zero coordinate) of every gene for a single read length. This example shows the positions of footprint 5′ ends, but 3′ ends may also be used. Since initiation is slower than elongation, a peak of footprint density is expected at the start codon. Thus the location of the 5′ end peak density indicates the distance between footprints 5′ ends and ribosome P‐site codon where tRNA‐Meti is being incorporated (offset). (b) Same as (a) but relative to annotated stop codons (zero coordinate). A drop of footprint density is observed upstream of the stop. (c) A start codon metagene profile constructed as a heatmap has the advantage of displaying multiple read lengths simultaneously. It can be seen that the distance between 5′ ends and P‐site codons vary depending on read lengths suggesting that different offsets should be applied to the reads depending on their length
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Translation > Translation Mechanisms
RNA Evolution and Genomics > Computational Analyses of RNA
Translation > Translation Regulation

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