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 de… Show more
“…This has some practical advantages. Ribosome profiling requires a time- and skill-intensive wet laboratory protocol ( 11 , 46 ), and specialized software is generally necessary to handle its valuable but specific data hallmarks ( 58 ). RNA-Seq on the other hand has a much easier library preparation protocol and is more accessible and more routinely applicable for most laboratories.…”
Proteogenomics suffers from statistical issues as the sequencing information inflates the database size. To compensate for this, rescoring with the machine learning-based spectrum predictors MS 2 PIP and Prosit was implemented in a proteogenomics approach. This was demonstrated for both ribosome profiling and nanopore RNA-Seq derived databases. Postprocessing with Percolator showed that these techniques result in recovered and often even elevated stringency levels and identification rates. In this way, it allows to validate novel proteoforms through proteogenomics with unsurpassed confidence levels.
Highlights• First proteogenomics with PSM rescoring using machine learning-predicted spectra • Demonstrated on both ribosome profiling and nanopore RNA-Seq-derived databases • Rescoring leads to elevated stringency and increased identification rates • Rescoring compensates for the search space size issues in proteogenomics
“…This has some practical advantages. Ribosome profiling requires a time- and skill-intensive wet laboratory protocol ( 11 , 46 ), and specialized software is generally necessary to handle its valuable but specific data hallmarks ( 58 ). RNA-Seq on the other hand has a much easier library preparation protocol and is more accessible and more routinely applicable for most laboratories.…”
Proteogenomics suffers from statistical issues as the sequencing information inflates the database size. To compensate for this, rescoring with the machine learning-based spectrum predictors MS 2 PIP and Prosit was implemented in a proteogenomics approach. This was demonstrated for both ribosome profiling and nanopore RNA-Seq derived databases. Postprocessing with Percolator showed that these techniques result in recovered and often even elevated stringency levels and identification rates. In this way, it allows to validate novel proteoforms through proteogenomics with unsurpassed confidence levels.
Highlights• First proteogenomics with PSM rescoring using machine learning-predicted spectra • Demonstrated on both ribosome profiling and nanopore RNA-Seq-derived databases • Rescoring leads to elevated stringency and increased identification rates • Rescoring compensates for the search space size issues in proteogenomics
“…This includes detection of novel translated open reading frames and pause sites, as well as identification of differentially translated genes, for reviews see ( 2–4 ). To date there has been a number of different software packages created to explore each of these aspects of ribosome profiling ( 5 ). Many of these require some computational expertise and familiarity with command line usage.…”
Trips-Viz (https://trips.ucc.ie/) is an interactive platform for the analysis and visualization of ribosome profiling (Ribo-Seq) and shotgun RNA sequencing (RNA-seq) data. This includes publicly available and user generated data, hence Trips-Viz can be classified as a database and as a server. As a database it provides access to many processed Ribo-Seq and RNA-seq data aligned to reference transcriptomes which has been expanded considerably since its inception. Here, we focus on the server functionality of Trips-viz which also has been greatly improved. Trips-viz now enables visualisation of proteomics data from a large number of processed mass spectrometry datasets. It can be used to support translation inferred from Ribo-Seq data. Users are now able to upload a custom reference transcriptome as well as data types other than Ribo-Seq/RNA-Seq. Incorporating custom data has been streamlined with RiboGalaxy (https://ribogalaxy.ucc.ie/) integration. The other new functionality is the rapid detection of translated open reading frames (ORFs) through a simple easy to use interface. The analysis of differential expression has been also improved via integration of DESeq2 and Anota2seq in addition to a number of other improvements of existing Trips-viz features.
“…Following the advent of RiboSeq, several bioinformatics tools were developed for analysis of the data generated [8] . Intuitively, one might imagine that RiboSeq and RNAseq data could be analyzed in the same way, but this is not the case due to certain specific features of RiboSeq data.…”
Section: Introductionmentioning
confidence: 99%
“…RiboDoc complies with FAIR (Findable, Accessible, Interoperable, Reusable) principles [11] to ensure the highest degree of reproducibility, and is presented in a Docker container for easy and reproducible usage with Linux, Windows or MacOS operating systems. Once these initial steps have been performed, more specific tools can be used to address specific questions [8] . Fig.…”
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