riboStreamR: A web application for quality control, analysis, and visualization of Ribo-seq data

Perkins, Patrick; Heber, Steffen

doi:10.1109/iccabs.2017.8114317

Cited by 4 publications

(5 citation statements)

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“…We have implemented BDUNN within riboStreamR, a platform for quality control, visualization, and analysis of RNAseq and Ribo-seq data [12]. Within this platform, users can upload their own data in the form of BAM alignment files.…”

Section: Methodsmentioning

confidence: 99%

“…We demonstrate that BDUNN can perform anomaly detection via one-class classification and matches the performance of comparable methods, and also show that BDUNN can be used to identify low quality RNA-seq and Ribo-seq samples using a set of informative data quality metrics. Additionally, we have implemented BDUNN within riboStreamR, a platform for quality control of RNA-seq and Ribo-seq data [12]. RiboStreamR uses BDUNN to highlight user-supplied samples which exhibit anomalous quality, and can be used to visualize characteristic features of the anomalous data.…”

Section: Introductionmentioning

confidence: 99%

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Using a Novel Negative Selection Inspired Anomaly Detection Algorithm to Identify Corrupted Ribo-seq and RNA-seq Samples

Perkins

Heber

2019

Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

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RNA-seq and Ribo-seq are popular techniques for quantifying cellular transcription and translation. These experiments use next-generation sequencing to produce genome-wide highresolution snapshots of the total populations of mRNAs and translating ribosomes within the investigated samples. When performed in concert, these experiments yield valuable information about protein synthesis rates and translational efficiency. Due to their intricate experimental protocols and demanding data processing requirements, quality control and analysis of such experiments are often challenging. Therefore, methods for accurately assessing data quality, and for identifying contaminated samples, are greatly needed. In the following we use a novel negative selection inspired algorithm called Boundary Detection Using Nearest Neighbors (BDUNN), for the identification of corrupted samples. Our algorithm constructs a detector set and reduced training set that defines the boundaries between normal data points and potential anomalies. Subsequently, a nearest neighbor algorithm is used to classify unseen observations. We compare the performance of BDUNN with other popular negative selection and one-class classification algorithms, and show that BDUNN is capable of accurately and efficiently detecting anomalies in standard anomaly detection datasets and simulated RNA-seq and Riboseq data sets. Furthermore, we have implemented our method within an existing R Shiny platform for analyzing RNA-seq an Ribo-seq datasets, which permits downstream analysis of anomalous samples.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using a Novel Negative Selection Inspired Anomaly Detection Algorithm to Identify Corrupted Ribo-seq and RNA-seq Samples

Perkins

Heber

2019

Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

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“…The user must also supply a file in GFF3 format that specifies the positions of the ORFs of interest within the transcripts. This flexibility distinguishes riboviz 2 from Ribo-seq bioinformatics pipelines that are organism or database specific, limiting their use to the scientific community (Wang et al, 2018;Perkins et al, 2019). We maintain a repository of example analyses, including transcriptome and contamination files, that span the major domains of life, at github.com/riboviz/example-datasets.…”

Section: New Features and Advantagesmentioning

confidence: 99%

riboviz 2: A flexible and robust ribosome profiling data analysis and visualization workflow

Anderson

Favate

et al. 2021

Preprint

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Motivation: Ribosome profiling, or Ribo-seq, is the state of the art method for quantifying protein synthesis in living cells. Computational analysis of Ribo-seq data remains challenging due to the complexity of the procedure, as well as variations introduced for specific organisms or specialized analyses. Many bioinformatic pipelines have been developed, but these pipelines have key limitations in terms of functionality or usability. Results: We present riboviz 2, an updated riboviz package, for the comprehensive transcript-centric analysis and visualization of Ribo-seq data. riboviz 2 includes an analysis workflow built on the Nextflow workflow management system, combining freely available software with custom code. The package is extensively documented and provides example configuration files for organisms spanning the domains of life. riboviz 2 is distinguished by clear separation of concerns between annotation and analysis: prior to a run, the user chooses a transcriptome in FASTA format, paired with annotation for the CDS locations in GFF3 format. The user is empowered to choose the relevant transcriptome for their biological question, or to run alternative analyses that address distinct questions. riboviz 2 has been extensively tested on various library preparation strategies, including multiplexed samples. riboviz 2 is flexible and uses open, documented file formats, allowing users to integrate new analyses with the pipeline.

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“…The periodicity can also be detected with Fourier (Calviello et al, ; Chun, Rodriguez, Todd, & Mills, ) and wavelets transformations (Xu et al, ). Both metagene profiles and triplet periodicity visualization plots can be produced by many different tools such as RiboStreamR (Perkins et al, ), Ribo‐seQC (Calviello, Sydow, et al, ), RiboGalaxy (Michel et al, ), Plastid (Dunn & Weissman, ), riboSeqR (Chung et al, ), and mQC (Verbruggen & Menschaert, ).…”

Section: Global Assessment Of the Data Qualitymentioning

confidence: 99%

Computational methods for ribosome profiling data analysis

2019

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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

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riboStreamR: A web application for quality control, analysis, and visualization of Ribo-seq data

Cited by 4 publications

References 0 publications

Using a Novel Negative Selection Inspired Anomaly Detection Algorithm to Identify Corrupted Ribo-seq and RNA-seq Samples

Using a Novel Negative Selection Inspired Anomaly Detection Algorithm to Identify Corrupted Ribo-seq and RNA-seq Samples

riboviz 2: A flexible and robust ribosome profiling data analysis and visualization workflow

Computational methods for ribosome profiling data analysis

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