scholarly journals Terminus enables the discovery of data-driven, robust transcript groups from RNA-seq data

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i102-i110
Author(s):  
Hirak Sarkar ◽  
Avi Srivastava ◽  
Héctor Corrada Bravo ◽  
Michael I Love ◽  
Rob Patro
Keyword(s):  
Transcript Level ◽  
Data Driven ◽  
Supplementary Information ◽  
Rna Seq ◽  
Large Numbers ◽  
Inference Algorithms ◽  
Data Driven Approach ◽  
Downstream Analysis ◽  
Transcriptional Output ◽  
Level Analysis

Abstract Motivation Advances in sequencing technology, inference algorithms and differential testing methodology have enabled transcript-level analysis of RNA-seq data. Yet, the inherent inferential uncertainty in transcript-level abundance estimation, even among the most accurate approaches, means that robust transcript-level analysis often remains a challenge. Conversely, gene-level analysis remains a common and robust approach for understanding RNA-seq data, but it coarsens the resulting analysis to the level of genes, even if the data strongly support specific transcript-level effects. Results We introduce a new data-driven approach for grouping together transcripts in an experiment based on their inferential uncertainty. Transcripts that share large numbers of ambiguously-mapping fragments with other transcripts, in complex patterns, often cannot have their abundances confidently estimated. Yet, the total transcriptional output of that group of transcripts will have greatly reduced inferential uncertainty, thus allowing more robust and confident downstream analysis. Our approach, implemented in the tool terminus, groups together transcripts in a data-driven manner allowing transcript-level analysis where it can be confidently supported, and deriving transcriptional groups where the inferential uncertainty is too high to support a transcript-level result. Availability and implementation Terminus is implemented in Rust, and is freely available and open source. It can be obtained from https://github.com/COMBINE-lab/Terminus. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Terminus enables the discovery of data-driven, robust transcript groups from RNA-seq data

2020 ◽  
Author(s):  
Hirak Sarkar ◽  
Avi Srivastava ◽  
Héctor Corrada Bravo ◽  
Michael I. Love ◽  
Rob Patro
Keyword(s):  
Transcript Level ◽  
Data Driven ◽  
Supplementary Information ◽  
Rna Seq ◽  
Large Numbers ◽  
Inference Algorithms ◽  
Data Driven Approach ◽  
Downstream Analysis ◽  
Transcriptional Output ◽  
Level Analysis

AbstractMotivationAdvances in sequencing technology, inference algorithms and differential testing methodology have enabled transcript-level analysis of RNA-seq data. Yet, the inherent inferential uncertainty in transcriptlevel abundance estimation, even among the most accurate approaches, means that robust transcript-level analysis often remains a challenge. Conversely, gene-level analysis remains a common and robust approach for understanding RNA-seq data, but it coarsens the resulting analysis to the level of genes, even if the data strongly support specific transcript-level effects.ResultsWe introduce a new data-driven approach for grouping together transcripts in an experiment based on their inferential uncertainty. Transcripts that share large numbers of ambiguously-mapping fragments with other transcripts, in complex patterns, often cannot have their abundances confidently estimated. Yet, the total transcriptional output of that group of transcripts will have greatly-reduced inferential uncertainty, thus allowing more robust and confident downstream analysis. Our approach, implemented in the tool terminus, groups together transcripts in a data-driven manner allowing transcript-level analysis where it can be confidently supported, and deriving transcriptional groups where the inferential uncertainty is too high to support a transcript-level result.AvailabilityTerminus is implemented in Rust, and is freely-available and open-source. It can be obtained from https://github.com/COMBINE-lab/[email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals Comparison and evaluation of statistical error models for scRNA-seq

2021 ◽  
Author(s):  
Saket Choudhary ◽  
Rahul Satija
Keyword(s):  
Linear Models ◽  
Negative Binomial ◽  
Statistical Error ◽  
Rna Seq ◽  
Multiple Sources ◽  
Error Models ◽  
Wide Range ◽  
Data Driven Approach ◽  
Downstream Analysis ◽  
Experimental Processing

Heterogeneity in single-cell RNA-seq (scRNA-seq) data is driven by multiple sources, including biological variation in cellular state as well as technical variation introduced during experimental processing. Deconvolving these effects is a key challenge for preprocessing workflows. Recent work has demonstrated the importance and utility of count models for scRNA-seq analysis, but there is a lack of consensus on which statistical distributions and parameter settings are appropriate. Here, we analyze 58 scRNA-seq datasets that span a wide range of technologies, systems, and sequencing depths in order to evaluate the performance of different error models. We find that while a Poisson error model appears appropriate for sparse datasets, we observe clear evidence of overdispersion for genes with sufficient sequencing depth in all biological systems, necessitating the use of a negative binomial model. Moreover, we find that the degree of overdispersion varies widely across datasets, systems, and gene abundances, and argues for a data-driven approach for parameter estimation. Based on these analyses, we provide a set of recommendations for modeling variation in scRNA-seq data, particularly when using generalized linear models or likelihood-based approaches for preprocessing and downstream analysis.

scholarly journals SINC: a scale-invariant deep-neural-network classifier for bulk and single-cell RNA-seq data

2019 ◽  
Vol 36 (6) ◽  
pp. 1779-1784 ◽  
Author(s):  
Chuanqi Wang ◽  
Jun Li
Keyword(s):  
Neural Network ◽  
Single Cell ◽  
Count Data ◽  
Deep Neural Network ◽  
Sequencing Depth ◽  
Supplementary Information ◽  
Neural Network Classifier ◽  
Rna Seq ◽  
Scale Invariant ◽  
Downstream Analysis

Abstract Motivation Scaling by sequencing depth is usually the first step of analysis of bulk or single-cell RNA-seq data, but estimating sequencing depth accurately can be difficult, especially for single-cell data, risking the validity of downstream analysis. It is thus of interest to eliminate the use of sequencing depth and analyze the original count data directly. Results We call an analysis method ‘scale-invariant’ (SI) if it gives the same result under different estimates of sequencing depth and hence can use the original count data without scaling. For the problem of classifying samples into pre-specified classes, such as normal versus cancerous, we develop a deep-neural-network based SI classifier named scale-invariant deep neural-network classifier (SINC). On nine bulk and single-cell datasets, the classification accuracy of SINC is better than or competitive to the best of eight other classifiers. SINC is easier to use and more reliable on data where proper sequencing depth is hard to determine. Availability and implementation This source code of SINC is available at https://www.nd.edu/∼jli9/SINC.zip. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals MAJIQ-SPEL: web-tool to interrogate classical and complex splicing variations from RNA-Seq data

2017 ◽  
Vol 34 (2) ◽  
pp. 300-302 ◽  
Author(s):  
Christopher J Green ◽  
Matthew R Gazzara ◽  
Yoseph Barash
Keyword(s):  
Experimental Validation ◽  
Ucsc Genome Browser ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Rna Seq ◽  
Web Tool ◽  
Rt Pcr ◽  
Design Algorithm ◽  
Gene Isoforms ◽  
Downstream Analysis

Abstract Summary Analysis of RNA sequencing (RNA-Seq) data have highlighted the fact that most genes undergo alternative splicing (AS) and that these patterns are tightly regulated. Many of these events are complex, resulting in numerous possible isoforms that quickly become difficult to visualize, interpret and experimentally validate. To address these challenges we developed MAJIQ-SPEL, a web-tool that takes as input local splicing variations (LSVs) quantified from RNA-Seq data and provides users with visualization and quantification of gene isoforms associated with those. Importantly, MAJIQ-SPEL is able to handle both classical (binary) and complex, non-binary, splicing variations. Using a matching primer design algorithm it also suggests to users possible primers for experimental validation by RT-PCR and displays those, along with the matching protein domains affected by the LSV, on UCSC Genome Browser for further downstream analysis. Availability and implementation Program and code will be available athttp://majiq.biociphers.org/majiq-spel. Supplementary information Supplementary data are available atBioinformatics online.

scholarly journals Continuous State HMMs for Modeling Time Series Single Cell RNA-Seq Data

2018 ◽  
Author(s):  
Chieh Lin ◽  
Ziv Bar-Joseph
Keyword(s):  
Time Series ◽  
Single Cell ◽  
Developmental Process ◽  
Developmental Trajectories ◽  
Cell Types ◽  
Supplementary Information ◽  
Rna Seq ◽  
Inference Algorithms ◽  
Continuous State ◽  
Efficient Learning

AbstractMotivationMethods for reconstructing developmental trajectories from time series single cell RNA-Seq (scRNA-Seq) data can be largely divided into two categories. The first, often referred to as pseudotime ordering methods, are deterministic and rely on dimensionality reduction followed by an ordering step. The second learns a probabilistic branching model to represent the developmental process. While both types have been successful, each suffers from shortcomings that can impact their accuracy.ResultsWe developed a new method based on continuous state HMMs (CSHMMs) for representing and modeling time series scRNA-Seq data. We define the CSHMM model and provide efficient learning and inference algorithms which allow the method to determine both the structure of the branching process and the assignment of cells to these branches. Analyzing several developmental single cell datasets we show that the CSHMM method accurately infers branching topology and correctly and continuously assign cells to paths, improving upon prior methods proposed for this task. Analysis of genes based on the continuous cell assignment identifies known and novel markers for different cell types.AvailabilitySoftware and Supporting website: www.andrew.cmu.edu/user/chiehll/CSHMM/[email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals Slinker: Visualising novel splicing events in RNA-Seq data

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1255
Author(s):  
Breon Schmidt ◽  
Marek Cmero ◽  
Paul Ekert ◽  
Nadia Davidson ◽  
Alicia Oshlack
Keyword(s):  
Rare Disease ◽  
Human Genome ◽  
Rna Sequencing ◽  
Reference Genome ◽  
Data Driven ◽  
Rna Seq ◽  
Bioinformatics Pipeline ◽  
Link Type ◽  
Muscular Disorders ◽  
Data Driven Approach

Visualisation of the transcriptome relative to a reference genome is fraught with sparsity. This is due to RNA sequencing (RNA-Seq) reads being predominantly mapped to exons that account for just under 3% of the human genome. Recently, we have used exon-only references, superTranscripts, to improve visualisation of aligned RNA-Seq data through the omission of supposedly unexpressed regions such as introns. However, variation within these regions can lead to novel splicing events that may drive a pathogenic phenotype. In these cases, the loss of information in only retaining annotated exons presents significant drawbacks. Here we present Slinker, a bioinformatics pipeline written in Python and Bpipe that uses a data-driven approach to assemble sample-specific superTranscripts. At its core, Slinker uses Stringtie2 to assemble transcripts with any sequence across any gene. This assembly is merged with reference transcripts, converted to a superTranscript, of which rich visualisations are made through Plotly with associated annotation and coverage information. Slinker was validated on five novel splicing events of rare disease samples from a cohort of primary muscular disorders. In addition, Slinker was shown to be effective in visualising deletion events within transcriptomes of tumour samples in the important leukemia gene, IKZF1. Slinker offers a succinct visualisation of RNA-Seq alignments across typically sparse regions and is freely available on Github.

scholarly journals orfipy: a fast and flexible tool for extracting ORFs

2020 ◽  
Author(s):  
Urminder Singh ◽  
Eve Syrkin Wurtele
Keyword(s):  
Open Reading Frames ◽  
Supplementary Information ◽  
Rna Seq ◽  
Flexible Tool ◽  
Coding Regions ◽  
Link Type ◽  
Alternative Reading Frames ◽  
Downstream Analysis ◽  
Fine Tune ◽  
Reading Frames

SummarySearching for ORFs in transcripts is a critical step prior to annotating coding regions in newly-sequenced genomes and to search for alternative reading frames within known genes. With the tremendous increase in RNA-Seq data, faster tools are needed to handle large input datasets. These tools should be versatile enough to fine-tune search criteria and allow efficient downstream analysis. Here we present a new python based tool, orfipy, which allows the user to flexibly search for open reading frames in fasta sequences. The search is rapid and is fully customizable, with a choice of Fasta and BED output formats.Availability and implementationorfipy is implemented in python and is compatible with python v3.6 and higher. Source code: https://github.com/urmi-21/orfipy. Installation: from the source, or via PyPi (https://pypi.org/project/orfipy) or bioconda (https://anaconda.org/bioconda/orfipy)[email protected], [email protected] informationSupplementary data are available at https://github.com/urmi-21/orfipy

scholarly journals Differential transcript usage analysis of bulk and single-cell RNA-seq data with DTUrtle

2021 ◽  
Author(s):  
Tobias Tekath ◽  
Martin Dugas
Keyword(s):  
Single Cell ◽  
Transcript Level ◽  
R Package ◽  
Supplementary Information ◽  
Data Sets ◽  
Rna Seq ◽  
Cell Type ◽  
Gene Level ◽  
Analysis Workflow ◽  
Usage Analysis

Abstract Motivation Each year, the number of published bulk and single-cell RNA-seq data sets is growing exponentially. Studies analyzing such data are commonly looking at gene-level differences, while the collected RNA-seq data inherently represents reads of transcript isoform sequences. Utilizing transcriptomic quantifiers, RNA-seq reads can be attributed to specific isoforms, allowing for analysis of transcript-level differences. A differential transcript usage (DTU) analysis is testing for proportional differences in a gene’s transcript composition, and has been of rising interest for many research questions, such as analysis of differential splicing or cell type identification. Results We present the R package DTUrtle, the first DTU analysis workflow for both bulk and single-cell RNA-seq data sets, and the first package to conduct a ‘classical’ DTU analysis in a single-cell context. DTUrtle extends established statistical frameworks, offers various result aggregation and visualization options and a novel detection probability score for tagged-end data. It has been successfully applied to bulk and single-cell RNA-seq data of human and mouse, confirming and extending key results. Additionally, we present novel potential DTU applications like the identification of cell type specific transcript isoforms as biomarkers. Availability The R package DTUrtle is available at https://github.com/TobiTekath/DTUrtle with extensive vignettes and documentation at https://tobitekath.github.io/DTUrtle/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals FuSe: a tool to move RNA-Seq analyses from chromosomal/gene loci to functional grouping of mRNA transcripts

2020 ◽  
Author(s):  
Rajinder Gupta ◽  
Yannick Schrooders ◽  
Marcha Verheijen ◽  
Adrian Roth ◽  
Jos Kleinjans ◽  
...  
Keyword(s):  
Transcript Level ◽  
Supplementary Information ◽  
Rna Seq ◽  
Functional Changes ◽  
Gene Level ◽  
Secondary Structure Of Proteins ◽  
The Impact ◽  
Structure Of Proteins ◽  
Functional Grouping

Abstract Summary Typical RNA sequencing (RNA-Seq) analyses are performed either at the gene level by summing all reads from the same locus, assuming that all transcripts from a gene make a protein or at the transcript level, assuming that each transcript displays unique function. However, these assumptions are flawed, as a gene can code for different types of transcripts and different transcripts are capable of synthesizing similar, different or no protein. As a consequence, functional changes are not well illustrated by either gene or transcript analyses. We propose to improve RNA-Seq analyses by grouping the transcripts based on their similar functions. We developed FuSe to predict functional similarities using the primary and secondary structure of proteins. To estimate the likelihood of proteins with similar functions, FuSe computes two confidence scores: knowledge (KS) and discovery (DS) for protein pairs. Overlapping protein pairs exhibiting high confidence are grouped to form ‘similar function protein groups’ and expression is calculated for each functional group. The impact of using FuSe is demonstrated on in vitro cells exposed to paracetamol, which highlight genes responsible for cell adhesion and glycogen regulation which were earlier shown to be not differentially expressed with traditional analysis methods. Availability and implementation The source code is available at https://github.com/rajinder4489/FuSe. Data for APAP exposure are available in the BioStudies database (http://www.ebi.ac.uk/biostudies) under accession numbers S-HECA143, S-HECA(158) and S-HECA139. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals snakePipes: facilitating flexible, scalable and integrative epigenomic analysis

2019 ◽  
Vol 35 (22) ◽  
pp. 4757-4759 ◽  
Author(s):  
Vivek Bhardwaj ◽  
Steffen Heyne ◽  
Katarzyna Sikora ◽  
Leily Rabbani ◽  
Michael Rauer ◽  
...  
Keyword(s):  
Single Cell ◽  
Source Code ◽  
Supplementary Information ◽  
Command Line ◽  
Supplementary Data ◽  
Rna Seq ◽  
Downstream Analysis ◽  
Scalable Analysis

Abstract Summary Due to the rapidly increasing scale and diversity of epigenomic data, modular and scalable analysis workflows are of wide interest. Here we present snakePipes, a workflow package for processing and downstream analysis of data from common epigenomic assays: ChIP-seq, RNA-seq, Bisulfite-seq, ATAC-seq, Hi-C and single-cell RNA-seq. snakePipes enables users to assemble variants of each workflow and to easily install and upgrade the underlying tools, via its simple command-line wrappers and yaml files. Availability and implementation snakePipes can be installed via conda: `conda install -c mpi-ie -c bioconda -c conda-forge snakePipes’. Source code (https://github.com/maxplanck-ie/snakepipes) and documentation (https://snakepipes.readthedocs.io/en/latest/) are available online. Supplementary information Supplementary data are available at Bioinformatics online.

Sign in / Sign up

Export Citation Format

Share Document