Slinker: Visualising novel splicing events in RNA-Seq data

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.

Download Full-text

Rare disease knowledge enrichment through a data-driven approach

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-019-0752-9 ◽

2019 ◽

Vol 19 (1) ◽

Cited By ~ 12

Author(s):

Feichen Shen ◽

Yiqing Zhao ◽

Liwei Wang ◽

Majid Rastegar Mojarad ◽

Yanshan Wang ◽

...

Keyword(s):

Rare Disease ◽

Data Driven ◽

Disease Knowledge ◽

Data Driven Approach ◽

Knowledge Enrichment

Download Full-text

Direct RNA Nanopore Sequencing of SARS-CoV-2 Extracted from Critical Material from Swabs

Life ◽

10.3390/life12010069 ◽

2022 ◽

Vol 12 (1) ◽

pp. 69

Author(s):

Davide Vacca ◽

Antonino Fiannaca ◽

Fabio Tramuto ◽

Valeria Cancila ◽

Laura La Paglia ◽

...

Keyword(s):

Rna Sequencing ◽

Systematic Errors ◽

N Gene ◽

Nanopore Sequencing ◽

Rna Seq ◽

Bioinformatics Pipeline ◽

Critical Material ◽

Oxford Nanopore ◽

Oropharyngeal Swab ◽

Generation Sequencing

In consideration of the increasing prevalence of COVID-19 cases in several countries and the resulting demand for unbiased sequencing approaches, we performed a direct RNA sequencing (direct RNA seq.) experiment using critical oropharyngeal swab samples collected from Italian patients infected with SARS-CoV-2 from the Palermo region in Sicily. Here, we identified the sequences SARS-CoV-2 directly in RNA extracted from critical samples using the Oxford Nanopore MinION technology without prior cDNA retrotranscription. Using an appropriate bioinformatics pipeline, we could identify mutations in the nucleocapsid (N) gene, which have been reported previously in studies conducted in other countries. In conclusion, to the best of our knowledge, the technique used in this study has not been used for SARS-CoV-2 detection previously owing to the difficulties in the extraction of RNA of sufficient quantity and quality from routine oropharyngeal swabs. Despite these limitations, this approach provides the advantages of true native RNA sequencing and does not include amplification steps that could introduce systematic errors. This study can provide novel information relevant to the current strategies adopted in SARS-CoV-2 next-generation sequencing.

Download Full-text

miQC: An adaptive probabilistic framework for quality control of single-cell RNA-sequencing data

10.1101/2021.03.03.433798 ◽

2021 ◽

Author(s):

Ariel A. Hippen ◽

Matias M. Falco ◽

Lukas M. Weber ◽

Erdogan Pekcan Erkan ◽

Kaiyang Zhang ◽

...

Keyword(s):

Quality Control ◽

Single Cell ◽

Rna Sequencing ◽

Data Driven ◽

Probabilistic Framework ◽

Sequencing Data ◽

Link Type ◽

Tumor Tissues ◽

Single Cell Rna Sequencing ◽

Different Types

AbstractMotivationSingle-cell RNA-sequencing (scRNA-seq) has made it possible to profile gene expression in tissues at high resolution. An important preprocessing step prior to performing downstream analyses is to identify and remove cells with poor or degraded sample quality using quality control (QC) metrics. Two widely used QC metrics to identify a ‘low-quality’ cell are (i) if the cell includes a high proportion of reads that map to mitochondrial DNA (mtDNA) encoded genes and (ii) if a small number of genes are detected. Current best practices use these QC metrics independently with either arbitrary, uniform thresholds (e.g. 5%) or biological context-dependent (e.g. species) thresholds, and fail to jointly model these metrics in a data-driven manner. Current practices are often overly stringent and especially untenable on lower-quality tissues, such as archived tumor tissues.ResultsWe propose a data-driven QC metric (miQC) that jointly models both the proportion of reads mapping to mtDNA genes and the number of detected genes with mixture models in a probabilistic framework to predict the low-quality cells in a given dataset. We demonstrate how our QC metric easily adapts to different types of single-cell datasets to remove low-quality cells while preserving high-quality cells that can be used for downstream analyses.AvailabilitySoftware available at https://github.com/greenelab/miQC. The code used to download datasets, perform the analyses, and reproduce the figures is available at https://github.com/greenelab/mito-filtering.ContactStephanie C. Hicks ([email protected]) and Anna Vähärautio ([email protected])

Download Full-text

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

Bioinformatics ◽

10.1093/bioinformatics/btaa448 ◽

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.

Download Full-text

Best Paper Selection

Yearbook of Medical Informatics ◽

10.1055/s-0040-1702011 ◽

2020 ◽

Vol 29 (01) ◽

pp. 167-168

Keyword(s):

Information Systems ◽

Electronic Health Record ◽

Rare Disease ◽

Data Driven ◽

Health Record ◽

Quality Changes ◽

Disease Knowledge ◽

Data Driven Approach ◽

Knowledge Enrichment ◽

Electronic Health

Burek P, Scherf N, Herre H. Ontology patterns for the representation of quality changes of cells in time. J Biomed Semantics 2019;10(1):16 https://jbiomedsem.biomedcentral.com/articles/10.1186/s13326-019-0206-4 Denaxas S, Gonzalez-Izquierdo A, Direk K, Fitzpatrick NK Fatemifar G, Banerjee A, Dobson RJB, Howe LJ, Kuan V, Lumbers RT, Pasea L, Patel RS, Shah AD, Hingorani AD, Sudlow C, Hemingway H. UK phenomics platform for developing and validating electronic health record phenotypes: CALIBER. J Am Med Inform Assoc 2019;26(12):1545-59 https://academic.oup.com/jamia/article/26/12/1545/5536916 Rector A, Schulz S, Rodrigues J-M, Chute CG, Solbrig H. On beyond Gruber: “Ontologies” in today's biomedical information systems and the limits of OWL. J Biomed Inform: X 2019 Jun 1;2:100002 https://academic.oup.com/jamia/article/26/12/1545/5536916 Shen F, Zhao Y, Wang L, Mojarad MR, Wang Y, Liu S, Liu H. Rare disease knowledge enrichment through a data-driven approach. BMC Med Inform Decis Mak 2019;19(1):32 https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-019-0752-9

Download Full-text

Scavenger: A pipeline for recovery of unaligned reads utilising similarity with aligned reads

F1000Research ◽

10.12688/f1000research.19426.1 ◽

2019 ◽

Vol 8 ◽

pp. 1587 ◽

Cited By ~ 1

Author(s):

Andrian Yang ◽

Joshua Y. S. Tang ◽

Michael Troup ◽

Joshua W. K. Ho

Keyword(s):

Single Cell ◽

Genetic Variants ◽

Reference Genome ◽

Sequence Similarity ◽

False Negative ◽

Rna Seq ◽

Bioinformatics Pipeline ◽

Alignment Problem ◽

Variable Mapping ◽

Reference Genomes

Read alignment is an important step in RNA-seq analysis as the result of alignment forms the basis for downstream analyses. However, recent studies have shown that published alignment tools have variable mapping sensitivity and do not necessarily align all the reads which should have been aligned, a problem we termed as the false-negative non-alignment problem. Here we present Scavenger, a python-based bioinformatics pipeline for recovering unaligned reads using a novel mechanism in which a putative alignment location is discovered based on sequence similarity between aligned and unaligned reads. We showed that Scavenger could recover unaligned reads in a range of simulated and real RNA-seq datasets, including single-cell RNA-seq data. We found that recovered reads tend to contain more genetic variants with respect to the reference genome compared to previously aligned reads, indicating that divergence between personal and reference genomes plays a role in the false-negative non-alignment problem. Even when the number of recovered reads is relatively small compared to the total number of reads, the addition of these recovered reads can impact downstream analyses, especially in terms of estimating the expression and differential expression of lowly expressed genes, such as pseudogenes.

Download Full-text

Host-virus chimeric events in SARS-CoV2 infected cells are infrequent and artifactual

Journal of Virology ◽

10.1128/jvi.00294-21 ◽

2021 ◽

Author(s):

Bingyu Yan ◽

Srishti Chakravorty ◽

Carmen Mirabelli ◽

Luopin Wang ◽

Jorge L. Trujillo-Ochoa ◽

...

Keyword(s):

Life Cycle ◽

Reverse Transcriptase ◽

Human Genome ◽

Rna Sequencing ◽

Rna Virus ◽

Template Switching ◽

Rna Seq ◽

Positive Sense ◽

Infected Cells ◽

Nuclear Phase

Pathogenic mechanisms underlying severe SARS-CoV2 infection remain largely unelucidated. High throughput sequencing technologies that capture genome and transcriptome information are key approaches to gain detailed mechanistic insights from infected cells. These techniques readily detect both pathogen and host-derived sequences, providing a means of studying host-pathogen interactions. Recent studies have reported the presence of host-virus chimeric (HVC) RNA in RNA-seq data from SARS-CoV2 infected cells and interpreted these findings as evidence of viral integration in the human genome as a potential pathogenic mechanism. Since SARS-CoV2 is a positive-sense RNA virus that replicates in the cytoplasm it does not have a nuclear phase in its life cycle. Thus, it is biologically unlikely to be in a location where splicing events could result in genome integration. Therefore, we investigated the biological authenticity of HVC events. In contrast to true biological events such as mRNA splicing and genome rearrangement events, which generate reproducible chimeric sequencing fragments across different biological isolates, we found that HVC events across >100 RNA-seq libraries from patients with COVID-19 and infected cell lines were highly irreproducible. RNA-seq library preparation is inherently error-prone due to random template switching during reverse transcription of RNA to cDNA. By counting chimeric events observed when constructing an RNA-seq library from human RNA and spike-in RNA from an unrelated species, such as fruit-fly, we estimated that ∼1% of RNA-seq reads are artifactually chimeric. In SARS-CoV2 RNA-seq we found that the frequency of HVC events was, in fact, not greater than this background “noise”. Finally, we developed a novel experimental approach to enrich SARS-CoV2 sequences from bulk RNA of infected cells. This method enriched viral sequences but did not enrich for HVC events, suggesting that the majority of HVC events are, in all likelihood, artifacts of library construction. In conclusion, our findings indicate that HVC events observed in RNA-sequencing libraries from SARS-CoV2 infected cells are extremely rare and are likely artifacts arising from either random template switching of reverse-transcriptase and/or sequence alignment errors. Therefore, the observed HVC events do not support SARS-CoV2 fusion to cellular genes and/or integration into human genomes. Importance The pathogenic mechanisms underlying SARS-CoV2, the virus responsible for COVID-19, are not fully understood. In particular, relatively little is known why some individuals develop life-threatening or persistent COVID-19. Recent studies identified host-virus chimeric (HVC) reads in RNA-sequencing data from SARS-CoV2 infected cells and suggested that HVC events support potential “human genome invasion” and “integration” by SARS-CoV2. This suggestion has fueled concerns about the long-term effects of current mRNA vaccines that incorporate elements of the viral genome. SARS-CoV2 is a positive-sense, single-stranded RNA virus that does not encode a reverse transcriptase and does not include a nuclear phase in its life cycle, so some doubts have rightfully been expressed regarding the authenticity of HVCs and the role played by endogenous retrotransposons in this phenomenon. Thus, it is important to independently authenticate these HVC events. Here we provide several evidences suggesting that the observed HVC events are likely artifactual.

Download Full-text

Genotype-free demultiplexing of pooled single-cell RNA-seq

Genome Biology ◽

10.1186/s13059-019-1852-7 ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 6

Author(s):

Jun Xu ◽

Caitlin Falconer ◽

Quan Nguyen ◽

Joanna Crawford ◽

Brett D. McKinnon ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Genetic Differences ◽

Rna Seq ◽

Link Type ◽

Genotype Information ◽

Single Cell Rna Sequencing ◽

Pooled Samples

AbstractA variety of methods have been developed to demultiplex pooled samples in a single cell RNA sequencing (scRNA-seq) experiment which either require hashtag barcodes or sample genotypes prior to pooling. We introduce scSplit which utilizes genetic differences inferred from scRNA-seq data alone to demultiplex pooled samples. scSplit also enables mapping clusters to original samples. Using simulated, merged, and pooled multi-individual datasets, we show that scSplit prediction is highly concordant with demuxlet predictions and is highly consistent with the known truth in cell-hashing dataset. scSplit is ideally suited to samples without external genotype information and is available at: https://github.com/jon-xu/scSplit

Download Full-text

Combining DGE and RNA-sequencing data to identify new polyA+ non-coding transcripts in the human genome

Nucleic Acids Research ◽

10.1093/nar/gkt1300 ◽

2013 ◽

Vol 42 (5) ◽

pp. 2820-2832 ◽

Cited By ~ 14

Author(s):

Nicolas Philippe ◽

Elias Bou Samra ◽

Anthony Boureux ◽

Alban Mancheron ◽

Florence Rufflé ◽

...

Keyword(s):

Human Genome ◽

Rna Sequencing ◽

Dynamic Range ◽

Tiling Array ◽

Expression Data ◽

Rna Seq ◽

Sequencing Data ◽

Data Set ◽

Protein Coding ◽

Protein Coding Genes

Abstract Recent sequencing technologies that allow massive parallel production of short reads are the method of choice for transcriptome analysis. Particularly, digital gene expression (DGE) technologies produce a large dynamic range of expression data by generating short tag signatures for each cell transcript. These tags can be mapped back to a reference genome to identify new transcribed regions that can be further covered by RNA-sequencing (RNA-Seq) reads. Here, we applied an integrated bioinformatics approach that combines DGE tags, RNA-Seq, tiling array expression data and species-comparison to explore new transcriptional regions and their specific biological features, particularly tissue expression or conservation. We analysed tags from a large DGE data set (designated as ‘TranscriRef’). We then annotated 750 000 tags that were uniquely mapped to the human genome according to Ensembl. We retained transcripts originating from both DNA strands and categorized tags corresponding to protein-coding genes, antisense, intronic- or intergenic-transcribed regions and computed their overlap with annotated non-coding transcripts. Using this bioinformatics approach, we identified ∼34 000 novel transcribed regions located outside the boundaries of known protein-coding genes. As demonstrated using sequencing data from human pluripotent stem cells for biological validation, the method could be easily applied for the selection of tissue-specific candidate transcripts. DigitagCT is available at http://cractools.gforge.inria.fr/softwares/digitagct.

Download Full-text

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

10.1101/2020.04.07.029967 ◽

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.

Download Full-text