SIRVs
A technology-agnostic long-read analysis pipeline for transcriptome discovery and quantification
Dana Wyman, Gabriela Balderrama-Gutierrez, Fairlie Reese, Shan Jiang, Sorena Rahmanian, Stefania Forner, Dina Matheos, Weihua Zeng, Brian Williams, Diane Trout, Whitney England, Shu-Hui Chu, Robert C. Spitale, Andrea J. Tenner, Barbara J. Wold, Ali Mortazavi
Alternative splicing is widely acknowledged to be a crucial regulator of gene expression and is a key contributor to both normal developmental processes and disease states. While cost-effective and accurate for quantification, short-read RNA-seq lacks the ability to resolve full-length transcript isoforms despite increasingly sophisticated computational methods. Long-read sequencing platforms such as Pacific Biosciences (PacBio) and Oxford Nanopore (ONT) bypass the transcript reconstruction challenges of short reads. Here we introduce TALON, the ENCODE4 pipeline for platform-independent analysis of long-read transcriptomes. We apply TALON to the GM12878 cell line and show that while both PacBio and ONT technologies perform well at full-transcript discovery and quantification, each displayed distinct technical artifacts. We further apply TALON to mouse hippocampus and cortex transcriptomes and find that 422 genes found in these regions have more reads associated with novel isoforms than with annotated ones. We demonstrate that TALON is a capable of tracking both known and novel transcript models as well as their expression levels across datasets for both simple studies and in larger projects. These properties will enable TALON users to move beyond the limitations of short-read data to perform isoform discovery and quantification in a uniform manner on existing and future long-read platforms.
Features SIRVs (Spike-in RNA Variant Control Mixes) – SIRV-Set 1, 2 & 3
Pi-starvation induced transcriptional changes in barley revealed by a comprehensive RNA-Seq and degradome analyses
Pawel Sega, Katarzyna Kruszka, Dawid Bielewicz, Wojciech Karlowski, Przemyslaw Nuc, Zofia Szweykowska-Kulinska, Andrzej Pacak
Background: Small RNAs (sRNAs) are 18–24 nt regulatory elements which are responsible for plant development regulation and participate in many plant stress responses. Insufficient inorganic phosphate (Pi) concentration triggers plant responses to balance the internal Pi level.
Results: In this study, we describe Pi-starvation-responsive small RNAs and transcriptome changes in barley (Hordeum vulgare L.) using Next-Generation Sequencing (NGS) data derived from three different types of NGS libraries: (i) small RNAs, (ii) degraded RNAs, and (iii) functional mRNAs. We find that differentially and significantly expressed miRNAs (DEMs, p-value < 0.05) are represented by 162 (44.88 % of total differentially expressed small RNAs) molecules in shoot and 138 (7.14 %) in root; mainly various miR399 and miR827 isomiRs. The remaining small RNAs (i.e., those without perfect match to reference sequences deposited in miRBase) are considered as differentially expressed other sRNAs (DESs, Bonferroni correction). In roots, a more abundant and diverse set of other sRNAs (1796 unique sequences, 0.13 % from total unique reads obtained under low-Pi) contributes more to the compensation of low-Pi stress than that in shoots (199 unique sequences, 0.01 %). More than 80 % of differentially expressed other sRNAs are upregulated in both organs. Additionally, in barley shoots, upregulation of small RNAs is accompanied by strong induction of two nucleases (S1/P1 endonuclease and 3’-5’ exonuclease). This suggests that most small RNAs may be generated upon endonucleolytic cleavage to increase the internal Pi pool. Transcriptomic profiling of Pi-starved barley shoots identify 98 differentially expressed genes (DEGs). A majority of the DEGs possess characteristic Pi-responsive cis-regulatory elements (P1BS and/or PHO element), located mostly in the proximal promoter regions. GO analysis shows that the discovered DEGs primarily alter plant defense, plant stress response, nutrient mobilization, or pathways involved in the gathering and recycling of phosphorus from organic pools.
Conclusions: Our results provide comprehensive data to demonstrate complex responses at the RNA level in barley to maintain Pi homeostasis and indicate that barley adapts to Pi scarcity through elicitation of RNA degradation. Novel P-responsive genes were selected as putative candidates to overcome low-Pi stress in barley plants.
Features SIRVs (Spike-in RNA Variant Control Mixes) and SENSE mRNA-Seq Library Prep Kit
Reference-free reconstruction and quantification of transcriptomes from long-read sequencing
Ivan de la Rubia, Joel A. Indi, Silvia Carbonell, Julien Lagarde, M Mar Albà, Eduardo Eyras
Single-molecule long-read sequencing provides an unprecedented opportunity to measure the transcriptome from any sample. However, current methods for the analysis of transcriptomes from long reads rely on the comparison with a genome or transcriptome reference, or use multiple sequencing technologies. These approaches preclude the cost-effective study of species with no reference available, and the discovery of new genes and transcripts in individuals underrepresented in the reference. Methods for the assembly of DNA long-reads cannot be directly transferred to transcriptomes since their consensus sequences lack the interpretability as genes with multiple transcript isoforms. To address these challenges, we have developed RATTLE, the first method for the reference-free reconstruction and quantification of transcripts from long reads. Using simulated data, transcript isoform spike-ins, and sequencing data from human and mouse tissues, we demonstrate that RATTLE accurately performs read clustering and error-correction. Furthermore, RATTLE predicts transcript sequences and their abundances with accuracy comparable to reference-based methods. RATTLE enables rapid and cost-effective long-read transcriptomics in any sample and any species, without the need of a genome or annotation reference and without using additional technologies.
Features SIRVs (Spike-in RNA Variant Control Mixes) – SIRV-Set 1, 2 & 3
Error correction enables use of Oxford Nanopore technology for reference-free transcriptome analysis
Kristoffer Sahlin, Botond Sipos, Phillip L James, Daniel J Turner, Paul Medvedev
Oxford Nanopore (ONT) is a leading long-read technology which has been revolutionizing transcriptome analysis through its capacity to sequence the majority of transcripts from end-to-end. This has greatly increased our ability to study the diversity of transcription mechanisms such as transcription initiation, termination, and alternative splicing. However, ONT still suffers from high error rates which have thus far limited it scope to reference-based analyses. When a reference is not available or is not a viable option due to reference-bias, error correction is a crucial step towards the reconstruction of the sequenced transcripts and downstream sequence analysis of transcripts. In this paper, we present a novel computational method to error-correct ONT cDNA sequencing data, called isONcorrect. IsONcorrect is able to jointly use all isoforms from a gene during error correction, thereby allowing it to correct reads at low sequencing depths. We are able to obtain an accuracy of 98.7-99.5%, demonstrating the feasibility of applying cost-effective cDNA full transcript length sequencing for reference-free transcriptome analysis.
Features SIRVs (Spike-in RNA Variant Control Mixes) – SIRV-Set 1, 2 & 3
The full-length transcriptome of C. elegans using direct RNA sequencing
Nathan P. Roach, Norah Sadowski, Amelia F. Alessi, Winston Timp, James Taylor, and John K. Kim
Current transcriptome annotations have largely relied on short read lengths intrinsic to the most widely used high-throughput cDNA sequencing technologies. For example, in the annotation of the Caenorhabditis elegans transcriptome, more than half of the transcript isoforms lack full-length support and instead rely on inference from short reads that do not span the full length of the isoform. We applied nanopore-based direct RNA sequencing to characterize the developmental polyadenylated transcriptome of C. elegans. Taking advantage of long reads spanning the full length of mRNA transcripts, we provide support for 23,865 splice isoforms across 14,611 genes, without the need for computational reconstruction of gene models. Of the isoforms identified, 3452 are novel splice isoforms not present in the WormBase WS265 annotation. Furthermore, we identified 16,342 isoforms in the 3′ untranslated region (3′ UTR), 2640 of which are novel and do not fall within 10 bp of existing 3′-UTR data sets and annotations. Combining 3′ UTRs and splice isoforms, we identified 28,858 full-length transcript isoforms. We also determined that poly(A) tail lengths of transcripts vary across development, as do the strengths of previously reported correlations between poly(A) tail length and expression level, and poly(A) tail length and 3′-UTR length. Finally, we have formatted this data as a publicly accessible track hub, enabling researchers to explore this data set easily in a genome browser.
Features SIRVs (Spike-in RNA Variant Control Mixes) – SIRV-Set 1, 2 & 3
Nijkamp and Parnham’s Principles of Immunopharmacology* BOOK CHAPTER
Michael J. Parnham, Frans P. Nijkamp, Adriano G. Rossi
Principles of Immunopharmacology, doi:10.1007/978-3-030-10811-3
Principles of Immunopharmacology provides a unique source of essential knowledge on the immune response, its diagnosis and its modification by drugs and chemicals. The 4th edition of this internationally recognized textbook has been revised to include recent developments, but continues the established format, dealing with four related fields in a single volume, thus obviating the need to refer to several different textbooks.
The first section of the book, providing a basic introduction to immunology and its relevance for human disease, has been updated to accommodate new immunological concepts, particularly the role of epigenetics and the latest understanding of cancer immunology. The second section on immunodiagnostics offers a topical description of widely used molecular techniques and a new chapter on imaging techniques. This is followed by a systematic coverage of drugs affecting the immune system, including natural products. This third section contains 15 updated chapters, covering classical immunopharmacological topics such as anti-asthmatic, anti-rheumatic and immunosuppressive drugs, but also deals with antibiotics, plant-derived and dietary agents, with new chapters on monoclonal antibodies, immunotherapy in sepsis and infection, drugs for soft-tissue autoimmunity and cell therapy. The book concludes with a chapter on immunotoxicology and drug safety tests.
Aids to the reader include a two-column format, glossaries of technical terms and appendix reference tables. The emphasis on illustrations is maintained from the first three editions.
The book is a valuable single reference for undergraduate and graduate medical and biomedical students, postgraduate chemistry and pharmacy students, researchers in chemistry, biochemistry and the pharmaceutical industry and researchers lacking basic immunological knowledge, who want to understand the actions of drugs on the immune system.
Features SIRVs (Spike-in RNA Variant Control Mixes) – SIRV-Set 1, 2 & 3
Computational Methods for Resolving Heterogeneity in Biological Data* PhD THESIS
Hongjiu Zhang
The complexity in biological data reflects the heterogeneous nature of biological processes. Computational methods need to preserve as much information regarding the biological process of interest as possible. In this work, we explore three specific tasks about resolving biological heterogeneity. The first task is to infer heterogeneous phylogenetic relationship using molecular data. The common likelihood models for phylogenetic inference often makes strong assumptions about the evolution process across different lineages and different mutation sites. We use convolutional neural network to infer phylogenies instead, allowing the model to describe more heterogeneous evolution process. The model outperformes commonly used algorithms on diverse simulation datasets. The second task is to infer the clonal composition and phylogeny from bulk DNA sequencing data of tumour samples. Estimating clonal information from bulk data often involves resolving mixture models. Unfortunately, simpler models are often unable to capture complex genetic alteration events in tumour cells, while more sophisticated models incur heavy computational burdens and are hard to converge. We solve the challenge through density-hinted optimization with post hoc adjustment. The model makes conservative predications but yields better accuracy in assessing co-clustering relationship among the somatic mutations. The third task is to estimate the abundance of splicing transcripts from full-length single-cell RNA sequencing data. Transcript inference from RNA sequencing data needs a plethora of reads for accurate abundance estimation. Yet single-cell sequencing yields much fewer reads than bulk sequencing. To recover transcripts from full-length single-cell RNA sequencing data, we pool reads from similar cells to help assign transcripts without disrupting the cluster structures. These methods describe complex biological processes with minimal runtime overhead. Taking these methods as examples, we will briefly discuss the rationale and some general principals in designing these methods.
Features SIRVs (Spike-in RNA Variant Control Mixes) – SIRV-Set 1, 2 & 3
SMUG1 Promotes Telomere Maintenance through Telomerase RNA Processing
Penelope Kroustallaki, Lisa Lirussi,Sergio Carracedo, Panpan You, Q. Ying Esbensen, Alexandra Götz, Laure Jobert, Lene Alsøe, Pål Sætrom, Sarantis Gagos, Hilde Nilsen
Telomerase biogenesis is a complex process where several steps remain poorly understood. Single-strand-selective uracil-DNA glycosylase (SMUG1) associates with the DKC1-containing H/ACA ribonucleoprotein complex, which is essential for telomerase biogenesis. Herein, we show that SMUG1 interacts with the telomeric RNA component (hTERC) and is required for co-transcriptional processing of the nascent transcript into mature hTERC. We demonstrate that SMUG1 regulates the presence of base modifications in hTERC, in a region between the CR4/CR5 domain and the H box. Increased levels of hTERC base modifications are accompanied by reduced DKC1 binding. Loss of SMUG1 leads to an imbalance between mature hTERC and its processing intermediates, leading to the accumulation of 3′-polyadenylated and 3′-extended intermediates that are degraded in an EXOSC10-independent RNA degradation pathway. Consequently, SMUG1-deprived cells exhibit telomerase deficiency, leading to impaired bone marrow proliferation in Smug1-knockout mice.
Features SIRVs (Spike-in RNA Variant Control Mixes) and SENSE Total RNA-Seq Library Prep Kit
ARID1A and PI3-kinase pathway mutations in the endometrium drive epithelial transdifferentiation and collective invasion
Mike R. Wilson, Jake J. Reske, Jeanne Holladay, Genna E. Wilber, Mary Rhodes, Julie Koeman, Marie Adams, Ben Johnson, Ren-Wei Su, Niraj R. Joshi, Amanda L. Patterson, Hui Shen, Richard E. Leach, Jose M. Teixeira, Asgerally T. Fazleabas & Ronald L. Chandler
ARID1A and PI3-Kinase (PI3K) pathway alterations are common in neoplasms originating from the uterine endometrium. Here we show that monoallelic loss of ARID1A in the mouse endometrial epithelium is sufficient for vaginal bleeding when combined with PI3K activation. Sorted mutant epithelial cells display gene expression and promoter chromatin signatures associated with epithelial-to-mesenchymal transition (EMT). We further show that ARID1A is bound to promoters with open chromatin, but ARID1A loss leads to increased promoter chromatin accessibility and the expression of EMT genes. PI3K activation partially rescues the mesenchymal phenotypes driven by ARID1A loss through antagonism of ARID1A target gene expression, resulting in partial EMT and invasion. We propose that ARID1A normally maintains endometrial epithelial cell identity by repressing mesenchymal cell fates, and that coexistent ARID1A and PI3K mutations promote epithelial transdifferentiation and collective invasion. Broadly, our findings support a role for collective epithelial invasion in the spread of abnormal endometrial tissue.
Features SIRVs (Spike-in RNA Variant Control Mixes) – SIRV-Set 1, 2 & 3
A comprehensive examination of Nanopore native RNA sequencing for characterization of complex transcriptomes
Charlotte Soneson, Yao Yao, Anna Bratus-Neuenschwander, Andrea Patrignani, Mark D. Robinson & Shobbir Hussain
A platform for highly parallel direct sequencing of native RNA strands was recently described by Oxford Nanopore Technologies, but despite initial efforts it remains crucial to further investigate the technology for quantification of complex transcriptomes. Here we undertake native RNA sequencing of polyA + RNA from two human cell lines, analysing ~5.2 million aligned native RNA reads. To enable informative comparisons, we also perform relevant ONT direct cDNA- and Illumina-sequencing. We find that while native RNA sequencing does enable some of the anticipated advantages, key unexpected aspects currently hamper its performance, most notably the quite frequent inability to obtain full-length transcripts from single reads, as well as difficulties to unambiguously infer their true transcript of origin. While characterising issues that need to be addressed when investigating more complex transcriptomes, our study highlights that with some defined improvements, native RNA sequencing could be an important addition to the mammalian transcriptomics toolbox.
Features SIRVs (Spike-in RNA Variant Control Mixes) – SIRV-Set 1, 2 & 3
Targeting destabilized DNA G-quadruplexes and aberrant splicing in drug-resistant glioblastoma
Deanna M Tiek, Roham Razaghi, Lu Jin, Norah Sadowski, Carla Alamillo-Ferrer, J Robert Hogg, Bassem R Haddad, David H Drewry, Carrow I Wells, Julie E. Pickett, William J Zuercher, Winston Timp, Rebecca B Riggins
Temozolomide (TMZ) is a chemotherapy agent that adds mutagenic adducts to guanine, and is first-line standard of care for the aggressive brain cancer glioblastoma (GBM). Methyl guanine methyl transferase (MGMT) is a DNA repair enzyme that can remove O6-methyl guanine adducts prior to the development of catastrophic mutations, and is associated with TMZ resistance. However, inhibition of MGMT fails to reverse TMZ resistance. Guanines are essential nucleotides in many DNA and RNA secondary structures. In several neurodegenerative diseases (NDs), disruption of these secondary structures is pathogenic. We therefore took a structural view of TMZ resistance, seeking to establish the role of guanine mutations in disrupting critical nucleotide secondary structures. To test whether these have functional impacts on TMZ-resistant GBM, we focused on two specific guanine-rich regions: G-quadruplexes (G4s) and splice sites. Here we report broad sequence- and conformation-based changes in G4s in acquired or intrinsic TMZ resistant vs. sensitive GBM cells, accompanied by nucleolar stress and enrichment of nucleolar RNA:DNA hybrids (r-loops). We further show widespread splice-altering mutations, exon skipping, and deregulation of splicing-regulatory serine/arginine rich (SR) protein phosphorylation in TMZ-resistant GBM cells. The G4-stabilizing ligand TMPyP4 and a novel inhibitor of cdc2-like kinases (CLKs) partially normalize G4 structure and SR protein phosphorylation, respectively, and are preferentially growth-inhibitory in TMZ-resistant cells. Lastly, we report that the G4- and RNA-binding protein EWSR1 forms aberrant cytoplasmic aggregates in response to acute TMZ treatment, and these aggregates are abundant in TMZ resistant cells. Preliminary evidence suggests these cytoplasmic EWSR1 aggregates are also present in GBM clinical samples. This work supports altered nucleotide secondary structure and splicing deregulation as pathogenic features of TMZ-resistant GBM. It further positions cytoplasmic aggregation of EWSR1 as a potential indicator for TMZ resistance, establishes the possibility of successful intervention with splicing modulatory or G4-targeting agents, and provides a new context in which to study aggregating RNA binding proteins.
Features SIRVs (Spike-in RNA Variant Control Mixes) – SIRV-Set 1, 2 & 3
ORF Capture-Seq: a versatile method for targeted identification of full-length isoforms
Gloria M. Sheynkman, Katharine S. Tuttle, Elizabeth Tseng, Jason G. Underwood, Liang Yu, Da Dong, Melissa L. Smith, Robert Sebra, Tong Hao, Michael A. Calderwood, David E. Hill, Marc Vidal
Most human protein-coding genes are expressed as multiple isoforms. This in turn greatly expands the functional repertoire of the encoded proteome. While at least one reliable open reading frame (ORF) model has been assigned for every gene, the majority of alternative isoforms remains uncharacterized experimentally. This is primarily due to: i) vast differences of overall levels between different isoforms expressed from common genes, and ii) the difficulty of obtaining contiguous full-length ORF sequences. Here, we present ORF Capture-Seq (OCS), a flexible and cost-effective method that addresses both challenges for targeted full-length isoform sequencing applications using collections of cloned ORFs as probes. As proof-of-concept, we show that an OCS pipeline focused on genes coding for transcription factors increases isoform detection by an order of magnitude, compared to unenriched sample. In short, OCS enables rapid discovery of isoforms from custom-selected genes and will allow mapping of the full set of human isoforms at reasonable cost.
Features SIRVs (Spike-in RNA Variant Control Mixes) – SIRV-Set 1, 2 & 3
Human co-transcriptional splicing kinetics and coordination revealed by direct nascent RNA sequencing
Heather L. Drexler, Karine Choquet, L. Stirling Churchman
Features SIRVs (Spike-in RNA Variant Control Mixes) – SIRV-Set 1
Single-cell RNA sequencing (scRNA-seq) has become an established approach to profile entire transcriptomes of individual cells from different cell types, tissues, species, and organisms. Single-cell tagged reverse transcription sequencing (STRT-seq) is one of the early single-cell methods which utilize 5′ tag counting of transcripts. STRT-seq performed on microfluidics Fluidigm C1 platform (STRT-C1) is a flexible scRNA-seq approach that allows for accurate, sensitive and importantly molecular counting of transcripts at single-cell level. Herein, I describe the STRT-C1 method and the steps involved in capturing 96 cells across C1 microfluidics chip, cDNA synthesis, and preparing single-cell libraries for Illumina short-read sequencing.
Features SIRVs (Spike-in RNA Variant Control Mixes) – SIRV-Set 1
Shedding light: The importance of reverse transcription efficiency standards in data interpretation* REVIEW
Jessica Schwaber, Stacey Andersen, Lars Nielsen
Biomolecular Detection and Quantification, doi: 10.1016/j.bdq.2018.12.002
The RNA-to-cDNA conversion step in transcriptomics experiments is widely recognised as inefficient and variable, casting doubt on the ability to do quantitative transcriptomics analyses. Multiple studies have focused on ways to optimise this process, resulting in contradictory recommendations. Here we explore the problem of reverse transcription efficiency using digital PCR and the RT method’s impact on subsequent data analysis. Using synthetic RNA standards, an example experiment is presented, outlining a method to (1) determine relevant efficiency and variability values and then to (2) incorporate this information into downstream analyses as a way to improve the accuracy of quantitative transcriptomics experiments.
Features SIRVs (Spike-in RNA Variant Control Mixes) – SIRV-Set 1
Improving nanopore read accuracy with the R2C2 method enables the sequencing of highly multiplexed full-length single-cell cDNA
Roger Volden, Theron Palmer, Ashley Byrne, Charles Cole, Robert J. Schmitz, Richard E. Green, and Christopher Vollmers
Proceedings of the National Academy of Sciences, doi: 10.1073/pnas.1806447115
Highly parallel direct RNA sequencing on an array of nanopores
Daniel R Garalde, Elizabeth A Snell, Daniel Jachimowicz, Botond Sipos, Joseph H Lloyd, Mark Bruce, Nadia Pantic, Tigist Admassu, Phillip James, Anthony Warland, Michael Jordan, Jonah Ciccone, Sabrina Serra, Jemma Keenan, Samuel Martin, Luke McNeill, E Jayne Wallace, Lakmal Jayasinghe, Chris Wright, Javier Blasco, Stephen Young, Denise Brocklebank, Sissel Juul, James Clarke, Andrew J Heron & Daniel J Turner
SpaRC: Scalable Sequence Clustering using Apache Spark* REVIEW
Lizhen Shi, Xiandong Meng, Elizabeth Tseng, Michael Mascagni, Zhong Wang
Global and targeted approaches to single-cell transcriptome characterization
Aleksandra A. Kolodziejczyk, Tapio Lönnberg
Single cell transcriptomics of pluripotent stem cells: reprogramming and differentiation
Kedar Nath Natarajan, Sarah A Teichmann, Aleksandra A Kolodziejczyk
Current Opinion in Genetics & Development, doi: 10.1016/j.gde.2017.06.003
Identifying cell populations with scRNASeq
Tallulah S. Andrews, Martin Hemberg
Molecular Aspects of Medicine, doi: 110.1016/j.mam.2017.07.002
A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications * REVIEW
Haque A., Engel J., Teichmann SA., Lönnberg T.
Reference standards for next-generation sequencing* REVIEW
Simon A. Hardwick, Ira W. Deveson & Tim R. Mercer
Nanopore Long-Read RNAseq Reveals Widespread Transcriptional Variation Among the Surface Receptors of Individual B cells
Ashley Byrne, Anna E Beaudin, Hugh E Olsen, Miten Jain, Charles Cole, Theron Palmer, Rebecca M DuBois, E. Camilla Forsberg, Mark Akeson, Christopher Vollmers
International Standards for Genomes, Transcriptomes, and Metagenomes* REVIEW
Christopher E. Mason, Ebrahim Afshinnekoo, Scott Tighe, Shixiu Wu, and Shawn Levy
Assessing The Reliability Of Spike-In Normalization For Analyses Of Single-Cell RNA Sequencing Data
Aaron TL Lun, Fernando J Calero-Nieto, Liora Haim-Vilmovsky, Berthold Gottgens, John C Marioni
SIRVs: Spike-In RNA Variants as External Isoform Controls in RNA-Sequencing
Lukas Paul, Petra Kubala, Gudrun Horner, Michael Ante, Igor Hollaender, Seitz Alexander, Torsten Reda
Power Analysis of Single Cell RNA‐Sequencing Experiments
Valentine Svensson, Kedar N Natarajan, Lam-Ha Ly, Ricardo J Miragaia, Charlotte Labalette, Iain C Macaulay, Ana Cvejic, Sarah A Teichmann
Comprehensive comparison of Pacific Biosciences and Oxford Nanopore Technologies and their applications to transcriptome analysis
Jason L Weirather, Mariateresa de Cesare, Yunhao Wang, Paolo Piazza, Vittorio Sebastiano, Xiu-Jie Wang, David Buck, Kin Fai Au
Results: PacBio shows overall better data quality, while ONT provides a higher yield. As with data quality, PacBio performs marginally better than ONT in most aspects for both long reads only and Hybrid-Seq strategies in transcriptome analysis. In addition, Hybrid-Seq shows superior performance over long reads only in most transcriptome analyses.
Conclusions: Both PacBio and ONT sequencing are suitable for full-length single-molecule transcriptome analysis. As this first use of ONT reads in a Hybrid-Seq analysis has shown, both PacBio and ONT can benefit from a combined Illumina strategy. The tools and analytical methods developed here provide a resource for future applications and evaluations of these rapidly-changing technologies.