Transcriptome assembly from long-read RNA-seq alignments with StringTie2.
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ABSTRACT:
SUBMITTER: Kovaka S
PROVIDER: S-EPMC6912988 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundRapid advances in next-generation sequencing methods have provided new opportunities for transcriptome sequencing (RNA-Seq). The unprecedented sequencing depth provided by RNA-Seq makes it a powerful and cost-efficient method for transcriptome study, and it has been widely used in model organisms and non-model organisms to identify and quantify RNA. For non-model organisms lacking well-defined genomes, de novo assembly is typically required for downstream RNA-Seq analyses, including SNP discovery and identification of genes differentially expressed by phenotypes. Although RNA-Seq has been successfully used to sequence many non-model organisms, the results of de novo assembly from short reads can still be improved by using recent bioinformatic developments.ResultsIn this study, we used 212.6 million pair-end reads, which accounted for 16.2 Gb, to assemble the hexaploid wheat transcriptome. Two state-of-the-art assemblers, Trinity and Trans-ABySS, which use the single and multiple k-mer methods, respectively, were used, and the whole de novo assembly process was divided into the following four steps: pre-assembly, merging different samples, removal of redundancy and scaffolding. We documented every detail of these steps and how these steps influenced assembly performance to gain insight into transcriptome assembly from short reads. After optimization, the assembled transcripts were comparable to Sanger-derived ESTs in terms of both continuity and accuracy. We also provided considerable new wheat transcript data to the community.ConclusionsIt is feasible to assemble the hexaploid wheat transcriptome from short reads. Special attention should be paid to dealing with multiple samples to balance the spectrum of expression levels and redundancy. To obtain an accurate overview of RNA profiling, removal of redundancy may be crucial in de novo assembly.
| S-EPMC3485621 | biostudies-literature
Project description:BackgroundWith the fast advances in nextgen sequencing technology, high-throughput RNA sequencing has emerged as a powerful and cost-effective way for transcriptome study. De novo assembly of transcripts provides an important solution to transcriptome analysis for organisms with no reference genome. However, there lacked understanding on how the different variables affected assembly outcomes, and there was no consensus on how to approach an optimal solution by selecting software tool and suitable strategy based on the properties of RNA-Seq data.ResultsTo reveal the performance of different programs for transcriptome assembly, this work analyzed some important factors, including k-mer values, genome complexity, coverage depth, directional reads, etc. Seven program conditions, four single k-mer assemblers (SK: SOAPdenovo, ABySS, Oases and Trinity) and three multiple k-mer methods (MK: SOAPdenovo-MK, trans-ABySS and Oases-MK) were tested. While small and large k-mer values performed better for reconstructing lowly and highly expressed transcripts, respectively, MK strategy worked well for almost all ranges of expression quintiles. Among SK tools, Trinity performed well across various conditions but took the longest running time. Oases consumed the most memory whereas SOAPdenovo required the shortest runtime but worked poorly to reconstruct full-length CDS. ABySS showed some good balance between resource usage and quality of assemblies.ConclusionsOur work compared the performance of publicly available transcriptome assemblers, and analyzed important factors affecting de novo assembly. Some practical guidelines for transcript reconstruction from short-read RNA-Seq data were proposed. De novo assembly of C. sinensis transcriptome was greatly improved using some optimized methods.
| S-EPMC3287467 | biostudies-literature
Project description:BackgroundIn recent years, massively parallel complementary DNA sequencing (RNA sequencing [RNA-Seq]) has emerged as a fast, cost-effective, and robust technology to study entire transcriptomes in various manners. In particular, for non-model organisms and in the absence of an appropriate reference genome, RNA-Seq is used to reconstruct the transcriptome de novo. Although the de novo transcriptome assembly of non-model organisms has been on the rise recently and new tools are frequently developing, there is still a knowledge gap about which assembly software should be used to build a comprehensive de novo assembly.ResultsHere, we present a large-scale comparative study in which 10 de novo assembly tools are applied to 9 RNA-Seq data sets spanning different kingdoms of life. Overall, we built >200 single assemblies and evaluated their performance on a combination of 20 biological-based and reference-free metrics. Our study is accompanied by a comprehensive and extensible Electronic Supplement that summarizes all data sets, assembly execution instructions, and evaluation results. Trinity, SPAdes, and Trans-ABySS, followed by Bridger and SOAPdenovo-Trans, generally outperformed the other tools compared. Moreover, we observed species-specific differences in the performance of each assembler. No tool delivered the best results for all data sets.ConclusionsWe recommend a careful choice and normalization of evaluation metrics to select the best assembling results as a critical step in the reconstruction of a comprehensive de novo transcriptome assembly.
| S-EPMC6511074 | biostudies-literature
Project description:Next-generation sequencing (NGS) has revolutionized genomic research by enabling high-throughput, cost-effective genome and transcriptome sequencing accelerating personalized medicine for complex diseases, including cancer. Whole genome/transcriptome sequencing (WGS/WTS) provides comprehensive insights, while targeted sequencing is more cost-effective and sensitive. In comparison to short-read sequencing, which still dominates the field due to high speed and cost-effectiveness, long-read sequencing can overcome alignment limitations and better discriminate similar sequences from alternative transcripts or repetitive regions. Hybrid sequencing combines the best strengths of different technologies for a more comprehensive view of genomic/transcriptomic variations. Understanding each technology's strengths and limitations is critical for translating cutting-edge technologies into clinical applications. In this study, we sequenced DNA and RNA libraries of reference samples using various targeted DNA and RNA panels and the whole transcriptome on both short-read and long-read platforms. This study design enables a comprehensive analysis of sequencing technologies, targeting protocols, and library preparation methods. Our expanded profiling landscape establishes a reference point for assessing current sequencing technologies, facilitating informed decision-making in genomic research and precision medicine.
| S-EPMC11329654 | biostudies-literature
Project description:Although long-read RNA-seq is increasingly applied to characterize full-length transcripts it can also enable detection of nucleotide variants, such as genetic mutations or RNA editing sites, which is significantly under-explored. Here, we present an in-depth study to detect and analyze RNA editing sites in long-read RNA-seq. Our new method, L-GIREMI, effectively handles sequencing errors and read biases. Applied to PacBio RNA-seq data, L-GIREMI affords a high accuracy in RNA editing identification. Additionally, our analysis uncovered novel insights about RNA editing occurrences in single molecules and double-stranded RNA structures. L-GIREMI provides a valuable means to study nucleotide variants in long-read RNA-seq.
| S-EPMC10360234 | biostudies-literature
Project description:The Tibetan Schizothoracinae fish Gymnocypris przewalskii has the ability to adapt to the extreme plateau environment, making it an ideal biological material for evolutionary biology research. However, the lack of well-annotated reference genomes has limited the study of the molecular genetics of G. przewalskii. To characterize its transcriptome features, we first used long-read sequencing technology in combination with RNA-seq for transcriptomic analysis. A total of 159,053 full-length (FL) transcripts were captured by Iso-Seq, having a mean length of 3,445 bp with N50 value of 4,348. Of all FL transcripts, 145,169 were well-annotated in the public database and 134,537 contained complete open reading frames. There were 4,149 pairs of alternative splicing events, of which three randomly selected were defined by RT-PCR and sequencing, and 13,293 long non-coding RNAs detected, based on all-vs.-all BLAST. A total of 118,185 perfect simple sequence repeats were identified from FL transcripts. The FL transcriptome might provide basis for further research of G. przewalskii.
| S-EPMC8320875 | biostudies-literature
Project description:RNA sequencing (RNA-seq) enables characterization and quantification of individual transcriptomes as well as detection of patterns of allelic expression and alternative splicing. Current RNA-seq protocols depend on high-throughput short-read sequencing of cDNA. However, as ongoing advances are rapidly yielding increasing read lengths, a technical hurdle remains in identifying the degree to which differences in read length influence various transcriptome analyses. In this study, we generated two paired-end RNA-seq datasets of differing read lengths (2×75 bp and 2×262 bp) for lymphoblastoid cell line GM12878 and compared the effect of read length on transcriptome analyses, including read-mapping performance, gene and transcript quantification, and detection of allele-specific expression (ASE) and allele-specific alternative splicing (ASAS) patterns. Our results indicate that, while the current long-read protocol is considerably more expensive than short-read sequencing, there are important benefits that can only be achieved with longer read length, including lower mapping bias and reduced ambiguity in assigning reads to genomic elements, such as mRNA transcript. We show that these benefits ultimately lead to improved detection of cis-acting regulatory and splicing variation effects within individuals.
| S-EPMC4176000 | biostudies-literature
Project description:BACKGROUND: RNA-seq has shown huge potential for phylogenomic inferences in non-model organisms. However, error, incompleteness, and redundant assembled transcripts for each gene in de novo assembly of short reads cause noise in analyses and a large amount of missing data in the aligned matrix. To address these problems, we compare de novo assemblies of paired end 90 bp RNA-seq reads using Oases, Trinity, Trans-ABySS and SOAPdenovo-Trans to transcripts from genome annotation of the model plant Ricinus communis. By doing so we evaluate strategies for optimizing total gene coverage and minimizing assembly chimeras and redundancy. RESULTS: We found that the frequency and structure of chimeras vary dramatically among different software packages. The differences were largely due to the number of trans-self chimeras that contain repeats in the opposite direction. More than half of the total chimeras in Oases and Trinity were trans-self chimeras. Within each package, we found a trade-off between maximizing reference coverage and minimizing redundancy and chimera rate. In order to reduce redundancy, we investigated three methods: 1) using cap3 and CD-HIT-EST to combine highly similar transcripts, 2) only retaining the transcript with the highest read coverage, or removing the transcript with the lowest read coverage for each subcomponent in Trinity, and 3) filtering Oases single k-mer assemblies by number of transcripts per locus and relative transcript length, and then finding the transcript with the highest read coverage. We then utilized results from blastx against model protein sequences to effectively remove trans chimeras. After optimization, seven assembly strategies among all four packages successfully assembled 42.9-47.1% of reference genes to more than 200 bp, with a chimera rate of 0.92-2.21%, and on average 1.8-3.1 transcripts per reference gene assembled. CONCLUSIONS: With rapidly improving sequencing and assembly tools, our study provides a framework to benchmark and optimize performance before choosing tools or parameter combinations for analyzing short-read RNA-seq data. Our study demonstrates that choice of assembly package, k-mer sizes, post-assembly redundancy-reduction and chimera cleanup, and strand-specific RNA-seq library preparation and assembly dramatically improves gene coverage by non-redundant and non-chimeric transcripts that are optimized for downstream phylogenomic analyses.
| S-EPMC3663818 | biostudies-literature
Project description:MotivationHigh-throughput sequencing has transformed the study of gene expression levels through RNA-seq, a technique that is now routinely used by various fields, such as genetic research or diagnostics. The advent of third generation sequencing technologies providing significantly longer reads opens up new possibilities. However, the high error rates common to these technologies set new bioinformatics challenges for the gapped alignment of reads to their genomic origin. In this study, we have explored how currently available RNA-seq splice-aware alignment tools cope with increased read lengths and error rates. All tested tools were initially developed for short NGS reads, but some have claimed support for long Pacific Biosciences (PacBio) or even Oxford Nanopore Technologies (ONT) MinION reads.ResultsThe tools were tested on synthetic and real datasets from two technologies (PacBio and ONT MinION). Alignment quality and resource usage were compared across different aligners. The effect of error correction of long reads was explored, both using self-correction and correction with an external short reads dataset. A tool was developed for evaluating RNA-seq alignment results. This tool can be used to compare the alignment of simulated reads to their genomic origin, or to compare the alignment of real reads to a set of annotated transcripts. Our tests show that while some RNA-seq aligners were unable to cope with long error-prone reads, others produced overall good results. We further show that alignment accuracy can be improved using error-corrected reads.Availability and implementationhttps://github.com/kkrizanovic/RNAseqEval, https://figshare.com/projects/RNAseq_benchmark/24391.Contactmile.sikic@fer.hr.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC6192213 | biostudies-literature
Project description:MotivationPercent Spliced-In (PSI) values are commonly used to report alternative pre-mRNA splicing (AS) changes. Previous PSI-detection tools were limited to specific AS events and were evaluated by in silico RNA-seq data. We developed PSI-Sigma, which uses a new PSI index, and we employed actual (non-simulated) RNA-seq data from spliced synthetic genes (RNA Sequins) to benchmark its performance (i.e. precision, recall, false positive rate and correlation) in comparison with three leading tools (rMATS, SUPPA2 and Whippet).ResultsPSI-Sigma outperformed these tools, especially in the case of AS events with multiple alternative exons and intron-retention events. We also briefly evaluated its performance in long-read RNA-seq analysis, by sequencing a mixture of human RNAs and RNA Sequins with nanopore long-read sequencers.Availability and implementationPSI-Sigma is implemented is available at https://github.com/wososa/PSI-Sigma.
| S-EPMC6901072 | biostudies-literature