The evaluation of RNA-Seq de novo assembly by PacBio long read sequencing

Mapping Intimacies ◽

10.1101/735621 ◽

2019 ◽

Author(s):

Yifan Yang ◽

Michael Gribskov

Keyword(s):

Real Time ◽

De Novo ◽

Critical Issue ◽

Evaluation Methods ◽

Model Organisms ◽

Rna Seq ◽

Long Reads ◽

Long Read ◽

Set Up ◽

Downstream Analysis

AbstractRNA-Seq de novo assembly is an important method to generate transcriptomes for non-model organisms before any downstream analysis. Given many great de novo assembly methods developed by now, one critical issue is that there is no consensus on the evaluation of de novo assembly methods yet. Therefore, to set up a benchmark for evaluating the quality of de novo assemblies is very critical. Addressing this challenge will help us deepen the insights on the properties of different de novo assemblers and their evaluation methods, and provide hints on choosing the best assembly sets as transcriptomes of non-model organisms for the further functional analysis. In this article, we generate a “real time” transcriptome using PacBio long reads as a benchmark for evaluating five de novo assemblers and two model-based de novo assembly evaluation methods. By comparing the de novo assmblies generated by RNA-Seq short reads with the “real time” transcriptome from the same biological sample, we find that Trinity is best at the completeness by generating more assemblies than the alternative assemblers, but less continuous and having more misassemblies; Oases is best at the continuity and specificity, but less complete; The performance of SOAPdenovo-Trans, Trans-AByss and IDBA-Tran are in between of five assemblers. For evaluation methods, DETONATE leverages multiple aspects of the assembly set and ranks the assembly set with an average performance as the best, meanwhile the contig score can serve as a good metric to select assemblies with high completeness, specificity, continuity but not sensitive to misassemblies; TransRate contig score is useful for removing misassemblies, yet often the assemblies in the optimal set is too few to be used as a transcriptome.

LongStitch: High-quality genome assembly correction and scaffolding using long reads

10.1101/2021.06.17.448848 ◽

2021 ◽

Author(s):

Lauren Coombe ◽

Janet X Li ◽

Theodora Lo ◽

Johnathan Wong ◽

Vladimir Nikolic ◽

...

Keyword(s):

Genome Assembly ◽

De Novo ◽

Draft Genome ◽

Model Organisms ◽

High Quality ◽

De Novo Genome Assembly ◽

Long Reads ◽

Long Read ◽

Genomic Regions ◽

Genome Assemblies

Background Generating high-quality de novo genome assemblies is foundational to the genomics study of model and non-model organisms. In recent years, long-read sequencing has greatly benefited genome assembly and scaffolding, a process by which assembled sequences are ordered and oriented through the use of long-range information. Long reads are better able to span repetitive genomic regions compared to short reads, and thus have tremendous utility for resolving problematic regions and helping generate more complete draft assemblies. Here, we present LongStitch, a scalable pipeline that corrects and scaffolds draft genome assemblies exclusively using long reads. Results LongStitch incorporates multiple tools developed by our group and runs in up to three stages, which includes initial assembly correction (Tigmint-long), followed by two incremental scaffolding stages (ntLink and ARKS-long). Tigmint-long and ARKS-long are misassembly correction and scaffolding utilities, respectively, previously developed for linked reads, that we adapted for long reads. Here, we describe the LongStitch pipeline and introduce our new long-read scaffolder, ntLink, which utilizes lightweight minimizer mappings to join contigs. LongStitch was tested on short and long-read assemblies of three different human individuals using corresponding nanopore long-read data, and improves the contiguity of each assembly from 2.0-fold up to 304.6-fold (as measured by NGA50 length). Furthermore, LongStitch generates more contiguous and correct assemblies compared to state-of-the-art long-read scaffolder LRScaf in most tests, and consistently runs in under five hours using less than 23GB of RAM. Conclusions Due to its effectiveness and efficiency in improving draft assemblies using long reads, we expect LongStitch to benefit a wide variety of de novo genome assembly projects. The LongStitch pipeline is freely available at https://github.com/bcgsc/longstitch.

Assembling Large Genomes with Single-Molecule Sequencing and Locality Sensitive Hashing

10.1101/008003 ◽

2014 ◽

Cited By ~ 13

Author(s):

Konstantin Berlin ◽

Sergey Koren ◽

Chen-Shan Chin ◽

James Drake ◽

Jane M Landolin ◽

...

Keyword(s):

Single Molecule ◽

De Novo ◽

Locality Sensitive Hashing ◽

Model Organisms ◽

Smrt Sequencing ◽

High Coverage ◽

Celera Assembler ◽

Single Molecule Sequencing ◽

Long Reads ◽

Long Read

We report reference-grade de novo assemblies of four model organisms and the human genome from single-molecule, real-time (SMRT) sequencing. Long-read SMRT sequencing is routinely used to finish microbial genomes, but the available assembly methods have not scaled well to larger genomes. Here we introduce the MinHash Alignment Process (MHAP) for efficient overlapping of noisy, long reads using probabilistic, locality-sensitive hashing. Together with Celera Assembler, MHAP was used to reconstruct the genomes of Escherichia coli, Saccharomyces cerevisiae, Arabidopsis thaliana, Drosophila melanogaster, and human from high-coverage SMRT sequencing. The resulting assemblies include fully resolved chromosome arms and close persistent gaps in these important reference genomes, including heterochromatic and telomeric transition sequences. For D. melanogaster, MHAP achieved a 600-fold speedup relative to prior methods and a cloud computing cost of a few hundred dollars. These results demonstrate that single-molecule sequencing alone can produce near-complete eukaryotic genomes at modest cost.

LongStitch: high-quality genome assembly correction and scaffolding using long reads

BMC Bioinformatics ◽

10.1186/s12859-021-04451-7 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Lauren Coombe ◽

Janet X. Li ◽

Theodora Lo ◽

Johnathan Wong ◽

Vladimir Nikolic ◽

...

Keyword(s):

Genome Assembly ◽

De Novo ◽

Draft Genome ◽

Model Organisms ◽

High Quality ◽

De Novo Genome Assembly ◽

Long Reads ◽

Long Read ◽

Genomic Regions ◽

Genome Assemblies

Abstract Background Generating high-quality de novo genome assemblies is foundational to the genomics study of model and non-model organisms. In recent years, long-read sequencing has greatly benefited genome assembly and scaffolding, a process by which assembled sequences are ordered and oriented through the use of long-range information. Long reads are better able to span repetitive genomic regions compared to short reads, and thus have tremendous utility for resolving problematic regions and helping generate more complete draft assemblies. Here, we present LongStitch, a scalable pipeline that corrects and scaffolds draft genome assemblies exclusively using long reads. Results LongStitch incorporates multiple tools developed by our group and runs in up to three stages, which includes initial assembly correction (Tigmint-long), followed by two incremental scaffolding stages (ntLink and ARKS-long). Tigmint-long and ARKS-long are misassembly correction and scaffolding utilities, respectively, previously developed for linked reads, that we adapted for long reads. Here, we describe the LongStitch pipeline and introduce our new long-read scaffolder, ntLink, which utilizes lightweight minimizer mappings to join contigs. LongStitch was tested on short and long-read assemblies of Caenorhabditis elegans, Oryza sativa, and three different human individuals using corresponding nanopore long-read data, and improves the contiguity of each assembly from 1.2-fold up to 304.6-fold (as measured by NGA50 length). Furthermore, LongStitch generates more contiguous and correct assemblies compared to state-of-the-art long-read scaffolder LRScaf in most tests, and consistently improves upon human assemblies in under five hours using less than 23 GB of RAM. Conclusions Due to its effectiveness and efficiency in improving draft assemblies using long reads, we expect LongStitch to benefit a wide variety of de novo genome assembly projects. The LongStitch pipeline is freely available at https://github.com/bcgsc/longstitch.

Metagenomics workflow for hybrid assembly, differential coverage binning, metatranscriptomics and pathway analysis (MUFFIN)

PLoS Computational Biology ◽

10.1371/journal.pcbi.1008716 ◽

2021 ◽

Vol 17 (2) ◽

pp. e1008716

Author(s):

Renaud Van Damme ◽

Martin Hölzer ◽

Adrian Viehweger ◽

Bettina Müller ◽

Erik Bongcam-Rudloff ◽

...

Keyword(s):

De Novo ◽

Rna Seq ◽

Kegg Pathways ◽

Sequencing Technologies ◽

Long Reads ◽

Metagenomic Sample ◽

Long Read ◽

Biogas Reactor ◽

Functional Pathway ◽

Workflow Orchestration

Metagenomics has redefined many areas of microbiology. However, metagenome-assembled genomes (MAGs) are often fragmented, primarily when sequencing was performed with short reads. Recent long-read sequencing technologies promise to improve genome reconstruction. However, the integration of two different sequencing modalities makes downstream analyses complex. We, therefore, developed MUFFIN, a complete metagenomic workflow that uses short and long reads to produce high-quality bins and their annotations. The workflow is written by using Nextflow, a workflow orchestration software, to achieve high reproducibility and fast and straightforward use. This workflow also produces the taxonomic classification and KEGG pathways of the bins and can be further used for quantification and annotation by providing RNA-Seq data (optionally). We tested the workflow using twenty biogas reactor samples and assessed the capacity of MUFFIN to process and output relevant files needed to analyze the microbial community and their function. MUFFIN produces functional pathway predictions and, if provided de novo metatranscript annotations across the metagenomic sample and for each bin. MUFFIN is available on github under GNUv3 licence: https://github.com/RVanDamme/MUFFIN.

Systematic assessment of long-read RNA-seq methods for transcript identification and quantification

10.21203/rs.3.rs-777702/v1 ◽

2021 ◽

Author(s):

Angela Brooks ◽

Francisco Pardo-Palacios ◽

Fairlie Reese ◽

Silvia Carbonell-Sala ◽

Mark Diekhans ◽

...

Keyword(s):

De Novo ◽

Simulated Data ◽

Model Organisms ◽

Rna Seq ◽

Sequencing Platform ◽

Systematic Assessment ◽

Sequencing Technologies ◽

Long Read ◽

Community Effort ◽

Transcriptome Analyses

Abstract With increased usage of long-read sequencing technologies to perform transcriptome analyses, there becomes a greater need to evaluate different methodologies including library preparation, sequencing platform, and computational analysis tools. Here, we report the study design of a community effort called the Long-read RNA-Seq Genome Annotation Assessment Project (LRGASP) Consortium, whose goals are characterizing the strengths and remaining challenges in using long-read approaches to identify and quantify the transcriptomes of both model and non-model organisms. The LRGASP organizers have generated cDNA and direct RNA datasets in human, mouse, and manatee samples using different protocols followed by sequencing on Illumina, Pacific Biosciences, and Oxford Nanopore Technologies platforms. Participants will use the provided data to submit predictions for three challenges: transcript isoform detection with a high-quality genome, transcript isoform quantification, and de novo transcript isoform identification. Evaluators from different institutions will determine which pipelines have the highest accuracy for a variety of metrics using benchmarks that include spike-in synthetic transcripts, simulated data, and a set of undisclosed, manually curated transcripts by GENCODE. We also describe plans for experimental validation of predictions that are platform-specific and computational tool-specific. We believe that a community effort to evaluate long-read RNA-seq methods will help move the field toward a better consensus on the best approaches to use for transcriptome analyses.

A practical guide to buildde-novoassemblies for single tissues of non-model organisms: the example of a Neotropical frog

PeerJ ◽

10.7717/peerj.3702 ◽

2017 ◽

Vol 5 ◽

pp. e3702 ◽

Cited By ~ 5

Author(s):

Santiago Montero-Mendieta ◽

Manfred Grabherr ◽

Henrik Lantz ◽

Ignacio De la Riva ◽

Jennifer A. Leonard ◽

...

Keyword(s):

Defense Mechanisms ◽

De Novo ◽

Transcriptome Assembly ◽

Cost Effective ◽

Model Organisms ◽

Rna Seq ◽

Assembly Pipeline ◽

Wide Variability ◽

History Of ◽

Inexperienced User

Whole genome sequencing (WGS) is a very valuable resource to understand the evolutionary history of poorly known species. However, in organisms with large genomes, as most amphibians, WGS is still excessively challenging and transcriptome sequencing (RNA-seq) represents a cost-effective tool to explore genome-wide variability. Non-model organisms do not usually have a reference genome and the transcriptome must be assembledde-novo. We used RNA-seq to obtain the transcriptomic profile forOreobates cruralis, a poorly known South American direct-developing frog. In total, 550,871 transcripts were assembled, corresponding to 422,999 putative genes. Of those, we identified 23,500, 37,349, 38,120 and 45,885 genes present in the Pfam, EggNOG, KEGG and GO databases, respectively. Interestingly, our results suggested that genes related to immune system and defense mechanisms are abundant in the transcriptome ofO. cruralis. We also present a pipeline to assist with pre-processing, assembling, evaluating and functionally annotating ade-novotranscriptome from RNA-seq data of non-model organisms. Our pipeline guides the inexperienced user in an intuitive way through all the necessary steps to buildde-novotranscriptome assemblies using readily available software and is freely available at:https://github.com/biomendi/TRANSCRIPTOME-ASSEMBLY-PIPELINE/wiki.

TrancriptomeReconstructoR, A Data-Driven Annotation of Complex Transcriptomes

10.21203/rs.3.rs-131404/v1 ◽

2020 ◽

Author(s):

Maxim Ivanov ◽

Albin Sandelin ◽

Sebastian Marquardt

Keyword(s):

De Novo ◽

Gene Annotation ◽

R Package ◽

Sequence Information ◽

Rna Seq ◽

Sequencing Data ◽

Gene Model ◽

Preparation Methods ◽

Downstream Analysis

Abstract Background: The quality of gene annotation determines the interpretation of results obtained in transcriptomic studies. The growing number of genome sequence information calls for experimental and computational pipelines for de novo transcriptome annotation. Ideally, gene and transcript models should be called from a limited set of key experimental data. Results: We developed TranscriptomeReconstructoR, an R package which implements a pipeline for automated transcriptome annotation. It relies on integrating features from independent and complementary datasets: i) full-length RNA-seq for detection of splicing patterns and ii) high-throughput 5' and 3' tag sequencing data for accurate definition of gene borders. The pipeline can also take a nascent RNA-seq dataset to supplement the called gene model with transient transcripts.We reconstructed de novo the transcriptional landscape of wild type Arabidopsis thaliana seedlings as a proof-of-principle. A comparison to the existing transcriptome annotations revealed that our gene model is more accurate and comprehensive than the two most commonly used community gene models, TAIR10 and Araport11. In particular, we identify thousands of transient transcripts missing from the existing annotations. Our new annotation promises to improve the quality of A.thaliana genome research.Conclusions: Our proof-of-concept data suggest a cost-efficient strategy for rapid and accurate annotation of complex eukaryotic transcriptomes. We combine the choice of library preparation methods and sequencing platforms with the dedicated computational pipeline implemented in the TranscriptomeReconstructoR package. The pipeline only requires prior knowledge on the reference genomic DNA sequence, but not the transcriptome. The package seamlessly integrates with Bioconductor packages for downstream analysis.

Platanus_B: an accurate de novo assembler for bacterial genomes using an iterative error-removal process

DNA Research ◽

10.1093/dnares/dsaa014 ◽

2020 ◽

Vol 27 (3) ◽

Cited By ~ 1

Author(s):

Rei Kajitani ◽

Dai Yoshimura ◽

Yoshitoshi Ogura ◽

Yasuhiro Gotoh ◽

Tetsuya Hayashi ◽

...

Keyword(s):

High Resolution ◽

Large Scale ◽

De Novo ◽

Bacterial Genomes ◽

Long Reads ◽

Wide Range ◽

Long Read ◽

Phylogenomic Analyses ◽

Error Removal ◽

Iterative Error

Abstract De novo assembly of short DNA reads remains an essential technology, especially for large-scale projects and high-resolution variant analyses in epidemiology. However, the existing tools often lack sufficient accuracy required to compare closely related strains. To facilitate such studies on bacterial genomes, we developed Platanus_B, a de novo assembler that employs iterations of multiple error-removal algorithms. The benchmarks demonstrated the superior accuracy and high contiguity of Platanus_B, in addition to its ability to enhance the hybrid assembly of both short and nanopore long reads. Although the hybrid strategies for short and long reads were effective in achieving near full-length genomes, we found that short-read-only assemblies generated with Platanus_B were sufficient to obtain ≥90% of exact coding sequences in most cases. In addition, while nanopore long-read-only assemblies lacked fine-scale accuracies, inclusion of short reads was effective in improving the accuracies. Platanus_B can, therefore, be used for comprehensive genomic surveillances of bacterial pathogens and high-resolution phylogenomic analyses of a wide range of bacteria.

De novo transcriptomes of 14 gammarid individuals for proteogenomic analysis of seven taxonomic groups

Scientific Data ◽

10.1038/s41597-019-0192-5 ◽

2019 ◽

Vol 6 (1) ◽

Cited By ~ 5

Author(s):

Yannick Cogne ◽

Davide Degli-Esposti ◽

Olivier Pible ◽

Duarte Gouveia ◽

Adeline François ◽

...

Keyword(s):

Aquatic Ecosystems ◽

Molecular Diversity ◽

De Novo ◽

Model Organisms ◽

Rna Seq ◽

Specific Expression ◽

Gammarus Fossarum ◽

Male Individual ◽

Taxonomic Groups ◽

Whole Transcriptome

Abstract Gammarids are amphipods found worldwide distributed in fresh and marine waters. They play an important role in aquatic ecosystems and are well established sentinel species in ecotoxicology. In this study, we sequenced the transcriptomes of a male individual and a female individual for seven different taxonomic groups belonging to the two genera Gammarus and Echinogammarus: Gammarus fossarum A, G. fossarum B, G. fossarum C, Gammarus wautieri, Gammarus pulex, Echinogammarus berilloni, and Echinogammarus marinus. These taxa were chosen to explore the molecular diversity of transcribed genes of genotyped individuals from these groups. Transcriptomes were de novo assembled and annotated. High-quality assembly was confirmed by BUSCO comparison against the Arthropod dataset. The 14 RNA-Seq-derived protein sequence databases proposed here will be a significant resource for proteogenomics studies of these ecotoxicologically relevant non-model organisms. These transcriptomes represent reliable reference sequences for whole-transcriptome and proteome studies on other gammarids, for primer design to clone specific genes or monitor their specific expression, and for analyses of molecular differences between gammarid species.

A comprehensive evaluation of long read error correction methods

BMC Genomics ◽

10.1186/s12864-020-07227-0 ◽

2020 ◽

Vol 21 (S6) ◽

Author(s):

Haowen Zhang ◽

Chirag Jain ◽

Srinivas Aluru

Keyword(s):

Error Correction ◽

Hybrid Methods ◽

Comprehensive Evaluation ◽

Data Sets ◽

Short Reads ◽

Long Reads ◽

Long Read ◽

Read Error Correction ◽

Downstream Analysis ◽

The Impact

Abstract Background Third-generation single molecule sequencing technologies can sequence long reads, which is advancing the frontiers of genomics research. However, their high error rates prohibit accurate and efficient downstream analysis. This difficulty has motivated the development of many long read error correction tools, which tackle this problem through sampling redundancy and/or leveraging accurate short reads of the same biological samples. Existing studies to asses these tools use simulated data sets, and are not sufficiently comprehensive in the range of software covered or diversity of evaluation measures used. Results In this paper, we present a categorization and review of long read error correction methods, and provide a comprehensive evaluation of the corresponding long read error correction tools. Leveraging recent real sequencing data, we establish benchmark data sets and set up evaluation criteria for a comparative assessment which includes quality of error correction as well as run-time and memory usage. We study how trimming and long read sequencing depth affect error correction in terms of length distribution and genome coverage post-correction, and the impact of error correction performance on an important application of long reads, genome assembly. We provide guidelines for practitioners for choosing among the available error correction tools and identify directions for future research. Conclusions Despite the high error rate of long reads, the state-of-the-art correction tools can achieve high correction quality. When short reads are available, the best hybrid methods outperform non-hybrid methods in terms of correction quality and computing resource usage. When choosing tools for use, practitioners are suggested to be careful with a few correction tools that discard reads, and check the effect of error correction tools on downstream analysis. Our evaluation code is available as open-source at https://github.com/haowenz/LRECE.