scholarly journals Establishing evidenced-based best practice for the de novo assembly and evaluation of transcriptomes from non-model organisms

2015 ◽  
Author(s):  
Matthew D MacManes
Keyword(s):  
Best Practice ◽  
High Throughput Sequencing ◽  
Population Genomics ◽  
De Novo ◽  
Transcriptome Assembly ◽  
Model Organisms ◽  
Single Individual ◽  
Biological Phenomena ◽  
Or Gene ◽  
Evidenced Based

Characterizing transcriptomes in both model and non-model organisms has resulted in a massive increase in our understanding of biological phenomena. This boon, largely made possible via high-throughput sequencing, means that studies of functional, evolutionary and population genomics are now being done by hundreds or even thousands of labs around the world. For many, these studies begin with a de novo transcriptome assembly, which is a technically complicated process involving several discrete steps. Each step may be accomplished in one of several different ways, using different software packages, each producing different results. This analytical complexity begs the question -- Which method(s) are optimal? Using reference and non-reference based evaluative methods, I propose a set of guidelines that aim to standardize and facilitate the process of transcriptome assembly. These recommendations include the generation of between 20 million and 40 million sequencing reads from single individual where possible, error correction of reads, gentle quality trimming, assembly filtering using Transrate and/or gene expression, annotation using dammit, and appropriate reporting. These recommendations have been extensively benchmarked and applied to publicly available transcriptomes, resulting in improvements in both content and contiguity. To facilitate the implementation of the proposed standardized methods, I have released a set of version controlled open-sourced code, The Oyster River Protocol for Transcriptome Assembly, available at http://oyster-river-protocol.rtfd.org/.

scholarly journals The Oyster River Protocol: A Multi Assembler and Kmer Approach For de novo Transcriptome Assembly

2017 ◽  
Author(s):  
Matthew D. MacManes
Keyword(s):  
High Throughput Sequencing ◽  
Population Genomics ◽  
De Novo ◽  
Transcriptome Assembly ◽  
Model Organisms ◽  
De Novo Transcriptome ◽  
Link Type ◽  
Biological Phenomena ◽  
Complicated Process ◽  
Downstream Analysis

AbstractCharacterizing transcriptomes in non-model organisms has resulted in a massive increase in our understanding of biological phenomena. This boon, largely made possible via high-throughput sequencing, means that studies of functional, evolutionary and population genomics are now being done by hundreds or even thousands of labs around the world. For many, these studies begin with a de novo transcriptome assembly, which is a technically complicated process involving several discrete steps. The Oyster River Protocol (ORP), described here, implements a standardized and benchmarked set of bioinformatic processes, resulting in an assembly with enhanced qualities over other standard assembly methods. Specifically, ORP produced assemblies have higher Detonate and TransRate scores and mapping rates, which is largely a product of the fact that it leverages a multi-assembler and kmer assembly process, thereby bypassing the shortcomings of any one approach. These improvements are important, as previously unassembled transcripts are included in ORP assemblies, resulting in a significant enhancement of the power of downstream analysis. Further, as part of this study, I show that assembly quality is unrelated with the number of reads generated, above 30 million reads. Code Availability: The version controlled open-source code is available at https://github.com/macmanes-lab/Oyster_River_Protocol. Instructions for software installation and use, and other details are available at http://oyster-river-protocol.rtfd.org/.

scholarly journals The Oyster River Protocol: a multi-assembler and kmer approach for de novo transcriptome assembly

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5428 ◽  
Author(s):  
Matthew D. MacManes
Keyword(s):  
High Throughput Sequencing ◽  
Population Genomics ◽  
De Novo ◽  
Transcriptome Assembly ◽  
Model Organisms ◽  
De Novo Transcriptome Assembly ◽  
De Novo Transcriptome ◽  
Biological Phenomena ◽  
Complicated Process ◽  
Downstream Analysis

Characterizing transcriptomes in non-model organisms has resulted in a massive increase in our understanding of biological phenomena. This boon, largely made possible via high-throughput sequencing, means that studies of functional, evolutionary, and population genomics are now being done by hundreds or even thousands of labs around the world. For many, these studies begin with a de novo transcriptome assembly, which is a technically complicated process involving several discrete steps. The Oyster River Protocol (ORP), described here, implements a standardized and benchmarked set of bioinformatic processes, resulting in an assembly with enhanced qualities over other standard assembly methods. Specifically, ORP produced assemblies have higher Detonate and TransRate scores and mapping rates, which is largely a product of the fact that it leverages a multi-assembler and kmer assembly process, thereby bypassing the shortcomings of any one approach. These improvements are important, as previously unassembled transcripts are included in ORP assemblies, resulting in a significant enhancement of the power of downstream analysis. Further, as part of this study, I show that assembly quality is unrelated with the number of reads generated, above 30 million reads. Code Availability: The version controlled open-source code is available at https://github.com/macmanes-lab/Oyster_River_Protocol. Instructions for software installation and use, and other details are available at http://oyster-river-protocol.rtfd.org/.

scholarly journals The brain transcriptome of the wolf spider, Schizocosa ocreata

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Daniel Stribling ◽  
Peter L. Chang ◽  
Justin E. Dalton ◽  
Christopher A. Conow ◽  
Malcolm Rosenthal ◽  
...  
Keyword(s):  
Gene Expression ◽  
De Novo ◽  
Transcriptome Assembly ◽  
Model Organisms ◽  
De Novo Transcriptome Assembly ◽  
De Novo Transcriptome ◽  
Wolf Spiders ◽  
Schizocosa Ocreata ◽  
Genomic Studies ◽  
The Brain

Abstract Objectives Arachnids have fascinating and unique biology, particularly for questions on sex differences and behavior, creating the potential for development of powerful emerging models in this group. Recent advances in genomic techniques have paved the way for a significant increase in the breadth of genomic studies in non-model organisms. One growing area of research is comparative transcriptomics. When phylogenetic relationships to model organisms are known, comparative genomic studies provide context for analysis of homologous genes and pathways. The goal of this study was to lay the groundwork for comparative transcriptomics of sex differences in the brain of wolf spiders, a non-model organism of the pyhlum Euarthropoda, by generating transcriptomes and analyzing gene expression. Data description To examine sex-differential gene expression, short read transcript sequencing and de novo transcriptome assembly were performed. Messenger RNA was isolated from brain tissue of male and female subadult and mature wolf spiders (Schizocosa ocreata). The raw data consist of sequences for the two different life stages in each sex. Computational analyses on these data include de novo transcriptome assembly and differential expression analyses. Sample-specific and combined transcriptomes, gene annotations, and differential expression results are described in this data note and are available from publicly-available databases.

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

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3702 ◽  
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.

scholarly journals dDocent: a RADseq, variant-calling pipeline designed for population genomics of non-model organisms

2014 ◽  
Author(s):  
Jonathan Puritz ◽  
Christopher M. Hollenbeck ◽  
John R. Gold
Keyword(s):  
Population Genomics ◽  
De Novo ◽  
Variant Calling ◽  
Population Level ◽  
Model Organisms ◽  
Effective Population ◽  
Reduction Techniques ◽  
Indel Polymorphisms ◽  
Indel Calling ◽  
Population Sizes

Restriction-site associated DNA sequencing (RADseq) has become a powerful and useful approach for population genomics. Currently, no software exists that utilizes both paired-end reads from RADseq data to efficiently produce population-informative variant calls, especially for organisms with large effective population sizes and high levels of genetic polymorphism but for which no genomic resources exist. dDocent is an analysis pipeline with a user-friendly, command-line interface designed to process individually barcoded RADseq data (with double cut sites) into informative SNPs/Indels for population-level analyses. The pipeline, written in BASH, uses data reduction techniques and other stand-alone software packages to perform quality trimming and adapter removal, de novo assembly of RAD loci, read mapping, SNP and Indel calling, and baseline data filtering. Double-digest RAD data from population pairings of three different marine fishes were used to compare dDocent with Stacks, the first generally available, widely used pipeline for analysis of RADseq data. dDocent consistently identified more SNPs shared across greater numbers of individuals and with higher levels of coverage. This is most likely due to the fact that dDocent quality trims instead of filtering and incorporates both forward and reverse reads in assembly, mapping, and SNP calling, thus enabling use of reads with Indel polymorphisms. The pipeline and a comprehensive user guide can be found at (http://dDocent.wordpress.com).

scholarly journals A Multireference-Based Whole Genome Assembly for the Obligate Ant-Following Antbird, Rhegmatorhina melanosticta (Thamnophilidae)

Diversity ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 144 ◽  
Author(s):  
Laís Coelho ◽  
Lukas Musher ◽  
Joel Cracraft
Keyword(s):  
Genome Assembly ◽  
High Throughput Sequencing ◽  
Population Genomics ◽  
De Novo ◽  
Structural Difference ◽  
Whole Genome ◽  
Sequencing Technology ◽  
A Genome ◽  
Avian Genomes ◽  
Chromosome Level

Current generation high-throughput sequencing technology has facilitated the generation of more genomic-scale data than ever before, thus greatly improving our understanding of avian biology across a range of disciplines. Recent developments in linked-read sequencing (Chromium 10×) and reference-based whole-genome assembly offer an exciting prospect of more accessible chromosome-level genome sequencing in the near future. We sequenced and assembled a genome of the Hairy-crested Antbird (Rhegmatorhina melanosticta), which represents the first publicly available genome for any antbird (Thamnophilidae). Our objectives were to (1) assemble scaffolds to chromosome level based on multiple reference genomes, and report on differences relative to other genomes, (2) assess genome completeness and compare content to other related genomes, and (3) assess the suitability of linked-read sequencing technology for future studies in comparative phylogenomics and population genomics studies. Our R. melanosticta assembly was both highly contiguous (de novo scaffold N50 = 3.3 Mb, reference based N50 = 53.3 Mb) and relatively complete (contained close to 90% of evolutionarily conserved single-copy avian genes and known tetrapod ultraconserved elements). The high contiguity and completeness of this assembly enabled the genome to be successfully mapped to the chromosome level, which uncovered a consistent structural difference between R. melanosticta and other avian genomes. Our results are consistent with the observation that avian genomes are structurally conserved. Additionally, our results demonstrate the utility of linked-read sequencing for non-model genomics. Finally, we demonstrate the value of our R. melanosticta genome for future researchers by mapping reduced representation sequencing data, and by accurately reconstructing the phylogenetic relationships among a sample of thamnophilid species.

scholarly journals An evaluation of pool-sequencing transcriptome-based exon capture for population genomics in non-model species

2019 ◽  
Author(s):  
Emeline Deleury ◽  
Thomas Guillemaud ◽  
Aurélie Blin ◽  
Eric Lombaert
Keyword(s):  
Evolutionary Biology ◽  
High Throughput Sequencing ◽  
Harmonia Axyridis ◽  
Population Genomics ◽  
De Novo ◽  
Cost Effective ◽  
Allele Frequencies ◽  
Technical Solution ◽  
Model Species ◽  
Exon Capture

AbstractExon capture coupled to high-throughput sequencing constitutes a cost-effective technical solution for addressing specific questions in evolutionary biology by focusing on expressed regions of the genome preferentially targeted by selection. Transcriptome-based capture, a process that can be used to capture the exons of non-model species, is use in phylogenomics. However, its use in population genomics remains rare due to the high costs of sequencing large numbers of indexed individuals across multiple populations. We evaluated the feasibility of combining transcriptome-based capture and the pooling of tissues from numerous individuals for DNA extraction as a cost-effective, generic and robust approach to estimating the variant allele frequencies of any species at the population level. We designed capture probes for ∼5 Mb of chosen de novo transcripts from the Asian ladybird Harmonia axyridis (5,717 transcripts). We called ∼300,000 bi-allelic SNPs for a pool of 36 non-indexed individuals. Capture efficiency was high, and pool-seq was as effective and accurate as individual-seq for detecting variants and estimating allele frequencies. Finally, we also evaluated an approach for simplifying bioinformatic analyses by mapping genomic reads directly to targeted transcript sequences to obtain coding variants. This approach is effective and does not affect the estimation of SNP allele frequencies, except for a small bias close to some exon ends. We demonstrate that this approach can also be used to predict the intron-exon boundaries of targeted de novo transcripts, making it possible to abolish genotyping biases near exon ends.

scholarly journals rnaSPAdes: a de novo transcriptome assembler and its application to RNA-Seq data

2018 ◽  
Author(s):  
Elena Bushmanova ◽  
Dmitry Antipov ◽  
Alla Lapidus ◽  
Andrey D. Prjibelski
Keyword(s):  
De Novo ◽  
Transcriptome Assembly ◽  
Model Organisms ◽  
Challenging Problem ◽  
Rna Seq ◽  
De Novo Transcriptome ◽  
Weak Points ◽  
Transcriptome Reconstruction ◽  
Evaluation Approaches ◽  
Genome Assembler

AbstractSummaryPossibility to generate large RNA-seq datasets has led to development of various reference-based and de novo transcriptome assemblers with their own strengths and limitations. While reference-based tools are widely used in various transcriptomic studies, their application is limited to the model organisms with finished and annotated genomes. De novo transcriptome reconstruction from short reads remains an open challenging problem, which is complicated by the varying expression levels across different genes, alternative splicing and paralogous genes. In this paper we describe a novel transcriptome assembler called rnaSPAdes, which is developed on top of SPAdes genome assembler and explores surprising computational parallels between assembly of transcriptomes and single-cell genomes. We also present quality assessment reports for rnaSPAdes assemblies, compare it with modern transcriptome assembly tools using several evaluation approaches on various RNA-Seq datasets, and briefly highlight strong and weak points of different assemblers.Availability and implementationrnaSPAdes is implemented in C++ and Python and is freely available at cab.spbu.ru/software/rnaspades/.

scholarly journals K-mer clustering algorithm using a MapReduce framework: application to the parallelization of the Inchworm module of Trinity

2017 ◽  
Author(s):  
Chang Sik Kim ◽  
Martyn D. Winn ◽  
Vipin Sachdeva ◽  
Kirk E. Jordan
Keyword(s):  
Clustering Algorithm ◽  
De Novo ◽  
Transcriptome Assembly ◽  
Initial Step ◽  
Computer Hardware ◽  
Model Organisms ◽  
De Bruijn Graph ◽  
Memory Representation ◽  
Novel Approach ◽  
Sequencing Problems

AbstractBackgroundDe novo transcriptome assembly is an important technique for understanding gene expression in non-model organisms. Many de novo assemblers using the de Bruijn graph of a set of the RNA sequences rely on in-memory representation of this graph. However, current methods analyse the complete set of read-derived k-mer sequence at once, resulting in the need for computer hardware with large shared memory.ResultsWe introduce a novel approach that clusters k-mers as the first step. The clusters correspond to small sets of gene products, which can be processed quickly to give candidate transcripts. We implement the clustering step using the MapReduce approach for parallelising the analysis of large datasets, which enables the use of compute clusters. The computational task is distributed across the compute system, and no specialised hardware is required. Using this approach, we have re-implemented the Inchworm module from the widely used Trinity pipeline, and tested the method in the context of the full Trinity pipeline. Validation tests on a range of real datasets show large reductions in the runtime and per-node memory requirements, when making use of a compute cluster.ConclusionsOur study shows that MapReduce-based clustering has great potential for distributing challenging sequencing problems, without loss of accuracy. Although we have focussed on the Trinity package, we propose that such clustering is a useful initial step for other assembly pipelines.

scholarly journals Proteotranscriptomics assisted gene annotation and spatial proteomics of Bombyx mori BmN4 cell line

2020 ◽  
Author(s):  
Michal Levin ◽  
Marion Scheibe ◽  
Falk Butter
Keyword(s):  
Mass Spectrometry ◽  
Bombyx Mori ◽  
Cell Line ◽  
De Novo ◽  
High Resolution Mass Spectrometry ◽  
Gene Annotation ◽  
Transcriptome Assembly ◽  
Model Organisms ◽  
Sequence Information ◽  
A Genome

Abstract BackgroundThe process of identifying all coding regions in a genome is crucial for any study at the level of molecular biology, ranging from single-gene cloning to genome-wide measurements using RNA-Seq or mass spectrometry. While satisfactory annotation has been made feasible for well-studied model organisms through great efforts of big consortia, for most systems this kind of data is either absent or not adequately precise. ResultsCombining in-depth transcriptome sequencing and high resolution mass spectrometry, we here use proteotranscriptomics to improve gene annotation of protein-coding genes in the Bombyx mori cell line BmN4 which is an increasingly used tool for the analysis of piRNA biogenesis and function. Using this approach we provide the exact coding sequence and evidence for more than 6,200 genes on the protein level. Furthermore using spatial proteomics, we establish the subcellular localization of thousands of these proteins. We show that our approach outperforms current Bombyx mori annotation attempts in terms of accuracy and coverage. ConclusionsWe show that proteotranscriptomics is an efficient, cost-effective and accurate approach to improve previous annotations or generate new gene models. As this technique is based on de-novo transcriptome assembly, it provides the possibility to study any species also in the absence of genome sequence information for which proteogenomics would be impossible.

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