K-mer Assemblies Research Articles

Strain-resolved de-novo metagenomic assembly of viral genomes and microbial 16S rRNAs

BackgroundMetagenomics is a powerful approach to study environmental and human-associated microbial communities and, in particular, the role of viruses in shaping them. Viral genomes are challenging to assemble from metagenomic samples due to their genomic diversity caused by high mutation rates. In the standard de Bruijn graph assemblers, this genomic diversity leads to complex k-mer assembly graphs with a plethora of loops and bulges that are challenging to resolve into strains or haplotypes because variants more than the k-mer size apart cannot be phased. In contrast, overlap assemblers can phase variants as long as they are covered by a single read.ResultsHere, we present PenguiN, a software for strain resolved assembly of viral DNA and RNA genomes and bacterial 16S rRNA from shotgun metagenomics. Its exhaustive detection of all read overlaps in linear time combined with a Bayesian model to select strain-resolved extensions allow it to assemble longer viral contigs, less fragmented genomes, and more strains than existing assembly tools, on both real and simulated datasets. We show a 3–40-fold increase in complete viral genomes and a 6-fold increase in bacterial 16S rRNA genes.ConclusionPenguiN is the first overlap-based assembler for viral genome and 16S rRNA assembly from large and complex metagenomic datasets, which we hope will facilitate studying the key roles of viruses in microbial communities.C22dm7AjjRZTtiuc33W1snVideo

A targeted bioinformatics approach identifies highly variable cell surface proteins that are unique to Glomeromycotina

Diversity in arbuscular mycorrhizal fungi (AMF) contributes to biodiversity and resilience in natural environments and healthy agricultural systems. Functional complementarity exists among species of AMF in symbiosis with their plant hosts, but the molecular basis of this is not known. We hypothesise this is in part due to the difficulties that current sequence assembly methodologies have assembling sequences for intrinsically disordered proteins (IDPs) due to their low sequence complexity. IDPs are potential candidates for functional complementarity because they often exist as extended (non-globular) proteins providing additional amino acids for molecular interactions. Rhizophagus irregularis arabinogalactan-protein-like proteins (AGLs) are small secreted IDPs with no known orthologues in AMF or other fungi. We developed a targeted bioinformatics approach to identify highly variable AGLs/IDPs in RNA-sequence datasets. The approach includes a modified multiple k-mer assembly approach (Oases) to identify candidate sequences, followed by targeted sequence capture and assembly (mirabait-mira). All AMF species analysed, including the ancestral family Paraglomeraceae, have small families of proteins rich in disorder promoting amino acids such as proline and glycine, or glycine and asparagine. Glycine- and asparagine-rich proteins also were found in Geosiphon pyriformis (an obligate symbiont of a cyanobacterium), from the same subphylum (Glomeromycotina) as AMF. The sequence diversity of AGLs likely translates to functional diversity, based on predicted physical properties of tandem repeats (elastic, amyloid, or interchangeable) and their broad pI ranges. We envisage that AGLs/IDPs could contribute to functional complementarity in AMF through processes such as self-recognition, retention of nutrients, soil stability, and water movement.

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

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/.

Informed kmer selection for de novo transcriptome assembly.

Motivation: De novo transcriptome assembly is an integral part for many RNA-seq workflows. Common applications include sequencing of non-model organisms, cancer or meta transcriptomes. Most de novo transcriptome assemblers use the de Bruijn graph (DBG) as the underlying data structure. The quality of the assemblies produced by such assemblers is highly influenced by the exact word length k. As such no single kmer value leads to optimal results. Instead, DBGs over different kmer values are built and the assemblies are merged to improve sensitivity. However, no studies have investigated thoroughly the problem of automatically learning at which kmer value to stop the assembly. Instead a suboptimal selection of kmer values is often used in practice.Results: Here we investigate the contribution of a single kmer value in a multi-kmer based assembly approach. We find that a comparative clustering of related assemblies can be used to estimate the importance of an additional kmer assembly. Using a model fit based algorithm we predict the kmer value at which no further assemblies are necessary. Our approach is tested with different de novo assemblers for datasets with different coverage values and read lengths. Further, we suggest a simple post processing step that significantly improves the quality of multi-kmer assemblies.Conclusion: We provide an automatic method for limiting the number of kmer values without a significant loss in assembly quality but with savings in assembly time. This is a step forward to making multi-kmer methods more reliable and easier to use.Availability and Implementation:A general implementation of our approach can be found under: https://github.com/SchulzLab/KREATION.Supplementary information: Supplementary data are available at Bioinformatics online.Contact: mschulz@mmci.uni-saarland.de

Inferring bona fide transfrags in RNA-Seq derived-transcriptome assemblies of non-model organisms.

BackgroundDe novo transcriptome assembly of short transcribed fragments (transfrags) produced from sequencing-by-synthesis technologies often results in redundant datasets with differing levels of unassembled, partially assembled or mis-assembled transcripts. Post-assembly processing intended to reduce redundancy typically involves reassembly or clustering of assembled sequences. However, these approaches are mostly based on common word heuristics and often create clusters of biologically unrelated sequences, resulting in loss of unique transfrags annotations and propagation of mis-assemblies.ResultsHere, we propose a structured framework that consists of a few steps in pipeline architecture for Inferring Functionally Relevant Assembly-derived Transcripts (IFRAT). IFRAT combines 1) removal of identical subsequences, 2) error tolerant CDS prediction, 3) identification of coding potential, and 4) complements BLAST with a multiple domain architecture annotation that reduces non-specific domain annotation. We demonstrate that independent of the assembler, IFRAT selects bona fide transfrags (with CDS and coding potential) from the transcriptome assembly of a model organism without relying on post-assembly clustering or reassembly. The robustness of IFRAT is inferred on RNA-Seq data of Neurospora crassa assembled using de Bruijn graph-based assemblers, in single (Trinity and Oases-25) and multiple (Oases-Merge and additive or pooled) k-mer modes. Single k-mer assemblies contained fewer transfrags compared to the multiple k-mer assemblies. However, Trinity identified a comparable number of predicted coding sequence and gene loci to Oases pooled assembly. IFRAT selects bona fide transfrags representing over 94% of cumulative BLAST-derived functional annotations of the unfiltered assemblies. Between 4-6% are lost when orphan transfrags are excluded and this represents only a tiny fraction of annotation derived from functional transference by sequence similarity. The median length of bona fide transfrags ranged from 1.5kb (Trinity) to 2kb (Oases), which is consistent with the average coding sequence length in fungi. The fraction of transfrags that could be associated with gene ontology terms ranged from 33-50%, which is also high for domain based annotation. We showed that unselected transfrags were mostly truncated and represent sequences from intronic, untranslated (5′ and 3′) regions and non-coding gene loci.ConclusionsIFRAT simplifies post-assembly processing providing a reference transcriptome enriched with functionally relevant assembly-derived transcripts for non-model organism.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0492-5) contains supplementary material, which is available to authorized users.

A consensus approach to vertebrate de novo transcriptome assembly from RNA-seq data: assembly of the duck (Anas platyrhynchos) transcriptome.

For vertebrate organisms where a reference genome is not available, de novo transcriptome assembly enables a cost effective insight into the identification of tissue specific or differentially expressed genes and variation of the coding part of the genome. However, since there are a number of different tools and parameters that can be used to reconstruct transcripts, it is difficult to determine an optimal method. Here we suggest a pipeline based on (1) assessing the performance of three different assembly tools (2) using both single and multiple k-mer (MK) approaches (3) examining the influence of the number of reads used in the assembly (4) merging assemblies from different tools. We use an example dataset from the vertebrate Anas platyrhynchos domestica (Pekin duck). We find that taking a subset of data enables a robust assembly to be produced by multiple methods without the need for very high memory capacity. The use of reads mapped back to transcripts (RMBT) and CEGMA (Core Eukaryotic Genes Mapping Approach) provides useful metrics to determine the completeness of assembly obtained. For this dataset the use of MK in the assembly generated a more complete assembly as measured by greater number of RMBT and CEGMA score. Merged single k-mer assemblies are generally smaller but consist of longer transcripts, suggesting an assembly consisting of fewer fragmented transcripts. We suggest that the use of a subset of reads during assembly allows the relatively rapid investigation of assembly characteristics and can guide the user to the most appropriate transcriptome for particular downstream use. Transcriptomes generated by the compared assembly methods and the final merged assembly are freely available for download at http://dx.doi.org/10.6084/m9.figshare.1032613.

A pipeline for the de novo assembly of the Themira biloba (Sepsidae: Diptera) transcriptome using a multiple k-mer length approach.

BackgroundThe Sepsidae family of flies is a model for investigating how sexual selection shapes courtship and sexual dimorphism in a comparative framework. However, like many non-model systems, there are few molecular resources available. Large-scale sequencing and assembly have not been performed in any sepsid, and the lack of a closely related genome makes investigation of gene expression challenging. Our goal was to develop an automated pipeline for de novo transcriptome assembly, and to use that pipeline to assemble and analyze the transcriptome of the sepsid Themira biloba.ResultsOur bioinformatics pipeline uses cloud computing services to assemble and analyze the transcriptome with off-site data management, processing, and backup. It uses a multiple k-mer length approach combined with a second meta-assembly to extend transcripts and recover more bases of transcript sequences than standard single k-mer assembly. We used 454 sequencing to generate 1.48 million reads from cDNA generated from embryo, larva, and pupae of T. biloba and assembled a transcriptome consisting of 24,495 contigs. Annotation identified 16,705 transcripts, including those involved in embryogenesis and limb patterning. We assembled transcriptomes from an additional three non-model organisms to demonstrate that our pipeline assembled a higher-quality transcriptome than single k-mer approaches across multiple species.ConclusionsThe pipeline we have developed for assembly and analysis increases contig length, recovers unique transcripts, and assembles more base pairs than other methods through the use of a meta-assembly. The T. biloba transcriptome is a critical resource for performing large-scale RNA-Seq investigations of gene expression patterns, and is the first transcriptome sequenced in this Dipteran family.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-188) contains supplementary material, which is available to authorized users.

Separating homeologs by phasing in the tetraploid wheat transcriptome

BackgroundThe high level of identity among duplicated homoeologous genomes in tetraploid pasta wheat presents substantial challenges for de novo transcriptome assembly. To solve this problem, we develop a specialized bioinformatics workflow that optimizes transcriptome assembly and separation of merged homoeologs. To evaluate our strategy, we sequence and assemble the transcriptome of one of the diploid ancestors of pasta wheat, and compare both assemblies with a benchmark set of 13,472 full-length, non-redundant bread wheat cDNAs.ResultsA total of 489 million 100 bp paired-end reads from tetraploid wheat assemble in 140,118 contigs, including 96% of the benchmark cDNAs. We used a comparative genomics approach to annotate 66,633 open reading frames. The multiple k-mer assembly strategy increases the proportion of cDNAs assembled full-length in a single contig by 22% relative to the best single k-mer size. Homoeologs are separated using a post-assembly pipeline that includes polymorphism identification, phasing of SNPs, read sorting, and re-assembly of phased reads. Using a reference set of genes, we determine that 98.7% of SNPs analyzed are correctly separated by phasing.ConclusionsOur study shows that de novo transcriptome assembly of tetraploid wheat benefit from multiple k-mer assembly strategies more than diploid wheat. Our results also demonstrate that phasing approaches originally designed for heterozygous diploid organisms can be used to separate the close homoeologous genomes of tetraploid wheat. The predicted tetraploid wheat proteome and gene models provide a valuable tool for the wheat research community and for those interested in comparative genomic studies.

Optimizing de novo assembly of short-read RNA-seq data for phylogenomics

BackgroundRNA-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.ResultsWe 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.ConclusionsWith 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.

Optimization of de novo transcriptome assembly from high-throughput short read sequencing data improves functional annotation for non-model organisms

BackgroundThe k-mer hash length is a key factor affecting the output of de novo transcriptome assembly packages using de Bruijn graph algorithms. Assemblies constructed with varying single k-mer choices might result in the loss of unique contiguous sequences (contigs) and relevant biological information. A common solution to this problem is the clustering of single k-mer assemblies. Even though annotation is one of the primary goals of a transcriptome assembly, the success of assembly strategies does not consider the impact of k-mer selection on the annotation output. This study provides an in-depth k-mer selection analysis that is focused on the degree of functional annotation achieved for a non-model organism where no reference genome information is available. Individual k-mers and clustered assemblies (CA) were considered using three representative software packages. Pair-wise comparison analyses (between individual k-mers and CAs) were produced to reveal missing Kyoto Encyclopedia of Genes and Genomes (KEGG) ortholog identifiers (KOIs), and to determine a strategy that maximizes the recovery of biological information in a de novo transcriptome assembly.ResultsAnalyses of single k-mer assemblies resulted in the generation of various quantities of contigs and functional annotations within the selection window of k-mers (k-19 to k-63). For each k-mer in this window, generated assemblies contained certain unique contigs and KOIs that were not present in the other k-mer assemblies. Producing a non-redundant CA of k-mers 19 to 63 resulted in a more complete functional annotation than any single k-mer assembly. However, a fraction of unique annotations remained (~0.19 to 0.27% of total KOIs) in the assemblies of individual k-mers (k-19 to k-63) that were not present in the non-redundant CA. A workflow to recover these unique annotations is presented.ConclusionsThis study demonstrated that different k-mer choices result in various quantities of unique contigs per single k-mer assembly which affects biological information that is retrievable from the transcriptome. This undesirable effect can be minimized, but not eliminated, with clustering of multi-k assemblies with redundancy removal. The complete extraction of biological information in de novo transcriptomics studies requires both the production of a CA and efforts to identify unique contigs that are present in individual k-mer assemblies but not in the CA.