Kollector: transcript-informed, targeted de novo assembly of gene loci.
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ABSTRACT:
SUBMITTER: Kucuk E
PROVIDER: S-EPMC5860073 | biostudies-literature | 2017 Sep
REPOSITORIES: biostudies-literature
Publications
Kollector: transcript-informed, targeted de novo assembly of gene loci.
Kucuk Erdi E Chu Justin J Vandervalk Benjamin P BP Austin Hammond S S Warren René L RL
Bioinformatics (Oxford, England) 20170901 17
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Project description:MotivationDespite considerable advancements in sequencing and computing technologies, de novo assembly of whole eukaryotic genomes is still a time-consuming task that requires a significant amount of computational resources and expertise. A targeted assembly approach to perform local assembly of sequences of interest remains a valuable option for some applications. This is especially true for gene-centric assemblies, whose resulting sequence can be readily utilized for more focused biological research. Here we describe Kollector, an alignment-free targeted assembly pipeline that uses thousands of transcript sequences concurrently to inform the localized assembly of corresponding gene loci. Kollector robustly reconstructs introns and novel sequences within these loci, and scales well to large genomes-properties that makes it especially useful for researchers working on non-model eukaryotic organisms.ResultsWe demonstrate the performance of Kollector for assembling complete or near-complete Caenorhabditis elegans and Homo sapiens gene loci from their respective, input transcripts. In a time- and memory-efficient manner, the Kollector pipeline successfully reconstructs respectively 99% and 80% (compared to 86% and 73% with standard de novo assembly techniques) of C.elegans and H.sapiens transcript targets in their corresponding genomic space using whole genome shotgun sequencing reads. We also show that Kollector outperforms both established and recently released targeted assembly tools. Finally, we demonstrate three use cases for Kollector, including comparative and cancer genomics applications.Availability and implementationKollector is implemented as a bash script, and is available at https://github.com/bcgsc/kollector.Contactibirol@bcgsc.ca.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC5572715 | biostudies-literature
Project description:MotivationDe 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.ResultsHere 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.ConclusionWe 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 implementationA general implementation of our approach can be found under: https://github.com/SchulzLab/KREATIONSupplementary information: Supplementary data are available at Bioinformatics online.Contactmschulz@mmci.uni-saarland.de.
| S-EPMC4892416 | biostudies-literature
Project description:Correct quantification of transcript expression is essential to understand the functional elements in different physiological conditions. For the organisms without the reference transcriptome, de novo transcriptome assembly must be carried out prior to quantification. However, a large number of erroneous contigs produced by the assemblers might result in unreliable estimation. In this regard, this study investigates how assembly quality affects the performance of quantification based on de novo transcriptome assembly. We examined the over-extended and incomplete contigs, and demonstrated that assembly completeness has a strong impact on the estimation of contig abundance. Then we investigated the behavior of the quantifiers with respect to sequence ambiguity which might be originally presented in the transcriptome or accidentally produced by assemblers. The results suggested that the quantifiers often over-estimate the expression of family-collapse contigs and under-estimate the expression of duplicated contigs. For organisms without reference transcriptome, it remains challenging to detect the inaccurate estimation on family-collapse contigs. On the contrary, we observed that the situation of under-estimation on duplicated contigs can be warned through analyzing the read proportion of estimated abundance (RPEA) of contigs in the connected component inferenced by the quantifiers. In addition, we suggest that the estimated quantification results on the connected component level have better accuracy over sequence level quantification. The analytic results conducted in this study provides valuable insights for future development of transcriptome assembly and quantification.
| S-EPMC6549443 | biostudies-literature
Project description:BackgroundAccurate fusion transcript detection is essential for comprehensive characterization of cancer transcriptomes. Over the last decade, multiple bioinformatic tools have been developed to predict fusions from RNA-seq, based on either read mapping or de novo fusion transcript assembly.ResultsWe benchmark 23 different methods including applications we develop, STAR-Fusion and TrinityFusion, leveraging both simulated and real RNA-seq. Overall, STAR-Fusion, Arriba, and STAR-SEQR are the most accurate and fastest for fusion detection on cancer transcriptomes.ConclusionThe lower accuracy of de novo assembly-based methods notwithstanding, they are useful for reconstructing fusion isoforms and tumor viruses, both of which are important in cancer research.
| S-EPMC6802306 | biostudies-literature
Project description:BackgroundHuman artificial chromosomes (HACs) are potentially useful vectors for gene transfer studies and for functional annotation of the genome because of their suitability for cloning, manipulating and transferring large segments of the genome. However, development of HACs for the transfer of large genomic loci into mammalian cells has been limited by difficulties in manipulating high-molecular weight DNA, as well as by the low overall frequencies of de novo HAC formation. Indeed, to date, only a small number of large (>100 kb) genomic loci have been reported to be successfully packaged into de novo HACs.ResultsWe have developed novel methodologies to enable efficient assembly of HAC vectors containing any genomic locus of interest. We report here the creation of a novel, bimolecular system based on bacterial artificial chromosomes (BACs) for the construction of HACs incorporating any defined genomic region. We have utilized this vector system to rapidly design, construct and validate multiple de novo HACs containing large (100-200 kb) genomic loci including therapeutically significant genes for human growth hormone (HGH), polycystic kidney disease (PKD1) and beta-globin. We report significant differences in the ability of different genomic loci to support de novo HAC formation, suggesting possible effects of cis-acting genomic elements. Finally, as a proof of principle, we have observed sustained beta-globin gene expression from HACs incorporating the entire 200 kb beta-globin genomic locus for over 90 days in the absence of selection.ConclusionTaken together, these results are significant for the development of HAC vector technology, as they enable high-throughput assembly and functional validation of HACs containing any large genomic locus. We have evaluated the impact of different genomic loci on the frequency of HAC formation and identified segments of genomic DNA that appear to facilitate de novo HAC formation. These genomic loci may be useful for identifying discrete functional elements that may be incorporated into future generations of HAC vectors.
| S-EPMC1182356 | biostudies-literature
Project description:Short-read sequencers provide highly accurate reads at very low cost. Unfortunately, short reads are often inadequate for important applications such as assembly in complex regions or phasing across distant heterozygous sites. In this study, we describe novel bench protocols and algorithms to obtain haplotype-phased sequence assemblies with ultra-low error for regions 10 kb and longer using short reads only. We accomplish this by imprinting each template strand from a target region with a dense and unique mutation pattern. The mutation process randomly and independently converts ∼50% of cytosines to uracils. Sequencing libraries are made from both mutated and unmutated templates. Using de Bruijn graphs and paired-end read information, we assemble each mutated template and use the unmutated library to correct the mutated bases. Templates are partitioned into two or more haplotypes, and the final haplotypes are assembled and corrected for residual template mutations and PCR errors. With sufficient template coverage, the final assemblies have per-base error rates below 10-9. We demonstrate this method on a four-member nuclear family, correctly assembling and phasing three genomic intervals, including the highly polymorphic HLA-B gene.
| S-EPMC9561374 | biostudies-literature
Project description:We reported an atlas of de novo-defined, full-length macaque gene models on the basis of single molecule long-read transcriptome sequencing (Iso-seq).
2021-04-17 | GSE158668 | GEO
Project description:DNA sequencing technology is becoming more accessible to a variety of researchers as costs continue to decline. As researchers begin to sequence novel transcriptomes, most of these data sets lack a reference genome and will have to rely on de novo assemblers. Making comparisons across assemblies can be difficult: each program has its strengths and weaknesses, and no tool exists to comparatively evaluate these data sets. We developed software in R, called Sequence Comparative Analysis using Networks (SCAN), to perform statistical comparisons between distinct assemblies. SCAN uses a reference data set to identify the most accurate de novo assembly and the "good" transcripts in the user's data. We tested SCAN on three publicly available transcriptomes, each assembled using three assembly programs. Moreover, we sequenced the transcriptome of the oomycete Achlya hypogyna and compared de novo assemblies from Velvet, ABySS, and the CLC Genomics Workbench assembly algorithms. One thousand one hundred twenty-eight of the CLC transcripts were statistically similar to the reference, compared with 49 of the Velvet transcripts and 937 of the ABySS transcripts. SCAN's strength is providing statistical support for transcript assemblies in a biological context. However, SCAN is designed to compare distinct node sets in networks, therefore it can also easily be extended to perform statistical comparisons on any network graph regardless of what the nodes represent.
| S-EPMC3708500 | biostudies-literature
Project description:Dysregulated endogenous retroelements (EREs) are increasingly implicated in the initiation, progression, and immune surveillance of human cancer. However, incomplete knowledge of ERE activity limits mechanistic studies. By using pan-cancer de novo transcript assembly, we uncover the extent and complexity of ERE transcription. The current assembly doubled the number of previously annotated transcripts overlapping with long-terminal repeat (LTR) elements, several thousand of which were expressed specifically in one or a few related cancer types. Exemplified in melanoma, LTR-overlapping transcripts were highly predictable, disease prognostic, and closely linked with molecularly defined subtypes. They further showed the potential to affect disease-relevant genes, as well as produce novel cancer-specific antigenic peptides. This extended view of LTR elements provides the framework for functional validation of affected genes and targets for cancer immunotherapy.
| S-EPMC6771403 | biostudies-literature
Project description:Hyperaccumulators are a group of plant species that accumulate high concentrations of one or more metal(loid)s in their above-ground tissues without showing any signs of toxicity. Several hyperaccumulating species belong to the Brassicaceae family, among them the Cd and Zn hyperaccumulator Noccaea praecox. In this paper, we present de novo transcriptome assembled from two naturally occurring N. praecox populations growing in (i) metal-enriched soil and (ii) soil non-contaminated with metals (control site). Total RNA was extracted from the leaves of both populations. We obtained 801,935,101 reads, which were successfully assembled and annotated. The resulting assembly contains 135,323 transcripts, with 103,396 transcripts (76.4%) annotated with at least one function and encoding 53,142 putative proteins. Due to its close relationship with the hyperaccumulating model species N. cearulescens, it will be possible to derive protein functions from sequence comparisons with this species. Comparisons will highlight common and differing pathways of metal acquisition, storage, and detoxification which will allow us to expand our knowledge of these processes.
| S-EPMC10692223 | biostudies-literature