K-mer Size Research Articles

Large-scale machine learning for metagenomics sequence classification

Motivation: Metagenomics characterizes the taxonomic diversity of microbial communities by sequencing DNA directly from an environmental sample. One of the main challenges in metagenomics data analysis is the binning step, where each sequenced read is assigned to a taxonomic clade. Because of the large volume of metagenomics datasets, binning methods need fast and accurate algorithms that can operate with reasonable computing requirements. While standard alignment-based methods provide state-of-the-art performance, compositional approaches that assign a taxonomic class to a DNA read based on the k-mers it contains have the potential to provide faster solutions.Results: We propose a new rank-flexible machine learning-based compositional approach for taxonomic assignment of metagenomics reads and show that it benefits from increasing the number of fragments sampled from reference genome to tune its parameters, up to a coverage of about 10, and from increasing the k-mer size to about 12. Tuning the method involves training machine learning models on about 108 samples in 107 dimensions, which is out of reach of standard softwares but can be done efficiently with modern implementations for large-scale machine learning. The resulting method is competitive in terms of accuracy with well-established alignment and composition-based tools for problems involving a small to moderate number of candidate species and for reasonable amounts of sequencing errors. We show, however, that machine learning-based compositional approaches are still limited in their ability to deal with problems involving a greater number of species and more sensitive to sequencing errors. We finally show that the new method outperforms the state-of-the-art in its ability to classify reads from species of lineage absent from the reference database and confirm that compositional approaches achieve faster prediction times, with a gain of 2–17 times with respect to the BWA-MEM short read mapper, depending on the number of candidate species and the level of sequencing noise.Availability and implementation: Data and codes are available at http://cbio.ensmp.fr/largescalemetagenomics.Contact: pierre.mahe@biomerieux.comSupplementary information: Supplementary data are available at Bioinformatics online.

Random sequential adsorption of straight rigid rods on a simple cubic lattice

Random sequential adsorption of straight rigid rods of length k (k-mers) on a simple cubic lattice has been studied by numerical simulations and finite-size scaling analysis. The k-mers were irreversibly and isotropically deposited into the lattice. The calculations were performed by using a new theoretical scheme, whose accuracy was verified by comparison with rigorous analytical data. The results, obtained for k ranging from 2 to 64, revealed that (i) the jamming coverage for dimers (k=2) is θj=0.918388(16). Our result corrects the previously reported value of θj=0.799(2) (Tarasevich and Cherkasova, 2007); (ii) θj exhibits a decreasing function when it is plotted in terms of the k-mer size, being θj(∞)=0.4045(19) the value of the limit coverage for large k’s; and (iii) the ratio between percolation threshold and jamming coverage shows a non-universal behavior, monotonically decreasing to zero with increasing k.

Optimizing Transcriptome Assemblies for Eleusine indica Leaf and Seedling by Combining Multiple Assemblies from Three De Novo Assemblers.

Due to rapid advances in sequencing technology, increasing amounts of genomic and transcriptomic data are available for plant species, presenting enormous challenges for biocomputing analysis. A crucial first step for a successful transcriptomics-based study is the building of a high-quality assembly. Here, we utilized three different de novo assemblers (Trinity, Velvet, and CLC) and the EvidentialGene pipeline tr2aacds to assemble two optimized transcript sets for the notorious weed species, Eleusine indica. Two RNA sequencing (RNA-seq) datasets from leaf and aboveground seedlings were processed using three assemblers, which resulted in 20 assemblies for each dataset. The contig numbers and N50 values of each assembly were compared to study the effect of read number, k-mer size, and in silico normalization on assembly output. The 20 assemblies were then processed through the tr2aacds pipeline to remove redundant transcripts and to select the transcript set with the best coding potential. Each assembly contributed a considerable proportion to the final transcript combination with the exception of the CLC-k14. Thus each assembler and parameter set did assemble better contigs for certain transcripts. The redundancy, total contig number, N50, fully assembled contig number, and transcripts related to target-site herbicide resistance were evaluated for the EvidentialGene and Trinity assemblies. Comparing the EvidentialGene set with the Trinity assembly revealed improved quality and reduced redundancy in both leaf and seedling EvidentialGene sets. The optimized transcriptome references will be useful for studying herbicide resistance in E. indica and the evolutionary process in the three allotetraploid E. indica offspring.

Advantages of distributed and parallel algorithms that leverage Cloud Computing platforms for large-scale genome assembly.

Background: The transition to Next Generation sequencing (NGS) sequencing technologies has had numerous applications in Plant, Microbial and Human genomics during the past decade. However, NGS sequencing trades high read throughput for shorter read length, increasing the difficulty for genome assembly. This research presents a comparison of traditional versus Cloud computing-based genome assembly software, using as examples the Velvet and Contrail assemblers and reads from the genome sequence of the zebrafish (Danio rerio) model organism.Results: The first phase of the analysis involved a subset of the zebrafish data set (2X coverage) and best results were obtained using K-mer size of 65, while it was observed that Velvet takes less time than Contrail to complete the assembly. In the next phase, genome assembly was attempted using the full dataset of read coverage 192x and while Velvet failed to complete on a 256GB memory compute server, Contrail completed but required 240hours of computation.Conclusion: This research concludes that for deciding on which assembler software to use, the size of the dataset and available computing hardware should be taken into consideration. For a relatively small sequencing dataset, such as microbial or small eukaryotic genome, the Velvet assembler is a good option. However, for larger datasets Velvet requires large-memory compute servers in the order of 1000GB or more. On the other hand, Contrail is implemented using Hadoop, which performs the assembly in parallel across nodes of a compute cluster. Furthermore, Hadoop clusters can be rented on-demand from Cloud computing providers, and therefore Contrail can provide a simple and cost effective way for genome assembly of data generated at laboratories that lack the infrastructure or funds to build their own clusters.

HyDA-Vista: towards optimal guided selection of k-mer size for sequence assembly

MotivationIntimately tied to assembly quality is the complexity of the de Bruijn graph built by the assembler. Thus, there have been many paradigms developed to decrease the complexity of the de Bruijn graph. One obvious combinatorial paradigm for this is to allow the value of k to vary; having a larger value of k where the graph is more complex and a smaller value of k where the graph would likely contain fewer spurious edges and vertices. One open problem that affects the practicality of this method is how to predict the value of k prior to building the de Bruijn graph. We show that optimal values of k can be predicted prior to assembly by using the information contained in a phylogenetically-close genome and therefore, help make the use of multiple values of k practical for genome assembly.ResultsWe present HyDA-Vista, which is a genome assembler that uses homology information to choose a value of k for each read prior to the de Bruijn graph construction. The chosen k is optimal if there are no sequencing errors and the coverage is sufficient. Fundamental to our method is the construction of the maximal sequence landscape, which is a data structure that stores for each position in the input string, the largest repeated substring containing that position. In particular, we show the maximal sequence landscape can be constructed in O(n + n log n)-time and O(n)-space. HyDA-Vista first constructs the maximal sequence landscape for a homologous genome. The reads are then aligned to this reference genome, and values of k are assigned to each read using the maximal sequence landscape and the alignments. Eventually, all the reads are assembled by an iterative de Bruijn graph construction method. Our results and comparison to other assemblers demonstrate that HyDA-Vista achieves the best assembly of E. coli before repeat resolution or scaffolding.AvailabilityHyDA-Vista is freely available [1]. The code for constructing the maximal sequence landscape and choosing the optimal value of k for each read is also separately available on the website and could be incorporated into any genome assembler.

The complex task of choosing a de novo assembly: Lessons from fungal genomes

Selecting the values of parameters used by de novo genomic assembly programs, or choosing an optimal de novo assembly from several runs obtained with different parameters or programs, are tasks that can require complex decision-making. A key parameter that must be supplied to typical next generation sequencing (NGS) assemblers is the k-mer length, i.e., the word size that determines which de Bruijn graph the program should map out and use. The topic of assembly selection criteria was recently revisited in the Assemblathon 2 study (Bradnam et al., 2013). Although no clear message was delivered with regard to optimal k-mer lengths, it was shown with examples that it is sometimes important to decide if one is most interested in optimizing the sequences of protein-coding genes (the gene space) or in optimizing the whole genome sequence including the intergenic DNA, as what is best for one criterion may not be best for the other. In the present study, our aim was to better understand how the assembly of unicellular fungi (which are typically intermediate in size and complexity between prokaryotes and metazoan eukaryotes) can change as one varies the k-mer values over a wide range. We used two different de novo assembly programs (SOAPdenovo2 and ABySS), and simple assembly metrics that also focused on success in assembling the gene space and repetitive elements. A recent increase in Illumina read length to around 150bp allowed us to attempt de novo assemblies with a larger range of k-mers, up to 127bp. We applied these methods to Illumina paired-end sequencing read sets of fungal strains of Paracoccidioides brasiliensis and other species. By visualizing the results in simple plots, we were able to track the effect of changing k-mer size and assembly program, and to demonstrate how such plots can readily reveal discontinuities or other unexpected characteristics that assembly programs can present in practice, especially when they are used in a traditional molecular microbiology laboratory with a ‘genomics corner’. Here we propose and apply a component of a first pass validation methodology for benchmarking and understanding fungal genome de novo assembly processes.

Employing whole genome mapping for optimal de novo assembly of bacterial genomes.

BackgroundDe novo genome assembly can be challenging due to inherent properties of the reads, even when using current state-of-the-art assembly tools based on de Bruijn graphs. Often users are not bio-informaticians and, in a black box approach, utilise assembly parameters such as contig length and N50 to generate whole genome sequences, potentially resulting in mis-assemblies.FindingsUtilising several assembly tools based on de Bruijn graphs like Velvet, SPAdes and IDBA, we demonstrate that at the optimal N50, mis-assemblies do occur, even when using the multi-k-mer approaches of SPAdes and IDBA. We demonstrate that whole genome mapping can be used to identify these mis-assemblies and can guide the selection of the best k-mer size which yields the highest N50 without mis-assemblies.ConclusionsWe demonstrate the utility of whole genome mapping (WGM) as a tool to identify mis-assemblies and to guide k-mer selection and higher quality de novo genome assembly of bacterial genomes.

Efficient de novo assembly of highly heterozygous genomes from whole-genome shotgun short reads

Although many de novo genome assembly projects have recently been conducted using high-throughput sequencers, assembling highly heterozygous diploid genomes is a substantial challenge due to the increased complexity of the de Bruijn graph structure predominantly used. To address the increasing demand for sequencing of nonmodel and/or wild-type samples, in most cases inbred lines or fosmid-based hierarchical sequencing methods are used to overcome such problems. However, these methods are costly and time consuming, forfeiting the advantages of massive parallel sequencing. Here, we describe a novel de novo assembler, Platanus, that can effectively manage high-throughput data from heterozygous samples. Platanus assembles DNA fragments (reads) into contigs by constructing de Bruijn graphs with automatically optimized k-mer sizes followed by the scaffolding of contigs based on paired-end information. The complicated graph structures that result from the heterozygosity are simplified during not only the contig assembly step but also the scaffolding step. We evaluated the assembly results on eukaryotic samples with various levels of heterozygosity. Compared with other assemblers, Platanus yields assembly results that have a larger scaffold NG50 length without any accompanying loss of accuracy in both simulated and real data. In addition, Platanus recorded the largest scaffold NG50 values for two of the three low-heterozygosity species used in the de novo assembly contest, Assemblathon 2. Platanus therefore provides a novel and efficient approach for the assembly of gigabase-sized highly heterozygous genomes and is an attractive alternative to the existing assemblers designed for genomes of lower heterozygosity.

KAnalyze: a fast versatile pipelined K-mer toolkit

Motivation: Converting nucleotide sequences into short overlapping fragments of uniform length, k-mers, is a common step in many bioinformatics applications. While existing software packages count k-mers, few are optimized for speed, offer an application programming interface (API), a graphical interface or contain features that make it extensible and maintainable. We designed KAnalyze to compete with the fastest k-mer counters, to produce reliable output and to support future development efforts through well-architected, documented and testable code. Currently, KAnalyze can output k-mer counts in a sorted tab-delimited file or stream k-mers as they are read. KAnalyze can process large datasets with 2 GB of memory. This project is implemented in Java 7, and the command line interface (CLI) is designed to integrate into pipelines written in any language.Results: As a k-mer counter, KAnalyze outperforms Jellyfish, DSK and a pipeline built on Perl and Linux utilities. Through extensive unit and system testing, we have verified that KAnalyze produces the correct k-mer counts over multiple datasets and k-mer sizes.Availability and implementation: KAnalyze is available on SourceForge:https://sourceforge.net/projects/kanalyze/Contact: fredrik.vannberg@biology.gatech.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Combining Transcriptome Assemblies from Multiple De Novo Assemblers in the Allo-Tetraploid Plant Nicotiana benthamiana

Background Nicotiana benthamiana is an allo-tetraploid plant, which can be challenging for de novo transcriptome assemblies due to homeologous and duplicated gene copies. Transcripts generated from such genes can be distinct yet highly similar in sequence, with markedly differing expression levels. This can lead to unassembled, partially assembled or mis-assembled contigs. Due to the different properties of de novo assemblers, no one assembler with any one given parameter space can re-assemble all possible transcripts from a transcriptome.ResultsIn an effort to maximise the diversity and completeness of de novo assembled transcripts, we utilised four de novo transcriptome assemblers, TransAbyss, Trinity, SOAPdenovo-Trans, and Oases, using a range of k-mer sizes and different input RNA-seq read counts. We complemented the parameter space biologically by using RNA from 10 plant tissues. We then combined the output of all assemblies into a large super-set of sequences. Using a method from the EvidentialGene pipeline, the combined assembly was reduced from 9.9 million de novo assembled transcripts to about 235,000 of which about 50,000 were classified as primary. Metrics such as average bit-scores, feature response curves and the ability to distinguish paralogous or homeologous transcripts, indicated that the EvidentialGene processed assembly was of high quality. Of 35 RNA silencing gene transcripts, 34 were identified as assembled to full length, whereas in a previous assembly using only one assembler, 9 of these were partially assembled.ConclusionsTo achieve a high quality transcriptome, it is advantageous to implement and combine the output from as many different de novo assemblers as possible. We have in essence taking the ‘best’ output from each assembler while minimising sequence redundancy. We have also shown that simultaneous assessment of a variety of metrics, not just focused on contig length, is necessary to gauge the quality of assemblies.

Construction of a Public CHO Cell Line Transcript Database Using Versatile Bioinformatics Analysis Pipelines

Chinese hamster ovary (CHO) cell lines represent the most commonly used mammalian expression system for the production of therapeutic proteins. In this context, detailed knowledge of the CHO cell transcriptome might help to improve biotechnological processes conducted by specific cell lines. Nevertheless, very few assembled cDNA sequences of CHO cells were publicly released until recently, which puts a severe limitation on biotechnological research. Two extended annotation systems and web-based tools, one for browsing eukaryotic genomes (GenDBE) and one for viewing eukaryotic transcriptomes (SAMS), were established as the first step towards a publicly usable CHO cell genome/transcriptome analysis platform. This is complemented by the development of a new strategy to assemble the ca. 100 million reads, sequenced from a broad range of diverse transcripts, to a high quality CHO cell transcript set. The cDNA libraries were constructed from different CHO cell lines grown under various culture conditions and sequenced using Roche/454 and Illumina sequencing technologies in addition to sequencing reads from a previous study. Two pipelines to extend and improve the CHO cell line transcripts were established. First, de novo assemblies were carried out with the Trinity and Oases assemblers, using varying k-mer sizes. The resulting contigs were screened for potential CDS using ESTScan. Redundant contigs were filtered out using cd-hit-est. The remaining CDS contigs were re-assembled with CAP3. Second, a reference-based assembly with the TopHat/Cufflinks pipeline was performed, using the recently published draft genome sequence of CHO-K1 as reference. Additionally, the de novo contigs were mapped to the reference genome using GMAP and merged with the Cufflinks assembly using the cuffmerge software. With this approach 28,874 transcripts located on 16,492 gene loci could be assembled. Combining the results of both approaches, 65,561 transcripts were identified for CHO cell lines, which could be clustered by sequence identity into 17,598 gene clusters.

Percolation of polyatomic species on a simple cubic lattice

In the present paper, the site-percolation problem corresponding to linear k-mers (containing k identical units, each one occupying a lattice site) on a simple cubic lattice has been studied. The k-mers were irreversibly and isotropically deposited into the lattice. Then, the percolation threshold and critical exponents were obtained by numerical simulations and finite-size scaling theory. The results, obtained for k ranging from 1 to 100, revealed that (i) the percolation threshold exhibits a decreasing function when it is plotted as a function of the k-mer size; and (ii) the phase transition occurring in the system belongs to the standard 3D percolation universality class regardless of the value of k considered.

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.

Fine de novo sequencing of a fungal genome using only SOLiD short read data: verification on Aspergillus oryzae RIB40.

The development of next-generation sequencing (NGS) technologies has dramatically increased the throughput, speed, and efficiency of genome sequencing. The short read data generated from NGS platforms, such as SOLiD and Illumina, are quite useful for mapping analysis. However, the SOLiD read data with lengths of <60 bp have been considered to be too short for de novo genome sequencing. Here, to investigate whether de novo sequencing of fungal genomes is possible using only SOLiD short read sequence data, we performed de novo assembly of the Aspergillus oryzae RIB40 genome using only SOLiD read data of 50 bp generated from mate-paired libraries with 2.8- or 1.9-kb insert sizes. The assembled scaffolds showed an N50 value of 1.6 Mb, a 22-fold increase than those obtained using only SOLiD short read in other published reports. In addition, almost 99% of the reference genome was accurately aligned by the assembled scaffold fragments in long lengths. The sequences of secondary metabolite biosynthetic genes and clusters, whose products are of considerable interest in fungal studies due to their potential medicinal, agricultural, and cosmetic properties, were also highly reconstructed in the assembled scaffolds. Based on these findings, we concluded that de novo genome sequencing using only SOLiD short reads is feasible and practical for molecular biological study of fungi. We also investigated the effect of filtering low quality data, library insert size, and k-mer size on the assembly performance, and recommend for the assembly use of mild filtered read data where the N50 was not so degraded and the library has an insert size of ∼2.0 kb, and k-mer size 33.

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.

Additive multiple k-mer transcriptome of the keelworm Pomatoceros lamarckii (Annelida; Serpulidae) reveals annelid trochophore transcription factor cassette

Recent advances in both next-generation sequencing and assembly programmes have made the low-cost construction of transcriptome datasets for non-model species feasible, capable of yielding a raft of information even from less well-transcribed genes. Here we present the results of assemblies performed on a 51-bp paired end Illumina dataset derived from a mixed larval sample of the annelid Pomatoceros lamarckii at 24, 48 and 72h post-fertilization. We used Oases to assemble 36.5 million paired end reads with k-mer sizes from 21 to 29, followed by amalgamation of assemblies, redundancy removal with Vmatch and TGICL and removal of contigs less than 500bp in length. This resulted in a final assembly of 50,151 contigs, with a mean length of 1,221bp and covering 61.3Mbp. A total of 34,846 (69.4%) of these returned a BlastX hit above a cutoff of 1.0e (-3), and 17,967 (35.8%) were assigned at least one GO annotation using Blast2GO. We used the assembly to identify genes belonging to the homeobox superclass and the Fox, Sox and Tbx classes, recovering 37, 16, four and three genes, respectively. This included orthologues of genes previously unidentified in lophotrochozoans and protostomes. Our study illustrates the utility of such transcriptomic assembly methods as a gene discovery tool and greatly expands our knowledge of transcription factor genes in annelids in general and in this species in particular.

Isotropic-nematic phase diagram for interacting rigid rods on two-dimensional lattices

The phase behavior of interacting rigid rods of length k (k-mers) on two-dimensional square and triangular lattices has been studied by theoretical calculations in the framework of the lattice-gas model. The process was analyzed by comparing the dependence on coverage of the free energy per site of an isotropic submonolayer of interacting k-mers f(iso)(θ) with that corresponding to a fully aligned (nematic) system f(nem)(θ). The existence of an intersection point between the curves f(iso)(θ) and f(nem)(θ), which is indicative of the occurrence of an isotropic-nematic phase transition in the adlayer, allowed us to obtain the complete (temperature, coverage, k-mer size) phase diagram of the system.

Cutoffs and k-mers: implications from a transcriptome study in allopolyploid plants

BackgroundTranscriptome analysis is increasingly being used to study the evolutionary origins and ecology of non-model plants. One issue for both transcriptome assembly and differential gene expression analyses is the common occurrence in plants of hybridisation and whole genome duplication (WGD) and hybridization resulting in allopolyploidy. The divergence of duplicated genes following WGD creates near identical homeologues that can be problematic for de novo assembly and also reference based assembly protocols that use short reads (35 - 100 bp).ResultsHere we report a successful strategy for the assembly of two transcriptomes made using 75 bp Illumina reads from Pachycladon fastigiatum and Pachycladon cheesemanii. Both are allopolyploid plant species (2n = 20) that originated in the New Zealand Alps about 0.8 million years ago. In a systematic analysis of 19 different coverage cutoffs and 20 different k-mer sizes we showed that i) none of the genes could be assembled across all of the parameter space ii) assembly of each gene required an optimal set of parameter values and iii) these parameter values could be explained in part by different gene expression levels and different degrees of similarity between genes.ConclusionsTo obtain optimal transcriptome assemblies for allopolyploid plants, k-mer size and k-mer coverage need to be considered simultaneously across a broad parameter space. This is important for assembling a maximum number of full length ESTs and for avoiding chimeric assemblies of homeologous and paralogous gene copies.

Evaluation of short read metagenomic assembly

BackgroundMetagenomic assembly is a challenging problem due to the presence of genetic material from multiple organisms. The problem becomes even more difficult when short reads produced by next generation sequencing technologies are used. Although whole genome assemblers are not designed to assemble metagenomic samples, they are being used for metagenomics due to the lack of assemblers capable of dealing with metagenomic samples. We present an evaluation of assembly of simulated short-read metagenomic samples using a state-of-art de Bruijn graph based assembler.ResultsWe assembled simulated metagenomic reads from datasets of various complexities using a state-of-art de Bruijn graph based parallel assembler. We have also studied the effect of k-mer size used in de Bruijn graph on metagenomic assembly and developed a clustering solution to pool the contigs obtained from different assembly runs, which allowed us to obtain longer contigs. We have also assessed the degree of chimericity of the assembled contigs using an entropy/impurity metric and compared the metagenomic assemblies to assemblies of isolated individual source genomes.ConclusionsOur results show that accuracy of the assembled contigs was better than expected for the metagenomic samples with a few dominant organisms and was especially poor in samples containing many closely related strains. Clustering contigs from different k-mer parameter of the de Bruijn graph allowed us to obtain longer contigs, however the clustering resulted in accumulation of erroneous contigs thus increasing the error rate in clustered contigs.

MiR-192 Mediates TGF-β/Smad3-Driven Renal Fibrosis

TGF-beta/Smad3 promotes renal fibrosis, but the mechanisms that regulate profibrotic genes remain unclear. We hypothesized that miR-192, a microRNA expressed in the kidney may mediate renal fibrosis in a Smad3-dependent manner. Microarray and real-time PCR demonstrated a tight association between upregulation of miR-192 in the fibrotic kidney and activation of TGF-beta/Smad signaling. Deletion of Smad7 promoted miR-192 expression and enhanced Smad signaling and fibrosis in obstructive kidney disease. In contrast, overexpression of Smad7 to block TGF-beta/Smad signaling inhibited miR-192 expression and renal fibrosis in the rat 5/6 nephrectomy model; in vitro, overexpression of Smad7 in tubular epithelial cells abolished TGF-beta1-induced miR-192 expression. Furthermore, Smad3 but not Smad2 mediated TGF-beta1-induced miR-192 expression by binding to the miR-192 promoter. Last, overexpression of a miR-192 mimic promoted and addition of a miR-192 inhibitor blocked TGF-beta1-induced collagen matrix expression. Taken together, miR-192 may be a critical downstream mediator of TGF-beta/Smad3 signaling in the development of renal fibrosis.