Genomic Reads Research Articles

Biological properties of bovine viral diarrhea virus quasispecies detected in the RK13 cell line

The rabbit kidney cell line RK13 has been reported to be contaminated with noncytopathogenic (ncp) bovine viral diarrhea virus (BVDV). Persistent infection was confirmed by demonstrating the stability of virus titers (10(4.6±0.5) TCID50/ml) and BVDV positive cells (71.9±3.12%), over six successive passages. Based on the "exaltation of Newcastle disease virus" (END) and reverse plaque formation methods, two types of ncp viruses were isolated, END-phenomenon-positive and negative. Isolates, RK13/E(+) and RK13/E(-), demonstrated (1) differing levels of reproducibility in cell cultures, (2) similar antigenicity against BVDV antisera, (3) identical 5'-UTR region nucleotide sequences, (4) four amino acid differences throughout the genomic open reading frame, and (5) better growth ability in primary rabbit cells than other laboratory strains when inoculated in parallel at an MOI of 0.01. Overall, the BVDV population in RK13 cells consists of at least two different END characteristic quasispecies that are adapted to cultures of rabbit origin, giving rise to naturally attenuated BVDV strains that can be used in vaccine development.

Filtering with alignment free distances for high throughput DNA reads assembly

Filtering with alignment free distances for high throughput DNA reads assembly

Population-scale sequencing reveals genetic differentiation due to local adaptation in Atlantic herring

The Atlantic herring (Clupea harengus), one of the most abundant marine fishes in the world, has historically been a critical food source in Northern Europe. It is one of the few marine species that can reproduce throughout the brackish salinity gradient of the Baltic Sea. Previous studies based on few genetic markers have revealed a conspicuous lack of genetic differentiation between geographic regions, consistent with huge population sizes and minute genetic drift. Here, we present a cost-effective genome-wide study in a species that lacks a genome sequence. We first assembled a muscle transcriptome and then aligned genomic reads to the transcripts, creating an "exome assembly," capturing both exons and flanking sequences. We then resequenced pools of fish from a wide geographic range, including the Northeast Atlantic, as well as different regions in the Baltic Sea, aligned the reads to the exome assembly, and identified 440,817 SNPs. The great majority of SNPs showed no appreciable differences in allele frequency among populations; however, several thousand SNPs showed striking differences, some approaching fixation for different alleles. The contrast between low genetic differentiation at most loci and striking differences at others implies that the latter category primarily reflects natural selection. A simulation study confirmed that the distribution of the fixation index F(ST) deviated significantly from expectation for selectively neutral loci. This study provides insights concerning the population structure of an important marine fish and establishes the Atlantic herring as a model for population genetic studies of adaptation and natural selection.

A Novel Computational Strategy to Identify A-to-I RNA Editing Sites by RNA-Seq Data: De Novo Detection in Human Spinal Cord Tissue

RNA editing is a post-transcriptional process occurring in a wide range of organisms. In human brain, the A-to-I RNA editing, in which individual adenosine (A) bases in pre-mRNA are modified to yield inosine (I), is the most frequent event. Modulating gene expression, RNA editing is essential for cellular homeostasis. Indeed, its deregulation has been linked to several neurological and neurodegenerative diseases. To date, many RNA editing sites have been identified by next generation sequencing technologies employing massive transcriptome sequencing together with whole genome or exome sequencing. While genome and transcriptome reads are not always available for single individuals, RNA-Seq data are widespread through public databases and represent a relevant source of yet unexplored RNA editing sites. In this context, we propose a simple computational strategy to identify genomic positions enriched in novel hypothetical RNA editing events by means of a new two-steps mapping procedure requiring only RNA-Seq data and no a priori knowledge of RNA editing characteristics and genomic reads. We assessed the suitability of our procedure by confirming A-to-I candidates using conventional Sanger sequencing and performing RNA-Seq as well as whole exome sequencing of human spinal cord tissue from a single individual.

Automated method for annotation of genes involved in the degradation of polycyclic aromatic hydrocarbons (PAH) from genomic, metagenomic and metatranscriptomic sequence reads

Automated method for annotation of genes involved in the degradation of polycyclic aromatic hydrocarbons (PAH) from genomic, metagenomic and metatranscriptomic sequence reads

Fast and accurate read alignment for resequencing

Next-generation sequence analysis has become an important task both in laboratory and clinical settings. A key stage in the majority sequence analysis workflows, such as resequencing, is the alignment of genomic reads to a reference genome. The accurate alignment of reads with large indels is a computationally challenging task for researchers. We introduce SeqAlto as a new algorithm for read alignment. For reads longer than or equal to 100 bp, SeqAlto is up to 10 × faster than existing algorithms, while retaining high accuracy and the ability to align reads with large (up to 50 bp) indels. This improvement in efficiency is particularly important in the analysis of future sequencing data where the number of reads approaches many billions. Furthermore, SeqAlto uses less than 8 GB of memory to align against the human genome. SeqAlto is benchmarked against several existing tools with both real and simulated data. Linux and Mac OS X binaries free for academic use are available at http://www.stanford.edu/group/wonglab/seqalto whwong@stanford.edu.

Triallelic SNP-mediated genotyping of regenerated protoplasts of the heterokaryotic fungus Rhizoctonia solani

The aneuploid and heterokaryotic nuclear condition of the soil fungus Rhizoctonia solani have provided challenges in obtaining a complete genome sequence. To better aid in the assembly and annotation process, a protoplast and single nucleotide polymorphism (SNP)-based method was developed to identify regenerated protoplasts with a reduced nuclear genome. Protocol optimization experiments showed that enzymatic digestion of mycelium from a 24 h culture of R. solani increased the proportion of protoplasts with a diameter of ≤7.5 μm and 1-4 nuclei. To determine whether strains regenerated from protoplasts with a reduced number of nuclei were genetically different from the parental strain, triallelic SNPs identified from variance records of the genomic DNA sequence reads of R. solani were used in PCR-based genotyping assays. Results from 16 of the 24 SNP-based PCR assays provided evidence that one of the three alleles was missing in the 11 regenerated protoplast strains, suggesting that these strains represent a reduced genomic complement of the parental strain. The protoplast and triallelic SNP-based method used in this study may be useful in strain development and analysis of other basidiomycete fungi with complex nuclear genomes.

Development and mapping of SNP assays in allotetraploid cotton

A narrow germplasm base and a complex allotetraploid genome have made the discovery of single nucleotide polymorphism (SNP) markers difficult in cotton (Gossypium hirsutum). To generate sequence for SNP discovery, we conducted a genome reduction experiment (EcoRI, BafI double digest, followed by adapter ligation, biotin–streptavidin purification, and agarose gel separation) on two accessions of G. hirsutum and two accessions of G. barbadense. From the genome reduction experiment, a total of 2.04 million genomic sequence reads were assembled into contigs with an N50 of 508 bp and analyzed for SNPs. A previously generated assembly of expressed sequence tags (ESTs) provided an additional source for SNP discovery. Using highly conservative parameters (minimum coverage of 8× at each SNP and 20% minor allele frequency), a total of 11,834 and 1,679 non-genic SNPs were identified between accessions of G. hirsutum and G. barbadense in genome reduction assemblies, respectively. An additional 4,327 genic SNPs were also identified between accessions of G. hirsutum in the EST assembly. KBioscience KASPar assays were designed for a portion of the intra-specific G. hirsutum SNPs. From 704 non-genic and 348 genic markers developed, a total of 367 (267 non-genic, 100 genic) mapped in a segregating F2 population (Acala Maxxa × TX2094) using the Fluidigm EP1 system. A G. hirsutum genetic linkage map of 1,688 cM was constructed based entirely on these new SNP markers. Of the genic-based SNPs, we were able to identify within which genome (‘A’ or ‘D’) each SNP resided using diploid species sequence data. Genetic maps generated by these newly identified markers are being used to locate quantitative, economically important regions within the cotton genome.Electronic supplementary materialThe online version of this article (doi:10.1007/s00122-011-1780-8) contains supplementary material, which is available to authorized users.

Towards accurate detection and genotyping of expressed variants from whole transcriptome sequencing data

BackgroundMassively parallel transcriptome sequencing (RNA-Seq) is becoming the method of choice for studying functional effects of genetic variability and establishing causal relationships between genetic variants and disease. However, RNA-Seq poses new technical and computational challenges compared to genome sequencing. In particular, mapping transcriptome reads onto the genome is more challenging than mapping genomic reads due to splicing. Furthermore, detection and genotyping of single nucleotide variants (SNVs) requires statistical models that are robust to variability in read coverage due to unequal transcript expression levels.ResultsIn this paper we present a strategy to more reliably map transcriptome reads by taking advantage of the availability of both the genome reference sequence and transcript databases such as CCDS. We also present a novel Bayesian model for SNV discovery and genotyping based on quality scores.ConclusionsExperimental results on RNA-Seq data generated from blood cell tissue of three Hapmap individuals show that our methods yield increased accuracy compared to several widely used methods. The open source code implementing our methods, released under the GNU General Public License, is available at http://dna.engr.uconn.edu/software/NGSTools/.

Spectral classification of short numerical exon and intron sequences

This research presents three new numerical representations for classifying short exon and intron sequences using discrete Fourier transform period-3 value. Based on the human genome, results indicate that the Complex Twin-Pair representation is attractive compared with other numerical representations and the approach has potential applications in genome annotation and read mapping.

Efficient Sequence Alignment Using MapReduce on the Cloud

Abstract Over the past few years, advances in the field of molecular biology and genomic technologies have led to an explosive growth of digital biological information. The analysis of this large amount of data is commonly based on the extensive and repeated use of conceptually parallel algorithms, most notably in the context of sequence alignment. Cloud computing provides scientists with a completely new model of utilizing the computing infrastructure. Cloud computing model is excellent in dealing with such bioinformatics applications, which require both management of huge amounts of data and heavy computations. The study aims at transforming a recently developed bioinformatics sequence alignment tool, named BFAST, to the cloud environment. The MapReduce version of the BFAST tool will be used to demonstrate the effectiveness of the MapReduce framework and the cloud-computing model in handling the intensive computations and management of the huge bioinformatics data. A number of existing tools and technologies are utilized in this study to achieve an efficient transformation of the BFAST tool into the cloud environment. The implementation is mainly based on two core components; BFAST and MapReduce. BFAST is a software package for aligning next generation genomic reads against a target genome with a very high accuracy and reasonable speed. MapReduce general-purpose parallelization technology [in its open source implementation, Hadoop] appears to be particularly well adapted to the intensive computations and huge data storage tasks involved in the BFAST sequence alignment tool. The MapReduce version of the BFAST tool is expected to offer better results than the original one in terms of maintaining good computational efficiency, accuracy, scalability, deployment and management efforts. The study demonstrates how a general-purpose parallelization technology, i.e. MapReduce running on the cloud, can be tailored to tackle the class of bioinformatics problems with good performance and scalability, and, more importantly, how this technology could be the basis of a computational parallel platform for several problems in the context of bioinformatics. Although the effort of transforming existing bioinformatics algorithms from local compute infrastructure is not trivial, the speed and flexibility of cloud computing environments provide a substantial boost with manageable cost.

Thoughts on the Manchester Manifesto

George Church is a professor at Harvard University and founder of the personal genome project. He has patents in the fields of genome reading and writing, automation and instrumentation, and advocates open source and transparency; for example, by developing the open access next generation sequencing platform (Polonator.org) and the open access human genome plus trait dataset (PersonalGenomes.org).

Mauve Assembly Metrics

Summary: High-throughput DNA sequencing technologies have spurred the development of numerous novel methods for genome assembly. With few exceptions, these algorithms are heuristic and require one or more parameters to be manually set by the user. One approach to parameter tuning involves assembling data from an organism with an available high-quality reference genome, and measuring assembly accuracy using some metrics.We developed a system to measure assembly quality under several scoring metrics, and to compare assembly quality across a variety of assemblers, sequence data types, and parameter choices. When used in conjunction with training data such as a high-quality reference genome and sequence reads from the same organism, our program can be used to manually identify an optimal sequencing and assembly strategy for de novo sequencing of related organisms.Availability: GPL source code and a usage tutorial is at http://ngopt.googlecode.comContact: aarondarling@ucdavis.eduSupplementary information: Supplementary data is available at Bioinformatics online.

Immunoglobulin heavy chains in medaka (Oryzias latipes).

BackgroundBony fish present an immunological system, which evolved independently from those of animals that migrated to land 400 million years ago. The publication of whole genome sequences and the availability of several cDNA libraries for medaka (Oryzias latipes) permitted us to perform a thorough analysis of immunoglobulin heavy chains present in this teleost.ResultsWe identified IgM and IgD coding ESTs, mainly in spleen, kidney and gills using published cDNA libraries but we did not find any sequence that coded for IgT or other heavy chain isotypes described in fish. The IgM - ESTs corresponded with the secreted and membrane forms and surprisingly, the latter form only presented two constant heavy chain domains. This is the first time that this short form of membrane IgM is described in a teleost. It is different from that identified in Notothenioid teleost because it does not present the typical splicing pattern of membrane IgM. The identified IgD-ESTs only present membrane transcripts, with Cμ1 and five Cδ exons. Furthermore, there are ESTs with sequences that do not have any VH which disrupt open reading frames.A scan of the medaka genome using transcripts and genomic short reads resulted in five zones within a region on chromosome 8 with Cμ and Cδ exons. Some of these exons do not form part of antibodies and were at times interspersed, suggesting a recombination process between zones. An analysis of the ESTs confirmed that no antibodies are expressed from zone 3.ConclusionsOur results suggest that the IGH locus duplication is very common among teleosts, wherein the existence of a recombination process explains the sequence homology between them.

Molecular karyotyping using massively parallel sequencing – a next generation approach to prenatal diagnosis?

Underlying chromosomal copy number variation is responsible for 10–15% of fetal structural abnormalities, but even in the presence of a normal karyotype neonatal outcome can be poor. Current whole genome...

Information-theoretic approaches to SVM feature selection for metagenome read classification

AbstractAnalysis of DNA sequences isolated directly from the environment, known as metagenomics, produces a large quantity of genome fragments that need to be classified into specific taxa. Most composition-based classification methods use all features instead of a subset of features that may maximize classifier accuracy. We show that feature selection methods can boost performance of taxonomic classifiers. This work proposes three different filter-based feature selection methods that stem from information theory: (1) a technique that combines Kullback–Leibler, Mutual Information, and distance information, (2) a text mining technique, TF-IDF, and (3) minimum redundancy-maximum-relevance (mRMR). The feature selection methods are compared by how well they improve support vector machine classification of genomic reads. Overall, the 6mer mRMR method performs well, especially on the phyla-level. If the number of total features is very large, feature selection becomes difficult because a small subset of features that captures a majority of the data variance is less likely to exist. Therefore, we conclude that there is a trade-off between feature set size and feature selection method to optimize classification performance. For larger feature set sizes, TF-IDF works better for finer-resolutions while mRMR performs the best out of any method for N=6 for all taxonomic levels.

Gene discovery in EST sequences from the wheat leaf rust fungus Puccinia triticina sexual spores, asexual spores and haustoria, compared to other rust and corn smut fungi

BackgroundRust fungi are biotrophic basidiomycete plant pathogens that cause major diseases on plants and trees world-wide, affecting agriculture and forestry. Their biotrophic nature precludes many established molecular genetic manipulations and lines of research. The generation of genomic resources for these microbes is leading to novel insights into biology such as interactions with the hosts and guiding directions for breakthrough research in plant pathology.ResultsTo support gene discovery and gene model verification in the genome of the wheat leaf rust fungus, Puccinia triticina (Pt), we have generated Expressed Sequence Tags (ESTs) by sampling several life cycle stages. We focused on several spore stages and isolated haustorial structures from infected wheat, generating 17,684 ESTs. We produced sequences from both the sexual (pycniospores, aeciospores and teliospores) and asexual (germinated urediniospores) stages of the life cycle. From pycniospores and aeciospores, produced by infecting the alternate host, meadow rue (Thalictrum speciosissimum), 4,869 and 1,292 reads were generated, respectively. We generated 3,703 ESTs from teliospores produced on the senescent primary wheat host. Finally, we generated 6,817 reads from haustoria isolated from infected wheat as well as 1,003 sequences from germinated urediniospores. Along with 25,558 previously generated ESTs, we compiled a database of 13,328 non-redundant sequences (4,506 singlets and 8,822 contigs). Fungal genes were predicted using the EST version of the self-training GeneMarkS algorithm. To refine the EST database, we compared EST sequences by BLASTN to a set of 454 pyrosequencing-generated contigs and Sanger BAC-end sequences derived both from the Pt genome, and to ESTs and genome reads from wheat. A collection of 6,308 fungal genes was identified and compared to sequences of the cereal rusts, Puccinia graminis f. sp. tritici (Pgt) and stripe rust, P. striiformis f. sp. tritici (Pst), and poplar leaf rust Melampsora species, and the corn smut fungus, Ustilago maydis (Um). While extensive homologies were found, many genes appeared novel and species-specific; over 40% of genes did not match any known sequence in existing databases. Focusing on spore stages, direct comparison to Um identified potential functional homologs, possibly allowing heterologous functional analysis in that model fungus. Many potentially secreted protein genes were identified by similarity searches against genes and proteins of Pgt and Melampsora spp., revealing apparent orthologs.ConclusionsThe current set of Pt unigenes contributes to gene discovery in this major cereal pathogen and will be invaluable for gene model verification in the genome sequence.

Ψ-RA: a parallel sparse index for genomic read alignment

BackgroundGenomic read alignment involves mapping (exactly or approximately) short reads from a particular individual onto a pre-sequenced reference genome of the same species. Because all individuals of the same species share the majority of their genomes, short reads alignment provides an alternative and much more efficient way to sequence the genome of a particular individual than does direct sequencing. Among many strategies proposed for this alignment process, indexing the reference genome and short read searching over the index is a dominant technique. Our goal is to design a space-efficient indexing structure with fast searching capability to catch the massive short reads produced by the next generation high-throughput DNA sequencing technology.ResultsWe concentrate on indexing DNA sequences via sparse suffix arrays (SSAs) and propose a new short read aligner named Ψ-RA (PSI-RA: parallel sparse index read aligner). The motivation in using SSAs is the ability to trade memory against time. It is possible to fine tune the space consumption of the index based on the available memory of the machine and the minimum length of the arriving pattern queries. Although SSAs have been studied before for exact matching of short reads, an elegant way of approximate matching capability was missing. We provide this by defining the rightmost mismatch criteria that prioritize the errors towards the end of the reads, where errors are more probable. Ψ-RA supports any number of mismatches in aligning reads. We give comparisons with some of the well-known short read aligners, and show that indexing a genome with SSA is a good alternative to the Burrows-Wheeler transform or seed-based solutions.ConclusionsΨ-RA is expected to serve as a valuable tool in the alignment of short reads generated by the next generation high-throughput sequencing technology. Ψ-RA is very fast in exact matching and also supports rightmost approximate matching. The SSA structure that Ψ-RA is built on naturally incorporates the modern multicore architecture and thus further speed-up can be gained. All the information, including the source code of Ψ-RA, can be downloaded at: http://www.busillis.com/o_kulekci/PSIRA.zip.

Uncovering the Human Methyltransferasome

We present a comprehensive analysis of the human methyltransferasome. Primary sequences, predicted secondary structures, and solved crystal structures of known methyltransferases were analyzed by hidden Markov models, Fisher-based statistical matrices, and fold recognition prediction-based threading algorithms to create a model, or profile, of each methyltransferase superfamily. These profiles were used to scan the human proteome database and detect novel methyltransferases. 208 proteins in the human genome are now identified as known or putative methyltransferases, including 38 proteins that were not annotated previously. To date, 30% of these proteins have been linked to disease states. Possible substrates of methylation for all of the SET domain and SPOUT methyltransferases as well as 100 of the 131 seven-β-strand methyltransferases were surmised from sequence similarity clusters based on alignments of the substrate-specific domains.

Next generation genome annotation with mGene.ngs

An increasingly large number of novel genomes is being sequenced and the task of automatic genome annotation has never been more important. The current revolution in sequencing technologies also allows us to obtain a detailed picture of the whole complement of expressed RNA transcripts. We have developed a novel de novo gene finding system mGene.ngs that combines the benefits of accurate ab initio gene finding with the rich information obtained in RNA sequencing (RNA-seq) experiments. The system is based on the recently developed accurate gene finding system mGene [1], which employs state-of-the-art prediction techniques and which has been shown to perform very well compared to established gene finding systems [2]. In contrast to many HMM-based gene finders, mGene has the conceptual advantage of being very flexible in terms of incorporating heterogeneous input data. The employed inference techniques can exploit the transcriptome information already at the learning stage to appropriately adapt to the relevance of the different evidences. We show that these advantages can be translated into more accurate gene predictions. Moreover, we developed extensions of mGene.ngs to predict and quantify alternative RNA transcripts. To provide de novo genome annotations based on RNA-seq experiments, we first construct a preliminary, highly specific gene set for genes that are well-covered with RNA-seq reads. In a second step, we train predictors for genomic signals on the preliminary gene set. In the third step we train mGene.ngs, using the preliminary gene models while taking advantage of the RNA-seq read coverage and genomic signal predictions. We illustrate the power of our approach for the C. elegans genome and 50M paired-end RNA-seq reads (Illumina; 76nt). Figure 1 shows transcript level evaluation results for all annotated genes (WS200) as a function of the expression level. The ab initio mGene-based system (blue) trained on the annotation achieves an average transcript-level F-score of 49.9%. We achieve a slightly better performance (51.8%) for the de novo annotation system (green) using RNA-seq reads, but without considering the existing genome annotation. If we use the RNA-seq reads and train on the existing annotation (red), we achieve 57.6%, and can therefore take advantage of the previous annotation. We find it remarkable that for medium to high expressed genes the de novo gene predictions are as similar to the genome annotation as the predictions of the system, that has seen parts of the annotation in training. Comparing these results to predictions from the recently published method cufflinks [3] (black) reveals that cufflinks seems not to be able to appropriately adapt to the RNA-seq data at hand. Investigating the contribution of individual features we found that spliced read alignments suggesting introns help most to increase the gene prediction performance; 91.6% of the achieved total improvement is due to spliced read alignments. The read coverage alone is much less informative and only leads to improvements similar to the ones achieved with transcriptome tiling arrays. We employed the developed annotation strategy for the re-annotation of the C. briggsae genome, for which only few transcriptome sequences are available yet. We can show that the new annotation is considerably more accurate than previous ones and additionally includes alternative RNA isoforms. mGene.ngs will be released as open source software on http://mgene.org and is already available as Galaxy-based web-service at http://galaxy.fml.mpg.de. * Correspondence: jonas.behr@tuebingen.mpg.de Friedrich Miescher Laboratory of the Max Planck Society, Tubingen, Germany Full list of author information is available at the end of the article Behr et al. BMC Bioinformatics 2010, 11(Suppl 10):O8 http://www.biomedcentral.com/1471-2105/11/S10/O8