Assembly and characterization of novel Alu inserts detected from next-generation sequencing data.
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ABSTRACT:
SUBMITTER: Mustafa H
PROVIDER: S-EPMC4589995 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Due to the complexity of the protocols and a limited knowledge of the nature of microbial communities, simulating metagenomic sequences plays an important role in testing the performance of existing tools and data analysis methods with metagenomic data. We developed metagenomic read simulators with platform-specific (Sanger, pyrosequencing, Illumina) base-error models, and simulated metagenomes of differing community complexities. We first evaluated the effect of rigorous quality control on Illumina data. Although quality filtering removed a large proportion of the data, it greatly improved the accuracy and contig lengths of resulting assemblies. We then compared the quality-trimmed Illumina assemblies to those from Sanger and pyrosequencing. For the simple community (10 genomes) all sequencing technologies assembled a similar amount and accurately represented the expected functional composition. For the more complex community (100 genomes) Illumina produced the best assemblies and more correctly resembled the expected functional composition. For the most complex community (400 genomes) there was very little assembly of reads from any sequencing technology. However, due to the longer read length the Sanger reads still represented the overall functional composition reasonably well. We further examined the effect of scaffolding of contigs using paired-end Illumina reads. It dramatically increased contig lengths of the simple community and yielded minor improvements to the more complex communities. Although the increase in contig length was accompanied by increased chimericity, it resulted in more complete genes and a better characterization of the functional repertoire. The metagenomic simulators developed for this research are freely available.
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Project description:As a result of improvements in genome assembly algorithms and the ever decreasing costs of high-throughput sequencing technologies, new high quality draft genome sequences are published at a striking pace. With well-established methodologies, larger and more complex genomes are being tackled, including polyploid plant genomes. Given the similarity between multiple copies of a basic genome in polyploid individuals, assembly of such data usually results in collapsed contigs that represent a variable number of homoeologous genomic regions. Unfortunately, such collapse is often not ideal, as keeping contigs separate can lead both to improved assembly and also insights about how haplotypes influence phenotype. Here, we describe a first step in avoiding inappropriate collapse during assembly. In particular, we describe ConPADE (Contig Ploidy and Allele Dosage Estimation), a probabilistic method that estimates the ploidy of any given contig/scaffold based on its allele proportions. In the process, we report findings regarding errors in sequencing. The method can be used for whole genome shotgun (WGS) sequencing data. We also show applicability of the method for variant calling and allele dosage estimation. Results for simulated and real datasets are discussed and provide evidence that ConPADE performs well as long as enough sequencing coverage is available, or the true contig ploidy is low. We show that ConPADE may also be used for related applications, such as the identification of duplicated genes in fragmented assemblies, although refinements are needed.
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Project description:The use of next generation sequencing (NGS) to identify novel viral sequences from eukaryotic tissue samples is challenging. Issues can include the low proportion and copy number of viral reads and the high number of contigs (post-assembly), making subsequent viral analysis difficult. Comparison of assembly algorithms with pre-assembly host-mapping subtraction using a short-read mapping tool, a k-mer frequency based filter and a low complexity filter, has been validated for viral discovery with Illumina data derived from naturally infected liver tissue and simulated data. Assembled contig numbers were significantly reduced (up to 99.97%) by the application of these pre-assembly filtering methods. This approach provides a validated method for maximizing viral contig size as well as reducing the total number of assembled contigs that require down-stream analysis as putative viral nucleic acids.
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Project description:Copy number variations (CNVs) can be used as significant bio-markers and next generation sequencing (NGS) provides a high resolution detection of these CNVs. But how to extract features from CNVs and further apply them to genomic studies such as population clustering have become a big challenge. In this paper, we propose a novel method for population clustering based on CNVs from NGS. First, CNVs are extracted from each sample to form a feature matrix. Then, this feature matrix is decomposed into the source matrix and weight matrix with non-negative matrix factorization (NMF). The source matrix consists of common CNVs that are shared by all the samples from the same group, and the weight matrix indicates the corresponding level of CNVs from each sample. Therefore, using NMF of CNVs one can differentiate samples from different ethnic groups, i.e. population clustering. To validate the approach, we applied it to the analysis of both simulation data and two real data set from the 1000 Genomes Project. The results on simulation data demonstrate that the proposed method can recover the true common CNVs with high quality. The results on the first real data analysis show that the proposed method can cluster two family trio with different ancestries into two ethnic groups and the results on the second real data analysis show that the proposed method can be applied to the whole-genome with large sample size consisting of multiple groups. Both results demonstrate the potential of the proposed method for population clustering.
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Project description:Transcriptome analysis has important applications in many biological fields. However, assembling a transcriptome without a known reference remains a challenging task requiring algorithmic improvements. We present two methods for substantially improving transcriptome de novo assembly. The first method relies on the observation that the use of a single k-mer length by current de novo assemblers is suboptimal to assemble transcriptomes where the sequence coverage of transcripts is highly heterogeneous. We present the Multiple-k method in which various k-mer lengths are used for de novo transcriptome assembly. We demonstrate its good performance by assembling de novo a published next-generation transcriptome sequence data set of Aedes aegypti, using the existing genome to check the accuracy of our method. The second method relies on the use of a reference proteome to improve the de novo assembly. We developed the Scaffolding using Translation Mapping (STM) method that uses mapping against the closest available reference proteome for scaffolding contigs that map onto the same protein. In a controlled experiment using simulated data, we show that the STM method considerably improves the assembly, with few errors. We applied these two methods to assemble the transcriptome of the non-model catfish Loricaria gr. cataphracta. Using the Multiple-k and STM methods, the assembly increases in contiguity and in gene identification, showing that our methods clearly improve quality and can be widely used. The new methods were used to assemble successfully the transcripts of the core set of genes regulating tooth development in vertebrates, while classic de novo assembly failed.
| S-EPMC2945192 | biostudies-literature
Project description:SummaryWith the rapid expansion of the capabilities of the DNA sequencers throughout the different sequencing generations, the quantity of generated data has likewise increased. This evolution has also led to new bioinformatical methods, for which in silico data have become crucial when verifying the accuracy of a model or the robustness of a genomic analysis pipeline. Here, we present a multithreaded next-generation simulator for next-generation sequencing data (NGSNGS), which simulates reads faster than currently available methods and programs. NGSNGS can simulate reads with platform-specific characteristics based on nucleotide quality score profiles as well as including a post-mortem damage model which is relevant for simulating ancient DNA. The simulated sequences are sampled (with replacement) from a reference DNA genome, which can represent a haploid genome, polyploid assemblies or even population haplotypes and allows the user to simulate known variable sites directly. The program is implemented in a multithreading framework and is factors faster than currently available tools while extending their feature set and possible output formats.Availability and implementationThe method and associated programs are released as open-source software, code and user manual are available at https://github.com/RAHenriksen/NGSNGS.Supplementary informationSupplementary data are available at Bioinformatics online.
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Project description:NGPS is a method for de-novo, full-length protein sequencing in high throughput. The method is based on cleavage of the protein at semi-random sites by microwave-assisted acid hydrolysis (MAAH), enrichment of LC-MS/MS amenable peptides from the hydrolysate by solid-phase-extraction, LC-MS/MS analysis, de-novo long peptide tag sequencing of resulting peptides and assembly of peptide tags into consensus contigs.
2017-04-03 | PXD003804 | Pride
Project description:BackgroundSplit-hand/foot malformation syndrome is a rare, clinically and genetically het-erogeneous group of limb malformations characterized by absence/hypoplasia and/or median cleft of hands and/or feet. It may occur as an isolated abnormality or it may be associated with a genetic syn-drome.Case reportIn the present case, isolated split-hand/split-foot malformation was diagnosed by prenatal ultrasound at 24 weeks in a male singleton fetus, with deep median cleft of the right hand, syndactyly and hypoplasia of phalanges in both hands, and oligodactyly of the right foot. During consultation, the father of the fetus revealed that he also had an isolated right foot dysplasia. The parents chose elective termination and autopsy confirmed prenatal ultrasound findings. Genetic testing of the aborted fetus with QF-PCR analysis for common aneuploidies and array comparative genomic hybridization (aCGH) showed a male genomic pattern, without aneuploidies or chromosomal imbalances. Further investigation with next generation sequencing of 49 clinically relevant genes revealed a novel heterozygous FGFR1 mutation c.787_789del (p.Ala263del) in the fetus; the father was heterozygous to the same mutation.ConclusionA novel heterozygous FGFR1 mutation causing split-hand/foot malformation syndrome is reported. Accurate genetic diagnosis allowed detailed counseling to be provided to the couple, including the underlying cause, recurrence risks, and detailed management plan with preimplantation genetic diag-nosis for future pregnancies.
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Project description:Next-generation-sequencing (NGS) has revolutionized the field of genome assembly because of its much higher data throughput and much lower cost compared with traditional Sanger sequencing. However, NGS poses new computational challenges to de novo genome assembly. Among the challenges, GC bias in NGS data is known to aggravate genome assembly. However, it is not clear to what extent GC bias affects genome assembly in general. In this work, we conduct a systematic analysis on the effects of GC bias on genome assembly. Our analyses reveal that GC bias only lowers assembly completeness when the degree of GC bias is above a threshold. At a strong GC bias, the assembly fragmentation due to GC bias can be explained by the low coverage of reads in the GC-poor or GC-rich regions of a genome. This effect is observed for all the assemblers under study. Increasing the total amount of NGS data thus rescues the assembly fragmentation because of GC bias. However, the amount of data needed for a full rescue depends on the distribution of GC contents. Both low and high coverage depths due to GC bias lower the accuracy of assembly. These pieces of information provide guidance toward a better de novo genome assembly in the presence of GC bias.
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Project description:BackgroundNext-generation sequencing technologies are rapidly generating whole-genome datasets for an increasing number of organisms. However, phylogenetic reconstruction of genomic data remains difficult because de novo assembly for non-model genomes and multi-genome alignment are challenging.ResultsTo greatly simplify the analysis, we present an Assembly and Alignment-Free (AAF) method ( https://sourceforge.net/projects/aaf-phylogeny ) that constructs phylogenies directly from unassembled genome sequence data, bypassing both genome assembly and alignment. Using mathematical calculations, models of sequence evolution, and simulated sequencing of published genomes, we address both evolutionary and sampling issues caused by direct reconstruction, including homoplasy, sequencing errors, and incomplete sequencing coverage. From these results, we calculate the statistical properties of the pairwise distances between genomes, allowing us to optimize parameter selection and perform bootstrapping. As a test case with real data, we successfully reconstructed the phylogeny of 12 mammals using raw sequencing reads. We also applied AAF to 21 tropical tree genome datasets with low coverage to demonstrate its effectiveness on non-model organisms.ConclusionOur AAF method opens up phylogenomics for species without an appropriate reference genome or high sequence coverage, and rapidly creates a phylogenetic framework for further analysis of genome structure and diversity among non-model organisms.
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