RAMBO-K: Rapid and Sensitive Removal of Background Sequences from Next Generation Sequencing Data.
Ontology highlight
ABSTRACT: Background
The assembly of viral or endosymbiont genomes from Next Generation Sequencing (NGS) data is often hampered by the predominant abundance of reads originating from the host organism. These reads increase the memory and CPU time usage of the assembler and can lead to misassemblies.Results
We developed RAMBO-K (Read Assignment Method Based On K-mers), a tool which allows rapid and sensitive removal of unwanted host sequences from NGS datasets. Reaching a speed of 10 Megabases/s on 4 CPU cores and a standard hard drive, RAMBO-K is faster than any tool we tested, while showing a consistently high sensitivity and specificity across different datasets.Conclusions
RAMBO-K rapidly and reliably separates reads from different species without data preprocessing. It is suitable as a straightforward standard solution for workflows dealing with mixed datasets. Binaries and source code (java and python) are available from http://sourceforge.net/projects/rambok/.
SUBMITTER: Tausch SH
PROVIDER: S-EPMC4574938 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
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.
| S-EPMC9891242 | biostudies-literature
Project description:BackgroundThe investigation of plant genome structure and evolution requires comprehensive characterization of repetitive sequences that make up the majority of higher plant nuclear DNA. Since genome-wide characterization of repetitive elements is complicated by their high abundance and diversity, novel approaches based on massively-parallel sequencing are being adapted to facilitate the analysis. It has recently been demonstrated that the low-pass genome sequencing provided by a single 454 sequencing reaction is sufficient to capture information about all major repeat families, thus providing the opportunity for efficient repeat investigation in a wide range of species. However, the development of appropriate data mining tools is required in order to fully utilize this sequencing data for repeat characterization.ResultsWe adapted a graph-based approach for similarity-based partitioning of whole genome 454 sequence reads in order to build clusters made of the reads derived from individual repeat families. The information about cluster sizes was utilized for assessing the proportion and composition of repeats in the genomes of two model species, Pisum sativum and Glycine max, differing in genome size and 454 sequencing coverage. Moreover, statistical analysis and visual inspection of the topology of the cluster graphs using a newly developed program tool, SeqGrapheR, were shown to be helpful in distinguishing basic types of repeats and investigating sequence variability within repeat families.ConclusionsRepetitive regions of plant genomes can be efficiently characterized by the presented graph-based analysis and the graph representation of repeats can be further used to assess the variability and evolutionary divergence of repeat families, discover and characterize novel elements, and aid in subsequent assembly of their consensus sequences.
| S-EPMC2912890 | biostudies-literature
Project description:The emergence of next-generation sequencing (NGS) has revolutionized the way of reaching a genome sequence, with the promise of potentially providing a comprehensive characterization of DNA variations. Nevertheless, detecting somatic mutations is still a difficult problem, in particular when trying to identify low abundance mutations, such as subclonal mutations, tumour-derived alterations in body fluids or somatic mutations from histological normal tissue. The main challenge is to precisely distinguish between sequencing artefacts and true mutations, particularly when the latter are so rare they reach similar abundance levels as artefacts. Here, we present needlestack, a highly sensitive variant caller, which directly learns from the data the level of systematic sequencing errors to accurately call mutations. Needlestack is based on the idea that the sequencing error rate can be dynamically estimated from analysing multiple samples together. We show that the sequencing error rate varies across alterations, illustrating the need to precisely estimate it. We evaluate the performance of needlestack for various types of variations, and we show that needlestack is robust among positions and outperforms existing state-of-the-art method for low abundance mutations. Needlestack, along with its source code is freely available on the GitHub platform: https://github.com/IARCbioinfo/needlestack.
| S-EPMC7182099 | biostudies-literature
Project description:Most tailed bacteriophages (phages) feature linear dsDNA genomes. Characterizing novel phages requires an understanding of complete genome sequences, including the definition of genome physical ends.We sequenced 48 Bacillus cereus phage isolates and analyzed Next-generation sequencing (NGS) data to resolve the genome configuration of these novel phages. Most assembled contigs featured reads that mapped to both contig ends and formed circularized contigs. Independent assemblies of 31 nearly identical I48-like Bacillus phage isolates allowed us to observe that the assembly programs tended to produce random cleavage on circularized contigs. However, currently available assemblers were not capable of reporting the underlying phage genome configuration from sequence data. To identify the genome configuration of sequenced phage in silico, a terminus prediction method was developed by means of 'neighboring coverage ratios' and 'read edge frequencies' from read alignment files. Termini were confirmed by primer walking and supported by phylogenetic inference of large DNA terminase protein sequences.The Terminus package using phage NGS data along with the contig circularity could efficiently identify the proximal positions of phage genome terminus. Complete phage genome sequences allow a proposed characterization of the potential packaging mechanisms and more precise genome annotation.
| S-EPMC5418689 | biostudies-literature
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:BackgroundGenetically engineered mice (GEM) are essential tools for understanding gene function and disease modeling. Historically, gene targeting was first done in embryonic stem cells (ESCs) derived from the 129 family of inbred strains, leading to a mixed background or congenic mice when crossed with C57BL/6 mice. Depending on the number of backcrosses and breeding strategies, genomic segments from 129-derived ESCs can be introgressed into the C57BL/6 genome, establishing a unique genetic makeup that needs characterization in order to obtain valid conclusions from experiments using GEM lines. Currently, SNP genotyping is used to detect the extent of 129-derived ESC genome introgression into C57BL/6 recipients; however, it fails to detect novel/rare variants.ResultsHere, we present a computational pipeline implemented in the Galaxy platform and in BASH/R script to determine genetic introgression of GEM using next generation sequencing data (NGS), such as whole genome sequencing (WGS), whole exome sequencing (WES) and RNA-Seq. The pipeline includes strategies to uncover variants linked to a targeted locus, genome-wide variant visualization, and the identification of potential modifier genes. Although these methods apply to congenic mice, they can also be used to describe variants fixed by genetic drift. As a proof of principle, we analyzed publicly available RNA-Seq data from five congenic knockout (KO) lines and our own RNA-Seq data from the Sall2 KO line. Additionally, we performed target validation using several genetics approaches.ConclusionsWe revealed the impact of the 129-derived ESC genome introgression on gene expression, predicted potential modifier genes, and identified potential phenotypic interference in KO lines. Our results demonstrate that our new approach is an effective method to determine genetic introgression of GEM.
| S-EPMC6373082 | biostudies-literature
Project description:MotivationNext-generation sequencing technology is increasingly being used for clinical diagnostic tests. Clinical samples are often genomically heterogeneous due to low sample purity or the presence of genetic subpopulations. Therefore, a variant calling algorithm for calling low-frequency polymorphisms in heterogeneous samples is needed.ResultsWe present a novel variant calling algorithm that uses a hierarchical Bayesian model to estimate allele frequency and call variants in heterogeneous samples. We show that our algorithm improves upon current classifiers and has higher sensitivity and specificity over a wide range of median read depth and minor allele fraction. We apply our model and identify 15 mutated loci in the PAXP1 gene in a matched clinical breast ductal carcinoma tumor sample; two of which are likely loss-of-heterozygosity events.Availability and implementationhttp://genomics.wpi.edu/rvd2/.Contactpjflaherty@wpi.eduSupplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC4547613 | biostudies-literature
Project description:Although pathway analysis methods have been developed and successfully applied to association studies of common variants, the statistical methods for pathway-based association analysis of rare variants have not been well developed. Many investigators observed highly inflated false-positive rates and low power in pathway-based tests of association of rare variants. The inflated false-positive rates and low true-positive rates of the current methods are mainly due to their lack of ability to account for gametic phase disequilibrium. To overcome these serious limitations, we develop a novel statistic that is based on the smoothed functional principal component analysis (SFPCA) for pathway association tests with next-generation sequencing data. The developed statistic has the ability to capture position-level variant information and account for gametic phase disequilibrium. By intensive simulations, we demonstrate that the SFPCA-based statistic for testing pathway association with either rare or common or both rare and common variants has the correct type 1 error rates. Also the power of the SFPCA-based statistic and 22 additional existing statistics are evaluated. We found that the SFPCA-based statistic has a much higher power than other existing statistics in all the scenarios considered. To further evaluate its performance, the SFPCA-based statistic is applied to pathway analysis of exome sequencing data in the early-onset myocardial infarction (EOMI) project. We identify three pathways significantly associated with EOMI after the Bonferroni correction. In addition, our preliminary results show that the SFPCA-based statistic has much smaller P-values to identify pathway association than other existing methods.
| S-EPMC4666565 | biostudies-literature
Project description:The Lundehund is a highly specialized breed characterized by a unique flexibility of the joints and polydactyly in all four limbs. The extremely small population size and high inbreeding has promoted a high frequency of diseased dogs affected by the Lundehund syndrome (LS), a severe gastro-enteropathic disease.Comprehensive analysis of bead chip and whole-genome sequencing data for LS in the Lundehund resulted in a genome-wide association signal on CFA 34 and LS-specific runs of homozygosity (ROH) in this region. Filtering analysis for variants with predicted high or moderate effects revealed a missense mutation in LEPREL1 1.2 Mb proximal to the region of the genome-wide association, which was shown to be significantly associated with LS. LS-affected Lundehund harbored the mutant LEPREL1:g.139212C>G genotype A/A whereas all controls of other breeds showed the C/C wild type. In addition, ROH analysis for the Lundehund indicated a high enrichment of genes in potential signatures of selection affecting protein activation and immunoregulatory processes like NOD1 potentially involved in LS breed disposition.Sequencing results for Lundehund specific traits reveal a potential causative mutation for LS in the neuropeptide operating gene LEPREL1 and suggests it as a precursor of the inflammatory process. Analyses of ROH regions give an insight into the genetic background of characteristic traits in the Lundehund that remain to be elucidated in the future.
| S-EPMC4971756 | biostudies-literature
Project description:Whole genome sequences are ideally suited for deriving evolutionary relationship among organisms. With the availability of Next Generation sequencing (NGS) datasets in an unprecedented scale, it will be highly desirable if phylogenetic analysis can be carried out using short read NGS data. We described here an anchor based approach NexABP for phylogenetic construction of closely related strains/isolates from NGS data. This approach can be used even in the absence of a fully assembled reference genome and works by reducing the complexity of the datasets without compromising results. NexABP was used for constructing phylogeny of different strains of some of the common pathogens, such as Mycobacterium tuberculosis, Vibrio cholera and Escherichia coli. In addition to classification into distinct lineages, NexABP could resolve inner branches and also allow statistical testing using bootstrap analysis. We believe that there are some clear advantages of using NexABP based phylogenetic analysis as compared to other methods.
| S-EPMC3769656 | biostudies-literature