Next-Generation Analytics for Omics Data.
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ABSTRACT:
SUBMITTER: Li J
PROVIDER: S-EPMC8336660 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:High-throughput experiments enable researchers to explore complex multifactorial diseases through large-scale analysis of omics data. Challenges for such high-dimensional data sets include storage, analyses, and sharing. Recent innovations in computational technologies and approaches, especially in cloud computing, offer a promising, low-cost, and highly flexible solution in the bioinformatics domain. Cloud computing is rapidly proving increasingly useful in molecular modeling, omics data analytics (eg, RNA sequencing, metabolomics, or proteomics data sets), and for the integration, analysis, and interpretation of phenotypic data. We review the adoption of advanced cloud-based and big data technologies for processing and analyzing omics data and provide insights into state-of-the-art cloud bioinformatics applications.
| S-EPMC8323418 | 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.
| S-EPMC9891242 | biostudies-literature
Project description:As the cost of sequencing drops rapidly, the amount of 'omics data increases exponentially, making data visualization and interpretation-'tertiary' analysis a bottleneck. Specialized analytical tools requiring technical expertise are available. However, consolidated and multi-faceted tools that are easy to use for life scientists is highly needed and currently lacking. Here we present Omics Playground, a user-friendly and interactive self-service bioinformatics platform for the in-depth analysis, visualization and interpretation of transcriptomics and proteomics data. It provides a large number of different tools in which special attention has been paid to single cell data. With Omics Playground, life scientists can easily perform complex data analysis and visualization without coding, and significantly reduce the time to discovery.
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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
Project description:High-throughput sequencing assays are now routinely used to study different aspects of genome organization. As decreasing costs and widespread availability of sequencing enable more laboratories to use sequencing assays in their research projects, the number of samples and replicates in these experiments can quickly grow to several dozens of samples and thus require standardized annotation, storage and management of preprocessing steps. As a part of the STATegra project, we have developed an Experiment Management System (EMS) for high throughput omics data that supports different types of sequencing-based assays such as RNA-seq, ChIP-seq, Methyl-seq, etc, as well as proteomics and metabolomics data. The STATegra EMS provides metadata annotation of experimental design, samples and processing pipelines, as well as storage of different types of data files, from raw data to ready-to-use measurements. The system has been developed to provide research laboratories with a freely-available, integrated system that offers a simple and effective way for experiment annotation and tracking of analysis procedures.
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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:Inference of population structure and individual ancestry is important both for population genetics and for association studies. With next generation sequencing technologies it is possible to obtain genetic data for all accessible genetic variations in the genome. Existing methods for admixture analysis rely on known genotypes. However, individual genotypes cannot be inferred from low-depth sequencing data without introducing errors. This article presents a new method for inferring an individual's ancestry that takes the uncertainty introduced in next generation sequencing data into account. This is achieved by working directly with genotype likelihoods that contain all relevant information of the unobserved genotypes. Using simulations as well as publicly available sequencing data, we demonstrate that the presented method has great accuracy even for very low-depth data. At the same time, we demonstrate that applying existing methods to genotypes called from the same data can introduce severe biases. The presented method is implemented in the NGSadmix software available at http://www.popgen.dk/software.
| S-EPMC3813857 | biostudies-other
Project description:Over the past few years, new high-throughput DNA sequencing technologies have dramatically increased speed and reduced sequencing costs. However, the use of these sequencing technologies is often challenged by errors and biases associated with the bioinformatical methods used for analyzing the data. In particular, the use of naïve methods to identify polymorphic sites and infer genotypes can inflate downstream analyses. Recently, explicit modeling of genotype probability distributions has been proposed as a method for taking genotype call uncertainty into account. Based on this idea, we propose a novel method for quantifying population genetic differentiation from next-generation sequencing data. In addition, we present a strategy for investigating population structure via principal components analysis. Through extensive simulations, we compare the new method herein proposed to approaches based on genotype calling and demonstrate a marked improvement in estimation accuracy for a wide range of conditions. We apply the method to a large-scale genomic data set of domesticated and wild silkworms sequenced at low coverage. We find that we can infer the fine-scale genetic structure of the sampled individuals, suggesting that employing this new method is useful for investigating the genetic relationships of populations sampled at low coverage.
| S-EPMC3813878 | biostudies-other
Project description:BackgroundThe discovery and mapping of genomic variants is an essential step in most analysis done using sequencing reads. There are a number of mature software packages and associated pipelines that can identify single nucleotide polymorphisms (SNPs) with a high degree of concordance. However, the same cannot be said for tools that are used to identify the other types of variants. Indels represent the second most frequent class of variants in the human genome, after single nucleotide polymorphisms. The reliable detection of indels is still a challenging problem, especially for variants that are longer than a few bases.ResultsWe have developed a set of algorithms and heuristics collectively called indelMINER to identify indels from whole genome resequencing datasets using paired-end reads. indelMINER uses a split-read approach to identify the precise breakpoints for indels of size less than a user specified threshold, and supplements that with a paired-end approach to identify larger variants that are frequently missed with the split-read approach. We use simulated and real datasets to show that an implementation of the algorithm performs favorably when compared to several existing tools.ConclusionsindelMINER can be used effectively to identify indels in whole-genome resequencing projects. The output is provided in the VCF format along with additional information about the variant, including information about its presence or absence in another sample. The source code and documentation for indelMINER can be freely downloaded from www.bx.psu.edu/miller_lab/indelMINER.tar.gz .
| S-EPMC4339746 | biostudies-literature
Project description:MotivationThe sequence alignment/map format (SAM) is a commonly used format to store the alignments between millions of short reads and a reference genome. Often certain positions within the reads are inherently more likely to contain errors due to the protocols used to prepare the samples. Such biases can have adverse effects on both mapping rate and accuracy. To understand the relationship between potential protocol biases and poor mapping we wrote SAMstat, a simple C program plotting nucleotide overrepresentation and other statistics in mapped and unmapped reads in a concise html page. Collecting such statistics also makes it easy to highlight problems in the data processing and enables non-experts to track data quality over time.ResultsWe demonstrate that studying sequence features in mapped data can be used to identify biases particular to one sequencing protocol. Once identified, such biases can be considered in the downstream analysis or even be removed by read trimming or filtering techniques.AvailabilitySAMStat is open source and freely available as a C program running on all Unix-compatible platforms. The source code is available from http://samstat.sourceforge.net.Contacttimolassmann@gmail.com.
| S-EPMC3008642 | biostudies-literature