Parallel Generalized Suffix Tree Construction for Genomic Data
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ABSTRACT:
SUBMITTER: Martin-Vide C
PROVIDER: S-EPMC7197101 | biostudies-literature | 2020 Feb
REPOSITORIES: biostudies-literature
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Project description:Background Several technological advancements and digitization of healthcare data have provided the scientific community with a large quantity of genomic data. Such datasets facilitated a deeper understanding of several diseases and our health in general. Strikingly, these genome datasets require a large storage volume and present technical challenges in retrieving meaningful information. Furthermore, the privacy aspects of genomic data limit access and often hinder timely scientific discovery. Methods In this paper, we utilize the Generalized Suffix Tree (GST); their construction and applications have been fairly studied in related areas. The main contribution of this article is the proposal of a privacy-preserving string query execution framework using GSTs and an additional tree-based hashing mechanism. Initially, we start by introducing an efficient GST construction in parallel that is scalable for a large genomic dataset. The secure indexing scheme allows the genomic data in a GST to be outsourced to an untrusted cloud server under encryption. Additionally, the proposed methods can perform several string search operations (i.e., exact, set-maximal matches) securely and efficiently using the outlined framework. Results The experimental results on different datasets and parameters in a real cloud environment exhibit the scalability of these methods as they also outperform the state-of-the-art method based on Burrows-Wheeler Transformation (BWT). The proposed method only takes around 36.7s to execute a set-maximal match whereas the BWT-based method takes around 160.85s, providing a 4× speedup. Supplementary Information The online version contains supplementary material available at (10.1186/s12863-022-01053-x).
| S-EPMC9206251 | biostudies-literature
Project description:BackgroundThe efficiencies of the stop codons TAA, TAG, and TGA in protein synthesis termination are not the same. These variations could allow many genes to be regulated. There are many similar nucleotide trimers found on the second and third reading-frames of a gene. They are called premature stop codons (PSC). Like stop codons, the PSC in bacterial genomes are also highly bias in terms of their quantities and qualities on the genes. Phylogenetically related species often share a similar PSC profile. We want to know whether the selective forces that influence the stop codons and the PSC usage biases in a genome are related. We also wish to know how strong these trimers in a genome are related to the natural history of the bacterium. Knowing these relations may provide better knowledge in the phylogeny of bacteriaResultsA 16SrRNA-alignment tree of 19 well-studied α-, β- and γ-Proteobacteria Type species is used as standard reference for bacterial phylogeny. The genomes of sixty-one bacteria, belonging to the α-, β- and γ-Proteobacteria subphyla, are used for this study. The stop codons and PSC are collectively termed "Translation Stop Signals" (TSS). A gene is represented by nine scalars corresponding to the numbers of counts of TAA, TAG, and TGA on each of the three reading-frames of that gene. "Translation Stop Signals Ratio" (TSSR) is the ratio between the TSS counts. Four types of TSSR are investigated. The TSSR-1, TSSR-2 and TSSR-3 are each a 3-scalar series corresponding respectively to the average ratio of TAA: TAG: TGA on the first, second, and third reading-frames of all genes in a genome. The Genomic-TSSR is a 9-scalar series representing the ratio of distribution of all TSS on the three reading-frames of all genes in a genome. Results show that bacteria grouped by their similarities based on TSSR-1, TSSR-2, or TSSR-3 values could only partially resolve the phylogeny of the species. However, grouping bacteria based on thier Genomic-TSSR values resulted in clusters of bacteria identical to those bacterial clusters of the reference tree. Unlike the 16SrRNA method, the Genomic-TSSR tree is also able to separate closely related species/strains at high resolution. Species and strains separated by the Genomic-TSSR grouping method are often in good agreement with those classified by other taxonomic methods. Correspondence analysis of individual genes shows that most genes in a bacterial genome share a similar TSSR value. However, within a chromosome, the Genic-TSSR values of genes near the replication origin region (Ori) are more similar to each other than those genes near the terminus region (Ter).ConclusionThe translation stop signals on the three reading-frames of the genes on a bacterial genome are interrelated, possibly due to frequent off-frame recombination facilitated by translational-associated recombination (TSR). However, TSR may not occur randomly in a bacterial chromosome. Genes near the Ori region are often highly expressed and a bacterium always maintains multiple copies of Ori. Frequent collisions between DNA- polymerase and RNA-polymerase would create many DNA strand-breaks on the genes; whereas DNA strand-break induced homologues-recombination is more likely to take place between genes with similar sequence. Thus, localized recombination could explain why the TSSR of genes near the Ori region are more similar to each other. The quantity and quality of these TSS in a genome strongly reflect the natural history of a bacterium. We propose that the Genomic- TSSR can be used as a subjective biomarker to represent the phyletic status of a bacterium.
| S-EPMC3466146 | biostudies-literature
Project description:Next generation sequencing technologies enable efficient and cost-effective genome sequencing. However, sequencing errors increase the complexity of the de novo assembly process, and reduce the quality of the assembled sequences. Many error correction techniques utilizing substring frequencies have been developed to mitigate this effect. In this paper, we present a novel and effective method called Pluribus, for correcting sequencing errors using a generalized suffix trie. Pluribus utilizes multiple manifestations of an error in the trie to accurately identify errors and suggest corrections. We show that Pluribus produces the least number of false positives across a diverse set of real sequencing datasets when compared to other methods. Furthermore, Pluribus can be used in conjunction with other contemporary error correction methods to achieve higher levels of accuracy than either tool alone. These increases in error correction accuracy are also realized in the quality of the contigs that are generated during assembly. We explore, in-depth, the behavior of Pluribus , to explain the observed improvement in accuracy and assembly performance. Pluribus is freely available at http://compbio.Caseedu/pluribus/.
| S-EPMC5754272 | biostudies-literature
Project description:The suffix array and its variants are text-indexing data structures that have become indispensable in the field of bioinformatics. With the uninitiated in mind, we provide an accessible exposition of the SA-IS algorithm, which is the state of the art in suffix array construction. We also describe DisLex, a technique that allows standard suffix array construction algorithms to create modified suffix arrays designed to enable a simple form of inexact matching needed to support 'spaced seeds' and 'subset seeds' used in many biological applications.
| S-EPMC3956071 | biostudies-literature
Project description:BackgroundLarge DNA sequence data sets require special bioinformatics tools to search and compare them. Such tools should be easy to use so that the data can be easily accessed by a wide array of researchers. In the past, the use of suffix trees for searching DNA sequences has been limited by a practical need to keep the trees in RAM. Newer algorithms solve this problem by using disk-based approaches. However, none of the fastest suffix tree algorithms have been implemented with a graphical user interface, preventing their incorporation into a feasible laboratory workflow.ResultsSuffix Tree Searcher (STS) is designed as an easy-to-use tool to index, search, and analyze very large DNA sequence datasets. The program accommodates very large numbers of very large sequences, with aggregate size reaching tens of billions of nucleotides. The program makes use of pre-sorted persistent "building blocks" to reduce the time required to construct new trees. STS is comprised of a graphical user interface written in Java, and four C modules. All components are automatically downloaded when a web link is clicked. The underlying suffix tree data structure permits extremely fast searching for specific nucleotide strings, with wild cards or mismatches allowed. Complete tree traversals for detecting common substrings are also very fast. The graphical user interface allows the user to transition seamlessly between building, traversing, and searching the dataset.ConclusionsThus, STS provides a new resource for the detection of substrings common to multiple DNA sequences or within a single sequence, for truly huge data sets. The re-searching of sequence hits, allowing wild card positions or mismatched nucleotides, together with the ability to rapidly retrieve large numbers of sequence hits from the DNA sequence files, provides the user with an efficient method of evaluating the similarity between nucleotide sequences by multiple alignment or use of Logos. The ability to re-use existing suffix tree pieces considerably shortens index generation time. The graphical user interface enables quick mastery of the analysis functions, easy access to the generated data, and seamless workflow integration.
| S-EPMC4118789 | biostudies-literature
Project description:As the apparent incidence of tree nut allergies rises, the development of MS methods that accurately identify tree nuts in food is critical. However, analyses are limited by few available tree nut protein sequences. We assess the utility of translated genomic and transcriptomic data for library construction with Juglans regia, walnut, as a model. Extracted walnuts were subjected to nano-liquid chromatography-mass spectrometry (n-LC-MS/MS), and spectra were searched against databases made from a six-frame translation of the genome (6FT), a transcriptome, and three proteomes. Searches against proteomic databases yielded a variable number of peptides (1156-1275), and only ten additional unique peptides were identified in the 6FT database. Searches against a transcriptomic database yielded results similar to those of the National Center for Biotechnology Information (NCBI) proteome (1200 and 1275 peptides, respectively). Performance of the transcriptomic database was improved via the adjustment of RNA-Seq read processing methods, which increased the number of identified peptides which align to seed allergen proteins by ~20%. Together, these findings establish a path towards the construction of robust proxy protein databases for tree nut species and other non-model organisms.
| S-EPMC7284556 | biostudies-literature
Project description:In untargeted metabolomics, multiple ions are often measured for each original metabolite, including isotopic forms and in-source modifications, such as adducts and fragments. Without prior knowledge of the chemical identity or formula, computational organization and interpretation of these ions is challenging, which is the deficit of previous software tools that perform the task using network algorithms. We propose here a generalized tree structure to annotate ions to relationships to the original compound and infer neutral mass. An algorithm is presented to convert mass distance networks to this tree structure with high fidelity. This method is useful for both regular untargeted metabolomics and stable isotope tracing experiments. It is implemented as a Python package (khipu), and provides a JSON format for easy data exchange and software interoperability. By generalized pre-annotation, khipu makes it feasible to connect metabolomics data with common data science tools, and supports flexible experimental designs.
| S-EPMC9881955 | biostudies-literature
Project description:The vast majority of methods available for sequence comparison rely on a first sequence alignment step, which requires a number of assumptions on evolutionary history and is sometimes very difficult or impossible to perform due to the abundance of gaps (insertions/deletions). In such cases, an alternative alignment-free method would prove valuable. Our method starts by a computation of a generalized suffix tree of all sequences, which is completed in linear time. Using this tree, the frequency of all possible words with a preset length L-L-words--in each sequence is rapidly calculated. Based on the L-words frequency profile of each sequence, a pairwise standard Euclidean distance is then computed producing a symmetric genetic distance matrix, which can be used to generate a neighbor joining dendrogram or a multidimensional scaling graph. We present an improvement to word counting alignment-free approaches for sequence comparison, by determining a single optimal word length and combining suffix tree structures to the word counting tasks. Our approach is, thus, a fast and simple application that proved to be efficient and powerful when applied to mitochondrial genomes. The algorithm was implemented in Python language and is freely available on the web.
| S-EPMC3444837 | biostudies-literature
Project description:The accelerating growth of the public microbial genomic data imposes substantial burden on the research community that uses such resources. Building databases for non-redundant reference sequences from massive microbial genomic data based on clustering analysis is essential. However, existing clustering algorithms perform poorly on long genomic sequences. In this article, we present Gclust, a parallel program for clustering complete or draft genomic sequences, where clustering is accelerated with a novel parallelization strategy and a fast sequence comparison algorithm using sparse suffix arrays (SSAs). Moreover, genome identity measures between two sequences are calculated based on their maximal exact matches (MEMs). In this paper, we demonstrate the high speed and clustering quality of Gclust by examining four genome sequence datasets. Gclust is freely available for non-commercial use at https://github.com/niu-lab/gclust. We also introduce a web server for clustering user-uploaded genomes at http://niulab.scgrid.cn/gclust.
| S-EPMC7056916 | biostudies-literature
Project description:Recently, there have been many studies in medicine related to genetic analysis. Many genetic studies have been studied to find genes associated with complex diseases. To find out how genes are related to disease, we need to understand not only the simple relationship of genotypes but also the way they are related to phenotype. Multi-block data, which is summation form of variable sets, is used for enhancing analysis of different block's relationship. By identifying relationships through multi-block data form, we can understand the association between the blocks is effective in understanding the correlation between them. Several statistical analysis methods have been developed to understand the relationship between multi-block data. In this paper, we will use generalized canonical correlation methodology to analyze multi-block data from Korean Association Resource (KARE) project which has combination of the SNP blocks, phenotype blocks, and disease block.
| S-EPMC6440675 | biostudies-literature