scholarly journals Alignment by numbers: sequence assembly using compressed numerical representations

2014 ◽  
Author(s):  
Avraam Tapinos ◽  
Bede Constantinides ◽  
Douglas B Kell ◽  
David L Robertson
Keyword(s):  
Dimensionality Reduction ◽  
Sequence Alignment ◽  
De Novo ◽  
Sequence Data ◽  
Sequence Assembly ◽  
Viral Population ◽  
Sequential Data ◽  
Data Intensive ◽  
Reduction Methods ◽  
Feature Selection Approach

Motivation: DNA sequencing instruments are enabling genomic analyses of unprecedented scope and scale, widening the gap between our abilities to generate and interpret sequence data. Established methods for computational sequence analysis generally use nucleotide-level resolution of sequences, and while such approaches can be very accurate, increasingly ambitious and data-intensive analyses are rendering them impractical for applications such as genome and metagenome assembly. Comparable analytical challenges are encountered in other data-intensive fields involving sequential data, such as signal processing, in which dimensionality reduction methods are routinely used to reduce the computational burden of analyses. We therefore seek to address the question of whether it is possible to improve the efficiency of sequence alignment by applying dimensionality reduction methods to numerically represented nucleotide sequences. Results: To explore the applicability of signal transformation and dimensionality reduction methods to sequence assembly, we implemented a short read aligner and evaluated its performance against simulated high diversity viral sequences alongside four existing aligners. Using our sequence transformation and feature selection approach, alignment time was reduced by up to 14-fold compared to uncompressed sequences and without reducing alignment accuracy. Despite using highly compressed sequence transformations, our implementation yielded alignments of similar overall accuracy to existing aligners, outperforming all other tools tested at high levels of sequence variation. Our approach was also applied to the de novo assembly of a simulated diverse viral population. Our results demonstrate that full sequence resolution is not a prerequisite of accurate sequence alignment and that analytical performance can be retained and even enhanced through appropriate dimensionality reduction of sequences.

scholarly journals The Utility of Data Transformation for Alignment, De Novo Assembly and Classification of Short Read Virus Sequences

Viruses ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 394
Author(s):  
Avraam Tapinos ◽  
Bede Constantinides ◽  
My V. T. Phan ◽  
Samaneh Kouchaki ◽  
Matthew Cotten ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
De Novo Assembly ◽  
De Novo ◽  
Sequence Data ◽  
Sequential Data ◽  
Biological Sequence ◽  
Viral Pathogen ◽  
Data Intensive ◽  
Reduction Methods ◽  
Virus Sequence

Advances in DNA sequencing technology are facilitating genomic analyses of unprecedented scope and scale, widening the gap between our abilities to generate and fully exploit biological sequence data. Comparable analytical challenges are encountered in other data-intensive fields involving sequential data, such as signal processing, in which dimensionality reduction (i.e., compression) methods are routinely used to lessen the computational burden of analyses. In this work, we explored the application of dimensionality reduction methods to numerically represent high-throughput sequence data for three important biological applications of virus sequence data: reference-based mapping, short sequence classification and de novo assembly. Leveraging highly compressed sequence transformations to accelerate sequence comparison, our approach yielded comparable accuracy to existing approaches, further demonstrating its suitability for sequences originating from diverse virus populations. We assessed the application of our methodology using both synthetic and real viral pathogen sequences. Our results show that the use of highly compressed sequence approximations can provide accurate results, with analytical performance retained and even enhanced through appropriate dimensionality reduction of sequence data.

scholarly journals The Utility of Data Transformation for Alignment, de novo Assembly and Classification of Short Read Virus Sequences

2019 ◽  
Author(s):  
Avraam Tapinos ◽  
Bede Constantinides ◽  
My VT Phan ◽  
Samaneh Kouchaki ◽  
Matthew Cotten ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
De Novo ◽  
Sequence Data ◽  
Sequential Data ◽  
Biological Sequence ◽  
Viral Pathogen ◽  
Data Intensive ◽  
Reduction Methods ◽  
Virus Sequence ◽  
Comparable Accuracy

Advances in DNA sequencing technology are facilitating genomic analyses of unprecedented scope and scale, widening the gap between our abilities to generate and fully exploit biological sequence data. Comparable analytical challenges are encountered in other data-intensive fields involving sequential data, such as signal processing, in which dimensionality reduction (i.e., compression) methods are routinely used to lessen the computational burden of analyses. In this work we explore the application of dimensionality reduction methods to numerically represent high-throughput sequence data for three important biological applications of virus sequence data: reference-based mapping, short sequence classification and de novo assembly. Despite using highly compressed sequence transformations to accelerate the processes, our sequence processing approach yielded comparable accuracy to existing approaches, and are ideally suited for sequences originating from highly diverse virus populations. We demonstrate the application of our methodology to both synthetic and real viral pathogen sequence data. Our results show that the use of highly compressed sequence approximations can provide accurate results and that useful analytical performance can be retained and even enhanced through appropriate dimensionality reduction of sequence data.

scholarly journals VGEA: an RNA viral assembly toolkit

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12129
Author(s):  
Paul E. Oluniyi ◽  
Fehintola Ajogbasile ◽  
Judith Oguzie ◽  
Jessica Uwanibe ◽  
Adeyemi Kayode ◽  
...  
Keyword(s):  
De Novo ◽  
Sequence Data ◽  
Workflow Management ◽  
Viral Population ◽  
Lassa Virus ◽  
Viral Genomes ◽  
Bioinformatics Tools ◽  
Reference Sequences ◽  
Genome Assemblies

Next generation sequencing (NGS)-based studies have vastly increased our understanding of viral diversity. Viral sequence data obtained from NGS experiments are a rich source of information, these data can be used to study their epidemiology, evolution, transmission patterns, and can also inform drug and vaccine design. Viral genomes, however, represent a great challenge to bioinformatics due to their high mutation rate and forming quasispecies in the same infected host, bringing about the need to implement advanced bioinformatics tools to assemble consensus genomes well-representative of the viral population circulating in individual patients. Many tools have been developed to preprocess sequencing reads, carry-out de novo or reference-assisted assembly of viral genomes and assess the quality of the genomes obtained. Most of these tools however exist as standalone workflows and usually require huge computational resources. Here we present (Viral Genomes Easily Analyzed), a Snakemake workflow for analyzing RNA viral genomes. VGEA enables users to map sequencing reads to the human genome to remove human contaminants, split bam files into forward and reverse reads, carry out de novo assembly of forward and reverse reads to generate contigs, pre-process reads for quality and contamination, map reads to a reference tailored to the sample using corrected contigs supplemented by the user’s choice of reference sequences and evaluate/compare genome assemblies. We designed a project with the aim of creating a flexible, easy-to-use and all-in-one pipeline from existing/stand-alone bioinformatics tools for viral genome analysis that can be deployed on a personal computer. VGEA was built on the Snakemake workflow management system and utilizes existing tools for each step: fastp (Chen et al., 2018) for read trimming and read-level quality control, BWA (Li & Durbin, 2009) for mapping sequencing reads to the human reference genome, SAMtools (Li et al., 2009) for extracting unmapped reads and also for splitting bam files into fastq files, IVA (Hunt et al., 2015) for de novo assembly to generate contigs, shiver (Wymant et al., 2018) to pre-process reads for quality and contamination, then map to a reference tailored to the sample using corrected contigs supplemented with the user’s choice of existing reference sequences, SeqKit (Shen et al., 2016) for cleaning shiver assembly for QUAST, QUAST (Gurevich et al., 2013) to evaluate/assess the quality of genome assemblies and MultiQC (Ewels et al., 2016) for aggregation of the results from fastp, BWA and QUAST. Our pipeline was successfully tested and validated with SARS-CoV-2 (n = 20), HIV-1 (n = 20) and Lassa Virus (n = 20) datasets all of which have been made publicly available. VGEA is freely available on GitHub at: https://github.com/pauloluniyi/VGEA under the GNU General Public License.

scholarly journals Validation of Variant Assembly Using HAPHPIPE with Next-Generation Sequence Data from Viruses

Viruses ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 758 ◽  
Author(s):  
Keylie M. Gibson ◽  
Margaret C. Steiner ◽  
Uzma Rentia ◽  
Matthew L. Bendall ◽  
Marcos Pérez-Losada ◽  
...  
Keyword(s):  
De Novo ◽  
Sequence Data ◽  
Consensus Sequence ◽  
Sequence Assembly ◽  
Next Generation ◽  
Consensus Sequences ◽  
Bioinformatic Tools ◽  
Hiv Gp120 ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data

Next-generation sequencing (NGS) offers a powerful opportunity to identify low-abundance, intra-host viral sequence variants, yet the focus of many bioinformatic tools on consensus sequence construction has precluded a thorough analysis of intra-host diversity. To take full advantage of the resolution of NGS data, we developed HAplotype PHylodynamics PIPEline (HAPHPIPE), an open-source tool for the de novo and reference-based assembly of viral NGS data, with both consensus sequence assembly and a focus on the quantification of intra-host variation through haplotype reconstruction. We validate and compare the consensus sequence assembly methods of HAPHPIPE to those of two alternative software packages, HyDRA and Geneious, using simulated HIV and empirical HIV, HCV, and SARS-CoV-2 datasets. Our validation methods included read mapping, genetic distance, and genetic diversity metrics. In simulated NGS data, HAPHPIPE generated pol consensus sequences significantly closer to the true consensus sequence than those produced by HyDRA and Geneious and performed comparably to Geneious for HIV gp120 sequences. Furthermore, using empirical data from multiple viruses, we demonstrate that HAPHPIPE can analyze larger sequence datasets due to its greater computational speed. Therefore, we contend that HAPHPIPE provides a more user-friendly platform for users with and without bioinformatics experience to implement current best practices for viral NGS assembly than other currently available options.

scholarly journals Single Nucleotide Polymorphisms Caused by Assembly Errors

2010 ◽  
Vol 3 ◽  
pp. GEI.S3653
Author(s):  
Jürgen Kleffe ◽  
Robert Weißmann ◽  
Florian F. Schmitzberger
Keyword(s):  
Single Nucleotide Polymorphisms ◽  
De Novo ◽  
Sequence Data ◽  
Bacterial Genome ◽  
Sequence Assembly ◽  
Nucleotide Polymorphisms ◽  
Base Pairs ◽  
Single Nucleotide ◽  
Current Sequence ◽  
Assembly Algorithms

We compare the results of three different assembler programs, Celera, Phrap and Mira2, for the same set of about a hundred thousand Sanger reads derived from an unknown bacterial genome. In difference to previous assembly comparisons we do not focus on speed of computation and numbers of assembled contigs but on how the different sequence assemblies agree by content. Threefold consistently assembled genome regions are identified in order to estimate a lower bound of erroneously identified single nucleotide polymorphisms (SNP) caused by nothing but the process of mathematical sequence assembly. We identified 509 sequence triplets common to all three de-novo assemblies spanning only 34% (3.3 Mb) of the bacterial genome with 175 of these regions (~1.5 Mb) including erroneous SNPs and insertion/deletions. Within these triplets this on average leads to one error per 7,155 base pairs. Replacing the assembler Mira2 by the most recent version Mira3, the letter number even drops to 5,923. Our results therefore suggest that a considerably high number of erroneous SNPs may be present in current sequence data and mathematicians should urgently take up research on numerical stability of sequence assembly algorithms. Furthermore, even the latest versions of currently used assemblers produce erroneous SNPs that depend on the order reads are used as input. Such errors will severely hamper molecular diagnostics as well as relating genome variation and disease. This issue needs to be addressed urgently as the field is moving fast into clinical applications.

scholarly journals Systematic Discovery of Conservation States for Single-Nucleotide Annotation of the Human Genome

2018 ◽  
Author(s):  
Adriana Sperlea ◽  
Jason Ernst
Keyword(s):  
Human Genome ◽  
Sequence Alignment ◽  
De Novo ◽  
Sequence Data ◽  
Biological Significance ◽  
Evolutionary Constraint ◽  
Single Nucleotide ◽  
Dna Sequence Alignment ◽  
Genome Annotations ◽  
Nucleotide Resolution

AbstractComparative genomics sequence data is an important source of information for interpreting genomes. Genome-wide annotations based on this data have largely focused on univariate scores or binary calls of evolutionary constraint. Here we present a complementary whole genome annotation approach, ConsHMM, which applies a multivariate hidden Markov model to learn de novo different ‘conservation states’ based on the combinatorial and spatial patterns of which species align to and match a reference genome in a multiple species DNA sequence alignment. We applied ConsHMM to a 100-way vertebrate sequence alignment to annotate the human genome at single nucleotide resolution into 100 different conservation states. These states have distinct enrichments for other genomic information including gene annotations, chromatin states, and repeat families, which were used to characterize their biological significance. Conservation states have greater or complementary predictive information than standard constraint based measures for a variety of genome annotations. Bases in constrained elements have distinct heritability enrichments depending on the conservation state assignment, demonstrating their relevance to analyzing phenotypic associated variation. The conservation states also highlight differences in the conservation patterns of bases prioritized by a number of scores used for variant prioritization. The ConsHMM method and conservation state annotations provide a valuable resource for interpreting genomes and genetic variation.

scholarly journals De novo genome sequence assembly of a filamentous fungus using Sanger, 454 and Illumina sequence data

2009 ◽  
Vol 10 (9) ◽  
pp. R94 ◽  
Author(s):  
Scott DiGuistini ◽  
Nancy Y Liao ◽  
Darren Platt ◽  
Gordon Robertson ◽  
Michael Seidel ◽  
...  
Keyword(s):  
Genome Sequence ◽  
Filamentous Fungus ◽  
De Novo ◽  
Sequence Data ◽  
Sequence Assembly ◽  
Genome Sequence Assembly ◽  
Illumina Sequence

Speech Emotion Recognition Based on Sparse Representation

2013 ◽  
Vol 38 (4) ◽  
pp. 465-470 ◽  
Author(s):  
Jingjie Yan ◽  
Xiaolan Wang ◽  
Weiyi Gu ◽  
LiLi Ma
Keyword(s):  
Dimensionality Reduction ◽  
Emotion Recognition ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Speech Emotion Recognition ◽  
Least Squares Regression ◽  
Computer Science Pedagogy ◽  
Reduction Methods ◽  
Analysis Computer

Abstract Speech emotion recognition is deemed to be a meaningful and intractable issue among a number of do- mains comprising sentiment analysis, computer science, pedagogy, and so on. In this study, we investigate speech emotion recognition based on sparse partial least squares regression (SPLSR) approach in depth. We make use of the sparse partial least squares regression method to implement the feature selection and dimensionality reduction on the whole acquired speech emotion features. By the means of exploiting the SPLSR method, the component parts of those redundant and meaningless speech emotion features are lessened to zero while those serviceable and informative speech emotion features are maintained and selected to the following classification step. A number of tests on Berlin database reveal that the recogni- tion rate of the SPLSR method can reach up to 79.23% and is superior to other compared dimensionality reduction methods.

scholarly journals An Integrated Framework for the Inference of Viral Population History From Reconstructed Genealogies

Genetics ◽  
2000 ◽  
Vol 155 (3) ◽  
pp. 1429-1437
Author(s):  
Oliver G Pybus ◽  
Andrew Rambaut ◽  
Paul H Harvey
Keyword(s):  
Maximum Likelihood ◽  
Sequence Data ◽  
Demographic History ◽  
Population History ◽  
Maximum Likelihood Estimates ◽  
Viral Population ◽  
True Parameter ◽  
Subtype B ◽  
Exponential Growth Model ◽  
Parameter Values

Abstract We describe a unified set of methods for the inference of demographic history using genealogies reconstructed from gene sequence data. We introduce the skyline plot, a graphical, nonparametric estimate of demographic history. We discuss both maximum-likelihood parameter estimation and demographic hypothesis testing. Simulations are carried out to investigate the statistical properties of maximum-likelihood estimates of demographic parameters. The simulations reveal that (i) the performance of exponential growth model estimates is determined by a simple function of the true parameter values and (ii) under some conditions, estimates from reconstructed trees perform as well as estimates from perfect trees. We apply our methods to HIV-1 sequence data and find strong evidence that subtypes A and B have different demographic histories. We also provide the first (albeit tentative) genetic evidence for a recent decrease in the growth rate of subtype B.

scholarly journals A long reads-based de-novo assembly of the genome of the Arlee homozygous line reveals chromosomal rearrangements in rainbow trout

2021 ◽  
Author(s):  
Guangtu Gao ◽  
Susana Magadan ◽  
Geoffrey C Waldbieser ◽  
Ramey C Youngblood ◽  
Paul A Wheeler ◽  
...  
Keyword(s):  
Rainbow Trout ◽  
Chromosome Number ◽  
Genome Assembly ◽  
De Novo Assembly ◽  
De Novo ◽  
Sequence Data ◽  
Structural Variations ◽  
High Coverage ◽  
Haploid Chromosome Number ◽  
Long Reads

Abstract Currently, there is still a need to improve the contiguity of the rainbow trout reference genome and to use multiple genetic backgrounds that will represent the genetic diversity of this species. The Arlee doubled haploid line was originated from a domesticated hatchery strain that was originally collected from the northern California coast. The Canu pipeline was used to generate the Arlee line genome de-novo assembly from high coverage PacBio long-reads sequence data. The assembly was further improved with Bionano optical maps and Hi-C proximity ligation sequence data to generate 32 major scaffolds corresponding to the karyotype of the Arlee line (2 N = 64). It is composed of 938 scaffolds with N50 of 39.16 Mb and a total length of 2.33 Gb, of which ∼95% was in 32 chromosome sequences with only 438 gaps between contigs and scaffolds. In rainbow trout the haploid chromosome number can vary from 29 to 32. In the Arlee karyotype the haploid chromosome number is 32 because chromosomes Omy04, 14 and 25 are divided into six acrocentric chromosomes. Additional structural variations that were identified in the Arlee genome included the major inversions on chromosomes Omy05 and Omy20 and additional 15 smaller inversions that will require further validation. This is also the first rainbow trout genome assembly that includes a scaffold with the sex-determination gene (sdY) in the chromosome Y sequence. The utility of this genome assembly is demonstrated through the improved annotation of the duplicated genome loci that harbor the IGH genes on chromosomes Omy12 and Omy13.

Sign in / Sign up

Export Citation Format

Share Document