scholarly journals PgRC: Pseudogenome based Read Compressor

2019 ◽  
Author(s):  
Tomasz Kowalski ◽  
Szymon Grabowski
Keyword(s):  
High Throughput ◽  
Compression Ratio ◽  
High Throughput Sequencing ◽  
Sequencing Data ◽  
High Quality ◽  
Link Type ◽  
Sequencing Technologies ◽  
Significant Interest ◽  
The One ◽  
Shortest Common Superstring

AbstractMotivationThe amount of sequencing data from High-Throughput Sequencing technologies grows at a pace exceeding the one predicted by Moore’s law. One of the basic requirements is to efficiently store and transmit such huge collections of data. Despite significant interest in designing FASTQ compressors, they are still imperfect in terms of compression ratio or decompression resources.ResultsWe present Pseudogenome-based Read Compressor (PgRC), an in-memory algorithm for compressing the DNA stream, based on the idea of building an approximation of the shortest common superstring over high-quality reads. Experiments show that PgRC wins in compression ratio over its main competitors, SPRING and Minicom, by up to 18 and 21 percent on average, respectively, while being at least comparably fast in decompression.AvailabilityPgRC can be downloaded from https://github.com/kowallus/[email protected]

scholarly journals PgRC: pseudogenome-based read compressor

2019 ◽  
Vol 36 (7) ◽  
pp. 2082-2089 ◽  
Author(s):  
Tomasz M Kowalski ◽  
Szymon Grabowski
Keyword(s):  
Compression Ratio ◽  
High Throughput Sequencing ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Sequencing Data ◽  
High Quality ◽  
Sequencing Technologies ◽  
Significant Interest ◽  
The One ◽  
Shortest Common Superstring

Abstract Motivation The amount of sequencing data from high-throughput sequencing technologies grows at a pace exceeding the one predicted by Moore’s law. One of the basic requirements is to efficiently store and transmit such huge collections of data. Despite significant interest in designing FASTQ compressors, they are still imperfect in terms of compression ratio or decompression resources. Results We present Pseudogenome-based Read Compressor (PgRC), an in-memory algorithm for compressing the DNA stream, based on the idea of building an approximation of the shortest common superstring over high-quality reads. Experiments show that PgRC wins in compression ratio over its main competitors, SPRING and Minicom, by up to 15 and 20% on average, respectively, while being comparably fast in decompression. Availability and implementation PgRC can be downloaded from https://github.com/kowallus/PgRC. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Engineering the Compression of Sequencing Reads

2020 ◽  
Author(s):  
Tomasz Kowalski ◽  
Szymon Grabowski
Keyword(s):  
High Throughput ◽  
Compression Ratio ◽  
High Throughput Sequencing ◽  
Sequencing Data ◽  
Current Version ◽  
High Quality ◽  
High Throughput Sequencing Data ◽  
Compression Time ◽  
Practical Performance ◽  
Shortest Common Superstring

AbstractMotivationFASTQ remains among the widely used formats for high-throughput sequencing data. Despite advances in specialized FASTQ compressors, they are still imperfect in terms of practical performance tradeoffs.ResultsWe present a multi-threaded version of Pseudogenome-based Read Compressor (PgRC), an in-memory algorithm for compressing the DNA stream, based on the idea of building an approximation of the shortest common superstring over high-quality reads. The current version, v1.2, practically preserves the compression ratio and decompression speed of the previous one, reducing the compression time by a factor of about 4–5 on a 6-core/12-thread machine.AvailabilityPgRC 1.2 can be downloaded from https://github.com/kowallus/[email protected]

scholarly journals MethylExtract: High-Quality methylation maps and SNV calling from whole genome bisulfite sequencing data

F1000Research ◽  
2014 ◽  
Vol 2 ◽  
pp. 217 ◽  
Author(s):  
Guillermo Barturen ◽  
Antonio Rueda ◽  
José L. Oliver ◽  
Michael Hackenberg
Keyword(s):  
High Throughput ◽  
Sequence Variation ◽  
High Throughput Sequencing ◽  
Whole Genome ◽  
Single Nucleotide Variants ◽  
High Quality ◽  
Single Nucleotide ◽  
Error Sources ◽  
Link Type ◽  
Genome Methylation

Whole genome methylation profiling at a single cytosine resolution is now feasible due to the advent of high-throughput sequencing techniques together with bisulfite treatment of the DNA. To obtain the methylation value of each individual cytosine, the bisulfite-treated sequence reads are first aligned to a reference genome, and then the profiling of the methylation levels is done from the alignments. A huge effort has been made to quickly and correctly align the reads and many different algorithms and programs to do this have been created. However, the second step is just as crucial and non-trivial, but much less attention has been paid to the final inference of the methylation states. Important error sources do exist, such as sequencing errors, bisulfite failure, clonal reads, and single nucleotide variants.We developed MethylExtract, a user friendly tool to: i) generate high quality, whole genome methylation maps and ii) detect sequence variation within the same sample preparation. The program is implemented into a single script and takes into account all major error sources. MethylExtract detects variation (SNVs – Single Nucleotide Variants) in a similar way to VarScan, a very sensitive method extensively used in SNV and genotype calling based on non-bisulfite-treated reads. The usefulness of MethylExtract is shown by means of extensive benchmarking based on artificial bisulfite-treated reads and a comparison to a recently published method, called Bis-SNP.MethylExtract is able to detect SNVs within High-Throughput Sequencing experiments of bisulfite treated DNA at the same time as it generates high quality methylation maps. This simultaneous detection of DNA methylation and sequence variation is crucial for many downstream analyses, for example when deciphering the impact of SNVs on differential methylation. An exclusive feature of MethylExtract, in comparison with existing software, is the possibility to assess the bisulfite failure in a statistical way. The source code, tutorial and artificial bisulfite datasets are available at http://bioinfo2.ugr.es/MethylExtract/ and http://sourceforge.net/projects/methylextract/, and also permanently accessible from 10.5281/zenodo.7144.

scholarly journals NASQAR: A web-based platform for high-throughput sequencing data analysis and visualization

2019 ◽  
Author(s):  
Ayman Yousif ◽  
Nizar Drou ◽  
Jillian Rowe ◽  
Mohammed Khalfan ◽  
Kristin C Gunsalus
Keyword(s):  
New York ◽  
Data Analysis ◽  
Open Source ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Web Applications ◽  
Rna Seq ◽  
Sequencing Data ◽  
Web Based ◽  
Link Type

AbstractBackgroundAs high-throughput sequencing applications continue to evolve, the rapid growth in quantity and variety of sequence-based data calls for the development of new software libraries and tools for data analysis and visualization. Often, effective use of these tools requires computational skills beyond those of many researchers. To ease this computational barrier, we have created a dynamic web-based platform, NASQAR (Nucleic Acid SeQuence Analysis Resource).ResultsNASQAR offers a collection of custom and publicly available open-source web applications that make extensive use of a variety of R packages to provide interactive data analysis and visualization. The platform is publicly accessible at http://nasqar.abudhabi.nyu.edu/. Open-source code is on GitHub at https://github.com/nasqar/NASQAR, and the system is also available as a Docker image at https://hub.docker.com/r/aymanm/nasqarall. NASQAR is a collaboration between the core bioinformatics teams of the NYU Abu Dhabi and NYU New York Centers for Genomics and Systems Biology.ConclusionsNASQAR empowers non-programming experts with a versatile and intuitive toolbox to easily and efficiently explore, analyze, and visualize their Transcriptomics data interactively. Popular tools for a variety of applications are currently available, including Transcriptome Data Preprocessing, RNA-seq Analysis (including Single-cell RNA-seq), Metagenomics, and Gene Enrichment.

scholarly journals fluff: exploratory analysis and visualization of high-throughput sequencing data

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2209 ◽  
Author(s):  
Georgios Georgiou ◽  
Simon J. van Heeringen
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Developmental Stages ◽  
Command Line ◽  
Clustering Methods ◽  
Sequencing Data ◽  
Link Type ◽  
High Throughput Sequencing Data ◽  
Genome Wide ◽  
Genome Wide Data

Summary.In this article we describe fluff, a software package that allows for simple exploration, clustering and visualization of high-throughput sequencing data mapped to a reference genome. The package contains three command-line tools to generate publication-quality figures in an uncomplicated manner using sensible defaults. Genome-wide data can be aggregated, clustered and visualized in a heatmap, according to different clustering methods. This includes a predefined setting to identify dynamic clusters between different conditions or developmental stages. Alternatively, clustered data can be visualized in a bandplot. Finally, fluff includes a tool to generate genomic profiles. As command-line tools, the fluff programs can easily be integrated into standard analysis pipelines. The installation is straightforward and documentation is available athttp://fluff.readthedocs.org.Availability.fluff is implemented in Python and runs on Linux. The source code is freely available for download athttps://github.com/simonvh/fluff.

scholarly journals PhageTerm: a Fast and User-friendly Software to Determine Bacteriophage Termini and Packaging Mode using randomly fragmented NGS data

2017 ◽  
Author(s):  
Julian Garneau ◽  
Florence Depardieu ◽  
Louis-Charles Fortier ◽  
David Bikard ◽  
Marc Monot
Keyword(s):  
High Throughput Sequencing ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Link Type ◽  
Sequencing Technologies ◽  
Statistical Framework ◽  
Fastq Format ◽  
Viral Particles ◽  
User Friendly ◽  
Ngs Data

ABSTRACTBacteriophages are the most abundant viruses on earth and display an impressive genetic as well as morphologic diversity. Among those, the most common order of phages is the Caudovirales, whose viral particles packages linear double stranded DNA (dsDNA). In this study we investigated how the information gathered by high throughput sequencing technologies can be used to determine the DNA termini and packaging mechanisms of dsDNA phages. The wet-lab procedures traditionally used for this purpose rely on the identification and cloning of restriction fragment which can be delicate and cumbersome. Here, we developed a theoretical and statistical framework to analyze DNA termini and phage packaging mechanisms using next-generation sequencing data. Our methods, implemented in the PhageTerm software, work with sequencing reads in fastq format and the corresponding assembled phage genome.PhageTerm was validated on a set of phages with well-established packaging mechanisms representative of the termini diversity: 5’cos (lambda), 3’cos (HK97), pac (P1), headful without a pac site (T4), DTR (T7) and host fragment (Mu). In addition, we determined the termini of 9Clostridium difficilephages and 6 phages whose sequences where retrieved from the sequence read archive (SRA).A direct graphical interface is available as a Galaxy wrapper version athttps://galaxy.pasteur.frand a standalone version is accessible athttps://sourceforge.net/projects/phageterm/.

scholarly journals ChiTaH: a fast and accurate tool for identifying known human chimeric sequences from high-throughput sequencing data

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Rajesh Detroja ◽  
Alessandro Gorohovski ◽  
Olawumi Giwa ◽  
Gideon Baum ◽  
Milana Frenkel-Morgenstern
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Complex Disease ◽  
Single Cells ◽  
Reference Database ◽  
Sequencing Data ◽  
Sequencing Technologies ◽  
High Throughput Sequencing Data ◽  
Chimeric Rnas ◽  
Sensitivity Specificity

Abstract Fusion genes or chimeras typically comprise sequences from two different genes. The chimeric RNAs of such joined sequences often serve as cancer drivers. Identifying such driver fusions in a given cancer or complex disease is important for diagnosis and treatment. The advent of next-generation sequencing technologies, such as DNA-Seq or RNA-Seq, together with the development of suitable computational tools, has made the global identification of chimeras in tumors possible. However, the testing of over 20 computational methods showed these to be limited in terms of chimera prediction sensitivity, specificity, and accurate quantification of junction reads. These shortcomings motivated us to develop the first ‘reference-based’ approach termed ChiTaH (Chimeric Transcripts from High–throughput sequencing data). ChiTaH uses 43,466 non–redundant known human chimeras as a reference database to map sequencing reads and to accurately identify chimeric reads. We benchmarked ChiTaH and four other methods to identify human chimeras, leveraging both simulated and real sequencing datasets. ChiTaH was found to be the most accurate and fastest method for identifying known human chimeras from simulated and sequencing datasets. Moreover, especially ChiTaH uncovered heterogeneity of the BCR-ABL1 chimera in both bulk and single-cells of the K-562 cell line, which was confirmed experimentally.

scholarly journals Discovery of tandem and interspersed segmental duplications using high throughput sequencing

2018 ◽  
Author(s):  
Arda Soylev ◽  
Thong Le ◽  
Hajar Amini ◽  
Can Alkan ◽  
Fereydoun Hormozdiari
Keyword(s):  
False Discovery Rate ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Real Data ◽  
Whole Genome Sequencing Data ◽  
Data Sets ◽  
Segmental Duplications ◽  
Sequencing Data ◽  
Link Type ◽  
False Discovery

AbstractMotivationSeveral algorithms have been developed that use high throughput sequencing technology to characterize structural variations. Most of the existing approaches focus on detecting relatively simple types of SVs such as insertions, deletions, and short inversions. In fact, complex SVs are of crucial importance and several have been associated with genomic disorders. To better understand the contribution of complex SVs to human disease, we need new algorithms to accurately discover and genotype such variants. Additionally, due to similar sequencing signatures, inverted duplications or gene conversion events that include inverted segmental duplications are often characterized as simple inversions; and duplications and gene conversions in direct orientation may be called as simple deletions. Therefore, there is still a need for accurate algorithms to fully characterize complex SVs and thus improve calling accuracy of more simple variants.ResultsWe developed novel algorithms to accurately characterize tandem, direct and inverted interspersed segmental duplications using short read whole genome sequencing data sets. We integrated these methods to our TARDIS tool, which is now capable of detecting various types of SVs using multiple sequence signatures such as read pair, read depth and split read. We evaluated the prediction performance of our algorithms through several experiments using both simulated and real data sets. In the simulation experiments, using a 30× coverage TARDIS achieved 96% sensitivity with only 4% false discovery rate. For experiments that involve real data, we used two haploid genomes (CHM1 and CHM13) and one human genome (NA12878) from the Illumina Platinum Genomes set. Comparison of our results with orthogonal PacBio call sets from the same genomes revealed higher accuracy for TARDIS than state of the art methods. Furthermore, we showed a surprisingly low false discovery rate of our approach for discovery of tandem, direct and inverted interspersed segmental duplications prediction on CHM1 (less than 5% for the top 50 predictions).AvailabilityTARDIS source code is available at https://github.com/BilkentCompGen/tardis, and a corresponding Docker image is available at https://hub.docker.com/r/alkanlab/tardis/[email protected] and [email protected]

scholarly journals Perspectives and benefits of high-throughput long-read sequencing in microbial ecology

2021 ◽  
Author(s):  
Leho Tedersoo ◽  
Mads Albertsen ◽  
Sten Anslan ◽  
Benjamin Callahan
Keyword(s):  
Microbial Ecology ◽  
High Throughput ◽  
Single Molecule ◽  
High Throughput Sequencing ◽  
Environmental Dna ◽  
Nanopore Sequencing ◽  
High Quality ◽  
Short Read ◽  
Sequencing Technologies ◽  
Long Read

Short-read, high-throughput sequencing (HTS) methods have yielded numerous important insights into microbial ecology and function. Yet, in many instances short-read HTS techniques are suboptimal, for example by providing insufficient phylogenetic resolution or low integrity of assembled genomes. Single-molecule and synthetic long-read (SLR) HTS methods have successfully ameliorated these limitations. In addition, nanopore sequencing has generated a number of unique analysis opportunities such as rapid molecular diagnostics and direct RNA sequencing, and both PacBio and nanopore sequencing support detection of epigenetic modifications. Although initially suffering from relatively low sequence quality, recent advances have greatly improved the accuracy of long read sequencing technologies. In spite of great technological progress in recent years, the long-read HTS methods (PacBio and nanopore sequencing) are still relatively costly, require large amounts of high-quality starting material, and commonly need specific solutions in various analysis steps. Despite these challenges, long-read sequencing technologies offer high-quality, cutting-edge alternatives for testing hypotheses about microbiome structure and functioning as well as assembly of eukaryote genomes from complex environmental DNA samples.

scholarly journals A Protocol for mtGenome Analysis on Large Sample Numbers

2014 ◽  
Vol 8 ◽  
pp. BBI.S14623 ◽  
Author(s):  
Igor G. Hamoy ◽  
André M. Ribeiro-Dos-Santos ◽  
Luiz Alvarez ◽  
Silvanira Barbosa ◽  
Artur Silva ◽  
...  
Keyword(s):  
Mitochondrial Genome ◽  
High Throughput ◽  
Genome Analysis ◽  
High Throughput Sequencing ◽  
Complete Mitochondrial Genome ◽  
High Quality ◽  
Sequencing Technologies ◽  
Ion Pgm ◽  
Total Variability ◽  
Run Up

The mitochondrial genome is widely studied in a variety of fields, such as population, forensic, and human and medical genetics. Most studies have been limited to a small portion of the sequence that, although highly diverse, does not describe the total variability. The arrival of modern high-throughput sequencing technologies has made it possible to investigate larger sequences in a shorter amount of time as well as in a more affordable fashion. This work aims to describe a protocol for sequencing and analyzing the complete mitochondrial genome with the Ion PGM™ platform. To evaluate the protocol, the mitochondrial genome was sequenced to approximately 210 Mbp, with high-quality sequences distributed between 12 samples that had an average coverage of 1023× per sample. Several variant callers were compared to improve the protocol outcome. The results suggest that it is possible to run up to 120 samples per run without any loss of any significant quality. Therefore, this protocol is an efficient and accurate tool for full mitochondrial genome analysis.

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