scholarly journals Combined assembly of long and short sequencing reads improve the efficiency of exploring the soil metagenome

BMC Genomics ◽  
2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Guoshun Xu ◽  
Liwen Zhang ◽  
Xiaoqing Liu ◽  
Feifei Guan ◽  
Yuquan Xu ◽  
...  
Keyword(s):  
Dna Sequencing ◽  
Species Abundance ◽  
Soil Microbial Communities ◽  
Science Research ◽  
Sequencing Data ◽  
Soil Dna ◽  
Maximum Information ◽  
Short Read ◽  
Sequencing Technologies ◽  
Plant Disease Management

Abstract Background Advances in DNA sequencing technologies have transformed our capacity to perform life science research, decipher the dynamics of complex soil microbial communities and exploit them for plant disease management. However, soil is a complex conglomerate, which makes functional metagenomics studies very challenging. Results Metagenomes were assembled by long-read (PacBio, PB), short-read (Illumina, IL), and mixture of PB and IL (PI) sequencing of soil DNA samples were compared. Ortholog analyses and functional annotation revealed that the PI approach significantly increased the contig length of the metagenomic sequences compared to IL and enlarged the gene pool compared to PB. The PI approach also offered comparable or higher species abundance than either PB or IL alone, and showed significant advantages for studying natural product biosynthetic genes in the soil microbiomes. Conclusion Our results provide an effective strategy for combining long and short-read DNA sequencing data to explore and distill the maximum information out of soil metagenomics.

scholarly journals Comprehensive identification of transposable element insertions using multiple sequencing technologies

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Chong Chu ◽  
Rebeca Borges-Monroy ◽  
Vinayak V. Viswanadham ◽  
Soohyun Lee ◽  
Heng Li ◽  
...  
Keyword(s):  
Transposable Element ◽  
Structure And Function ◽  
Endogenous Retroviruses ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Short Read ◽  
Sequencing Technologies ◽  
Long Read ◽  
And Function

AbstractTransposable elements (TEs) help shape the structure and function of the human genome. When inserted into some locations, TEs may disrupt gene regulation and cause diseases. Here, we present xTea (x-Transposable element analyzer), a tool for identifying TE insertions in whole-genome sequencing data. Whereas existing methods are mostly designed for short-read data, xTea can be applied to both short-read and long-read data. Our analysis shows that xTea outperforms other short read-based methods for both germline and somatic TE insertion discovery. With long-read data, we created a catalogue of polymorphic insertions with full assembly and annotation of insertional sequences for various types of retroelements, including pseudogenes and endogenous retroviruses. Notably, we find that individual genomes have an average of nine groups of full-length L1s in centromeres, suggesting that centromeres and other highly repetitive regions such as telomeres are a significant yet unexplored source of active L1s. xTea is available at https://github.com/parklab/xTea.

scholarly journals Splicing Express: a software suite for alternative splicing analysis using next-generation sequencing data

PeerJ ◽  
2015 ◽  
Vol 3 ◽  
pp. e1419 ◽  
Author(s):  
Jose E. Kroll ◽  
Jihoon Kim ◽  
Lucila Ohno-Machado ◽  
Sandro J. de Souza
Keyword(s):  
Alternative Splicing ◽  
Dna Sequencing ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Software Suite ◽  
Next Generation Dna Sequencing ◽  
Sequencing Technologies ◽  
Body Map ◽  
Sequencing Platforms

Motivation.Alternative splicing events (ASEs) are prevalent in the transcriptome of eukaryotic species and are known to influence many biological phenomena. The identification and quantification of these events are crucial for a better understanding of biological processes. Next-generation DNA sequencing technologies have allowed deep characterization of transcriptomes and made it possible to address these issues. ASEs analysis, however, represents a challenging task especially when many different samples need to be compared. Some popular tools for the analysis of ASEs are known to report thousands of events without annotations and/or graphical representations. A new tool for the identification and visualization of ASEs is here described, which can be used by biologists without a solid bioinformatics background.Results.A software suite namedSplicing Expresswas created to perform ASEs analysis from transcriptome sequencing data derived from next-generation DNA sequencing platforms. Its major goal is to serve the needs of biomedical researchers who do not have bioinformatics skills.Splicing Expressperforms automatic annotation of transcriptome data (GTF files) using gene coordinates available from the UCSC genome browser and allows the analysis of data from all available species. The identification of ASEs is done by a known algorithm previously implemented in another tool namedSplooce. As a final result,Splicing Expresscreates a set of HTML files composed of graphics and tables designed to describe the expression profile of ASEs among all analyzed samples. By using RNA-Seq data from the Illumina Human Body Map and the Rat Body Map, we show thatSplicing Expressis able to perform all tasks in a straightforward way, identifying well-known specific events.Availability and Implementation.Splicing Expressis written in Perl and is suitable to run only in UNIX-like systems. More details can be found at:http://www.bioinformatics-brazil.org/splicingexpress.

Improved Compression of DNA Sequencing Data with Cascading Bloom Filters

2018 ◽  
Vol 29 (08) ◽  
pp. 1249-1255
Author(s):  
Kamil Salikhov
Keyword(s):  
Dna Sequencing ◽  
Sequence Data ◽  
Real Data ◽  
Compression Algorithm ◽  
Computational Experiments ◽  
Bloom Filters ◽  
Dna Fragments ◽  
Sequencing Data ◽  
Sequencing Technologies ◽  
Memory Reduction

Modern DNA sequencing technologies generate prodigious volumes of sequence data consisting of short DNA fragments (reads). Storing and transferring this data is often challenging. With this motivation, several specialized compression methods have been developed. In this paper, we present an improvement of the lossless reference-free compression algorithm, suggested by Rozov et al., based on the technique of cascading Bloom filters. Through computational experiments on real data, we demonstrate that our method results in a significant associated memory reduction in practice.

scholarly journals A Transposon Story: From TE Content to TE Dynamic Invasion of Drosophila Genomes Using the Single-Molecule Sequencing Technology from Oxford Nanopore

Cells ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1776
Author(s):  
Mourdas Mohamed ◽  
Nguyet Thi-Minh Dang ◽  
Yuki Ogyama ◽  
Nelly Burlet ◽  
Bruno Mugat ◽  
...  
Keyword(s):  
Single Molecule ◽  
Wild Type ◽  
Sequencing Data ◽  
Short Read ◽  
Short Read Sequencing ◽  
Sequencing Technologies ◽  
Oxford Nanopore ◽  
In The Wild ◽  
Successive Generations ◽  
Type Strains

Transposable elements (TEs) are the main components of genomes. However, due to their repetitive nature, they are very difficult to study using data obtained with short-read sequencing technologies. Here, we describe an efficient pipeline to accurately recover TE insertion (TEI) sites and sequences from long reads obtained by Oxford Nanopore Technology (ONT) sequencing. With this pipeline, we could precisely describe the landscapes of the most recent TEIs in wild-type strains of Drosophila melanogaster and Drosophila simulans. Their comparison suggests that this subset of TE sequences is more similar than previously thought in these two species. The chromosome assemblies obtained using this pipeline also allowed recovering piRNA cluster sequences, which was impossible using short-read sequencing. Finally, we used our pipeline to analyze ONT sequencing data from a D. melanogaster unstable line in which LTR transposition was derepressed for 73 successive generations. We could rely on single reads to identify new insertions with intact target site duplications. Moreover, the detailed analysis of TEIs in the wild-type strains and the unstable line did not support the trap model claiming that piRNA clusters are hotspots of TE insertions.

scholarly journals Systematic analysis of dark and camouflaged genes: disease-relevant genes hiding in plain sight

2019 ◽  
Author(s):  
Mark T. W. Ebbert ◽  
Tanner D. Jensen ◽  
Karen Jansen-West ◽  
Jonathon P. Sens ◽  
Joseph S. Reddy ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Sequencing Data ◽  
Systematic Analysis ◽  
Protein Coding ◽  
Short Read ◽  
Sequencing Project ◽  
Short Read Sequencing ◽  
Sequencing Technologies ◽  
Long Read

AbstractBackgroundThe human genome contains ‘dark’ gene regions that cannot be adequately assembled or aligned using standard short-read sequencing technologies, preventing researchers from identifying mutations within these gene regions that may be relevant to human disease. Here, we identify regions that are ‘dark by depth’ (few mappable reads) and others that are ‘camouflaged’ (ambiguous alignment), and we assess how well long-read technologies resolve these regions. We further present an algorithm to resolve most camouflaged regions (including in short-read data) and apply it to the Alzheimer’s Disease Sequencing Project (ADSP; 13142 samples), as a proof of principle.ResultsBased on standard whole-genome lllumina sequencing data, we identified 37873 dark regions in 5857 gene bodies (3635 protein-coding) from pathways important to human health, development, and reproduction. Of the 5857 gene bodies, 494 (8.4%) were 100% dark (142 protein-coding) and 2046 (34.9%) were ≥5% dark (628 protein-coding). Exactly 2757 dark regions were in protein-coding exons (CDS) across 744 genes. Long-read sequencing technologies from 10x Genomics, PacBio, and Oxford Nanopore Technologies reduced dark CDS regions to approximately 45.1%, 33.3%, and 18.2% respectively. Applying our algorithm to the ADSP, we rescued 4622 exonic variants from 501 camouflaged genes, including a rare, ten-nucleotide frameshift deletion in CR1, a top Alzheimer’s disease gene, found in only five ADSP cases and zero controls.ConclusionsWhile we could not formally assess the CR1 frameshift mutation in Alzheimer’s disease (insufficient sample-size), we believe it merits investigating in a larger cohort. There remain thousands of potentially important genomic regions overlooked by short-read sequencing that are largely resolved by long-read technologies.

scholarly journals CaBagE: A Cas9-based Background Elimination strategy for targeted, long-read DNA sequencing

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0241253
Author(s):  
Amelia D. Wallace ◽  
Thomas A. Sasani ◽  
Jordan Swanier ◽  
Brooke L. Gates ◽  
Jeff Greenland ◽  
...  
Keyword(s):  
Dna Sequencing ◽  
Tandem Repeats ◽  
Short Read ◽  
Background Elimination ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Long Read ◽  
Repeat Expansions ◽  
Sequencing Platforms ◽  
Als Patients

A substantial fraction of the human genome is difficult to interrogate with short-read DNA sequencing technologies due to paralogy, complex haplotype structures, or tandem repeats. Long-read sequencing technologies, such as Oxford Nanopore’s MinION, enable direct measurement of complex loci without introducing many of the biases inherent to short-read methods, though they suffer from relatively lower throughput. This limitation has motivated recent efforts to develop amplification-free strategies to target and enrich loci of interest for subsequent sequencing with long reads. Here, we present CaBagE, a method for target enrichment that is efficient and useful for sequencing large, structurally complex targets. The CaBagE method leverages the stable binding of Cas9 to its DNA target to protect desired fragments from digestion with exonuclease. Enriched DNA fragments are then sequenced with Oxford Nanopore’s MinION long-read sequencing technology. Enrichment with CaBagE resulted in a median of 116X coverage (range 39–416) of target loci when tested on five genomic targets ranging from 4-20kb in length using healthy donor DNA. Four cancer gene targets were enriched in a single reaction and multiplexed on a single MinION flow cell. We further demonstrate the utility of CaBagE in two ALS patients with C9orf72 short tandem repeat expansions to produce genotype estimates commensurate with genotypes derived from repeat-primed PCR for each individual. With CaBagE there is a physical enrichment of on-target DNA in a given sample prior to sequencing. This feature allows adaptability across sequencing platforms and potential use as an enrichment strategy for applications beyond sequencing. CaBagE is a rapid enrichment method that can illuminate regions of the ‘hidden genome’ underlying human disease.

scholarly journals SVJedi: Genotyping structural variations with long reads

2019 ◽  
Author(s):  
Lolita Lecompte ◽  
Pierre Peterlongo ◽  
Dominique Lavenier ◽  
Claire Lemaitre
Keyword(s):  
Sequencing Data ◽  
Structural Variations ◽  
Short Read ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Oxford Nanopore ◽  
Clinical Diagnoses ◽  
Long Read ◽  
Reference Sequences ◽  
The One

AbstractMotivationStudies on structural variants (SV) are expanding rapidly. As a result, and thanks to third generation sequencing technologies, the number of discovered SVs is increasing, especially in the human genome. At the same time, for several applications such as clinical diagnoses, it is important to genotype newly sequenced individuals on well defined and characterized SVs. Whereas several SV genotypers have been developed for short read data, there is a lack of such dedicated tool to assess whether known SVs are present or not in a new long read sequenced sample, such as the one produced by Pacific Biosciences or Oxford Nanopore Technologies.ResultsWe present a novel method to genotype known SVs from long read sequencing data. The method is based on the generation of a set of reference sequences that represent the two alleles of each structural variant. Long reads are aligned to these reference sequences. Alignments are then analyzed and filtered out to keep only informative ones, to quantify and estimate the presence of each SV allele and the allele frequencies. We provide an implementation of the method, SVJedi, to genotype insertions and deletions with long reads. The tool has been applied to both simulated and real human datasets and achieves high genotyping accuracy. We also demonstrate that SV genotyping is considerably improved with SVJedi compared to other approaches, namely SV discovery and short read SV genotyping approaches.Availabilityhttps://github.com/llecompte/[email protected]

scholarly journals A MapReduce Framework for DNA Sequencing Data Processing

2016 ◽  
Vol 4 (4) ◽  
pp. 11 ◽  
Author(s):  
Samy Ghoneimy ◽  
Samir Abou El-Seoud
Keyword(s):  
Data Processing ◽  
Dna Sequencing ◽  
High Performance ◽  
Alternative Methods ◽  
Alignment Algorithm ◽  
Sequencing Data ◽  
Illumina Hiseq ◽  
Data Set ◽  
Short Read ◽  
Short Oligonucleotide

<p class="Els-1storder-head">Genomics and Next Generation Sequencers (NGS) like Illumina Hiseq produce data in the order of ‎‎200 billion base pairs in a single one-week run for a 60x human genome coverage, which ‎requires modern high-throughput experimental technologies that can ‎only be tackled with high performance computing (HPC) and specialized software algorithms called ‎‎“short read aligners”. This paper focuses on the implementation of the DNA sequencing as a set of MapReduce programs that will accept a DNA data set as a FASTQ file and finally generate a VCF (variant call format) file, which has variants for a given DNA data set. In this paper MapReduce/Hadoop along with Burrows-Wheeler Aligner (BWA), Sequence Alignment/Map (SAM) ‎tools, are fully utilized to provide various utilities for manipulating alignments, including sorting, merging, indexing, ‎and generating alignments. The Map-Sort-Reduce process is designed to be suited for a Hadoop framework in ‎which each cluster is a traditional N-node Hadoop cluster to utilize all of the Hadoop features like HDFS, program ‎management and fault tolerance. The Map step performs multiple instances of the short read alignment algorithm ‎‎(BoWTie) that run in parallel in Hadoop. The ordered list of the sequence reads are used as input tuples and the ‎output tuples are the alignments of the short reads. In the Reduce step many parallel instances of the Short ‎Oligonucleotide Analysis Package for SNP (SOAPsnp) algorithm run in the cluster. Input tuples are sorted ‎alignments for a partition and the output tuples are SNP calls. Results are stored via HDFS, and then archived in ‎SOAPsnp format. ‎ The proposed framework enables extremely fast discovering somatic mutations, inferring population genetical ‎parameters, and performing association tests directly based on sequencing data without explicit genotyping or ‎linkage-based imputation. It also demonstrate that this method achieves comparable accuracy to alternative ‎methods for sequencing data processing.‎‎</p><p class="Abstract"><em></em><em><br /></em></p>

scholarly journals Methods for Copy Number Aberration Detection from Single-cell DNA Sequencing Data

2019 ◽  
Author(s):  
Xian Fan ◽  
Mohammadamin Edrisi ◽  
Nicholas Navin ◽  
Luay Nakhleh
Keyword(s):  
Dna Sequencing ◽  
Single Cell ◽  
Copy Number ◽  
Sequencing Data ◽  
Computationally Efficient ◽  
New Methods ◽  
Sequencing Technologies ◽  
Copy Numbers ◽  
Aberration Detection ◽  
Evolutionary Trajectories

AbstractSingle-cell DNA sequencing technologies are enabling the study of mutations and their evolutionary trajectories in cancer. Somatic copy number aberrations (CNAs) have been implicated in the development and progression of various types of cancer. A wide array of methods for CNA detection has been either developed specifically for or adapted to single-cell DNA sequencing data. Understanding the strengths and limitations that are unique to each of these methods is very important for obtaining accurate copy number profiles from single-cell DNA sequencing data. Here we review the major steps that are followed by these methods when analyzing such data, and then review the strengths and limitations of the methods individually. In terms of segmenting the genome into regions of different copy numbers, we categorize the methods into three groups, select a representative method from each group that has been commonly used in this context, and benchmark them on simulated as well as real datasets. While single-cell DNA sequencing is very promising for elucidating and understanding CNAs, even the best existing method does not exceed 80% accuracy. New methods that significantly improve upon the accuracy of these three methods are needed. Furthermore, with the large datasets being generated, the methods must be computationally efficient.

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