scholarly journals RECORD: Reference-Assisted Genome Assembly for Closely Related Genomes

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Krisztian Buza ◽  
Bartek Wilczynski ◽  
Norbert Dojer
Keyword(s):  
Reference Genome ◽  
De Novo ◽  
Real Data ◽  
Reference Sequence ◽  
Individual Genome ◽  
Single Experiment ◽  
Sequencing Technologies ◽  
Sequencing Cost ◽  
The Individual ◽  
Assembly Software

Background. Next-generation sequencing technologies are now producing multiple times the genome size in total reads from a single experiment. This is enough information to reconstruct at least some of the differences between the individual genome studied in the experiment and the reference genome of the species. However, in most typical protocols, this information is disregarded and the reference genome is used.Results. We provide a new approach that allows researchers to reconstruct genomes very closely related to the reference genome (e.g., mutants of the same species) directly from the reads used in the experiment. Our approach applies de novo assembly software to experimental reads and so-called pseudoreads and uses the resulting contigs to generate a modified reference sequence. In this way, it can very quickly, and at no additional sequencing cost, generate new, modified reference sequence that is closer to the actual sequenced genome and has a full coverage. In this paper, we describe our approach and test its implementation called RECORD. We evaluate RECORD on both simulated and real data. We made our software publicly available on sourceforge.Conclusion. Our tests show that on closely related sequences RECORD outperforms more general assisted-assembly software.

scholarly journals De novo assembly of viral quasispecies using overlap graphs

2016 ◽  
Author(s):  
Jasmijn A. Baaijens ◽  
Amal Zine El Aabidine ◽  
Eric Rivals ◽  
Alexander Schönhuth
Keyword(s):  
Ad Hoc ◽  
Reference Genome ◽  
De Novo ◽  
Reference Sequence ◽  
Viral Quasispecies ◽  
Recombination Rates ◽  
High Quality ◽  
Assembly Algorithm ◽  
Overlap Graphs ◽  
The Individual

AbstractA viral quasispecies, the ensemble of viral strains populating an infected person, can be highly diverse. For optimal assessment of virulence, pathogenesis and therapy selection, determining the haplotypes of the individual strains can play a key role. As many viruses are subject to high mutation and recombination rates, high-quality reference genomes are often not available at the time of a new disease outbreak. We present SAVAGE, a computational tool for reconstructing individual haplotypes of intrahost virus strains without the need for a high-quality reference genome. SAVAGE makes use of either FM-index based data structures or ad-hoc consensus reference sequence for constructing overlap graphs from patient sample data. In this overlap graph, nodes represent reads and/or contigs, while edges reflect that two reads/contigs, based on sound statistical considerations, represent identical haplotypic sequence. Following an iterative scheme, a new overlap assembly algorithm that is based on the enumeration of statistically well-calibrated groups of reads/contigs then efficiently reconstructs the individual haplotypes from this overlap graph. In benchmark experiments on simulated and on real deep coverage data, SAV-AGE drastically outperforms generic de novo assemblers as well as the only specialized de novo viral quasispecies assembler available so far. When run on ad-hoc consensus reference sequence, SAVAGE performs very favorably in comparison with state-of-the-art reference genome guided tools. We also apply SAVAGE on two deep coverage samples of patients infected by the Zika and the hepatitis C virus, respectively, which sheds light on the genetic structures of the respective viral quasispecies.

scholarly journals CAMISIM: Simulating metagenomes and microbial communities

2018 ◽  
Author(s):  
Adrian Fritz ◽  
Peter Hofmann ◽  
Stephan Majda ◽  
Eik Dahms ◽  
Johannes Dröge ◽  
...  
Keyword(s):  
Microbial Communities ◽  
De Novo ◽  
Real Data ◽  
Small Data ◽  
Data Sets ◽  
Sequencing Data ◽  
Taxonomic Profiling ◽  
Benchmark Data ◽  
Sequencing Technologies ◽  
Wide Range

Shotgun metagenome data sets of microbial communities are highly diverse, not only due to the natural variation of the underlying biological systems, but also due to differences in laboratory protocols, replicate numbers, and sequencing technologies. Accordingly, to effectively assess the performance of metagenomic analysis software, a wide range of benchmark data sets are required. Here, we describe the CAMISIM microbial community and metagenome simulator. The software can model different microbial abundance profiles, multi-sample time series and differential abundance studies, includes real and simulated strain-level diversity, and generates second and third generation sequencing data from taxonomic profiles or de novo. Gold standards are created for sequence assembly, genome binning, taxonomic binning, and taxonomic profiling. CAMSIM generated the benchmark data sets of the first CAMI challenge. For two simulated multi-sample data sets of the human and mouse gut microbiomes we observed high functional congruence to the real data. As further applications, we investigated the effect of varying evolutionary genome divergence, sequencing depth, and read error profiles on two popular metagenome assemblers, MEGAHIT and metaSPAdes, on several thousand small data sets generated with CAMISIM. CAMISIM can simulate a wide variety of microbial communities and metagenome data sets together with truth standards for method evaluation. All data sets and the software are freely available at: https://github.com/CAMI-challenge/CAMISIM

scholarly journals De novo Assembly of the Brugia malayi Genome Using Long Reads from a Single MinION Flowcell

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Joseph R. Fauver ◽  
John Martin ◽  
Gary J. Weil ◽  
Makedonka Mitreva ◽  
Peter U. Fischer
Keyword(s):  
Single Molecule ◽  
New Technologies ◽  
Reference Genome ◽  
De Novo ◽  
Complete Mitochondrial Genome ◽  
Nuclear Genome ◽  
Brugia Malayi ◽  
Field Isolates ◽  
Sequencing Technologies ◽  
Long Reads

AbstractFilarial nematode infections cause a substantial global disease burden. Genomic studies of filarial worms can improve our understanding of their biology and epidemiology. However, genomic information from field isolates is limited and available reference genomes are often discontinuous. Single molecule sequencing technologies can reduce the cost of genome sequencing and long reads produced from these devices can improve the contiguity and completeness of genome assemblies. In addition, these new technologies can make generation and analysis of large numbers of field isolates feasible. In this study, we assessed the performance of the Oxford Nanopore Technologies MinION for sequencing and assembling the genome of Brugia malayi, a human parasite widely used in filariasis research. Using data from a single MinION flowcell, a 90.3 Mb nuclear genome was assembled into 202 contigs with an N50 of 2.4 Mb. This assembly covered 96.9% of the well-defined B. malayi reference genome with 99.2% identity. The complete mitochondrial genome was obtained with individual reads and the nearly complete genome of the endosymbiotic bacteria Wolbachia was assembled alongside the nuclear genome. Long-read data from the MinION produced an assembly that approached the quality of a well-established reference genome using comparably fewer resources.

scholarly journals Illumina TruSeq synthetic long-reads empowerde novoassembly and resolve complex, highly repetitive transposable elements

2014 ◽  
Author(s):  
Rajiv C McCoy ◽  
Ryan W Taylor ◽  
Timothy A Blauwkamp ◽  
Joanna L Kelley ◽  
Michael Kertesz ◽  
...  
Keyword(s):  
Transposable Elements ◽  
Reference Genome ◽  
De Novo ◽  
Model Organism ◽  
Genomic Analysis ◽  
High Sequence Identity ◽  
Current Reference ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Whole Genomes

High-throughput DNA sequencing technologies have revolutionized genomic analysis, including thede novoassembly of whole genomes. Nevertheless, assembly of complex genomes remains challenging, in part due to the presence of dispersed repeats which introduce ambiguity during genome reconstruction. Transposable elements (TEs) can be particularly problematic, especially for TE families exhibiting high sequence identity, high copy number, or present in complex genomic arrangements. While TEs strongly affect genome function and evolution, most currentde novoassembly approaches cannot resolve long, identical, and abundant families of TEs. Here, we applied a novel Illumina technology called TruSeq synthetic long-reads, which are generated through highly parallel library preparation and local assembly of short read data and achieve lengths of 1.5-18.5 Kbp with an extremely low error rate (∼0.03% per base). To test the utility of this technology, we sequenced and assembled the genome of the model organismDrosophila melanogaster(reference genome strainy;cn,bw,sp) achieving an N50 contig size of 69.7 Kbp and covering 96.9% of the euchromatic chromosome arms of the current reference genome. TruSeq synthetic long-read technology enables placement of individual TE copies in their proper genomic locations as well as accurate reconstruction of TE sequences. We entirely recovered and accurately placed 4,229 (77.8%) of the 5,434 of annotated transposable elements with perfect identity to the current reference genome. As TEs are ubiquitous features of genomes of many species, TruSeq synthetic long- reads, and likely other methods that generate long reads, offer a powerful approach to improvede novoassemblies of whole genomes.

scholarly journals Customized de novo mutation detection for any variant calling pipeline: SynthDNM

2021 ◽  
Author(s):  
Aojie Lian ◽  
James Guevara ◽  
Kun Xia ◽  
Jonathan Sebat
Keyword(s):  
Mutation Detection ◽  
De Novo ◽  
Variant Calling ◽  
Real Data ◽  
Supplementary Information ◽  
De Novo Mutation ◽  
Flexible Approach ◽  
Sequencing Technologies ◽  
Training Examples ◽  
Simulated Training

Abstract Motivation As sequencing technologies and analysis pipelines evolve, de novo mutation (DNM) calling tools must be adapted. Therefore, a flexible approach is needed that can accurately identify DNMs from genome or exome sequences from a variety of datasets and variant calling pipelines. Results Here, we describe SynthDNM, a random-forest based classifier that can be readily adapted to new sequencing or variant-calling pipelines by applying a flexible approach to constructing simulated training examples from real data. The optimized SynthDNM classifiers predict de novo SNPs and indels with robust accuracy across multiple methods of variant calling. Availabilityand implementation SynthDNM is freely available on Github (https://github.com/james-guevara/synthdnm). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals AlleleAnalyzer: a tool for personalized and allele-specific sgRNA design

2018 ◽  
Author(s):  
Kathleen C. Keough ◽  
Svetlana Lyalina ◽  
Michael P. Olvera ◽  
Sean Whalen ◽  
Bruce R. Conklin ◽  
...  
Keyword(s):  
Genome Editing ◽  
Reference Genome ◽  
Individual Genome ◽  
Experimental Performance ◽  
Human Genomes ◽  
Large Populations ◽  
Allele Specific ◽  
Sgrna Design ◽  
Single Base Pair ◽  
The Individual

AbstractThe CRISPR/Cas system is a highly specific genome editing tool capable of distinguishing alleles differing by even a single base pair. However, current tools only design sgRNAs for a reference genome, not taking into account individual variants which may generate, remove, or modify CRISPR/Cas sgRNA sites. This may cause mismatches between designed sgRNAs and the individual genome they are intended to target, leading to decreased experimental performance. Here we describe AlleleAnalyzer, a tool for designing personalized and allele-specific sgRNAs for genome editing. We leverage >2,500 human genomes to identify optimized pairs of sgRNAs that can be used for human therapeutic editing in large populations in the future.

scholarly journals Recycler: an algorithm for detecting plasmids from de novo assembly graphs

2015 ◽  
Author(s):  
Roye Rozov ◽  
Aya Brown Kav ◽  
David Bogumil ◽  
Naama Shterzer ◽  
Eran Halperin ◽  
...  
Keyword(s):  
De Novo ◽  
Real Data ◽  
Microbial Evolution ◽  
Reference Sequence ◽  
Data Types ◽  
High Agreement ◽  
Assembly Algorithm ◽  
E Coli ◽  
Agricultural Applications

AbstractPlasmids are central contributors to microbial evolution and genome innovation. Recently, they have been found to have important roles in antibiotic resistance and in affecting production of metabolites used in industrial and agricultural applications. However, their characterization through deep sequencing remains challenging, in spite of rapid drops in cost and throughput increases for sequencing. Here, we attempt to ameliorate this situation by introducing a new plasmid-specific assembly algorithm, leveraging assembly graphs provided by a conventional de novo assembler and alignments of paired- end reads to assembled graph nodes. We introduce the first tool for this task, called Recycler, and demonstrate its merits in comparison with extant approaches. We show that Recycler greatly increases the number of true plasmids recovered while remaining highly accurate. On simulated plasmidomes, Recycler recovered 5-14% more true plasmids compared to the best extant method with overall precision of about 90%. We validated these results in silico on real data, as well as in vitro by PCR validation performed on a subset of Recycler’s predictions on different data types. All 12 of Recycler’s outputs on isolate samples matched known plasmids or phages, and had alignments having at least 97% identity over at least 99% of the reported reference sequence lengths. For the two E. Coli strains examined, most known plasmid sequences were recovered, while in both cases additional plasmids only known to be present in different hosts were found. Recycler also generated plasmids in high agreement with known annotation on real plasmidome data. Moreover, in PCR validations performed on 77 sequences, Recycler showed mean accuracy of 89% across all data types – isolate, microbiome, and plasmidome. Recycler is available at http://github.com/Shamir-Lab/Recycler

scholarly journals Index suffix–prefix overlaps by (w, k)-minimizer to generate long contigs for reads compression

2018 ◽  
Vol 35 (12) ◽  
pp. 2066-2074 ◽  
Author(s):  
Yuansheng Liu ◽  
Zuguo Yu ◽  
Marcel E Dinger ◽  
Jinyan Li
Keyword(s):  
High Throughput Sequencing ◽  
De Novo ◽  
Reference Sequence ◽  
Supplementary Information ◽  
The Novel ◽  
Rna Seq ◽  
File Size ◽  
Sequencing Technologies ◽  
Efficient Storage ◽  
Merging Process

Abstract Motivation Advanced high-throughput sequencing technologies have produced massive amount of reads data, and algorithms have been specially designed to contract the size of these datasets for efficient storage and transmission. Reordering reads with regard to their positions in de novo assembled contigs or in explicit reference sequences has been proven to be one of the most effective reads compression approach. As there is usually no good prior knowledge about the reference sequence, current focus is on the novel construction of de novo assembled contigs. Results We introduce a new de novo compression algorithm named minicom. This algorithm uses large k-minimizers to index the reads and subgroup those that have the same minimizer. Within each subgroup, a contig is constructed. Then some pairs of the contigs derived from the subgroups are merged into longer contigs according to a (w, k)-minimizer-indexed suffix–prefix overlap similarity between two contigs. This merging process is repeated after the longer contigs are formed until no pair of contigs can be merged. We compare the performance of minicom with two reference-based methods and four de novo methods on 18 datasets (13 RNA-seq datasets and 5 whole genome sequencing datasets). In the compression of single-end reads, minicom obtained the smallest file size for 22 of 34 cases with significant improvement. In the compression of paired-end reads, minicom achieved 20–80% compression gain over the best state-of-the-art algorithm. Our method also achieved a 10% size reduction of compressed files in comparison with the best algorithm under the reads-order preserving mode. These excellent performances are mainly attributed to the exploit of the redundancy of the repetitive substrings in the long contigs. Availability and implementation https://github.com/yuansliu/minicom Supplementary information Supplementary data are available at Bioinformatics online.

PARALLEL ALGORITHMS FOR MAPPING SHORT DEGENERATE AND WEIGHTED DNA SEQUENCES TO A REFERENCE GENOME

2012 ◽  
Vol 23 (02) ◽  
pp. 249-259
Author(s):  
COSTAS S. ILIOPOULOS ◽  
MIRKA MILLER ◽  
SOLON P. PISSIS
Keyword(s):  
Parallel Algorithms ◽  
Dna Sequences ◽  
Message Passing ◽  
Large Scale ◽  
Reference Genome ◽  
Single Experiment ◽  
Sequencing Technologies ◽  
Mapping Process ◽  
Specific Position ◽  
Parallelism Model

One of the most ambitious trends in current biomedical research is the large-scale genomic sequencing of patients. Novel high-throughput (or next-generation) sequencing technologies have redefined the way genome sequencing is performed. They are able to produce millions of short sequences (reads) in a single experiment, and with a much lower cost than previously possible. Due to this massive amount of data, efficient algorithms for mapping these sequences to a reference genome are in great demand, and recently, there has been ample work for publishing such algorithms. One important feature of these algorithms is the support of multithreaded parallel computing in order to speedup the mapping process. In this paper, we design parallel algorithms, which make use of the message-passing parallelism model, to address this problem efficiently. The proposed algorithms also take into consideration the probability scores assigned to each base for occurring in a specific position of a sequence. In particular, we present parallel algorithms for mapping short degenerate and weighted DNA sequences to a reference genome.

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