scholarly journals Parallel String Graph Construction and Transitive Reduction for De Novo Genome Assembly

2021 ◽  
Author(s):  
Giulia Guidi ◽  
Oguz Selvitopi ◽  
Marquita Ellis ◽  
Leonid Oliker ◽  
Katherine Yelick ◽  
...  
Keyword(s):  
Genome Assembly ◽  
De Novo ◽  
De Novo Genome Assembly ◽  
Transitive Reduction ◽  
String Graph

scholarly journals Using Apache Spark on genome assembly for scalable overlap-graph reduction

2019 ◽  
Vol 13 (S1) ◽  
Author(s):  
Alexander J. Paul ◽  
Dylan Lawrence ◽  
Myoungkyu Song ◽  
Seung-Hwan Lim ◽  
Chongle Pan ◽  
...  
Keyword(s):  
Genome Assembly ◽  
De Novo ◽  
Time Frame ◽  
Apache Spark ◽  
Reference Sequence ◽  
Graph Reduction ◽  
De Novo Genome Assembly ◽  
String Graph ◽  
Edge Graph ◽  
Generation Sequencing

Abstract Background De novo genome assembly is a technique that builds the genome of a specimen using overlaps of genomic fragments without additional work with reference sequence. Sequence fragments (called reads) are assembled as contigs and scaffolds by the overlaps. The quality of the de novo assembly depends on the length and continuity of the assembly. To enable faster and more accurate assembly of species, existing sequencing techniques have been proposed, for example, high-throughput next-generation sequencing and long-reads-producing third-generation sequencing. However, these techniques require a large amounts of computer memory when very huge-size overlap graphs are resolved. Also, it is challenging for parallel computation. Results To address the limitations, we propose an innovative algorithmic approach, called Scalable Overlap-graph Reduction Algorithms (SORA). SORA is an algorithm package that performs string graph reduction algorithms by Apache Spark. The SORA’s implementations are designed to execute de novo genome assembly on either a single machine or a distributed computing platform. SORA efficiently compacts the number of edges on enormous graphing paths by adapting scalable features of graph processing libraries provided by Apache Spark, GraphX and GraphFrames. Conclusions We shared the algorithms and the experimental results at our project website, https://github.com/BioHPC/SORA. We evaluated SORA with the human genome samples. First, it processed a nearly one billion edge graph on a distributed cloud cluster. Second, it processed mid-to-small size graphs on a single workstation within a short time frame. Overall, SORA achieved the linear-scaling simulations for the increased computing instances.

scholarly journals GraphSeq: Accelerating String Graph Construction for De Novo Assembly on Spark

2018 ◽  
Author(s):  
Chung-Tsai Su ◽  
Ming-Tai Chang ◽  
Yun-Chian Cheng ◽  
Yun-Lung Li ◽  
Yao-Ting Wang
Keyword(s):  
Genome Assembly ◽  
De Novo Assembly ◽  
De Novo ◽  
Data Representation ◽  
Important Application ◽  
Supplementary Information ◽  
De Novo Genome Assembly ◽  
String Graph ◽  
Computing Framework ◽  
Variant Identification

AbstractSummary: De novo genome assembly is an important application on both uncharacterized genome assembly and variant identification in a reference-unbiased way. In comparison with de Brujin graph, string graph is a lossless data representation for de novo assembly. However, string graph construction is computational intensive. We propose GraphSeq to accelerate string graph construction by leveraging the distributed computing framework.Availability and Implementation: GraphSeq is implemented with Scala on Spark and freely available at https://www.atgenomix.com/blog/graphseq.Supplementary information: Supplementary data are available at Bioinformatics online.

scholarly journals Improved hybrid de novo genome assembly of domesticated apple (Malus x domestica)

GigaScience ◽  
2016 ◽  
Vol 5 (1) ◽  
Author(s):  
Xuewei Li ◽  
Ling Kui ◽  
Jing Zhang ◽  
Yinpeng Xie ◽  
Liping Wang ◽  
...  
Keyword(s):  
Genome Assembly ◽  
De Novo ◽  
Malus X Domestica ◽  
De Novo Genome Assembly

scholarly journals Meraculous: De Novo Genome Assembly with Short Paired-End Reads

PLoS ONE ◽  
2011 ◽  
Vol 6 (8) ◽  
pp. e23501 ◽  
Author(s):  
Jarrod A. Chapman ◽  
Isaac Ho ◽  
Sirisha Sunkara ◽  
Shujun Luo ◽  
Gary P. Schroth ◽  
...  
Keyword(s):  
Genome Assembly ◽  
De Novo ◽  
De Novo Genome Assembly

Ultra Efficient Acceleration for De Novo Genome Assembly via Near-Memory Computing

2021 ◽  
Author(s):  
Minxuan Zhou ◽  
Lingxi Wu ◽  
Muzhou Li ◽  
Niema Moshiri ◽  
Kevin Skadron ◽  
...  
Keyword(s):  
Genome Assembly ◽  
De Novo ◽  
De Novo Genome Assembly

scholarly journals Optimizing de novo genome assembly from PCR-amplified metagenomes

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6902 ◽  
Author(s):  
Simon Roux ◽  
Gareth Trubl ◽  
Danielle Goudeau ◽  
Nandita Nath ◽  
Estelle Couradeau ◽  
...  
Keyword(s):  
Genome Assembly ◽  
De Novo ◽  
Pcr Amplification ◽  
Error Rates ◽  
De Novo Genome Assembly ◽  
Low Input ◽  
Assembly Algorithm ◽  
Coverage Bias ◽  
Size Number ◽  
Assembly Pipeline

Background Metagenomics has transformed our understanding of microbial diversity across ecosystems, with recent advances enabling de novo assembly of genomes from metagenomes. These metagenome-assembled genomes are critical to provide ecological, evolutionary, and metabolic context for all the microbes and viruses yet to be cultivated. Metagenomes can now be generated from nanogram to subnanogram amounts of DNA. However, these libraries require several rounds of PCR amplification before sequencing, and recent data suggest these typically yield smaller and more fragmented assemblies than regular metagenomes. Methods Here we evaluate de novo assembly methods of 169 PCR-amplified metagenomes, including 25 for which an unamplified counterpart is available, to optimize specific assembly approaches for PCR-amplified libraries. We first evaluated coverage bias by mapping reads from PCR-amplified metagenomes onto reference contigs obtained from unamplified metagenomes of the same samples. Then, we compared different assembly pipelines in terms of assembly size (number of bp in contigs ≥ 10 kb) and error rates to evaluate which are the best suited for PCR-amplified metagenomes. Results Read mapping analyses revealed that the depth of coverage within individual genomes is significantly more uneven in PCR-amplified datasets versus unamplified metagenomes, with regions of high depth of coverage enriched in short inserts. This enrichment scales with the number of PCR cycles performed, and is presumably due to preferential amplification of short inserts. Standard assembly pipelines are confounded by this type of coverage unevenness, so we evaluated other assembly options to mitigate these issues. We found that a pipeline combining read deduplication and an assembly algorithm originally designed to recover genomes from libraries generated after whole genome amplification (single-cell SPAdes) frequently improved assembly of contigs ≥10 kb by 10 to 100-fold for low input metagenomes. Conclusions PCR-amplified metagenomes have enabled scientists to explore communities traditionally challenging to describe, including some with extremely low biomass or from which DNA is particularly difficult to extract. Here we show that a modified assembly pipeline can lead to an improved de novo genome assembly from PCR-amplified datasets, and enables a better genome recovery from low input metagenomes.

scholarly journals Accurate long-read de novo assembly evaluation with Inspector

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yu Chen ◽  
Yixin Zhang ◽  
Amy Y. Wang ◽  
Min Gao ◽  
Zechen Chong
Keyword(s):  
Genome Assembly ◽  
De Novo Assembly ◽  
In Silico ◽  
Large Scale ◽  
De Novo ◽  
Small Scale ◽  
De Novo Genome Assembly ◽  
Consensus Sequences ◽  
Assembly Evaluation ◽  
Long Read

AbstractLong-read de novo genome assembly continues to advance rapidly. However, there is a lack of effective tools to accurately evaluate the assembly results, especially for structural errors. We present Inspector, a reference-free long-read de novo assembly evaluator which faithfully reports types of errors and their precise locations. Notably, Inspector can correct the assembly errors based on consensus sequences derived from raw reads covering erroneous regions. Based on in silico and long-read assembly results from multiple long-read data and assemblers, we demonstrate that in addition to providing generic metrics, Inspector can accurately identify both large-scale and small-scale assembly errors.

scholarly journals Gene Annotation and Transcriptome Delineation on a De Novo Genome Assembly for the Reference Leishmania major Friedlin Strain

Genes ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1359
Author(s):  
Esther Camacho ◽  
Sandra González-de la Fuente ◽  
Jose C. Solana ◽  
Alberto Rastrojo ◽  
Fernando Carrasco-Ramiro ◽  
...  
Keyword(s):  
Genome Sequence ◽  
Genome Assembly ◽  
Molecular Mechanisms ◽  
High Throughput Sequencing ◽  
Leishmania Major ◽  
De Novo ◽  
Gene Annotation ◽  
Leishmania Species ◽  
De Novo Genome Assembly ◽  
Sequencing Platforms

Leishmania major is the main causative agent of cutaneous leishmaniasis in humans. The Friedlin strain of this species (LmjF) was chosen when a multi-laboratory consortium undertook the objective of deciphering the first genome sequence for a parasite of the genus Leishmania. The objective was successfully attained in 2005, and this represented a milestone for Leishmania molecular biology studies around the world. Although the LmjF genome sequence was done following a shotgun strategy and using classical Sanger sequencing, the results were excellent, and this genome assembly served as the reference for subsequent genome assemblies in other Leishmania species. Here, we present a new assembly for the genome of this strain (named LMJFC for clarity), generated by the combination of two high throughput sequencing platforms, Illumina short-read sequencing and PacBio Single Molecular Real-Time (SMRT) sequencing, which provides long-read sequences. Apart from resolving uncertain nucleotide positions, several genomic regions were reorganized and a more precise composition of tandemly repeated gene loci was attained. Additionally, the genome annotation was improved by adding 542 genes and more accurate coding-sequences defined for around two hundred genes, based on the transcriptome delimitation also carried out in this work. As a result, we are providing gene models (including untranslated regions and introns) for 11,238 genes. Genomic information ultimately determines the biology of every organism; therefore, our understanding of molecular mechanisms will depend on the availability of precise genome sequences and accurate gene annotations. In this regard, this work is providing an improved genome sequence and updated transcriptome annotations for the reference L. major Friedlin strain.

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