<i>Snowball</i>: strain aware gene assembly of metagenomes

Gregor, Ivan; Schönhuth, Alexander; McHardy, Alice C.

doi:10.1093/bioinformatics/btw426

Cited by 20 publications

(17 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Note that this contig is longer than the individual reads participating in the contig and that sequencing errors can be eliminated by raising majority votes among the reads participating in the maximal clique. While this reflects an approved procedure in its generic form (Baaijens et al, 2017;Gregor et al, 2016;Töpfer et al, 2014), accounting for the particular setting we are facing here-namely low coverage in combination with sequence-based edge definition-requires particular care.…”

Section: Correction Of Sequencing Errorsmentioning

confidence: 99%

“…Thereby, contigs grow along the iterations, while preserving their haplotype identity. POLYTE adopts ideas from earlier work that either focused on variant discovery (Marschall et al, 2012), viral quasispecies assembly (Baaijens et al, 2017;Töpfer et al, 2014) or metagenome gene assembly (Gregor et al, 2016). However, Marschall et al (2012) only make use of a rudimentary type of (overlap like) graph, where edges are agnostic to sequence content, and does not follow an iterative scheme.…”

Section: Introductionmentioning

confidence: 99%

“…However, Marschall et al (2012) only make use of a rudimentary type of (overlap like) graph, where edges are agnostic to sequence content, and does not follow an iterative scheme. Second, Gregor et al (2016) (2014)-dealing with real overlap graphs and contig computation-on the other hand. That is, POLYTE brings forward an iterative overlap graph based scheme for contig generation that reliably works in low coverage settings, requiring coverage of only as low as 5x per haplotype.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Overlap graph-based generation of haplotigs for diploids and polyploids

Baaijens

Schoenhuth

2018

Preprint

View full text Add to dashboard Cite

Haplotype aware genome assembly plays an important role in genetics, medicine, and various other disciplines, yet generation of haplotype-resolved de novo assemblies remains a major challenge. Beyond distinguishing between errors and true sequential variants, one needs to assign the true variants to the different genome copies. Recent work has pointed out that the enormous quantities of traditional NGS read data have been greatly underexploited in terms of haplotig computation so far, which reflects that methodology for reference independent haplotig computation has not yet reached maturity. We present POLYTE (POLYploid genome fitTEr) as a new approach to de novo generation of haplotigs for diploid and polyploid genomes. Our method follows an iterative scheme where in each iteration reads or contigs are joined, based on their interplay in terms of an underlying haplotype-aware overlap graph. Along the iterations, contigs grow while preserving their haplotype identity. Benchmarking experiments on both real and simulated data demonstrate that POLYTE establishes new standards in terms of error-free reconstruction of haplotype-specific sequence. As a consequence, POLYTE outperforms state-of-the-art approaches in various relevant aspects, where advantages become particularly distinct in polyploid settings. POLYTE is freely available as part of the HaploConduct package at https://github.com/HaploConduct/HaploConduct, implemented in Python and C++.

show abstract

Section: Correction Of Sequencing Errorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Overlap graph-based generation of haplotigs for diploids and polyploids

Baaijens

Schoenhuth

2018

Preprint

View full text Add to dashboard Cite

show abstract

“…A common prerequisite for these tools is a highquality alignment of the reads (e.g., T€ opfer et al, 2014). However, tools exist that allow haplotype calling without a reference genome as presented in Gregor et al (2016). Nevertheless, the short-read-based discovery of viral sequences in mixed samples remains challenging (Marschall et al, 2016) because most analysis steps are not easily automated and various technical or biological limitations exist (Fricke et al, 2017).…”

Section: Viral Quasispeciesmentioning

confidence: 99%

Software Dedicated to Virus Sequence Analysis “Bioinformatics Goes Viral”

Hölzer

2017

In Loeffler’s Footsteps – Viral Genomics in the Era of High-Throughput Sequencing

View full text Add to dashboard Cite

Computer-assisted technologies of the genomic structure, biological function, and evolution of viruses remain a largely neglected area of research. The attention of bioinformaticians to this challenging field is currently unsatisfying in respect to its medical and biological importance. The power of new genome sequencing technologies, associated with new tools to handle "big data", provides unprecedented opportunities to address fundamental questions in virology. Here, we present an overview of the current technologies, challenges, and advantages of Next-Generation Sequencing (NGS) in relation to the field of virology. We present how viral sequences can be detected de novo out of current short-read NGS data. Furthermore, we discuss the challenges and applications of viral quasispecies and how secondary structures, commonly shaped by RNA viruses, can be computationally predicted. The phylogenetic analysis of viruses, as another ubiquitous field in virology, forms an essential element of describing viral epidemics and challenges current algorithms. Recently, the first specialized virus-bioinformatic organizations have been established. We need to bring together virologists and bioinformaticians and provide a platform for the implementation of interdisciplinary collaborative projects at local and international scales. Above all, there is an urgent need for dedicated software tools to tackle various challenges in virology.

show abstract

“…VGA (Mangul et al 2014) addresses to make use of ad-hoc consensus sequence for viral quasispecies assembly. There are also de novo metagenome assembly approaches (Laserson et al 2011;Nurk et al 2016) which expect collections of genomes from different species as input, and gene assembly methods (Gregor et al 2016;Zhang et al 2014b), which operate reference-free by aligning reads to gene template sequence as provided by suitable databases.…”

Section: Introductionmentioning

confidence: 99%

De novo assembly of viral quasispecies using overlap graphs

Baaijens

Aabidine

Rivals

et al. 2016

Preprint

View full text Add to dashboard Cite

A 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.

show abstract

Snowball: strain aware gene assembly of metagenomes

Cited by 20 publications

References 29 publications

Overlap graph-based generation of haplotigs for diploids and polyploids

Overlap graph-based generation of haplotigs for diploids and polyploids

Software Dedicated to Virus Sequence Analysis “Bioinformatics Goes Viral”

De novo assembly of viral quasispecies using overlap graphs

Contact Info

Product

Resources

About