scholarly journals Panaconda: Application of pan-synteny graph models to genome content analysis

2017 ◽  
Author(s):  
Andrew S. Warren ◽  
James J. Davis ◽  
Alice R. Wattam ◽  
Dustin Machi ◽  
João C. Setubal ◽  
...  
Keyword(s):  
Gene Families ◽  
De Bruijn Graph ◽  
Genome Alignment ◽  
Whole Genome ◽  
Sequence Comparisons ◽  
Multiple Sequence ◽  
Link Type ◽  
De Bruijn ◽  
Similarities And Differences ◽  
Whole Genome Alignment

AbstractMotivationWhole-genome alignment and pan-genome analysis are useful tools in understanding the similarities and differences of many genomes in an evolutionary context. Here we introduce the concept of pan-synteny graphs, an analysis method that combines elements of both to represent conservation and change of multiple prokaryotic genomes at an architectural level. Pan-synteny graphs represent a reference free approach for the comparison of many genomes and allows for the identification of synteny, insertion, deletion, replacement, inversion, recombination, missed assembly joins, evolutionary hotspots, and reference based scaffolding.ResultsWe present an algorithm for creating whole genome multiple sequence comparisons and a model for representing the similarities and differences among sequences as a graph of syntenic gene families. As part of the pan-synteny graph creation, we first create a de Bruijn graph. Instead of the alphabet of nucleotides commonly used in genome assembly, we use an alphabet of gene families. This de Bruijn graph is then processed to create the pan-synteny graph. Our approach is novel in that it explicitly controls how regions from the same sequence and genome are aligned and generates a graph in which all sequences are fully represented as paths. This method harnesses previous computation involved in protein family calculation to speed up the creation of whole genome alignment for many genomes. We provide the software suite Panaconda, for the calculation of pan-synteny graphs given annotation input, and an implementation of methods for their layout and visualization.AvailabilityPanaconda is available at https://github.com/aswarren/pangenome_graphs and datasets used in examples are available at https://github.com/aswarren/pangenome_examplesContactAndrew Warren [email protected]

scholarly journals Scalable multiple whole-genome alignment and locally collinear block construction with SibeliaZ

2019 ◽  
Author(s):  
Ilia Minkin ◽  
Paul Medvedev
Keyword(s):  
Single Machine ◽  
De Bruijn Graph ◽  
Genome Alignment ◽  
Whole Genome ◽  
Reconstruction Algorithms ◽  
De Bruijn Graphs ◽  
Significant Step ◽  
De Bruijn ◽  
Whole Genome Alignment ◽  
Computational Resources

AbstractMultiple whole-genome alignment is a challenging problem in bioinformatics. Despite many successes, current methods are not able to keep up with the growing number, length, and complexity of assembled genomes, especially when computational resources are limited. Approaches based on compacted de Bruijn graphs to identify and extend anchors into locally collinear blocks have potential for scalability, but current methods do not scale to mammalian genomes. We present an algorithm, SibeliaZ-LCB, for identifying collinear blocks in closely related genomes based on analysis of the de Bruijn graph. We further incorporate this into a multiple whole-genome alignment pipeline called SibeliaZ. SibeliaZ shows run-time improvements over other methods while maintaining accuracy. On sixteen recently-assembled strains of mice, SibeliaZ runs in under 16 hours on a single machine, while other tools did not run to completion for eight mice within a week. SibeliaZ makes a significant step towards improving scalability of multiple whole-genome alignment and collinear block reconstruction algorithms on a single machine.

scholarly journals Scalable multiple whole-genome alignment and locally collinear block construction with SibeliaZ

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Ilia Minkin ◽  
Paul Medvedev
Keyword(s):  
Single Machine ◽  
De Bruijn Graph ◽  
Genome Alignment ◽  
Whole Genome ◽  
Reconstruction Algorithms ◽  
De Bruijn Graphs ◽  
Significant Step ◽  
De Bruijn ◽  
Whole Genome Alignment ◽  
Computational Resources

AbstractMultiple whole-genome alignment is a challenging problem in bioinformatics. Despite many successes, current methods are not able to keep up with the growing number, length, and complexity of assembled genomes, especially when computational resources are limited. Approaches based on compacted de Bruijn graphs to identify and extend anchors into locally collinear blocks have potential for scalability, but current methods do not scale to mammalian genomes. We present an algorithm, SibeliaZ-LCB, for identifying collinear blocks in closely related genomes based on analysis of the de Bruijn graph. We further incorporate this into a multiple whole-genome alignment pipeline called SibeliaZ. SibeliaZ shows run-time improvements over other methods while maintaining accuracy. On sixteen recently-assembled strains of mice, SibeliaZ runs in under 16 hours on a single machine, while other tools did not run to completion for eight mice within a week. SibeliaZ makes a significant step towards improving scalability of multiple whole-genome alignment and collinear block reconstruction algorithms on a single machine.

scholarly journals Alignathon: A competitive assessment of whole genome alignment methods.

2014 ◽  
Author(s):  
Dent Earl ◽  
Ngan K Nguyen ◽  
Glenn Hickey ◽  
Robert S. Harris ◽  
Stephen Fitzgerald ◽  
...  
Keyword(s):  
Genome Alignment ◽  
Whole Genome ◽  
Alignment Quality ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Simulation Based ◽  
Benchmark Datasets ◽  
Whole Genome Alignment

Background: Multiple sequence alignments (MSAs) are a prerequisite for a wide variety of evolutionary analyses. Published assessments and benchmark datasets for protein and, to a lesser extent, global nucleotide MSAs are available, but less effort has been made to establish benchmarks in the more general problem of whole genome alignment (WGA). Results: Using the same model as the successful Assemblathon competitions, we organized a competitive evaluation in which teams submitted their alignments, and assessments were performed collectively after all the submissions were received. Three datasets were used: two of simulated primate and mammalian phylogenies, and one of 20 real fly genomes. In total 35 submissions were assessed, submitted by ten teams using 12 different alignment pipelines. Conclusions: We found agreement between independent simulation-based and statistical assessments, indicating that there are substantial accuracy differences between contemporary alignment tools. We saw considerable difference in the alignment quality of differently annotated regions, and found few tools aligned the duplications analysed. We found many tools worked well at shorter evolutionary distances, but fewer performed competitively at longer distances. We provide all datasets, submissions and assessment programs for further study, and provide, as a resource for future benchmarking, a convenient repository of code and data for reproducing the simulation assessments.

scholarly journals Discrimination of hospital isolates of Acinetobacter baumannii using repeated sequences and whole genome alignment differential analysis

2021 ◽  
Author(s):  
Roman Kotłowski ◽  
Alicja Nowak-Zaleska ◽  
Grzegorz Węgrzyn
Keyword(s):  
Acinetobacter Baumannii ◽  
Time Frame ◽  
Repeated Sequences ◽  
Hospital Environment ◽  
Genome Alignment ◽  
Whole Genome ◽  
Differential Analysis ◽  
Gene Encoding ◽  
Resistance Patterns ◽  
Whole Genome Alignment

AbstractAn optimized method for bacterial strain differentiation, based on combination of Repeated Sequences and Whole Genome Alignment Differential Analysis (RS&WGADA), is presented in this report. In this analysis, 51 Acinetobacter baumannii multidrug-resistance strains from one hospital environment and patients from 14 hospital wards were classified on the basis of polymorphisms of repeated sequences located in CRISPR region, variation in the gene encoding the EmrA-homologue of E. coli, and antibiotic resistance patterns, in combination with three newly identified polymorphic regions in the genomes of A. baumannii clinical isolates. Differential analysis of two similarity matrices between different genotypes and resistance patterns allowed to distinguish three significant correlations (p < 0.05) between 172 bp DNA insertion combined with resistance to chloramphenicol and gentamycin. Interestingly, 45 and 55 bp DNA insertions within the CRISPR region were identified, and combined during analyses with resistance/susceptibility to trimethoprim/sulfamethoxazole. Moreover, 184 or 1374 bp DNA length polymorphisms in the genomic region located upstream of the GTP cyclohydrolase I gene, associated mainly with imipenem susceptibility, was identified. In addition, considerable nucleotide polymorphism of the gene encoding the gamma/tau subunit of DNA polymerase III, an enzyme crucial for bacterial DNA replication, was discovered. The differentiation analysis performed using the above described approach allowed us to monitor the distribution of A. baumannii isolates in different wards of the hospital in the time frame of several years, indicating that the optimized method may be useful in hospital epidemiological studies, particularly in identification of the source of primary infections.

scholarly journals MUM&Co: accurate detection of all SV types through whole-genome alignment

2020 ◽  
Vol 36 (10) ◽  
pp. 3242-3243 ◽  
Author(s):  
Samuel O’Donnell ◽  
Gilles Fischer
Keyword(s):  
De Novo ◽  
Supplementary Information ◽  
Genome Alignment ◽  
Whole Genome ◽  
Structural Variations ◽  
Sequencing Technologies ◽  
Third Generation Sequencing ◽  
Human Genomes ◽  
Whole Genome Alignment ◽  
Primary Output

Abstract Summary MUM&Co is a single bash script to detect structural variations (SVs) utilizing whole-genome alignment (WGA). Using MUMmer’s nucmer alignment, MUM&Co can detect insertions, deletions, tandem duplications, inversions and translocations greater than 50 bp. Its versatility depends upon the WGA and therefore benefits from contiguous de-novo assemblies generated by third generation sequencing technologies. Benchmarked against five WGA SV-calling tools, MUM&Co outperforms all tools on simulated SVs in yeast, plant and human genomes and performs similarly in two real human datasets. Additionally, MUM&Co is particularly unique in its ability to find inversions in both simulated and real datasets. Lastly, MUM&Co’s primary output is an intuitive tabulated file containing a list of SVs with only necessary genomic details. Availability and implementation https://github.com/SAMtoBAM/MUMandCo. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals REINDEER: efficient indexing of k-mer presence and abundance in sequencing datasets

2020 ◽  
Author(s):  
Camille Marchet ◽  
Zamin Iqbal ◽  
Daniel Gautheret ◽  
Mikael Salson ◽  
Rayan Chikhi
Keyword(s):  
Large Datasets ◽  
Computational Method ◽  
De Bruijn Graph ◽  
Rna Seq ◽  
Indexing Methods ◽  
Link Type ◽  
De Bruijn

AbstractMotivationIn this work we present REINDEER, a novel computational method that performs indexing of sequences and records their abundances across a collection of datasets. To the best of our knowledge, other indexing methods have so far been unable to record abundances efficiently across large datasets.ResultsWe used REINDEER to index the abundances of sequences within 2,585 human RNA-seq experiments in 45 hours using only 56 GB of RAM. This makes REINDEER the first method able to record abundances at the scale of 4 billion distinct k-mers across 2,585 datasets. REINDEER also supports exact presence/absence queries of k-mers. Briefly, REINDEER constructs the compacted de Bruijn graph (DBG) of each dataset, then conceptually merges those DBGs into a single global one. Then, REINDEER constructs and indexes monotigs, which in a nutshell are groups of k-mers of similar abundances.Availabilityhttps://github.com/kamimrcht/[email protected]

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