scholarly journals MUMdex: MUM-based structural variation detection

2016 ◽  
Author(s):  
Peter A. Andrews ◽  
Ivan Iossifov ◽  
Jude Kendall ◽  
Steven Marks ◽  
Lakshmi Muthuswamy ◽  
...  
Keyword(s):  
Reference Genome ◽  
De Novo ◽  
Sequence Data ◽  
Genomic Analysis ◽  
Supplementary Information ◽  
Whole Genome ◽  
Analysis Software ◽  
Population Database ◽  
Genome Data ◽  
Split Read

AbstractMotivationStandard genome sequence alignment tools primarily designed to find one alignment per read have difficulty detecting inversion, translocation and large insertion and deletion (indel) events. Moreover, dedicated split read alignment methods that depend only upon the reference genome may misidentify or find too many potential split read alignments because of reference genome anomalies.MethodsWe introduce MUMdex, a Maximal Unique Match (MUM)-based genomic analysis software package consisting of a sequence aligner to the reference genome, a storage-indexing format and analysis software. Discordant reference alignments of MUMs are especially suitable for identifying inversion, translocation and large indel differences in unique regions. Extracted population databases are used as filters for flaws in the reference genome. We describe the concepts underlying MUM-based analysis, the software implementation and its usage.ResultsWe demonstrate via simulation that the MUMdex aligner and alignment format are able to correctly detect and record genomic events. We characterize alignment performance and output file sizes for human whole genome data and compare to Bowtie 2 and the BAM format. Preliminary results demonstrate the practicality of the analysis approach by detecting de novo mutation candidates in human whole genome DNA sequence data from 510 families. We provide a population database of events from these families for use by others.Availabilityhttp://mumdex.com/[email protected] (or [email protected])Supplementary informationSupplementary data are available online.

scholarly journals SCGid: a consensus approach to contig filtering and genome prediction from single-cell sequencing libraries of uncultured eukaryotes

2019 ◽  
Vol 36 (7) ◽  
pp. 1994-2000
Author(s):  
Kevin R Amses ◽  
William J Davis ◽  
Timothy Y James
Keyword(s):  
Single Cell ◽  
De Novo ◽  
Sequence Data ◽  
Multiple Displacement Amplification ◽  
Supplementary Information ◽  
Whole Genome ◽  
Consensus Approach ◽  
Data Repositories ◽  
Manual Curation ◽  
Genome Prediction

Abstract Motivation Whole-genome sequencing of uncultured eukaryotic genomes is complicated by difficulties in acquiring sufficient amounts of tissue. Single-cell genomics (SCG) by multiple displacement amplification provides a technical workaround, yielding whole-genome libraries which can be assembled de novo. Downsides of multiple displacement amplification include coverage biases and exacerbation of contamination. These factors affect assembly continuity and fidelity, complicating discrimination of genomes from contamination and noise by available tools. Uncultured eukaryotes and their relatives are often underrepresented in large sequence data repositories, further impairing identification and separation. Results We compare the ability of filtering approaches to remove contamination and resolve eukaryotic draft genomes from SCG metagenomes, finding significant variation in outcomes. To address these inconsistencies, we introduce a consensus approach that is codified in the SCGid software package. SCGid parallelly filters assemblies using different approaches, yielding three intermediate drafts from which consensus is drawn. Using genuine and mock SCG metagenomes, we show that our approach corrects for variation among draft genomes predicted by individual approaches and outperforms them in recapitulating published drafts in a fast and repeatable way, providing a useful alternative to available methods and manual curation. Availability and implementation The SCGid package is implemented in python and R. Source code is available at http://www.github.com/amsesk/SCGid under the GNU GPL 3.0 license. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Mycobacterium tuberculosis complex lineage 5 exhibits high levels of within-lineage genomic diversity and differing gene content compared to the type strain H37Rv

2020 ◽  
Author(s):  
C. N’Dira Sanoussi ◽  
Mireia Coscolla ◽  
Boatema Ofori-Anyinam ◽  
Isaac Darko Otchere ◽  
Martin Antonio ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Reference Genome ◽  
De Novo ◽  
Genome Mapping ◽  
Sequence Data ◽  
Gene Diversity ◽  
Genomic Analysis ◽  
Genomic Diversity ◽  
Gene Content ◽  
Link Type

AbstractPathogens of the Mycobacterium tuberculosis complex (MTBC) are considered monomorphic, with little gene content variation between strains. Nevertheless, several genotypic and phenotypic factors separate the different MTBC lineages (L), especially L5 and L6 (traditionally termed Mycobacterium africanum), from each other. However, genome variability and gene content especially of L5 and L6 strains have not been fully explored and may be potentially important for pathobiology and current approaches for genomic analysis of MTBC isolates, including transmission studies.We compared the genomes of 358 L5 clinical isolates (including 3 completed genomes and 355 Illumina WGS (whole genome sequenced) isolates) to the L5 complete genomes and H37Rv, and identified multiple genes differentially present or absent between H37Rv and L5 strains. Additionally, considerable gene content variability was found across L5 strains, including a split in the L5.3 sublineage into L5.3.1 and L5.3.2. These gene content differences had a small knock on effect on transmission cluster estimation, with clustering rates influenced by the selection of reference genome, and with potential over-estimation of recent transmission when using H37Rv as the reference genome.Our data show that the use of H37Rv as reference genome results in missing SNPs in genes unique for L5 strains. This potentially leads to an underestimation of the diversity present in the genome of L5 strains and in turn affects the transmission clustering rates. As such, a full capture of the gene diversity, especially for high resolution outbreak analysis, requires a variation of the single H37Rv-centric reference genome mapping approach currently used in most WGS data analysis pipelines. Moreover, the high within-lineage gene content variability suggests that the pan-genome of M. tuberculosis is at least several kilobases larger than previously thought, implying a concatenated or reference-free genome assembly (de novo) approach may be needed for particular questions.Data summarySequence data for the Illumina dataset are available at European Genome-phenome Archive (EGA; https://www.ebi.ac.uk/ega/) under the study accession numbers PRJEB38317 and PRJEB38656. Individual runs accession numbers are indicated in Table S8.PacBio raw reads for the L5 Benin genome are available on the ENA accession SAME3170744. The assembled L5 Benin genome is available on NCBI with accession PRJNA641267. To ensure naming conventions of the genes in the three L5 genomes can be followed, we have uploaded these annotated GFF files to figshare at https://doi.org/10.6084/m9.figshare.12911849.v1.Custom python scripts used in this analysis can be found at https://github.com/conmeehan/pathophy.

TIGER: inferring DNA replication timing from whole-genome sequence data

2021 ◽  
Author(s):  
Amnon Koren ◽  
Dashiell J Massey ◽  
Alexa N Bracci
Keyword(s):  
Dna Replication ◽  
Genome Sequence ◽  
Genomic Dna ◽  
Sequence Data ◽  
Replication Timing ◽  
Whole Genome Sequence ◽  
Supplementary Information ◽  
Whole Genome ◽  
Genome Sequence Data ◽  
Dna Replication Timing

Abstract Motivation Genomic DNA replicates according to a reproducible spatiotemporal program, with some loci replicating early in S phase while others replicate late. Despite being a central cellular process, DNA replication timing studies have been limited in scale due to technical challenges. Results We present TIGER (Timing Inferred from Genome Replication), a computational approach for extracting DNA replication timing information from whole genome sequence data obtained from proliferating cell samples. The presence of replicating cells in a biological specimen leads to non-uniform representation of genomic DNA that depends on the timing of replication of different genomic loci. Replication dynamics can hence be observed in genome sequence data by analyzing DNA copy number along chromosomes while accounting for other sources of sequence coverage variation. TIGER is applicable to any species with a contiguous genome assembly and rivals the quality of experimental measurements of DNA replication timing. It provides a straightforward approach for measuring replication timing and can readily be applied at scale. Availability and Implementation TIGER is available at https://github.com/TheKorenLab/TIGER. Supplementary information Supplementary data are available at Bioinformatics online

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.

AStrap: identification of alternative splicing from transcript sequences without a reference genome

2018 ◽  
Vol 35 (15) ◽  
pp. 2654-2656 ◽  
Author(s):  
Guoli Ji ◽  
Wenbin Ye ◽  
Yaru Su ◽  
Moliang Chen ◽  
Guangzao Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alternative Splicing ◽  
Single Molecule ◽  
Reference Genome ◽  
De Novo ◽  
Supplementary Information ◽  
Model Organisms ◽  
Sequencing Data ◽  
Extensive Evaluation ◽  
Reference Genomes

Abstract Summary Alternative splicing (AS) is a well-established mechanism for increasing transcriptome and proteome diversity, however, detecting AS events and distinguishing among AS types in organisms without available reference genomes remains challenging. We developed a de novo approach called AStrap for AS analysis without using a reference genome. AStrap identifies AS events by extensive pair-wise alignments of transcript sequences and predicts AS types by a machine-learning model integrating more than 500 assembled features. We evaluated AStrap using collected AS events from reference genomes of rice and human as well as single-molecule real-time sequencing data from Amborella trichopoda. Results show that AStrap can identify much more AS events with comparable or higher accuracy than the competing method. AStrap also possesses a unique feature of predicting AS types, which achieves an overall accuracy of ∼0.87 for different species. Extensive evaluation of AStrap using different parameters, sample sizes and machine-learning models on different species also demonstrates the robustness and flexibility of AStrap. AStrap could be a valuable addition to the community for the study of AS in non-model organisms with limited genetic resources. Availability and implementation AStrap is available for download at https://github.com/BMILAB/AStrap. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Genomic Analysis of Sarcomyxa edulis Reveals the Basis of Its Medicinal Properties and Evolutionary Relationships

2021 ◽  
Vol 12 ◽  
Author(s):  
Fenghua Tian ◽  
Changtian Li ◽  
Yu Li
Keyword(s):  
Single Molecule ◽  
De Novo ◽  
Genomic Analysis ◽  
Single Copy ◽  
Whole Genome Sequence ◽  
Type I ◽  
Whole Genome ◽  
Uridine Diphosphate ◽  
Protein Coding ◽  
Medicinal Value

Yuanmo [Sarcomyxa edulis (Y.C. Dai, Niemelä & G.F. Qin) T. Saito, Tonouchi & T. Harada] is an important edible and medicinal mushroom endemic to Northeastern China. Here we report the de novo sequencing and assembly of the S. edulis genome using single-molecule real-time sequencing technology. The whole genome was approximately 35.65 Mb, with a G + C content of 48.31%. Genome assembly generated 41 contigs with an N50 length of 1,772,559 bp. The genome comprised 9,364 annotated protein-coding genes, many of which encoded enzymes involved in the modification, biosynthesis, and degradation of glycoconjugates and carbohydrates or enzymes predicted to be involved in the biosynthesis of secondary metabolites such as terpene, type I polyketide, siderophore, and fatty acids, which are responsible for the pharmacodynamic activities of S. edulis. We also identified genes encoding 1,3-β-glucan synthase and endo-1,3(4)-β-glucanase, which are involved in polysaccharide and uridine diphosphate glucose biosynthesis. Phylogenetic and comparative analyses of Basidiomycota fungi based on a single-copy orthologous protein indicated that the Sarcomyxa genus is an independent group that evolved from the Pleurotaceae family. The annotated whole-genome sequence of S. edulis can serve as a reference for investigations of bioactive compounds with medicinal value and the development and commercial production of superior S. edulis varieties.

scholarly journals Genome assembly of the JD17 soybean provides a new reference genome for Comparative genomics

2021 ◽  
Author(s):  
Xinxin Yi ◽  
Jing Liu ◽  
Shengcai Chen ◽  
Hao Wu ◽  
Min Liu ◽  
...  
Keyword(s):  
Nitrogen Fixation ◽  
Genome Assembly ◽  
Reference Genome ◽  
De Novo ◽  
Genomic Analysis ◽  
Comparative Genomic ◽  
High Quality ◽  
Genome Wide ◽  
A Genome ◽  
Cultivated Soybean

Cultivated soybean (Glycine max) is an important source for protein and oil. Many elite cultivars with different traits have been developed for different conditions. Each soybean strain has its own genetic diversity, and the availability of more high-quality soybean genomes can enhance comparative genomic analysis for identifying genetic underpinnings for its unique traits. In this study, we constructed a high-quality de novo assembly of an elite soybean cultivar Jidou 17 (JD17) with chromsome contiguity and high accuracy. We annotated 52,840 gene models and reconstructed 74,054 high-quality full-length transcripts. We performed a genome-wide comparative analysis based on the reference genome of JD17 with three published soybeans (WM82, ZH13 and W05) , which identified five large inversions and two large translocations specific to JD17, 20,984 - 46,912 PAVs spanning 13.1 - 46.9 Mb in size, and 5 - 53 large PAV clusters larger than 500kb. 1,695,741 - 3,664,629 SNPs and 446,689 - 800,489 Indels were identified and annotated between JD17 and them. Symbiotic nitrogen fixation (SNF) genes were identified and the effects from these variants were further evaluated. It was found that the coding sequences of 9 nitrogen fixation-related genes were greatly affected. The high-quality genome assembly of JD17 can serve as a valuable reference for soybean functional genomics research.

scholarly journals Rapture-ready darters: choice of reference genome and genotyping method (whole-genome or sequence capture) influence population genomic inference in Etheostoma

2020 ◽  
Author(s):  
Brendan N. Reid ◽  
Rachel L. Moran ◽  
Christopher J. Kopack ◽  
Sarah W. Fitzpatrick
Keyword(s):  
Reference Genome ◽  
Sequence Data ◽  
Low Cost ◽  
Read Depth ◽  
Model Organisms ◽  
Whole Genome ◽  
Reduced Representation ◽  
Sequence Capture ◽  
Population Genomic ◽  
The Impact

AbstractResearchers studying non-model organisms have an increasing number of methods available for generating genomic data. However, the applicability of different methods across species, as well as the effect of reference genome choice on population genomic inference, are still difficult to predict in many cases. We evaluated the impact of data type (whole-genome vs. reduced representation) and reference genome choice on data quality and on population genomic and phylogenomic inference across several species of darters (subfamily Etheostomatinae), a highly diverse radiation of freshwater fish. We generated a high-quality reference genome and developed a hybrid RADseq/sequence capture (Rapture) protocol for the Arkansas darter (Etheostoma cragini). Rapture data from 1900 individuals spanning four darter species showed recovery of most loci across darter species at high depth and consistent estimates of heterozygosity regardless of reference genome choice. Loci with baits spanning both sides of the restriction enzyme cut site performed especially well across species. For low-coverage whole-genome data, choice of reference genome affected read depth and inferred heterozygosity. For similar amounts of sequence data, Rapture performed better at identifying fine-scale genetic structure compared to whole-genome sequencing. Rapture loci also recovered an accurate phylogeny for the study species and demonstrated high phylogenetic informativeness across the evolutionary history of the genus Etheostoma. Low cost and high cross-species effectiveness regardless of reference genome suggest that Rapture and similar sequence capture methods may be worthwhile choices for studies of diverse species radiations.

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 A benchmarking of human mitochondrial DNA haplogroup classifiers from whole-genome and whole-exome sequence data

2021 ◽  
Author(s):  
Víctor García-Olivares ◽  
Adrián Muñoz-Barrera ◽  
José Miguel Lorenzo-Salazar ◽  
Carlos Zaragoza-Trello ◽  
Luis A. Rubio-Rodríguez ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
De Novo ◽  
Sequence Data ◽  
Qualitative Assessment ◽  
Whole Genome ◽  
Third Generation ◽  
Sequencing Data ◽  
Short Read ◽  
Bioinformatic Tools ◽  
Whole Exome

AbstractThe mitochondrial genome (mtDNA) is of interest for a range of fields including evolutionary, forensic, and medical genetics. Human mitogenomes can be classified into evolutionary related haplogroups that provide ancestral information and pedigree relationships. Because of this and the advent of high-throughput sequencing (HTS) technology, there is a diversity of bioinformatic tools for haplogroup classification. We present a benchmarking of the 11 most salient tools for human mtDNA classification using empirical whole-genome (WGS) and whole-exome (WES) short-read sequencing data from 36 unrelated donors. Besides, because of its relevance, we also assess the best performing tool in third-generation long noisy read WGS data obtained with nanopore technology for a subset of the donors. We found that, for short-read WGS, most of the tools exhibit high accuracy for haplogroup classification irrespective of the input file used for the analysis. However, for short-read WES, Haplocheck and MixEmt were the most accurate tools. Based on the performance shown for WGS and WES, and the accompanying qualitative assessment, Haplocheck stands out as the most complete tool. For third-generation HTS data, we also showed that Haplocheck was able to accurately retrieve mtDNA haplogroups for all samples assessed, although only after following assembly-based approaches (either based on a referenced-based assembly or a hybrid de novo assembly). Taken together, our results provide guidance for researchers to select the most suitable tool to conduct the mtDNA analyses from HTS data.

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