scholarly journals HaplotypeTools: a toolkit for accurately identifying recombination and recombinant genotypes

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Rhys A. Farrer
Keyword(s):  
Sequence Analysis ◽  
Batrachochytrium Dendrobatidis ◽  
Fungal Isolate ◽  
Computer Cluster ◽  
Phylogenetic Placement ◽  
Long Reads ◽  
Illumina Data ◽  
Mitotic Gene Conversion ◽  
Downstream Analysis ◽  
Genomic Regions

Abstract Background Identifying haplotypes is central to sequence analysis in diploid or polyploid genomes. Despite this, there remains a lack of research and tools designed for physical phasing and its downstream analysis. Results HaplotypeTools is a new toolset to phase variant sites using VCF and BAM files and to analyse phased VCFs. Phasing is achieved via the identification of reads overlapping ≥ 2 heterozygous positions and then extended by additional reads, a process that can be parallelized across a computer cluster. HaplotypeTools includes various utility scripts for downstream analysis including crossover detection and phylogenetic placement of haplotypes to other lineages or species. HaplotypeTools was assessed for accuracy against WhatsHap using simulated short and long reads, demonstrating higher accuracy, albeit with reduced haplotype length. HaplotypeTools was also tested on real Illumina data to determine the ancestry of hybrid fungal isolate Batrachochytrium dendrobatidis (Bd) SA-EC3, finding 80% of haplotypes across the genome phylogenetically cluster with parental lineages BdGPL (39%) and BdCAPE (41%), indicating those are the parental lineages. Finally, ~ 99% of phasing was conserved between overlapping phase groups between SA-EC3 and either parental lineage, indicating mitotic gene conversion/parasexuality as the mechanism of recombination for this hybrid isolate. HaplotypeTools is open source and freely available from https://github.com/rhysf/HaplotypeTools under the MIT License. Conclusions HaplotypeTools is a powerful resource for analyzing hybrid or recombinant diploid or polyploid genomes and identifying parental ancestry for sub-genomic regions.

scholarly journals LRSDAY: Long-read Sequencing Data Analysis for Yeasts

2017 ◽  
Author(s):  
Jia-Xing Yue ◽  
Gianni Liti
Keyword(s):  
Genome Assembly ◽  
Model Organism ◽  
Sequencing Data ◽  
Protein Coding ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Long Read ◽  
Downstream Analysis ◽  
Eukaryotic Organisms ◽  
Genomic Regions

AbstractLong-read sequencing technologies have become increasingly popular in genome projects due to their strengths in resolving complex genomic regions. As a leading model organism with small genome size and great biotechnological importance, the budding yeast, Saccharomyces cerevisiae, has many isolates currently being sequenced with long reads. However, analyzing long-read sequencing data to produce high-quality genome assembly and annotation remains challenging. Here we present LRSDAY, the first one-stop solution to streamline this process. LRSDAY can produce chromosome-level end-to-end genome assembly and comprehensive annotations for various genomic features (including centromeres, protein-coding genes, tRNAs, transposable elements and telomere-associated elements) that are ready for downstream analysis. Although tailored for S. cerevisiae, we designed LRSDAY to be highly modular and customizable, making it adaptable for virtually any eukaryotic organisms. Applying LRSDAY to a S. cerevisiae strain takes ∼43 hrs to generate a complete and well-annotated genome from ∼100X Pacific Biosciences (PacBio) reads using four threads.

scholarly journals Haplotype threading: accurate polyploid phasing from long reads

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Sven D. Schrinner ◽  
Rebecca Serra Mari ◽  
Jana Ebler ◽  
Mikko Rautiainen ◽  
Lancelot Seillier ◽  
...  
Keyword(s):  
Open Source ◽  
Evolutionary History ◽  
State Of The Art ◽  
Regions Of Interest ◽  
Breeding Strategies ◽  
Polyploid Species ◽  
Two Stage ◽  
Long Reads ◽  
History Of ◽  
Genomic Regions

Abstract Resolving genomes at haplotype level is crucial for understanding the evolutionary history of polyploid species and for designing advanced breeding strategies. Polyploid phasing still presents considerable challenges, especially in regions of collapsing haplotypes.We present WhatsHap polyphase, a novel two-stage approach that addresses these challenges by (i) clustering reads and (ii) threading the haplotypes through the clusters. Our method outperforms the state-of-the-art in terms of phasing quality. Using a real tetraploid potato dataset, we demonstrate how to assemble local genomic regions of interest at the haplotype level. Our algorithm is implemented as part of the widely used open source tool WhatsHap.

scholarly journals A comprehensive evaluation of long read error correction methods

BMC Genomics ◽  
2020 ◽  
Vol 21 (S6) ◽  
Author(s):  
Haowen Zhang ◽  
Chirag Jain ◽  
Srinivas Aluru
Keyword(s):  
Error Correction ◽  
Hybrid Methods ◽  
Comprehensive Evaluation ◽  
Data Sets ◽  
Short Reads ◽  
Long Reads ◽  
Long Read ◽  
Read Error Correction ◽  
Downstream Analysis ◽  
The Impact

Abstract Background Third-generation single molecule sequencing technologies can sequence long reads, which is advancing the frontiers of genomics research. However, their high error rates prohibit accurate and efficient downstream analysis. This difficulty has motivated the development of many long read error correction tools, which tackle this problem through sampling redundancy and/or leveraging accurate short reads of the same biological samples. Existing studies to asses these tools use simulated data sets, and are not sufficiently comprehensive in the range of software covered or diversity of evaluation measures used. Results In this paper, we present a categorization and review of long read error correction methods, and provide a comprehensive evaluation of the corresponding long read error correction tools. Leveraging recent real sequencing data, we establish benchmark data sets and set up evaluation criteria for a comparative assessment which includes quality of error correction as well as run-time and memory usage. We study how trimming and long read sequencing depth affect error correction in terms of length distribution and genome coverage post-correction, and the impact of error correction performance on an important application of long reads, genome assembly. We provide guidelines for practitioners for choosing among the available error correction tools and identify directions for future research. Conclusions Despite the high error rate of long reads, the state-of-the-art correction tools can achieve high correction quality. When short reads are available, the best hybrid methods outperform non-hybrid methods in terms of correction quality and computing resource usage. When choosing tools for use, practitioners are suggested to be careful with a few correction tools that discard reads, and check the effect of error correction tools on downstream analysis. Our evaluation code is available as open-source at https://github.com/haowenz/LRECE.

scholarly journals Pan-genome sequence analysis using Panseq: an online tool for the rapid analysis of core and accessory genomic regions

2010 ◽  
Vol 11 (1) ◽  
pp. 461 ◽  
Author(s):  
Chad Laing ◽  
Cody Buchanan ◽  
Eduardo N Taboada ◽  
Yongxiang Zhang ◽  
Andrew Kropinski ◽  
...  
Keyword(s):  
Sequence Analysis ◽  
Genome Sequence ◽  
Rapid Analysis ◽  
Online Tool ◽  
Genome Sequence Analysis ◽  
Pan Genome ◽  
Genomic Regions

Sequence Analysis of Genomic Regions Containing Trinucleotide Repeats Isolated by a Novel Cloning Method

Genomics ◽  
1999 ◽  
Vol 57 (1) ◽  
pp. 169-172 ◽  
Author(s):  
Shinichi Inoue ◽  
Kazuhiko Takahashi ◽  
Masataka Ohta
Keyword(s):  
Sequence Analysis ◽  
Trinucleotide Repeats ◽  
Cloning Method ◽  
Genomic Regions

scholarly journals Reduced representation optical methylation mapping (R2OM2)

2017 ◽  
Author(s):  
Assaf Grunwald ◽  
Hila Sharim ◽  
Tslil Gabrieli ◽  
Yael Michaeli ◽  
Dmitry Torchinsky ◽  
...  
Keyword(s):  
Single Molecule ◽  
Genome Mapping ◽  
Single Cells ◽  
Methylation Status ◽  
Next Generation ◽  
Whole Genome Analysis ◽  
Reduced Representation ◽  
Long Reads ◽  
Structural Aberrations ◽  
Genomic Regions

ABSTRACTReduced representation methylation profiling is a method of analysis in which a subset of CpGs is used to report the overall methylation status of the probed genomic regions. This approach has been widely adopted for genome-scale bisulfite sequencing since it requires fewer sequencing reads and uses significantly less starting material than whole-genome analysis. Consequently, this method is suitable for profiling medical samples and single cells at high throughput and reduced costs. Here, we use this concept in order to create a pattern of fluorescent optical methylation profiles along individual DNA molecules. Reduced representation optical methylation mapping (R2OM2) in combination with Bionano Genomics next generation genome mapping (NGM) technology provides a hybrid genetic/epigenetic genome map of individual chromosome segments spanning hundreds of kilobase pairs (kbp). These long reads, along with the single-molecule resolution, allow for epigenetic variation calling and methylation analysis of large structural aberrations such as pathogenic macrosatellite arrays not accessible to single-cell next generation sequencing (NGS). We apply this method to facioscapulohumeral dystrophy (FSHD) showing both structural variation and hypomethylation status of a disease-associated, highly repetitive locus on chromosome 4q.

scholarly journals NanoCaller for accurate detection of SNPs and indels in difficult-to-map regions from long-read sequencing by haplotype-aware deep neural networks

2019 ◽  
Author(s):  
Umair Ahsan ◽  
Qian Liu ◽  
Li Fang ◽  
Kai Wang
Keyword(s):  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Variant Calling ◽  
Sequencing Data ◽  
Long Reads ◽  
Novel Variants ◽  
Long Read ◽  
Variant Detection ◽  
Genomic Regions ◽  
Haplotype Information

AbstractVariant (SNPs/indels) detection from high-throughput sequencing data remains an important yet unresolved problem. Long-read sequencing enables variant detection in difficult-to-map genomic regions that short-read sequencing cannot reliably examine (for example, only ~80% of genomic regions are marked as “high-confidence region” to have SNP/indel calls in the Genome In A Bottle project); however, the high per-base error rate poses unique challenges in variant detection. Existing methods on long-read data typically rely on analyzing pileup information from neighboring bases surrounding a candidate variant, similar to short-read variant callers, yet the benefits of much longer read length are not fully exploited. Here we present a deep neural network called NanoCaller, which detects SNPs by examining pileup information solely from other nonadjacent candidate SNPs that share the same long reads using long-range haplotype information. With called SNPs by NanoCaller, NanoCaller phases long reads and performs local realignment on two sets of phased reads to call indels by another deep neural network. Extensive evaluation on 5 human genomes (sequenced by Nanopore and PacBio long-read techniques) demonstrated that NanoCaller greatly improved performance in difficult-to-map regions, compared to other long-read variant callers. We experimentally validated 41 novel variants in difficult-to-map regions in a widely-used benchmarking genome, which cannot be reliably detected previously. We extensively evaluated the run-time characteristics and the sensitivity of parameter settings of NanoCaller to different characteristics of sequencing data. Finally, we achieved the best performance in Nanopore-based variant calling from MHC regions in the PrecisionFDA Variant Calling Challenge on Difficult-to-Map Regions by ensemble calling. In summary, by incorporating haplotype information in deep neural networks, NanoCaller facilitates the discovery of novel variants in complex genomic regions from long-read sequencing data.

scholarly journals Impact of short-read sequencing on the misassembly of a plant genome

2021 ◽  
Author(s):  
Peipei Wang ◽  
Fanrui Meng ◽  
Bethany M. Moore ◽  
Shin-Han Shiu
Keyword(s):  
Great Majority ◽  
Plant Genome ◽  
High Coverage ◽  
Short Read ◽  
Short Read Sequencing ◽  
Sequencing Technologies ◽  
Long Read ◽  
Downstream Analysis ◽  
Genomic Regions ◽  
Simple Sequence

Abstract Background: Availability of plant genome sequences has led to significant advances. However, with few exceptions, the great majority of existing genome assemblies are derived from short read sequencing technologies with highly uneven read coverages indicative of sequencing and assembly issues that could significantly impact any downstream analysis of plant genomes. In tomato for example, 0.6% (5.1 Mb) and 9.7% (79.6 Mb) of short-read based assembly had significantly higher and lower coverage compared to background, respectively.Results: To understand what the causes may be for such uneven coverage, we first established machine learning models capable of predicting genomic regions with variable coverages and found that high coverage regions tend to have higher simple sequence repeat and tandem gene densities compared to background regions. To determine if the high coverage regions were misassembled, we examined a recently available tomato long-read based assembly and found that 27.8% (1.41 Mb) of high coverage regions were potentially misassembled of duplicate sequences, compared to 1.4% in background regions. In addition, using a predictive model that can distinguish correctly and incorrectly assembled high coverage regions, we found that misassembled, high coverage regions tend to be flanked by simple sequence repeats, pseudogenes, and transposon elements. Conclusions: Our study provides insights on the causes of variable coverage regions and a quantitative assessment of factors contributing to plant genome misassembly when using short reads.

scholarly journals The evaluation of RNA-Seq de novo assembly by PacBio long read sequencing

2019 ◽  
Author(s):  
Yifan Yang ◽  
Michael Gribskov
Keyword(s):  
Real Time ◽  
De Novo ◽  
Critical Issue ◽  
Evaluation Methods ◽  
Model Organisms ◽  
Rna Seq ◽  
Long Reads ◽  
Long Read ◽  
Set Up ◽  
Downstream Analysis

AbstractRNA-Seq de novo assembly is an important method to generate transcriptomes for non-model organisms before any downstream analysis. Given many great de novo assembly methods developed by now, one critical issue is that there is no consensus on the evaluation of de novo assembly methods yet. Therefore, to set up a benchmark for evaluating the quality of de novo assemblies is very critical. Addressing this challenge will help us deepen the insights on the properties of different de novo assemblers and their evaluation methods, and provide hints on choosing the best assembly sets as transcriptomes of non-model organisms for the further functional analysis. In this article, we generate a “real time” transcriptome using PacBio long reads as a benchmark for evaluating five de novo assemblers and two model-based de novo assembly evaluation methods. By comparing the de novo assmblies generated by RNA-Seq short reads with the “real time” transcriptome from the same biological sample, we find that Trinity is best at the completeness by generating more assemblies than the alternative assemblers, but less continuous and having more misassemblies; Oases is best at the continuity and specificity, but less complete; The performance of SOAPdenovo-Trans, Trans-AByss and IDBA-Tran are in between of five assemblers. For evaluation methods, DETONATE leverages multiple aspects of the assembly set and ranks the assembly set with an average performance as the best, meanwhile the contig score can serve as a good metric to select assemblies with high completeness, specificity, continuity but not sensitive to misassemblies; TransRate contig score is useful for removing misassemblies, yet often the assemblies in the optimal set is too few to be used as a transcriptome.

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