Improving read alignment through the generation of alternative reference via iterative strategy

Abstract There is generally one standard reference sequence for each species. When extensive variations exist in other breeds of the species, it can lead to ambiguous alignment and inaccurate variant calling and, in turn, compromise the accuracy of downstream analysis. Here, with the help of the FPGA hardware platform, we present a method that generates an alternative reference via an iterative strategy to improve the read alignment for breeds that are genetically distant to the reference breed. Compared to the published reference genomes, by using the alternative reference sequences we built, the mapping rates of Chinese indigenous pigs and chickens were improved by 0.61–1.68% and 0.09–0.45%, respectively. These sequences also enable researchers to recover highly variable regions that could be missed using public reference sequences. We also determined that the optimal number of iterations needed to generate alternative reference sequences were seven and five for pigs and chickens, respectively. Our results show that, for genetically distant breeds, generating an alternative reference sequence can facilitate read alignment and variant calling and improve the accuracy of downstream analyses.

Download Full-text

AirLift: A Fast and Comprehensive Technique for Remapping Alignments between Reference Genomes

10.1101/2021.02.16.431517 ◽

2021 ◽

Author(s):

Jeremie S. Kim ◽

Can Firtina ◽

Meryem Banu Cavlak ◽

Damla Senol Cali ◽

Nastaran Hajinazar ◽

...

Keyword(s):

Reference Genome ◽

State Of The Art ◽

Variant Calling ◽

Ground Truth ◽

Data Set ◽

C Elegans ◽

A Genome ◽

Downstream Analysis ◽

Similar Accuracy ◽

Reference Genomes

AbstractAs genome sequencing tools and techniques improve, researchers are able to incrementally assemble more accurate reference genomes, which enable sensitivity in read mapping and downstream analysis such as variant calling. A more sensitive downstream analysis is critical for a better understanding of the genome donor (e.g., health characteristics). Therefore, read sets from sequenced samples should ideally be mapped to the latest available reference genome that represents the most relevant population. Unfortunately, the increasingly large amount of available genomic data makes it prohibitively expensive to fully re-map each read set to its respective reference genome every time the reference is updated. There are several tools that attempt to accelerate the process of updating a read data set from one reference to another (i.e., remapping) by 1) identifying regions that appear similarly between two references and 2) updating the mapping location of reads that map to any of the identified regions in the old reference to the corresponding similar region in the new reference. The main drawback of existing approaches is that if a read maps to a region in the old reference that does not appear with a reasonable degree of similarity in the new reference, the read cannot be remapped. We find that, as a result of this drawback, a significant portion of annotations (i.e., coding regions in a genome) are lost when using state-of-the-art remapping tools. To address this major limitation in existing tools, we propose AirLift, a fast and comprehensive technique for remapping alignments from one genome to another. Compared to the state-of-the-art method for remapping reads (i.e., full mapping), AirLift reduces 1) the number of reads (out of the entire read set) that need to be fully mapped to the new reference by up to 99.99% and 2) the overall execution time to remap read sets between two reference genome versions by 6.7×, 6.6×, and 2.8× for large (human), medium (C. elegans), and small (yeast) reference genomes, respectively. We validate our remapping results with GATK and find that AirLift provides similar accuracy in identifying ground truth SNP and INDEL variants as the baseline of fully mapping a read set.Code AvailabilityAirLift source code and readme describing how to reproduce our results are available at https://github.com/CMU-SAFARI/AirLift.

Download Full-text

Assessing Bos taurus introgression in the UOA Bos indicus assembly

Genetics Selection Evolution ◽

10.1186/s12711-021-00688-1 ◽

2021 ◽

Vol 53 (1) ◽

Author(s):

Maulana M. Naji ◽

Yuri T. Utsunomiya ◽

Johann Sölkner ◽

Benjamin D. Rosen ◽

Gábor Mészáros

Keyword(s):

Bos Taurus ◽

Sequence Data ◽

Variant Calling ◽

Principal Component ◽

Reference Sequence ◽

Sequencing Analysis ◽

Single Nucleotide Variants ◽

Reference Allele ◽

Brahman Cattle ◽

Reference Genomes

Abstract Background Reference genomes are essential in the analysis of genomic data. As the cost of sequencing decreases, multiple reference genomes are being produced within species to alleviate problems such as low mapping accuracy and reference allele bias in variant calling that can be associated with the alignment of divergent samples to a single reference individual. The latest reference sequence adopted by the scientific community for the analysis of cattle data is ARS_UCD1.2, built from the DNA of a Hereford cow (Bos taurus taurus—B. taurus). A complementary genome assembly, UOA_Brahman_1, was recently built to represent the other cattle subspecies (Bos taurus indicus—B. indicus) from a Brahman cow haplotype to further support analysis of B. indicus data. In this study, we aligned the sequence data of 15 B. taurus and B. indicus breeds to each of these references. Results The alignment of B. taurus individuals against UOA_Brahman_1 detected up to five million more single-nucleotide variants (SNVs) compared to that against ARS_UCD1.2. Similarly, the alignment of B. indicus individuals against ARS_UCD1.2 resulted in one and a half million more SNVs than that against UOA_Brahman_1. The number of SNVs with nearly fixed alternative alleles also increased in the alignments with cross-subspecies. Interestingly, the alignment of B. taurus cattle against UOA_Brahman_1 revealed regions with a smaller than expected number of counts of SNVs with nearly fixed alternative alleles. Since B. taurus introgression represents on average 10% of the genome of Brahman cattle, we suggest that these regions comprise taurine DNA as opposed to indicine DNA in the UOA_Brahman_1 reference genome. Principal component and admixture analyses using genotypes inferred from this region support these taurine-introgressed loci. Overall, the flagged taurine segments represent 13.7% of the UOA_Brahman_1 assembly. The genes located within these segments were previously reported to be under positive selection in Brahman cattle, and include functional candidate genes implicated in feed efficiency, development and immunity. Conclusions We report a list of taurine segments that are in the UOA_Brahman_1 assembly, which will be useful for the interpretation of interesting genomic features (e.g., signatures of selection, runs of homozygosity, increased mutation rate, etc.) that could appear in future re-sequencing analysis of indicine cattle.

Download Full-text

Fast and Accurate Genomic Analyses using Genome Graphs

10.1101/194530 ◽

2017 ◽

Cited By ~ 8

Author(s):

Goran Rakocevic ◽

Vladimir Semenyuk ◽

James Spencer ◽

John Browning ◽

Ivan Johnson ◽

...

Keyword(s):

Large Scale ◽

Reference Genome ◽

Variant Calling ◽

Simulated Data ◽

Significant Advance ◽

Unified Framework ◽

Structural Variations ◽

Read Alignment ◽

Genomic Analyses ◽

Downstream Analysis

AbstractThe human reference genome serves as the foundation for genomics by providing a scaffold for alignment of sequencing reads, but currently only reflects a single consensus haplotype, which impairs read alignment and downstream analysis accuracy. Reference genome structures incorporating known genetic variation have been shown to improve the accuracy of genomic analyses, but have so far remained computationally prohibitive for routine large-scale use. Here we present a graph genome implementation that enables read alignment across 2,800 diploid genomes encompassing 12.6 million SNPs and 4.0 million indels. Our Graph Genome Pipeline requires 6.5 hours to process a 30x coverage WGS sample on a system with 36 CPU cores compared with 11 hours required by the GATK Best Practices pipeline. Using complementary benchmarking experiments based on real and simulated data, we show that using a graph genome reference improves read mapping sensitivity and produces a 0.5% increase in variant calling recall, or about 20,000 additional variants being detected per sample, while variant calling specificity is unaffected. Structural variations (SVs) incorporated into a graph genome can be genotyped accurately under a unified framework. Finally, we show that iterative augmentation of graph genomes yields incremental gains in variant calling accuracy. Our implementation is a significant advance towards fulfilling the promise of graph genomes to radically enhance the scalability and accuracy of genomic analyses.

Download Full-text

Multiple Variant Calling Pipelines in Wheat Whole Exome Sequencing

International Journal of Molecular Sciences ◽

10.3390/ijms221910400 ◽

2021 ◽

Vol 22 (19) ◽

pp. 10400

Author(s):

H. Busra Cagirici ◽

Bala Ani Akpinar ◽

Taner Z. Sen ◽

Hikmet Budak

Keyword(s):

Exome Sequencing ◽

Whole Exome Sequencing ◽

Variant Calling ◽

High Sensitivity ◽

Whole Exome ◽

The Impact ◽

Multiple Reference ◽

Reference Genomes ◽

Selection Of ◽

Variant Identification

The highly challenging hexaploid wheat (Triticum aestivum) genome is becoming ever more accessible due to the continued development of multiple reference genomes, a factor which aids in the plight to better understand variation in important traits. Although the process of variant calling is relatively straightforward, selection of the best combination of the computational tools for read alignment and variant calling stages of the analysis and efficient filtering of the false variant calls are not always easy tasks. Previous studies have analyzed the impact of methods on the quality metrics in diploid organisms. Given that variant identification in wheat largely relies on accurate mining of exome data, there is a critical need to better understand how different methods affect the analysis of whole exome sequencing (WES) data in polyploid species. This study aims to address this by performing whole exome sequencing of 48 wheat cultivars and assessing the performance of various variant calling pipelines at their suggested settings. The results show that all the pipelines require filtering to eliminate false-positive calls. The high consensus among the reference SNPs called by the best-performing pipelines suggests that filtering provides accurate and reproducible results. This study also provides detailed comparisons for high sensitivity and precision at individual and population levels for the raw and filtered SNP calls.

Download Full-text

Best practices for variant calling in clinical sequencing

Genome Medicine ◽

10.1186/s13073-020-00791-w ◽

2020 ◽

Vol 12 (1) ◽

Author(s):

Daniel C. Koboldt

Keyword(s):

Best Practices ◽

Single Molecule ◽

Best Practice ◽

Variant Calling ◽

Clinical Samples ◽

Clinical Genetic ◽

Inherited Disorders ◽

Clinical Sequencing ◽

Sequencing Technologies ◽

Downstream Analysis

Abstract Next-generation sequencing technologies have enabled a dramatic expansion of clinical genetic testing both for inherited conditions and diseases such as cancer. Accurate variant calling in NGS data is a critical step upon which virtually all downstream analysis and interpretation processes rely. Just as NGS technologies have evolved considerably over the past 10 years, so too have the software tools and approaches for detecting sequence variants in clinical samples. In this review, I discuss the current best practices for variant calling in clinical sequencing studies, with a particular emphasis on trio sequencing for inherited disorders and somatic mutation detection in cancer patients. I describe the relative strengths and weaknesses of panel, exome, and whole-genome sequencing for variant detection. Recommended tools and strategies for calling variants of different classes are also provided, along with guidance on variant review, validation, and benchmarking to ensure optimal performance. Although NGS technologies are continually evolving, and new capabilities (such as long-read single-molecule sequencing) are emerging, the “best practice” principles in this review should be relevant to clinical variant calling in the long term.

Download Full-text

QuanTest2: benchmarking multiple sequence alignments using secondary structure prediction

Bioinformatics ◽

10.1093/bioinformatics/btz552 ◽

2019 ◽

Cited By ~ 3

Author(s):

Fabian Sievers ◽

Desmond G Higgins

Keyword(s):

Secondary Structure ◽

Structure Prediction ◽

Secondary Structure Prediction ◽

Reference Sequence ◽

Supplementary Information ◽

Sequence Alignments ◽

Multiple Sequence ◽

Multiple Sequence Alignments ◽

Reference Sequences ◽

Selection Of

Abstract Motivation Secondary structure prediction accuracy (SSPA) in the QuanTest benchmark can be used to measure accuracy of a multiple sequence alignment. SSPA correlates well with the sum-of-pairs score, if the results are averaged over many alignments but not on an alignment-by-alignment basis. This is due to a sub-optimal selection of reference and non-reference sequences in QuanTest. Results We develop an improved strategy for selecting reference and non-reference sequences for a new benchmark, QuanTest2. In QuanTest2, SSPA and SP correlate better on an alignment-by-alignment basis than in QuanTest. Guide-trees for QuanTest2 are more balanced with respect to reference sequences than in QuanTest. QuanTest2 scores correlate well with other well-established benchmarks. Availability and implementation QuanTest2 is available at http://bioinf.ucd.ie/quantest2.tar, comprises of reference and non-reference sequence sets and a scoring script. Supplementary information Supplementary data are available at Bioinformatics online

Download Full-text

SMRT Genome Assembly Corrects Reference Errors, Resolving the Genetic Basis of Virulence in Mycobacterium tuberculosis

10.1101/064840 ◽

2016 ◽

Author(s):

Afif Elghraoui ◽

Samuel J Modlin ◽

Faramarz Valafar

Keyword(s):

Mycobacterium Tuberculosis ◽

Single Molecule ◽

Genetic Basis ◽

Reference Genome ◽

Reference Sequence ◽

Smrt Sequencing ◽

Virulence Attenuation ◽

Sequencing Platforms ◽

Genome Comparisons ◽

Reference Genomes

AbstractThe genetic basis of virulence in Mycobacterium tuberculosis has been investigated through genome comparisons of its virulent (H37Rv) and attenuated (H37Ra) sister strains. Such analysis, however, relies heavily on the accuracy of the sequences. While the H37Rv reference genome has had several corrections to date, that of H37Ra is unmodified since its original publication. Here, we report the assembly and finishing of the H37Ra genome from single-molecule, real-time (SMRT) sequencing. Our assembly reveals that the number of H37Ra-specific variants is less than half of what the Sanger-based H37Ra reference sequence indicates, undermining and, in some cases, invalidating the conclusions of several studies. PE_PPE family genes, which are intractable to commonly-used sequencing platforms because of their repetitive and GC-rich nature, are overrepresented in the set of genes in which all reported H37Ra-specific variants are contradicted. We discuss how our results change the picture of virulence attenuation and the power of SMRT sequencing for producing high-quality reference genomes.

Download Full-text

Information theoretic alignment free variant calling

PeerJ Computer Science ◽

10.7717/peerj-cs.71 ◽

2016 ◽

Vol 2 ◽

pp. e71

Author(s):

Justin Bedo ◽

Benjamin Goudey ◽

Jeremy Wazny ◽

Zeyu Zhou

Keyword(s):

Sequence Data ◽

Multinomial Distribution ◽

Variant Calling ◽

Whole Genome Sequence ◽

Reference Sequence ◽

Information Theoretic ◽

Learning Tasks ◽

Leibler Divergence ◽

Suitable Reference ◽

Mouse Dataset

While traditional methods for calling variants across whole genome sequence data rely on alignment to an appropriate reference sequence, alternative techniques are needed when a suitable reference does not exist. We present a novel alignment and assembly free variant calling method based on information theoretic principles designed to detect variants have strong statistical evidence for their ability to segregate samples in a given dataset. Our method uses the context surrounding a particular nucleotide to define variants. Given a set of reads, we model the probability of observing a given nucleotide conditioned on the surrounding prefix and suffixes of lengthkas a multinomial distribution. We then estimate which of these contexts are stable intra-sample and varying inter-sample using a statistic based on the Kullback–Leibler divergence.The utility of the variant calling method was evaluated through analysis of a pair of bacterial datasets and a mouse dataset. We found that our variants are highly informative for supervised learning tasks with performance similar to standard reference based calls and another reference free method (DiscoSNP++). Comparisons against reference based calls showed our method was able to capture very similar population structure on the bacterial dataset. The algorithm’s focus on discriminatory variants makes it suitable for many common analysis tasks for organisms that are too diverse to be mapped back to a single reference sequence.

Download Full-text

Supervised extraction of near-complete genomes from metagenomic samples: A new service in PATRIC

PLoS ONE ◽

10.1371/journal.pone.0250092 ◽

2021 ◽

Vol 16 (4) ◽

pp. e0250092

Author(s):

Bruce Parrello ◽

Rory Butler ◽

Philippe Chlenski ◽

Gordon D. Pusch ◽

Ross Overbeek

Keyword(s):

Draft Genome ◽

Single Copy ◽

Metagenomic Data ◽

Use Case ◽

High Quality ◽

Complete Genomes ◽

Downstream Analysis ◽

Low Coverage ◽

Derived Data ◽

Reference Genomes

Large amounts of metagenomically-derived data are submitted to PATRIC for analysis. In the future, we expect even more jobs submitted to PATRIC will use metagenomic data. One in-demand use case is the extraction of near-complete draft genomes from assembled contigs of metagenomic origin. The PATRIC metagenome binning service utilizes the PATRIC database to furnish a large, diverse set of reference genomes. We provide a new service for supervised extraction and annotation of high-quality, near-complete genomes from metagenomically-derived contigs. Reference genomes are assigned to putative draft genome bins based on the presence of single-copy universal marker roles in the sample, and contigs are sorted into these bins by their similarity to reference genomes in PATRIC. Each set of binned contigs represents a draft genome that will be annotated by RASTtk in PATRIC. A structured-language binning report is provided containing quality measurements and taxonomic information about the contig bins. The PATRIC metagenome binning service emphasizes extraction of high-quality genomes for downstream analysis using other PATRIC tools and services. Due to its supervised nature, the binning service is not appropriate for mining novel or extremely low-coverage genomes from metagenomic samples.

Download Full-text

FaStore – a space-saving solution for raw sequencing data

10.1101/168096 ◽

2017 ◽

Cited By ~ 1

Author(s):

Łukasz Roguski ◽

Idoia Ochoa ◽

Mikel Hernaez ◽

Sebastian Deorowicz

Keyword(s):

Decision Making ◽

Compression Ratio ◽

Clinical Decision Making ◽

Variant Calling ◽

Clinical Decision ◽

Genomic Information ◽

Sequencing Data ◽

Space Saving ◽

Reference Sequences ◽

Significant Compression

AbstractThe affordability of DNA sequencing has led to the generation of unprecedented volumes of raw sequencing data. These data must be stored, processed, and transmitted, which poses significant challenges. To facilitate this effort, we introduce FaStore, a specialized compressor for FASTQ files. The proposed algorithm does not use any reference sequences for compression, and permits the user to choose from several lossy modes to improve the overall compression ratio, depending on the specific needs. We demonstrate through extensive simulations that FaStore achieves a significant improvement in compression ratio with respect to previously proposed algorithms for this task. In addition, we perform an analysis on the effect that the different lossy modes have on variant calling, the most widely used application for clinical decision making, especially important in the era of precision medicine. We show that lossy compression can offer significant compression gains, while preserving the essential genomic information and without affecting the variant calling performance.

Download Full-text