Population-specific genome graphs improve high-throughput sequencing data analysis: A case study on the Pan-African genome

2021 ◽  
Author(s):  
H. Serhat Tetikol ◽  
Kubra Narci ◽  
Deniz Turgut ◽  
Gungor Budak ◽  
Ozem Kalay ◽  
...  

ABSTRACTGraph-based genome reference representations have seen significant development, motivated by the inadequacy of the current human genome reference for capturing the diverse genetic information from different human populations and its inability to maintain the same level of accuracy for non-European ancestries. While there have been many efforts to develop computationally efficient graph-based bioinformatics toolkits, how to curate genomic variants and subsequently construct genome graphs remains an understudied problem that inevitably determines the effectiveness of the end-to-end bioinformatics pipeline. In this study, we discuss major obstacles encountered during graph construction and propose methods for sample selection based on population diversity, graph augmentation with structural variants and resolution of graph reference ambiguity caused by information overload. Moreover, we present the case for iteratively augmenting tailored genome graphs for targeted populations and test the proposed approach on the whole-genome samples of African ancestry. Our results show that, as more representative alternatives to linear or generic graph references, population-specific graphs can achieve significantly lower read mapping errors, increased variant calling sensitivity and provide the improvements of joint variant calling without the need of computationally intensive post-processing steps.

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Gwenna Breton ◽  
Anna C. V. Johansson ◽  
Per Sjödin ◽  
Carina M. Schlebusch ◽  
Mattias Jakobsson

Abstract Background Population genetic studies of humans make increasing use of high-throughput sequencing in order to capture diversity in an unbiased way. There is an abundance of sequencing technologies, bioinformatic tools and the available genomes are increasing in number. Studies have evaluated and compared some of these technologies and tools, such as the Genome Analysis Toolkit (GATK) and its “Best Practices” bioinformatic pipelines. However, studies often focus on a few genomes of Eurasian origin in order to detect technical issues. We instead surveyed the use of the GATK tools and established a pipeline for processing high coverage full genomes from a diverse set of populations, including Sub-Saharan African groups, in order to reveal challenges from human diversity and stratification. Results We surveyed 29 studies using high-throughput sequencing data, and compared their strategies for data pre-processing and variant calling. We found that processing of data is very variable across studies and that the GATK “Best Practices” are seldom followed strictly. We then compared three versions of a GATK pipeline, differing in the inclusion of an indel realignment step and with a modification of the base quality score recalibration step. We applied the pipelines on a diverse set of 28 individuals. We compared the pipelines in terms of count of called variants and overlap of the callsets. We found that the pipelines resulted in similar callsets, in particular after callset filtering. We also ran one of the pipelines on a larger dataset of 179 individuals. We noted that including more individuals at the joint genotyping step resulted in different counts of variants. At the individual level, we observed that the average genome coverage was correlated to the number of variants called. Conclusions We conclude that applying the GATK “Best Practices” pipeline, including their recommended reference datasets, to underrepresented populations does not lead to a decrease in the number of called variants compared to alternative pipelines. We recommend to aim for coverage of > 30X if identifying most variants is important, and to work with large sample sizes at the variant calling stage, also for underrepresented individuals and populations.


2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Gundula Povysil ◽  
Monika Heinzl ◽  
Renato Salazar ◽  
Nicholas Stoler ◽  
Anton Nekrutenko ◽  
...  

Abstract Duplex sequencing is currently the most reliable method to identify ultra-low frequency DNA variants by grouping sequence reads derived from the same DNA molecule into families with information on the forward and reverse strand. However, only a small proportion of reads are assembled into duplex consensus sequences (DCS), and reads with potentially valuable information are discarded at different steps of the bioinformatics pipeline, especially reads without a family. We developed a bioinformatics toolset that analyses the tag and family composition with the purpose to understand data loss and implement modifications to maximize the data output for the variant calling. Specifically, our tools show that tags contain polymerase chain reaction and sequencing errors that contribute to data loss and lower DCS yields. Our tools also identified chimeras, which likely reflect barcode collisions. Finally, we also developed a tool that re-examines variant calls from raw reads and provides different summary data that categorizes the confidence level of a variant call by a tier-based system. With this tool, we can include reads without a family and check the reliability of the call, that increases substantially the sequencing depth for variant calling, a particular important advantage for low-input samples or low-coverage regions.


2019 ◽  
Author(s):  
Elena Nabieva ◽  
Satyarth Mishra Sharma ◽  
Yermek Kapushev ◽  
Sofya K. Garushyants ◽  
Anna V. Fedotova ◽  
...  

AbstractHigh-throughput sequencing of fetal DNA is a promising and increasingly common method for the discovery of all (or all coding) genetic variants in the fetus, either as part of prenatal screening or diagnosis, or for genetic diagnosis of spontaneous abortions. In many cases, the fetal DNA (from chorionic villi, amniotic fluid, or abortive tissue) can be contaminated with maternal cells, resulting in the mixture of fetal and maternal DNA. This maternal cell contamination (MCC) undermines the assumption, made by traditional variant callers, that each allele in a heterozygous site is covered, on average, by 50% of the reads, and therefore can lead to erroneous genotype calls. We present a panel of methods for reducing the genotyping error in the presence of MCC. All methods start with the output of GATK HaplotypeCaller on the sequencing data for the (contaminated) fetal sample and both of its parents, and additionally rely on information about the MCC fraction (which itself is readily estimated from the high-throughput sequencing data). The first of these methods uses a Bayesian probabilistic model to correct the fetal genotype calls produced by MCC-unaware HaplotypeCaller. The other two methods “learn” the genotype-correction model from examples. We use simulated contaminated fetal data to train and test the models. Using the test sets, we show that all three methods lead to substantially improved accuracy when compared with the original MCC-unaware HaplotypeCaller calls. We then apply the best-performing method to three chorionic villus samples from spontaneously terminated pregnancies.Code and training data availabilityhttps://github.com/bazykinlab/ML-maternal-cell-contamination


Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 5453-5453
Author(s):  
Katerina Gemenetzi ◽  
Andreas Agathangelidis ◽  
Fotis Psomopoulos ◽  
Karla Plevova ◽  
Lesley-Ann Sutton ◽  
...  

Stereotyped subset #2 (IGHV3-21/IGLV3-21) is the largest subset in CLL (~3% of all patients). Membership in subset #2 is clinically relevant since these patients experience an aggressive disease irrespective of the somatic hypermutation (SHM) status of the clonotypic immunoglobulin heavy variable (IGHV) gene. Low-throughput evidence suggests that stereotyped subset #169, a minor CLL subset (~0.2% of all CLL), resembles subset #2 at the immunogenetic level. More specifically: (i) the clonotypic heavy chain (HC) of subset #169 is encoded by the IGHV3-48 gene which is closely related to the IGHV3-21 gene; (ii) both subsets carry VH CDR3s comprising 9-amino acids (aa) with a conserved aspartic acid (D) at VH CDR3 position 3; (iii) both subsets bear light chains (LC) encoded by the IGLV3-21 gene with a restricted VL CDR3; and, (iv) both subsets have borderline SHM status. Here we comprehensively assessed the ontogenetic relationship between CLL subsets #2 and #169 by analyzing their immunogenetic signatures. Utilizing next-generation sequencing (NGS) we studied the HC and LC gene rearrangements of 6 subset #169 patients and 20 subset #2 cases. In brief, IGHV-IGHD-IGHJ and IGLV-IGLJ gene rearrangements were RT-PCR amplified using subgroup-specific leader primers as well as IGHJ and IGLC primers, respectively. Libraries were sequenced on the MiSeq Illumina instrument. IG sequence annotation was performed with IMGT/HighV-QUEST and metadata analysis conducted using an in-house, validated bioinformatics pipeline. Rearrangements with identical CDR3 aa sequences were herein defined as clonotypes, whereas clonotypes with different aa substitutions within the V-domain were defined as subclones. For the HC analysis of subset #169, we obtained 894,849 productive sequences (mean: 127,836, range: 87,509-208,019). On average, each analyzed sample carried 54 clonotypes (range: 44-68); the dominant clonotype had a mean frequency of 99.1% (range: 98.8-99.2%) and displayed considerable intraclonal heterogeneity with a mean of 2,641 subclones/sample (range: 1,566-6,533). For the LCs of subset #169, we obtained 2,096,728 productive sequences (mean: 299,533, range: 186,637-389,258). LCs carried a higher number of distinct clonotypes/sample compared to their partner HCs (mean: 148, range: 110-205); the dominant clonotype had a mean frequency of 98.1% (range: 97.2-98.6%). Intraclonal heterogeneity was also observed in the LCs, with a mean of 6,325 subclones/sample (range: 4,651-11,444), hence more pronounced than in their partner HCs. Viewing each of the cumulative VH and VL CDR3 sequence datasets as a single entity branching through diversification enabled the identification of common sequences. In particular, 2 VH clonotypes were present in 3/6 cases, while a single VL clonotype was present in all 6 cases, albeit at varying frequencies; interestingly, this VL CDR3 sequence was also detected in all subset #2 cases, underscoring the molecular similarities between the two subsets. Focusing on SHM, the following observations were made: (i) the frequent 3-nucleotide (AGT) deletion evidenced in the VH CDR2 of subset #2 (leading to the deletion of one of 5 consecutive serine residues) was also detected in all subset #169 cases at subclonal level (average: 6% per sample, range: 0.1-10.8%); of note, the 5-serine stretch is also present in the germline VH CDR2 of the IGHV3-48 gene; (ii) the R-to-G substitution at the VL-CL linker, a ubiquitous SHM in subset #2 and previously reported as critical for IG self-association leading to cell autonomous signaling in this subset, was present in all subset #169 samples as a clonal event with a mean frequency of 98.3%; and, finally, (iii) the S-to-G substitution at position 6 of the VL CDR3, present in all subset #2 cases (mean : 44.2% ,range: 6.3-87%), was also found in all #169 samples, representing a clonal event in 1 case (97.2% of all clonotypes) and a subclonal event in the remaining 5 cases (mean: 0.6%, range: 0.4-1.1%). In conclusion, the present high-throughput sequencing data cements the immunogenetic relatedness of CLL stereotyped subsets #2 and #169, further highlighting the role of antigen selection throughout their natural history. These findings also argue for a similar pathophysiology for these subsets that could also be reflected in a similar clonal behavior, with implications for risk stratification. Disclosures Sutton: Abbvie: Honoraria; Gilead: Honoraria; Janssen: Honoraria. Stamatopoulos:Abbvie: Honoraria, Research Funding; Janssen: Honoraria, Research Funding. Chatzidimitriou:Janssen: Honoraria.


Author(s):  
Melissa Davis ◽  
Rachel Martini ◽  
Lisa Newman ◽  
Olivier Elemento ◽  
Jason White ◽  
...  

Triple negative breast cancers (TNBCs) are molecularly heterogeneous, and the link between their aggressiveness with African ancestry is not established. We investigated primary TNBCs for gene expression among self-reported race (SRR) groups of African American (AA, n=42) and European American (EA, n=33) women. Using The Cancer Genome Atlas (TCGA) approaches, we analyzed RNA sequencing data to measure changes in genome-wide expression and used logistic regressions to identify ancestry-associated gene expression signatures. To determine global ancestry, GATK best practices were followed for variant calling, and used the 1000 Genomes Project as reference data. We identified >150 African ancestry-associated genes and found that, compared to SRR, quantitative genetic analysis was a more robust method to identify racial/ethnic-specific genes that were differentially expressed. A subset of African ancestry-specific genes that were upregulated in TNBCs of our AA patients were validated in TCGA data. In AA patients, there was a higher incidence of basal-like 2 tumors and altered TP53, NFB1, and AKT pathways. The distinct distribution of TNBC subtypes and altered oncologic pathways show that the ethnic variations in TNBCs are driven by shared genetic ancestry. Thus, to appreciate the molecular diversity of TNBCs, tumors from patients of various ancestral origins should be evaluated.


2019 ◽  
Vol 35 (22) ◽  
pp. 4716-4723 ◽  
Author(s):  
Daniel Tello ◽  
Juanita Gil ◽  
Cristian D Loaiza ◽  
John J Riascos ◽  
Nicolás Cardozo ◽  
...  

Abstract Motivation Accurate detection, genotyping and downstream analysis of genomic variants from high-throughput sequencing data are fundamental features in modern production pipelines for genetic-based diagnosis in medicine or genomic selection in plant and animal breeding. Our research group maintains the Next-Generation Sequencing Experience Platform (NGSEP) as a precise, efficient and easy-to-use software solution for these features. Results Understanding that incorrect alignments around short tandem repeats are an important source of genotyping errors, we implemented in NGSEP new algorithms for realignment and haplotype clustering of reads spanning indels and short tandem repeats. We performed extensive benchmark experiments comparing NGSEP to state-of-the-art software using real data from three sequencing protocols and four species with different distributions of repetitive elements. NGSEP consistently shows comparative accuracy and better efficiency compared to the existing solutions. We expect that this work will contribute to the continuous improvement of quality in variant calling needed for modern applications in medicine and agriculture. Availability and implementation NGSEP is available as open source software at http://ngsep.sf.net. Supplementary information Supplementary data are available at Bioinformatics online.


2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Marwan A. Hawari ◽  
Celine S. Hong ◽  
Leslie G. Biesecker

Abstract Background Somatic single nucleotide variants have gained increased attention because of their role in cancer development and the widespread use of high-throughput sequencing techniques. The necessity to accurately identify these variants in sequencing data has led to a proliferation of somatic variant calling tools. Additionally, the use of simulated data to assess the performance of these tools has become common practice, as there is no gold standard dataset for benchmarking performance. However, many existing somatic variant simulation tools are limited because they rely on generating entirely synthetic reads derived from a reference genome or because they do not allow for the precise customizability that would enable a more focused understanding of single nucleotide variant calling performance. Results SomatoSim is a tool that lets users simulate somatic single nucleotide variants in sequence alignment map (SAM/BAM) files with full control of the specific variant positions, number of variants, variant allele fractions, depth of coverage, read quality, and base quality, among other parameters. SomatoSim accomplishes this through a three-stage process: variant selection, where candidate positions are selected for simulation, variant simulation, where reads are selected and mutated, and variant evaluation, where SomatoSim summarizes the simulation results. Conclusions SomatoSim is a user-friendly tool that offers a high level of customizability for simulating somatic single nucleotide variants. SomatoSim is available at https://github.com/BieseckerLab/SomatoSim.


2019 ◽  
Author(s):  
Xing Wu ◽  
Christopher Heffelfinger ◽  
Hongyu Zhao ◽  
Stephen L. Dellaporta

Abstract Background The ability to accurately and comprehensively identify genomic variations is critical for plant studies utilizing high-throughput sequencing. Most bioinformatics tools for processing next-generation sequencing data were originally developed and tested in human studies, raising questions as to their efficacy for plant research. A detailed evaluation of the entire variant calling pipeline, including alignment, variant calling, variant filtering, and imputation was performed on different programs using both simulated and real plant genomic datasets. Results A comparison of SOAP2, Bowtie2, and BWA-MEM found that BWA-MEM was consistently able to align the most reads with high accuracy, whereas Bowtie2 had the highest overall accuracy. Comparative results of GATK HaplotypCaller versus SAMtools mpileup indicated that the choice of variant caller affected precision and recall differentially depending on the levels of diversity, sequence coverage and genome complexity. A cross-reference experiment of S. lycopersicum and S. pennellii reference genomes revealed the inadequacy of single reference genome for variant discovery that includes distantly-related plant individuals. Machine-learning-based variant filtering strategy outperformed the traditional hard-cutoff strategy resulting in higher number of true positive variants and fewer false positive variants. A 2-step imputation method, which utilized a set of high-confidence SNPs as the reference panel, showed up to 60% higher accuracy than direct LD-based imputation. Conclusions Programs in the variant discovery pipeline have different performance on plant genomic dataset. Choice of the programs is subjected to the goal of the study and available resources. This study serves as an important guiding information for plant biologists utilizing next-generation sequencing data for diversity characterization and crop improvement.


2013 ◽  
Vol 7 (Suppl 6) ◽  
pp. S8 ◽  
Author(s):  
Takahiro Mimori ◽  
Naoki Nariai ◽  
Kaname Kojima ◽  
Mamoru Takahashi ◽  
Akira Ono ◽  
...  

2020 ◽  
Vol 36 (9) ◽  
pp. 2725-2730
Author(s):  
Keisuke Shimmura ◽  
Yuki Kato ◽  
Yukio Kawahara

Abstract Motivation Genetic variant calling with high-throughput sequencing data has been recognized as a useful tool for better understanding of disease mechanism and detection of potential off-target sites in genome editing. Since most of the variant calling algorithms rely on initial mapping onto a reference genome and tend to predict many variant candidates, variant calling remains challenging in terms of predicting variants with low false positives. Results Here we present Bivartect, a simple yet versatile variant caller based on direct comparison of short sequence reads between normal and mutated samples. Bivartect can detect not only single nucleotide variants but also insertions/deletions, inversions and their complexes. Bivartect achieves high predictive performance with an elaborate memory-saving mechanism, which allows Bivartect to run on a computer with a single node for analyzing small omics data. Tests with simulated benchmark and real genome-editing data indicate that Bivartect was comparable to state-of-the-art variant callers in positive predictive value for detection of single nucleotide variants, even though it yielded a substantially small number of candidates. These results suggest that Bivartect, a reference-free approach, will contribute to the identification of germline mutations as well as off-target sites introduced during genome editing with high accuracy. Availability and implementation Bivartect is implemented in C++ and available along with in silico simulated data at https://github.com/ykat0/bivartect. Supplementary information Supplementary data are available at Bioinformatics online.


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