Low-coverage Next-generation Sequencing Data Research Articles

Nuclear and chloroplast genome diversity revealed by low-coverage whole-genome shotgun sequence in 44 Brassica oleracea breeding lines

Whole-genome shogun sequence (WGS) data generated by next-generation sequencing (NGS) platforms are a valuable resource for crop improvement. We produced 5–6 × WGS coverage of 44 Brassica oleracea breeding lines representing seven subspecies/morphotypes: cabbage, broccoli, cauliflower, kailan, kale, Brussels sprout, and kohlrabi to systematically evaluate the nuclear and chloroplast (Cp) diversity in the 44 B. oleracea breeding lines. We then exploited the impact of low-coverage NGS by evaluating nuclear genome diversity and assembly, annotation of complete chloroplast (Cp) genomes and 45S nuclear ribosomal DNA (45S nrDNA) sequences, and copy number variation for major repeats. Nuclear genome diversity analysis has revealed a total of 496 463 SNPs and 37 493 indels in the nuclear genome across the 44 accessions. Interestingly, some SNPs showed subspecies enrichment at certain chromosomal regions. The assembly of complete Cp genomes contained 153 361–153 372 bp with 37 variants including SNPs and indels. The 45S nrDNA transcription unit was 5 802 bp long with a total of 31 SNPs from the 44 lines. The phylogenetic tree inferred from the nuclear and Cp genomes coincided and clustered broccoli, cauliflower, and kailan in one group and cabbage, Brussels sprout, kale, and kohlrabi in another group. The morphotypes diverged during the last 0.17 million years. The Cp genome diversity reflected the unique cytoplasm of each subspecies, and revealed that the cytoplasm of many breeding lines was replaced and intermingled via inter-subspecies crosses during the breeding process instead. The polymorphic Cp markers provide a classification system for the cytoplasm types in B. oleracea. Furthermore, copy numbers of major transposable elements (TEs) showed high diversity among the 44 accessions, indicating that many TEs have become active recently. Overall, we demonstrated a comprehensive utilization of low-coverage NGS data and might shed light on the genetic diversity and evolution of diverse B. oleracea morphotypes.

The major satellite DNA families of the diploid Chenopodium album aggregate species: Arguments for and against the "library hypothesis".

Satellite DNA (satDNA) is one of the major fractions of the eukaryotic nuclear genome. Highly variable satDNA is involved in various genome functions, and a clear link between satellites and phenotypes exists in a wide range of organisms. However, little is known about the origin and temporal dynamics of satDNA. The “library hypothesis” indicates that the rapid evolutionary changes experienced by satDNAs are mostly quantitative. Although this hypothesis has received some confirmation, a number of its aspects are still controversial. A recently developed next-generation sequencing (NGS) method allows the determination of the satDNA landscape and could shed light on unresolved issues. Here, we explore low-coverage NGS data to infer satDNA evolution in the phylogenetic context of the diploid species of the Chenopodium album aggregate. The application of the Illumina read assembly algorithm in combination with Oxford Nanopore sequencing and fluorescent in situ hybridization allowed the estimation of eight satDNA families within the studied group, six of which were newly described. The obtained set of satDNA families of different origins can be divided into several categories, namely group-specific, lineage-specific and species-specific. In the process of evolution, satDNA families can be transmitted vertically and can be eliminated over time. Moreover, transposable element-derived satDNA families may appear repeatedly in the satellitome, creating an illusion of family conservation. Thus, the obtained data refute the “library hypothesis”, rather than confirming it, and in our opinion, it is more appropriate to speak about “the library of the mechanisms of origin”.

Next-Generation Sequencing Data Analysis on Pool-Seq and Low-Coverage Retinoblastoma Data.

Next-generation sequencing (NGS) is related to massively parallel or deep deoxyribonucleic acid (DNA) sequencing technology which has revolutionized genomic researches in recent years. Although the cost of generating NGS data was decreased compared to the one at the time of emerging this technology, its cost might still be somewhat a problem. Hence, new strategies as pool-seq and low-coverage NGS data have been developed to overcome the cost problem. Despite decreasing cost, it is important to elucidate whether they are efficient in NGS studies. We applied a bioinformatics pipeline on pool-seq and low-coverage retinoblastoma data retrieved from only tumor data. Retinoblastoma is an eye malignancy in childhood that is initiated by RB1 mutation or MYCN amplification and can lead to the loss of vision of eye(s), and even sometimes life. We applied our pipeline on both retinoblastoma disease data and two other particular data to testify the validity and also for comparison purposes in the aspect of performance. High-confidence variant calls from Genome in a Bottle Consortium were used for fulfilling these purposes. We observed that our pipeline successfully called higher number of variants than a standard pipeline for all these three different data. Besides, the recall and F-score values were quite better in our pipeline as being noteworthy. We further presented our results on disease data in the aspects of the variants, variant types and disease-related genes. This study provides a guideline for performing NGS data analysis pipeline on pool-seq and low-coverage sequencing data in conjunction. To get more conclusive outcomes of these two strategies, we recommend using cancer data having higher mutation rates and larger pools.

Robust Linear Trend Test for Low-Coverage Next-Generation Sequence Data Controlling for Covariates

Low-coverage next-generation sequencing experiments assisted by statistical methods are popular in a genetic association study. Next-generation sequencing experiments produce genotype data that include allele read counts and read depths. For low sequencing depths, the genotypes tend to be highly uncertain; therefore, the uncertain genotypes are usually removed or imputed before performing a statistical analysis. It may result in the inflated type I error rate and in a loss of statistical power. In this paper, we propose a mixture-based penalized score association test adjusting for non-genetic covariates. The proposed score test statistic is based on a sandwich variance estimator so that it is robust under the model misspecification between the covariates and the latent genotypes. The proposed method takes advantage of not requiring either external imputation or elimination of uncertain genotypes. The results of our simulation study show that the type I error rates are well controlled and the proposed association test have reasonable statistical power. As an illustration, we apply our statistic to pharmacogenomics data for drug responsiveness among 400 epilepsy patients.

Mind the gap! The mitochondrial control region and its power as a phylogenetic marker in echinoids

BackgroundIn Metazoa, mitochondrial markers are the most commonly used targets for inferring species-level molecular phylogenies due to their extremely low rate of recombination, maternal inheritance, ease of use and fast substitution rate in comparison to nuclear DNA. The mitochondrial control region (CR) is the main non-coding area of the mitochondrial genome and contains the mitochondrial origin of replication and transcription.While sequences of the cytochrome oxidase subunit 1 (COI) and 16S rRNA genes are the prime mitochondrial markers in phylogenetic studies, the highly variable CR is typically ignored and not targeted in such analyses. However, the higher substitution rate of the CR can be harnessed to infer the phylogeny of closely related species, and the use of a non-coding region alleviates biases resulting from both directional and purifying selection. Additionally, complete mitochondrial genome assemblies utilizing next generation sequencing (NGS) data often show exceptionally low coverage at specific regions, including the CR. This can only be resolved by targeted sequencing of this region.ResultsHere we provide novel sequence data for the echinoid mitochondrial control region in over 40 species across the echinoid phylogenetic tree. We demonstrate the advantages of directly targeting the CR and adjacent tRNAs to facilitate complementing low coverage NGS data from complete mitochondrial genome assemblies. Finally, we test the performance of this region as a phylogenetic marker both in the lab and in phylogenetic analyses, and demonstrate its superior performance over the other available mitochondrial markers in echinoids.ConclusionsOur target region of the mitochondrial CR (1) facilitates the first thorough investigation of this region across a wide range of echinoid taxa, (2) provides a tool for complementing missing data in NGS experiments, and (3) identifies the CR as a powerful, novel marker for phylogenetic inference in echinoids due to its high variability, lack of selection, and high compatibility across the entire class, outperforming conventional mitochondrial markers.

Heap: a highly sensitive and accurate SNP detection tool for low-coverage high-throughput sequencing data.

Recent availability of large-scale genomic resources enables us to conduct so called genome-wide association studies (GWAS) and genomic prediction (GP) studies, particularly with next-generation sequencing (NGS) data. The effectiveness of GWAS and GP depends on not only their mathematical models, but the quality and quantity of variants employed in the analysis. In NGS single nucleotide polymorphism (SNP) calling, conventional tools ideally require more reads for higher SNP sensitivity and accuracy. In this study, we aimed to develop a tool, Heap, that enables robustly sensitive and accurate calling of SNPs, particularly with a low coverage NGS data, which must be aligned to the reference genome sequences in advance. To reduce false positive SNPs, Heap determines genotypes and calls SNPs at each site except for sites at the both ends of reads or containing a minor allele supported by only one read. Performance comparison with existing tools showed that Heap achieved the highest F-scores with low coverage (7X) restriction-site associated DNA sequencing reads of sorghum and rice individuals. This will facilitate cost-effective GWAS and GP studies in this NGS era. Code and documentation of Heap are freely available from https://github.com/meiji-bioinf/heap (29 March 2017, date last accessed) and our web site (http://bioinf.mind.meiji.ac.jp/lab/en/tools.html (29 March 2017, date last accessed)).

Genome-wide copy number variation in the bovine genome detected using low coverage sequence of popular beef breeds.

Copy number variations (CNVs) are large insertions, deletions or duplications in the genome that vary between members of a species and are known to affect a wide variety of phenotypic traits. In this study, we identified CNVs in a population of bulls using low coverage next-generation sequence data. First, in order to determine a suitable strategy for CNV detection in our data, we compared the performance of three distinct CNV detection algorithms on benchmark CNV datasets and concluded that using the multiple sample read depth approach was the best method for identifying CNVs in our sequences. Using this technique, we identified a total of 1341 copy number variable regions (CNVRs) from genome sequences of 154 purebred sires used in Cycle VII of the USMARC Germplasm Evaluation Project. These bulls represented the seven most popular beef breeds in the United States: Hereford, Charolais, Angus, Red Angus, Simmental, Gelbvieh and Limousin. The CNVRs covered 6.7% of the bovine genome and spanned 2465 protein-coding genes and many known quantitative trait loci (QTL). Genes harbored in the CNVRs were further analyzed to determine their function as well as to find any breed-specific differences that may shed light on breed differences in adaptation, health and production.

Transmission Disequilibrium Tests Based on Read Counts for Low-Coverage Next-Generation Sequence Data

The purpose of this paper is the introduction of new statistical methods for case-parent trio association studies based on the read counts that can be obtained from next-generation sequencing (NGS) experiments. This work focuses on the inclusion of low-coverage data into the case-parent trio design without genotype classification or imputation. Two different approaches are considered: (1) a likelihood-based approach implementing a 15-component parametric mixture model and (2) a model-free approach that applies non-parametric statistical methods to the ratios of the read counts to coverage. Simulation studies are conducted to evaluate the performances of the proposed tests. In addition, the non-centrality parameters of the mixture likelihood-based tests are derived to determine sample sizes and coverage for a NGS experimental design. As an example, the sample sizes to maintain specified powers of a published adolescent idiopathic scoliosis (AIS) study are presented. The simulation results show that the tests using the genotypes classified by the maximum Bayesian posterior probability have significantly inflated type I error rates for low-coverage data. The tests using the posterior probabilities instead of the classified genotypes show lower power than the proposed tests. Generally, power for the likelihood-based approach is higher than that for the non-parametric ratio-based approach. For the AIS example, approximately 654 trios with 4× coverage are necessary to maintain 90% power when detecting an association of odds ratio 2 at a locus with a minor allele frequency of 0.35 at the level of significance α = 5 × 10^-8. By comparison, approximately 416 trios with 25× coverage are required to maintain the same power with the same settings. The R and C source codes to calculate the proposed test statistics, the sample sizes and power can be obtained by contacting the author (wkim@cau.ac.kr).

Estimating optimal window size for analysis of low-coverage next-generation sequence data

Current high-throughput sequencing has greatly transformed genome sequence analysis. In the context of very low-coverage sequencing (<0.1×), performing 'binning' or 'windowing' on mapped short sequences ('reads') is critical to extract genomic information of interest for further evaluation, such as copy-number alteration analysis. If the window size is too small, many windows will exhibit zero counts and almost no pattern can be observed. In contrast, if the window size is too wide, the patterns or genomic features will be 'smoothed out'. Our objective is to identify an optimal window size in between the two extremes. We assume the reads density to be a step function. Given this model, we propose a data-based estimation of optimal window size based on Akaike's information criterion (AIC) and cross-validation (CV) log-likelihood. By plotting the AIC and CV log-likelihood curve as a function of window size, we are able to estimate the optimal window size that minimizes AIC or maximizes CV log-likelihood. The proposed methods are of general purpose and we illustrate their application using low-coverage next-generation sequence datasets from real tumour samples and simulated datasets. An R package to estimate optimal window size is available at http://www1.maths.leeds.ac.uk/∼arief/R/win/.

Calculation of Tajima’s D and other neutrality test statistics from low depth next-generation sequencing data

BackgroundA number of different statistics are used for detecting natural selection using DNA sequencing data, including statistics that are summaries of the frequency spectrum, such as Tajima’s D. These statistics are now often being applied in the analysis of Next Generation Sequencing (NGS) data. However, estimates of frequency spectra from NGS data are strongly affected by low sequencing coverage; the inherent technology dependent variation in sequencing depth causes systematic differences in the value of the statistic among genomic regions.ResultsWe have developed an approach that accommodates the uncertainty of the data when calculating site frequency based neutrality test statistics. A salient feature of this approach is that it implicitly solves the problems of varying sequencing depth, missing data and avoids the need to infer variable sites for the analysis and thereby avoids ascertainment problems introduced by a SNP discovery process.ConclusionUsing an empirical Bayes approach for fast computations, we show that this method produces results for low-coverage NGS data comparable to those achieved when the genotypes are known without uncertainty. We also validate the method in an analysis of data from the 1000 genomes project. The method is implemented in a fast framework which enables researchers to perform these neutrality tests on a genome-wide scale.

A dynamic Bayesian Markov model for phasing and characterizing haplotypes in next-generation sequencing

Next-generation sequencing (NGS) technologies have enabled whole-genome discovery and analysis of genetic variants in many species of interest. Individuals are often sequenced at low coverage for detecting novel variants, phasing haplotypes and inferring population structures. Although several tools have been developed for SNP and genotype calling in NGS data, haplotype phasing is often done separately on the called genotypes. We propose a dynamic Bayesian Markov model (DBM) for simultaneous genotype calling and haplotype phasing in low-coverage NGS data of unrelated individuals. Our method is fully probabilistic that produces consistent inference of genotypes, haplotypes and recombination probabilities. Using data from the 1000 Genomes Project, we demonstrate that DBM not only yields more accurate results than some popular methods, but also provides novel characterization of haplotype structures at the individual level for visualization, interpretation and comparison in downstream analysis. DBM is a powerful and flexible tool that can be applied to many sequencing studies. Its statistical framework can also be extended to accommodate broader scopes of data. http://stat.psu.edu/∼yuzhang/software/dbm.tar. Supplementary data are available at Bioinformatics online.

Estimation of metagenome size and structure in an experimental soil microbiota from low coverage next-generation sequence data

A major challenge in metagenome studies is to estimate the true size of all combined genomes. Here, we present a novel approach to estimate the size of all combined genomes for low coverage next-generation sequencing (NGS) data through empirically determined copy numbers of random DNA fragments. Size estimates were made based on analyses of two experimental soil micro-ecosystems - simulating soil with and without earthworms. Our analyses showed combined genome sizes of about log 11 nucleotides for each of the soil micro-ecosystems, as estimated from qPCR determined copy numbers of random DNA fragments. This corresponds to more than 20000 unique bacterial genomes in each sample. There seemed, however, to be a bacterial subpopulation in the earthworm soil, not being present in the nonearthworm soil. To describe the structure of the metagenomes, both total DNA and amplified 16S rRNA gene sequence libraries were generated with 454-sequencing. Bioinformatic analysis of 454 sequence libraries showed a large functional but low taxonomic overlap between the samples with and without earthworms. A neutrality test indicated that rare species have a competitive advantage over abundant species in both micro-ecosystems providing a potential explanation for the large metagenome sizes. We have shown that the soil metagenome is very large and that the large size is probably a consequence of top-down selection of the dominant bacterial species. Estimates of metagenome size from low coverage NGS data will be important for guiding future NGS set-ups.