SNP Calling Pipelines Research Articles

The Effect of Genome Parametrization and SNP Marker Subsetting on Genomic Selection in Autotetraploid Alfalfa.

Alfalfa, the most economically important forage legume worldwide, features modest genetic progress due to long selection cycles and the extent of the non-additive genetic variance associated with its autotetraploid genome. To improve the efficiency of genomic selection in alfalfa, we explored the effects of genome parametrization (as tetraploid and diploid dosages, plus allele ratios) and SNP marker subsetting (all available SNPs, only genic regions, and only non-genic regions) on genomic regressions, together with various levels of filtering on reading depth and missing rates. We used genotyping by sequencing-generated data and focused on traits of different genetic complexity, i.e., dry biomass yield in moisture-favorable (FE) and drought stress (SE) environments, leaf size, and the onset of flowering, which were assessed in 143 genotyped plants from a genetically broad European reference population and their phenotyped half-sib progenies. On average, the allele ratio improved the predictive ability compared with other genome parametrizations (+7.9% vs. tetraploid dosage, +12.6% vs. diploid dosage), while using all the SNPs offered an advantage compared with any specific SNP subsetting (+3.7% vs. genic regions, +7.6% vs. non-genic regions). However, when focusing on specific traits, different combinations of genome parametrization and subsetting achieved better performances. We also released Legpipe2, an SNP calling pipeline tailored for reduced representation (GBS, RAD) in medium-sized genotyping experiments.

Prevalence and antimicrobial resistance of Salmonella in conventional and no antibiotics ever broiler farms in the United States

Non-typhoidal Salmonella is the second most common bacterial cause of foodborne illness in the United States. More than 50% of the poultry meat sold in the U.S. is categorized as no antibiotics ever (NAE), yet it is unclear how these practices affect the prevalence and antimicrobial resistance (AMR) of Salmonella . The aim of this study was to evaluate and compare the prevalence of Salmonella and the incidence of AMR in conventional (n=2) and NAE (n = 2) broiler farms in the Southeast US. Broiler cecal (n = 420) and ileal contents (n = 420), along with litter (n = 24), feed (n = 24) and water (n = 24) samples were collected from four broiler farms. Sample collection was carried out during a full production cycle and following the birds to the processing facilities. The BAX® Real-Time PCR system was used to detect Salmonella positive samples. Phenotypic antimicrobial susceptibility was evaluated following the National Antimicrobial Resistance Monitoring System (NARMS) method, and whole genome sequencing (WGS) was carried out for screening of AMR genes. The software tool SeqSero2 and the McOutbryk SNP calling pipeline were used for serotype identification and phylogenetic analysis, respectively. Conventional broilers had the highest Salmonella prevalence (80% and 73%; P ≤ 0.05) at day 21. The Salmonella prevalence declined as the birds aged. Antimicrobial resistance among Salmonella isolates from all sample types was high in all four farms (76%), exhibiting frequent resistance to tetracycline (76%) and streptomycin (70%). Salmonella isolates recovered from NAE broilers carried higher (P ≤ 0.05) rates of AMR than isolates from conventionally raised chicken. Salmonella enterica serotype Kentucky was the predominant serotype, harboring genes tet(B), aph(3″)-Ib and aph(6)-Id (which encode resistance to tetracycline and streptomycin, respectively). Farm-related strains of the serotype were identified, exhibiting persistence in the litter environment. • Broilers from NAE farms had lower Salmonella prevalence in the Southeast US. • Resistance to tetracycline and streptomycin among Salmonella was widespread. • Salmonella Kentucky was the predominant serotype on farms in the Southeast US. • Phylogeny of S. Kentucky strains exhibited farm-specific association. • Antimicrobial resistant genes tet(B), aph(3″)‐Ib , aph(6)‐Id and fosA7 were found.

Comparison of seven SNP calling pipelines for the next-generation sequencing data of chickens.

Single nucleotide polymorphisms (SNPs) are widely used in genome-wide association studies and population genetics analyses. Next-generation sequencing (NGS) has become convenient, and many SNP-calling pipelines have been developed for human NGS data. We took advantage of a gap knowledge in selecting the appropriated SNP calling pipeline to handle with high-throughput NGS data. To fill this gap, we studied and compared seven SNP calling pipelines, which include 16GT, genome analysis toolkit (GATK), Bcftools-single (Bcftools single sample mode), Bcftools-multiple (Bcftools multiple sample mode), VarScan2-single (VarScan2 single sample mode), VarScan2-multiple (VarScan2 multiple sample mode) and Freebayes pipelines, using 96 NGS data with the different depth gradients of approximately 5X, 10X, 20X, 30X, 40X, and 50X coverage from 16 Rhode Island Red chickens. The sixteen chickens were also genotyped with a 50K SNP array, and the sensitivity and specificity of each pipeline were assessed by comparison to the results of SNP arrays. For each pipeline, except Freebayes, the number of detected SNPs increased as the input read depth increased. In comparison with other pipelines, 16GT, followed by Bcftools-multiple, obtained the most SNPs when the input coverage exceeded 10X, and Bcftools-multiple obtained the most when the input was 5X and 10X. The sensitivity and specificity of each pipeline increased with increasing input. Bcftools-multiple had the highest sensitivity numerically when the input ranged from 5X to 30X, and 16GT showed the highest sensitivity when the input was 40X and 50X. Bcftools-multiple also had the highest specificity, followed by GATK, at almost all input levels. For most calling pipelines, there were no obvious changes in SNP numbers, sensitivities or specificities beyond 20X. In conclusion, (1) if only SNPs were detected, the sequencing depth did not need to exceed 20X; (2) the Bcftools-multiple may be the best choice for detecting SNPs from chicken NGS data, but for a single sample or sequencing depth greater than 20X, 16GT was recommended. Our findings provide a reference for researchers to select suitable pipelines to obtain SNPs from the NGS data of chickens or nonhuman animals.

Read trimming has minimal effect on bacterial SNP-calling accuracy.

Read alignment is the central step of many analytic pipelines that perform variant calling. To reduce error, it is common practice to pre-process raw sequencing reads to remove low-quality bases and residual adapter contamination, a procedure collectively known as ‘trimming’. Trimming is widely assumed to increase the accuracy of variant calling, although there are relatively few systematic evaluations of its effects and no clear consensus on its efficacy. As sequencing datasets increase both in number and size, it is worthwhile reappraising computational operations of ambiguous benefit, particularly when the scope of many analyses now routinely incorporates thousands of samples, increasing the time and cost required. Using a curated set of 17 Gram-negative bacterial genomes, this study initially evaluated the impact of four read-trimming utilities (Atropos, fastp, Trim Galore and Trimmomatic), each used with a range of stringencies, on the accuracy and completeness of three bacterial SNP-calling pipelines. It was found that read trimming made only small, and statistically insignificant, increases in SNP-calling accuracy even when using the highest-performing pre-processor in this study, fastp. To extend these findings, >6500 publicly archived sequencing datasets from Escherichia coli , Mycobacterium tuberculosis and Staphylococcus aureus were re-analysed using a common analytic pipeline. Of the approximately 125 million SNPs and 1.25 million indels called across all samples, the same bases were called in 98.8 and 91.9 % of cases, respectively, irrespective of whether raw reads or trimmed reads were used. Nevertheless, the proportion of mixed calls (i.e. calls where <100 % of the reads support the variant allele; considered a proxy of false positives) was significantly reduced after trimming, which suggests that while trimming rarely alters the set of variant bases, it can affect the proportion of reads supporting each call. It was concluded that read quality- and adapter-trimming add relatively little value to a SNP-calling pipeline and may only be necessary if small differences in the absolute number of SNP calls, or the false call rate, are critical. Broadly similar conclusions can be drawn about the utility of trimming to an indel-calling pipeline. Read trimming remains routinely performed prior to variant calling likely out of concern that doing otherwise would typically have negative consequences. While historically this may have been the case, the data in this study suggests that read trimming is not always a practical necessity.

Genomic diversity affects the accuracy of bacterial single-nucleotide polymorphism-calling pipelines.

BackgroundAccurately identifying single-nucleotide polymorphisms (SNPs) from bacterial sequencing data is an essential requirement for using genomics to track transmission and predict important phenotypes such as antimicrobial resistance. However, most previous performance evaluations of SNP calling have been restricted to eukaryotic (human) data. Additionally, bacterial SNP calling requires choosing an appropriate reference genome to align reads to, which, together with the bioinformatic pipeline, affects the accuracy and completeness of a set of SNP calls obtained. This study evaluates the performance of 209 SNP-calling pipelines using a combination of simulated data from 254 strains of 10 clinically common bacteria and real data from environmentally sourced and genomically diverse isolates within the genera Citrobacter, Enterobacter, Escherichia, and Klebsiella.ResultsWe evaluated the performance of 209 SNP-calling pipelines, aligning reads to genomes of the same or a divergent strain. Irrespective of pipeline, a principal determinant of reliable SNP calling was reference genome selection. Across multiple taxa, there was a strong inverse relationship between pipeline sensitivity and precision, and the Mash distance (a proxy for average nucleotide divergence) between reads and reference genome. The effect was especially pronounced for diverse, recombinogenic bacteria such as Escherichia coli but less dominant for clonal species such as Mycobacterium tuberculosis.ConclusionsThe accuracy of SNP calling for a given species is compromised by increasing intra-species diversity. When reads were aligned to the same genome from which they were sequenced, among the highest-performing pipelines was Novoalign/GATK. By contrast, when reads were aligned to particularly divergent genomes, the highest-performing pipelines often used the aligners NextGenMap or SMALT, and/or the variant callers LoFreq, mpileup, or Strelka.

Better quality score compression through sequence-based quality smoothing

MotivationCurrent NGS techniques are becoming exponentially cheaper. As a result, there is an exponential growth of genomic data unfortunately not followed by an exponential growth of storage, leading to the necessity of compression. Most of the entropy of NGS data lies in the quality values associated to each read. Those values are often more diversified than necessary. Because of that, many tools such as Quartz or GeneCodeq, try to change (smooth) quality scores in order to improve compressibility without altering the important information they carry for downstream analysis like SNP calling.ResultsWe use the FM-Index, a type of compressed suffix array, to reduce the storage requirements of a dictionary of k-mers and an effective smoothing algorithm to maintain high precision for SNP calling pipelines, while reducing quality scores entropy.We present YALFF (Yet Another Lossy Fastq Filter), a tool for quality scores compression by smoothing leading to improved compressibility of FASTQ files. The succinct k-mers dictionary allows YALFF to run on consumer computers with only 5.7 GB of available free RAM. YALFF smoothing algorithm can improve genotyping accuracy while using less resources.Availabilityhttps://github.com/yhhshb/yalff

Corrigendum to: gmRAD: an integrated SNP calling pipeline for genetic mapping with RADseq across a hybrid population.

Corrigendum to: gmRAD: an integrated SNP calling pipeline for genetic mapping with RADseq across a hybrid population.

GBS-derived SNP catalogue unveiled wide genetic variability and geographical relationships of Italian olive cultivars

Information on the distribution of genetic variation is essential to preserve olive germplasm from erosion and to recover alleles lost through selective breeding. In addition, knowledge on population structure and genotype–phenotype associations is crucial to support modern olive breeding programs that must respond to new environmental conditions imposed by climate change and novel biotic/abiotic stressors. To further our understanding of genetic variation in the olive, we performed genotype-by-sequencing on a panel of 94 Italian olive cultivars. A reference-based and a reference-independent SNP calling pipeline generated 22,088 and 8,088 high-quality SNPs, respectively. Both datasets were used to model population structure via parametric and non parametric clustering. Although the two pipelines yielded a 3-fold difference in the number of SNPs, both described wide genetic variability among our study panel and allowed individuals to be grouped based on fruit weight and the geographical area of cultivation. Multidimensional scaling analysis on identity-by-state allele-sharing values as well as inference of population mixtures from genome-wide allele frequency data corroborated the clustering pattern we observed. These findings allowed us to formulate hypotheses about geographical relationships of Italian olive cultivars and to confirm known and uncover novel cases of synonymy.

High-resolution genetic linkage map of European pear (Pyrus communis) and QTL fine-mapping of vegetative budbreak time

BackgroundGenomic analysis technologies can promote efficient fruit tree breeding. Genotyping by sequencing (GBS) enables generating efficient data for high-quality genetic map construction and QTL analysis in a relatively accessible way. Furthermore, High-resolution genetic map construction and accurate QTL detection can significantly narrow down the putative candidate genes associated with important plant traits.ResultsWe genotyped 162 offspring in the F1 ‘Spadona’ x ‘Harrow Sweet’ pear population using GBS. An additional 21 pear accessions, including the F1 population’s parents, from our germplasm collection were subjected to GBS to examine diverse genetic backgrounds that are associated to agriculturally relevant traits and to enhance the power of SNP calling. A standard SNP calling pipeline identified 206,971 SNPs with Asian pear (‘Suli’) as the reference genome and 148,622 SNPs with the European genome (‘Bartlett’). These results enabled constructing a genetic map, after further stringent SNP filtering, consisting of 2036 markers on 17 linkage groups with a length of 1433 cM and an average marker interval of 0.7 cM. We aligned 1030 scaffolds covering a total size of 165.5 Mbp (29%) of the European pear genome to the 17 linkage groups. For high-resolution QTL analysis covering the whole genome, we used phenotyping for vegetative budbreak time in the F1 population. New QTLs associated to vegetative budbreak time were detected on linkage groups 5, 13 and 15. A major QTL on linkage group 8 and an additional QTL on linkage group 9 were confirmed. Due to the significant genotype-by-environment (GxE) effect, we were able to identify novel interaction QTLs on linkage groups 5, 8, 9 and 17. Phenotype–genotype association analysis in the pear accessions for main genotype effect was conducted to support the QTLs detected in the F1 population. Significant markers were detected on every linkage group to which main genotype effect QTLs were mapped.ConclusionsThis is the first vegetative budbreak study of European pear that makes use of high-resolution genetic mapping. These results provide tools for marker-assisted selection and accurate QTL analysis in pear, and specifically at vegetative budbreak, considering the significant GxE and phenotype-plasticity effects.

Rice SNP-seek database update: new SNPs, indels, and queries.

We describe updates to the Rice SNP-Seek Database since its first release. We ran a new SNP-calling pipeline followed by filtering that resulted in complete, base, filtered and core SNP datasets. Besides the Nipponbare reference genome, the pipeline was run on genome assemblies of IR 64, 93-11, DJ 123 and Kasalath. New genotype query and display features are added for reference assemblies, SNP datasets and indels. JBrowse now displays BAM, VCF and other annotation tracks, the additional genome assemblies and an embedded VISTA genome comparison viewer. Middleware is redesigned for improved performance by using a hybrid of HDF5 and RDMS for genotype storage. Query modules for genotypes, varieties and genes are improved to handle various constraints. An integrated list manager allows the user to pass query parameters for further analysis. The SNP Annotator adds traits, ontology terms, effects and interactions to markers in a list. Web-service calls were implemented to access most data. These features enable seamless querying of SNP-Seek across various biological entities, a step toward semi-automated gene-trait association discovery. URL: http://snp-seek.irri.org.