Genotype calling and haplotype inference from low coverage sequence data in heterozygous plant genome using HetMap

Many software packages and pipelines had been developed to handle the sequence data of the model species. However, Genotyping from complex heterozygous plant genome needs further improvement on the previous methods. Here we present a new pipeline available at https://github.com/Ncgrhg/HetMapv1) for variant calling and missing genotype imputation from low coverage sequence data for heterozygous plant genomes. To check the performance of the HetMap on the real sequence data, HetMap was applied to both the F1 hybrid rice population which consists of 1495 samples and wild rice population with 446 samples. Four high coverage sequence hybrid rice accessions and two high coverage sequence wild rice accessions, which were also included in low coverage sequence data, are used to validate the genotype inference accuracy. The validation results showed that HetMap archived significant improvement in heterozygous genotype inference accuracy (13.65% for hybrid rice, 26.05% for wild rice) and total accuracy compared with other similar software packages. The application of the new genotype with the genome wide association study also showed improvement of association power in two wild rice phenotypes. It could archive high genotype inference accuracy with low sequence coverage with a small population size with both the natural population and constructed recombination population. HetMap provided a powerful tool for the heterozygous plant genome sequence data analysis, which may help the discover of new phenotype regions for the plant species with complex heterozygous genome.

Demuxalot: scaled up genetic demultiplexing for single-cell sequencing

Demultiplexing methods have facilitated the widespread use of single-cell RNA sequencing (scRNAseq) experiments by lowering costs and reducing technical variations. Here, we present demuxalot: a method for probabilistic genotype inference from aligned reads, with no assumptions about allele ratios and efficient incorporation of prior genotype information from historical experiments in a multi-batch setting. Our method efficiently incorporates additional information across reads originating from the same transcript, enabling up to 3x more calls per read relative to naive approaches. We also propose a novel and highly performant tradeoff between methods that rely on reference genotypes and methods that learn variants from the data, by selecting a small number of highly informative variants that maximize the marginal information with respect to reference single nucleotide variants (SNVs). Our resulting improved SNV-based demultiplex method is up to 3x faster, 3x more data efficient, and achieves significantly more accurate doublet discrimination than previously published methods. This approach renders scRNAseq feasible for the kind of large multi-batch, multi-donor studies that are required to prosecute diseases with heterogeneous genetic backgrounds.

Predicting clone genotypes from tumor bulk sequencing of multiple samples

MotivationAnalyses of data generated from bulk sequencing of tumors have revealed extensive genomic heterogeneity within patients. Many computational methods have been developed to enable the inference of genotypes of tumor cell populations (clones) from bulk sequencing data. However, the relative and absolute accuracy of available computational methods in estimating clone counts and clone genotypes is not yet known.ResultsWe have assessed the performance of nine methods, including eight previously-published and one new method (CloneFinder), by analyzing computer simulated datasets. CloneFinder, LICHeE, CITUP, and cloneHD inferred clone genotypes with low error (<5% per clone) for a majority of datasets in which the tumor samples contained evolutionarily-related clones. Computational methods did not perform well for datasets in which tumor samples contained mixtures of clones from different clonal lineages. Generally, the number of clones was underestimated by cloneHD and overestimated by Phy-loWGS, and BayClone2, Canopy, and Clomial required prior information regarding the number of clones. AncesTree and Canopy did not produce results for a large number of datasets.ConclusionsDeconvolution of clone genotypes from single nucleotide variant (SNV) frequency differences among tumor samples remains challenging, so there is a need to develop more accurate computational methods and robust software for clone genotype inference.Availability and ImplementationCloneFinder is implemented in Python and is available from https://github.com/gstecher/[email protected] informationSupplementary data are available at Bioinformatics online

Twins, Tissue, and Time: An Assessment of SNPs and CNVs

With the desire to assess genetic variation across the lifespan in large-scale collaborative projects, one question is whether inference of copy number (CN) is sensitive to the source of material for deoxyribonucleic acid (DNA) analysis (e.g., blood and buccal) and another question is whether CN is stable as individuals age. Here, we address these questions by applying Affymetrix 6.0 single nucleotide polymorphism (SNP) micro-arrays to 1,472 DNA samples from 710 individuals from the Netherlands Twin Register, including twin and non-twin individuals (372 with buccal and blood derived DNA and 388 with longitudinal data). Similar concordance for CN and genotype inference between samples from the same individual [or from the monozygotic (MZ) co-twins] was found for blood and buccal tissues. There was a small but statistically significant decrease in across-tissue concordance compared with concordance of samples from the same tissue type. No temporal effect was seen on CN variation from the 388 individuals sampled at two time points ranging from 1 to 12 years apart. The majority of our individuals were sampled at age younger than 20 years. Genotype concordance was very high (R2 > 99%) between co-twins from 43 MZ pairs. For 75 dizygotic (DZ) pairs, R2 was ≈65%. CN estimates were highly consistent between co-twins from MZ pairs for both deletions (R2 ≈ 90%) and duplications (R2 ≈ 86%). For DZ, these were similar for within-individual comparisons, but naturally lower between co-twins (R2 ≈ 50–60%). These results suggest that DNA from buccal samples perform as well as DNA from blood samples on the current generation of micro-array technologies.