Multi-GBS: A massively multiplexed GBS-based protocol optimized for large, repetitive conifer genomes

Reduced representation library approaches are still a valuable tool for breeding and population and ecological genomics, even with impressive increases in sequencing capacity in recent years. Unfortunately, current approaches only allow for multiplexing up to 384 samples. To take advantage of increased sequencing capacity, we present Multi-GBS, a massively multiplexable extension to Genotyping-by-Sequencing that is also optimized for large conifer genomes. In Norway Spruce, a highly repetitive 20Gbp diploid genome with high population genetic variation, we call over a million variants in 32 genotypes from three populations, two natural forest in the Alps and Bohemian Alps, and a managed population from southeastern Austria using the existing TASSEL GBSv2 pipeline. Metric MDS analysis of replicated genotypes shows that technical bias in resulting genotype calling is minimal and that populations cluster in biologically meaningful ways.

Streamlined and quantitative detection of chimerism in mouse tissue using digital PCR

ABSTRACTAnimal chimeras are widely used for biomedical discoveries, from developmental biology to cancer research. However, the accurate quantitation of mixed cell types in chimeric and mosaic tissues has challenges. Here, we have developed and characterized a droplet digital PCR single-nucleotide discrimination assay to detect chimerism among albino and non-albino mouse strains. In addition, we have validated that this assay is compatible with crude lysate from most organs, drastically streamlining sample preparation. This chimerism detection assay has many additional advantages over existing methods including its robust nature, minimal technical bias, and ability to report the total number of cells in a prepared sample. Importantly, the concepts developed and discussed here are readily adapted to other genomic loci to accurately measure mixed cell populations in any tissue.

A Novel Signature of 23 Immunity-related Gene Pairs is Prognostic of Cutaneous Melanoma

Background: We aimed to identify immune-related signature for predicting cutaneous melanoma (CM) prognosis. Methods: We used TCGA samples (n=471) to develop the best 23 Immune related gene pairs (23-IRGP) prognostic signature and divided patients into high- and low-immune risk group in TCGA dataset and validation datasets: GSE65904 (n=214), GSE59455 (n=141), and GSE22153 (n=79). Results: 23-IRGP presented precise ability in cutaneous melanoma (CM) which high-risk groups showed poor prognosis and indicated significant predict power in immune micro-environment and biological analysis as well. Conclusions: we established a novel promising prognostic model in CM and built the bridge between immune micro-environment and CM patient results. This approach can be applied to discover the signatures in other diseases without technical bias from different platforms.

Quantitative comparison of within-sample heterogeneity scores for DNA methylation data

DNA methylation is an epigenetic mark with important regulatory roles in cellular identity and can be quantified at base resolution using bisulfite sequencing. Most studies are limited to the average DNA methylation levels of individual CpGs and thus neglect heterogeneity within the profiled cell populations. To assess this within-sample heterogeneity (WSH) several window-based scores that quantify variability in DNA methylation in sequencing reads have been proposed. We performed the first systematic comparison of four published WSH scores based on simulated and publicly available datasets. Moreover, we propose two new scores and provide guidelines for selecting appropriate scores to address cell-type heterogeneity, cellular contamination and allele-specific methylation. Most of the measures were sensitive in detecting DNA methylation heterogeneity in these scenarios, while we detected differences in susceptibility to technical bias. Using recently published DNA methylation profiles of Ewing sarcoma samples, we show that DNA methylation heterogeneity provides information complementary to the DNA methylation level. WSH scores are powerful tools for estimating variance in DNA methylation patterns and have the potential for detecting novel disease-associated genomic loci not captured by established statistics. We provide an R-package implementing the WSH scores for integration into analysis workflows.

Coexpression enables multi-study cellular trajectories of development and disease

Single-cell transcriptomic studies of diverse and complex systems are becoming ubiquitous. Algorithms now attempt to integrate patterns across these studies by removing all study-specific information, without distinguishing unwanted technical bias from relevant biological variation. Integration remains difficult when capturing biological variation that is distributed across studies, as when combining disparate temporal snapshots into a panoramic, multi-study trajectory of cellular development. Here, we show that a fundamental analytic shift to gene coexpression within clusters of cells, rather than gene expression within individual cells, balances robustness to bias with preservation of meaningful inter-study differences. We leverage this insight in Trajectorama, an algorithm which we use to unify trajectories of neuronal development and hematopoiesis across studies that each profile separate developmental stages, a highly challenging task for existing methods. Trajectorama also reveals systems-level processes relevant to disease pathogenesis within the microglial response to myelin injury. Trajectorama benefits from efficiency and scalability, processing nearly one million cells in around an hour.

Systematic assessment of next generation sequencing for quantitative small RNA profiling: a multiple protocol study across multiple laboratories

Small RNA-seq is increasingly being used for profiling of small RNAs. Quantitative characteristics of long RNA-seq have been extensively described, but small RNA-seq involves fundamentally different methods for library preparation, with distinct protocols and technical variations that have not been fully and systematically studied. We report here the results of a study using common references (synthetic RNA pools of defined composition, as well as plasma-derived RNA) to evaluate the accuracy, reproducibility and bias of small RNA-seq library preparation for five distinct protocols and across nine different laboratories. We observed protocol-specific and sequence-specific bias, which was ameliorated using adapters for ligation with randomized end-nucleotides, and computational correction factors. Despite this technical bias, relative quantification using small RNA-seq was remarkably accurate and reproducible, even across multiple laboratories using different methods. These results provide strong evidence for the feasibility of reproducible cross-laboratory small RNA-seq studies, even those involving analysis of data generated using different protocols.

Tools and best practices for allelic expression analysis

Allelic expression (AE) analysis has become an important tool for integrating genome and transcriptome data to characterize various biological phenomena such as cis-regulatory variation and nonsense-mediated decay. In this paper, we systematically analyze the properties of AE read count data and technical sources of error, such as low-quality or double-counted RNA-seq reads, genotyping errors, allelic mapping bias, and technical covariates due to sample preparation and sequencing, and variation in total read depth. We provide guidelines for correcting and filtering for such errors, and show that the resulting AE data has extremely low technical noise. Finally, we introduce novel software for high-throughput production of AE data from RNA-sequencing data, implemented in the GATK framework. These improved tools and best practices for AE analysis yield higher quality AE data by reducing technical bias. This provides a practical framework for wider adoption of AE analysis by the genomics community.

Technical bias of microcultivation environments on single-cell physiology

The cross-platform comparison of three different single-cell cultivation methods demonstrates technical influences on biological key parameters like specific growth rate, division rate and cellular morphology.