Integrated analysis of multimodal single-cell data.

The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce "weighted-nearest neighbor" analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.

Clonal Hematopoiesis Before, During, and After Human Spaceflight.

Clonal hematopoiesis (CH) occurs when blood cells harboring an advantageous mutation propagate faster than others. These mutations confer a risk for hematological cancers and cardiovascular disease. Here, we analyze CH in blood samples from a pair of twin astronauts over 4 years in bulk and fractionated cell populations using a targeted CH panel, linked-read whole-genome sequencing, and deep RNA sequencing. We show CH with distinct mutational profiles and increasing allelic fraction that includes a high-risk, TET2 clone in one subject and two DNMT3A mutations on distinct alleles in the other twin. These astronauts exhibit CH almost two decades prior to the mean age at which it is typically detected and show larger shifts in clone size than age-matched controls or radiotherapy patients, based on a longitudinal cohort of 157 cancer patients. As such, longitudinal monitoring of CH may serve as an important metric for overall cancer and cardiovascular risk in astronauts.

scHLAcount: allele-specific HLA expression from single-cell gene expression data.

SUMMARY: Bulk RNA sequencing studies have demonstrated that human leukocyte antigen (HLA) genes may be expressed in a cell type-specific and allele-specific fashion. Single-cell gene expression assays have the potential to further resolve these expression patterns, but currently available methods do not perform allele-specific quantification at the molecule level. Here, we present scHLAcount, a post-processing workflow for single-cell RNA-seq data that computes allele-specific molecule counts of the HLA genes based on a personalized reference constructed from the sample's HLA genotypes. AVAILABILITY AND IMPLEMENTATION: scHLAcount is available under the MIT license at https://github.com/10XGenomics/scHLAcount. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Vargas: heuristic-free alignment for assessing linear and graph read aligners.

MOTIVATION: Read alignment is central to many aspects of modern genomics. Most aligners use heuristics to accelerate processing, but these heuristics can fail to find the optimal alignments of reads. Alignment accuracy is typically measured through simulated reads; however, the simulated location may not be the (only) location with the optimal alignment score. RESULTS: Vargas implements a heuristic-free algorithm guaranteed to find the highest-scoring alignment for real sequencing reads to a linear or graph genome. With semiglobal and local alignment modes and affine gap and quality-scaled mismatch penalties, it can implement the scoring functions of commonly used aligners to calculate optimal alignments. While this is computationally intensive, Vargas uses multi-core parallelization and vectorized (SIMD) instructions to make it practical to optimally align large numbers of reads, achieving a maximum speed of 456 billion cell updates per second. We demonstrate how these 'gold standard' Vargas alignments can be used to improve heuristic alignment accuracy by optimizing command-line parameters in Bowtie 2, BWA-maximal exact match and vg to align more reads correctly. AVAILABILITY AND IMPLEMENTATION: Source code implemented in C++ and compiled binary releases are available at https://github.com/langmead-lab/vargas under the MIT license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Samovar: Single-Sample Mosaic Single-Nucleotide Variant Calling with Linked Reads.

Linked-read sequencing enables greatly improves haplotype assembly over standard paired-end analysis. The detection of mosaic single-nucleotide variants benefits from haplotype assembly when the model is informed by the mapping between constituent reads and linked reads. Samovar evaluates haplotype-discordant reads identified through linked-read sequencing, thus enabling phasing and mosaic variant detection across the entire genome. Samovar trains a random forest model to score candidate sites using a dataset that considers read quality, phasing, and linked-read characteristics. Samovar calls mosaic single-nucleotide variants (SNVs) within a single sample with accuracy comparable with what previously required trios or matched tumor/normal pairs and outperforms single-sample mosaic variant callers at minor allele frequency 5%-50% with at least 30X coverage. Samovar finds somatic variants in both tumor and normal whole-genome sequencing from 13 pediatric cancer cases that can be corroborated with high recall with whole exome sequencing. Samovar is available open-source at https://github.com/cdarby/samovar under the MIT license.

Piercing the dark matter: bioinformatics of long-range sequencing and mapping.

Several new genomics technologies have become available that offer long-read sequencing or long-range mapping with higher throughput and higher resolution analysis than ever before. These long-range technologies are rapidly advancing the field with improved reference genomes, more comprehensive variant identification and more complete views of transcriptomes and epigenomes. However, they also require new bioinformatics approaches to take full advantage of their unique characteristics while overcoming their complex errors and modalities. Here, we discuss several of the most important applications of the new technologies, focusing on both the currently available bioinformatics tools and opportunities for future research.

LRSim: A Linked-Reads Simulator Generating Insights for Better Genome Partitioning.

Linked-read sequencing, using highly-multiplexed genome partitioning and barcoding, can span hundreds of kilobases to improve de novo assembly, haplotype phasing, and other applications. Based on our analysis of 14 datasets, we introduce LRSim that simulates linked-reads by emulating the library preparation and sequencing process with fine control over variants, linked-read characteristics, and the short-read profile. We conclude from the phasing and assembly of multiple datasets, recommendations on coverage, fragment length, and partitioning when sequencing genomes of different sizes and complexities. These optimizations improve results by orders of magnitude, and enable the development of novel methods. LRSim is available at https://github.com/aquaskyline/LRSIM.

Xenolog classification.

Motivation: Orthology analysis is a fundamental tool in comparative genomics. Sophisticated methods have been developed to distinguish between orthologs and paralogs and to classify paralogs into subtypes depending on the duplication mechanism and timing, relative to speciation. However, no comparable framework exists for xenologs: gene pairs whose history, since their divergence, includes a horizontal transfer. Further, the diversity of gene pairs that meet this broad definition calls for classification of xenologs with similar properties into subtypes. Results: We present a xenolog classification that uses phylogenetic reconciliation to assign each pair of genes to a class based on the event responsible for their divergence and the historical association between genes and species. Our classes distinguish between genes related through transfer alone and genes related through duplication and transfer. Further, they separate closely-related genes in distantly-related species from distantly-related genes in closely-related species. We present formal rules that assign gene pairs to specific xenolog classes, given a reconciled gene tree with an arbitrary number of duplications and transfers. These xenology classification rules have been implemented in software and tested on a collection of ∼13 000 prokaryotic gene families. In addition, we present a case study demonstrating the connection between xenolog classification and gene function prediction. Availability and Implementation: The xenolog classification rules have been implemented in N otung 2.9, a freely available phylogenetic reconciliation software package. http://www.cs.cmu.edu/~durand/Notung . Gene trees are available at http://dx.doi.org/10.7488/ds/1503 . Contact: durand@cmu.edu. Supplementary information: Supplementary data are available at Bioinformatics online.