Biology subjects

Waterston, R. H.

Publications and source records attributed to Waterston, R. H..

8 recordsLinked to original sources

bioRxiv2024

Binding profiles for 954 Drosophila and C. elegans transcription factors revealtissue specific regulatory relationships

A catalog of transcription factor (TF) binding sites in the genome is critical for deciphering regulatory relationships. Here we present the culmination of the modERN (model organism Encyclopedia of Regulatory Networks) consortium that systematically assayed TF binding events in vivo in two major model organisms, Drosophila melanogaster (fly) and Caenorhabditis elegans (worm). We describe key features of these datasets, comprising 604 TFs identifying 3.6M sites in the fly and 350 TFs identifying 0.9 M sites in the worm. Applying a machine learning model to these data identifies sets of TFs with a prominent role in promoting target gene expression in specific cell types. TF binding data are available through the ENCODE Data Coordinating Center and at https://epic.gs.washington.edu/modERNresource, which provides access to processed and summary data, as well as widgets to probe cell type-specific TF-target relationships. These data are a rich resource that should fuel investigations into TF function during development.

genomics↗

bioRxiv2021

An integrated analysis tool reveals intrinsic biases in gene set enrichment

Generating meaningful interpretations of gene lists remains a challenge for all large-scale studies. Many approaches exist, often based on evaluating gene enrichment among pre-determined gene classes. Here, we conceived and implemented yet another analysis tool (YAAT), specifically for data from the widely-used model organism C. elegans. YAAT extends standard enrichment analyses, using a combination of co-expression data and profiles of phylogenetic conservation, to identify groups of functionally-related genes. It additionally allows class clustering, providing inference of functional links between groups of genes. We give examples of the utility of YAAT for uncovering unsuspected links between genes and show how the approach can be used to prioritise genes for in-depth study. Our analyses revealed several limitations to the meaningful interpretation of gene lists, specifically related to data sources and the "universe" of gene lists used. We hope that YAAT will represent a model for integrated analysis that could be useful for large-scale exploration of biological function in other species.

bioinformatics↗

bioRxiv2020

Comprehensive characterization of tissue-specific chromatin accessibility in L2 Caenorhabditis elegans nematodes

Recently developed single cell technologies allow researchers to characterize cell states at ever greater resolution and scale. C. elegans is a particularly tractable system for studying development, and recent single cell RNA-seq studies characterized the gene expression patterns for nearly every cell type in the embryo and at the second larval stage (L2). Gene expression patterns are useful for learning about gene function and give insight into the biochemical state of different cell types; however, in order to understand these cell types, we must also determine how these gene expression levels are regulated. We present the first single cell ATAC-seq study in C. elegans. We collected data in L2 larvae to match the available single cell RNA-seq data set, and we identify tissue-specific chromatin accessibility patterns that align well with existing data, including the L2 single cell RNA-seq results. Using a novel implementation of the latent Dirichlet allocation algorithm, we leverage the single-cell resolution of the sci-ATAC-seq data to identify accessible loci at the level of individual cell types, providing new maps of putative cell type-specific gene regulatory sites, with promise for better understanding of cellular differentiation and gene regulation in the worm.

genomics↗

bioRxiv2019

To mock or not: a comprehensive comparison of mock IP and DNA input for ChIP-seq

Chromatin immunoprecipitation (IP) followed by sequencing (ChIP-seq) is the gold standard to detect genome-wide DNA-protein binding. The binding sites of transcription factors facilitate many biological studies. Of emerging concern is the abundance of spurious sites in ChIP-seq, which are mainly caused by uneven genomic sonication and nonspecific interactions between chromatin and antibody. A "mock" IP is designed to correct for both factors, whereas a DNA input control corrects only for uneven sonication. However, a mock IP is more susceptible to technical noise than a DNA input, and empirically, these two controls perform similarly for ChIP-seq. Therefore, DNA input is currently being used almost exclusively. With a large dataset, we demonstrate that using a DNA input control results in a definable set of spurious sites, and their abundance is tightly associated with the intrinsic properties of the ChIP-seq sample. For example, compared to human cell lines, samples such as human tissues and whole worm and fly have more accessible genomes, and thus have more spurious sites. The large and varying abundance of spurious sites may impede comparative studies across multiple samples. In contrast, using a mock IP as control substantially removes these spurious sites, resulting in high-quality binding sites and facilitating their comparability across samples. Although outperformed by mock IP, DNA input is still informative and has unique advantages. Therefore, we have developed a method to use both controls in combination to further improve binding site detection.

genomics↗

bioRxiv2019

PhenoMIP: High Throughput Phenotyping of Diverse C. elegans Populations via Molecular Inversion Probes

Whether generated within a lab setting or isolated from the wild, variant alleles continue to be an important resource for decoding gene function in model organisms such as Caenorhabditis elegans. With advances in massively parallel sequencing, multiple whole-genome sequenced (WGS) strain collections are now available to the research community. The Million Mutation Project (MMP) for instance, analysed 2007 N2-derived, mutagenized strains. Individually, each strain averages [~]400 single nucleotide variants amounting to [~]80 protein coding variants. The effects of these variants, however, remain largely uncharacterized and querying the breadth of these strains for phenotypic changes requires a method amenable to rapid and sensitive high-throughput analysis. Here we present a pooled competitive fitness approach to quantitatively phenotype subpopulations of sequenced collections via molecular inversion probes (PhenoMIP). We phenotyped the relative fitness of 217 mutant strains on multiple food sources and classified these into five categories. We also demonstrate on a subset of these strains, that their fitness defects can be genetically mapped. Overall, our results suggest that approximately 80% of MMP mutant strains may have a decreased fitness relative to the lab reference, N2. The costs of generating this form of analysis through WGS methods would be prohibitive while PhenoMIP analysis in this manner is accomplished at less than 1% of projected WGS costs. We propose methods for applying PhenoMIP to a broad range of population selection experiments in a cost-efficient manner that would be useful to the community at large.

genetics↗

bioRxiv2019

A lineage-resolved molecular atlas of C. elegans embryogenesis at single cell resolution

C. elegans is an animal with few cells, but a striking diversity of cell types. Here, we characterize the molecular basis for their specification by profiling the transcriptomes of 84,625 single embryonic cells. We identify 284 terminal and pre-terminal cell types, mapping most single cell transcriptomes to their exact position in C. elegans invariant lineage. We use these annotations to perform the first quantitative analysis of the relationship between lineage and the transcriptome for a whole organism. We find that a strong lineage-transcriptome correlation in the early embryo breaks down in the final two cell divisions as cells adopt their terminal fates and that most distinct lineages that produce the same anatomical cell type converge to a homogenous transcriptomic state. Users can explore our data with a graphical application "VisCello".

genomics↗

bioRxiv2017

MIP-MAP: High-Throughput Mapping of Caenorhabditis elegans Temperature Sensitive Mutants via Molecular Inversion Probes

Temperature sensitive (TS) alleles are important tools for the genetic and functional analysis of essential genes in many model organisms. While isolating TS alleles is not difficult, determining the TS-conferring mutation can be problematic. Even with whole-genome sequencing (WGS) data there is a paucity of predictive methods for identifying TS alleles from DNA sequence alone. We assembled 173 TS lethal mutants of Caenorhabditis elegans and used WGS to identify several hundred mutations per strain. We leveraged single molecule molecular inversion probes (MIPs) to sequence variant sites at high depth in the cross-progeny of TS mutants and a mapping strain with identified sequence variants but no apparent phenotypic differences from the reference N2 strain. By sampling for variants at ~1Mb intervals across the genome we genetically mapped mutant alleles at a resolution comparable to current standards in a process we call MIP-MAP. The MIP-MAP protocol, however, permits high-throughput sequencing of multiple TS mutation mapping libraries at less than 200K reads per library. Using MIP-MAP on a subset of TS mutants, via a competitive selection assay and standard recombinant mutant selection, we defined TS-associated intervals of 3Mb or less. Our results suggest this collection of strains contains a diverse library of TS alleles for genes involved in development and reproduction. MIP-MAP is a robust method to genetically map mutations in both viable and essential genes. The MIPs protocol should allow high-throughput tracking of genetic variants in any mixed population.

genomics↗

bioRxiv2017

Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing

Conventional methods for profiling the molecular content of biological samples fail to resolve heterogeneity that is present at the level of single cells. In the past few years, single cell RNA sequencing has emerged as a powerful strategy for overcoming this challenge. However, its adoption has been limited by a paucity of methods that are at once simple to implement and cost effective to scale massively. Here, we describe a combinatorial indexing strategy to profile the transcriptomes of large numbers of single cells or single nuclei without requiring the physical isolation of each cell (Single cell Combinatorial Indexing RNA-seq or sci-RNA-seq). We show that sci-RNA-seq can be used to efficiently profile the transcriptomes of tens-of-thousands of single cells per experiment, and demonstrate that we can stratify cell types from these data. Key advantages of sci-RNA-seq over contemporary alternatives such as droplet-based single cell RNA-seq include sublinear cost scaling, a reliance on widely available reagents and equipment, the ability to concurrently process many samples within a single workflow, compatibility with methanol fixation of cells, cell capture based on DNA content rather than cell size, and the flexibility to profile either cells or nuclei. As a demonstration of sci-RNA-seq, we profile the transcriptomes of 42,035 single cells from C. elegans at the L2 stage, effectively 50-fold \"shotgun cellular coverage\" of the somatic cell composition of this organism at this stage. We identify 27 distinct cell types, including rare cell types such as the two distal tip cells of the developing gonad, estimate consensus expression profiles and define cell-type specific and selective genes. Given that C. elegans is the only organism with a fully mapped cellular lineage, these data represent a rich resource for future methods aimed at defining cell types and states. They will advance our understanding of developmental biology, and constitute a major step towards a comprehensive, single-cell molecular atlas of a whole animal.

genomics↗