Updating RNA-Seq analyses after re-annotation

The estimation of isoform abundances from RNA-Seq data requires a time-intensive step of mapping reads to either an assembled or previously annotated transcriptome, followed by an optimization procedure for deconvolution of multi-mapping reads. These procedures are essential for downstream analysis such as differential expression. In cases where it is desirable to adjust the underlying annotation, for example, on the discovery of novel isoforms or errors in existing annotations, current pipelines must be rerun from scratch. This makes it difficult to update abundance estimates after re-annotation, or to explore the effect of changes in the transcriptome on analyses. We present a novel efficient algorithm for updating abundance estimates from RNA-Seq experiments on re-annotation that does not require re-analysis of the entire dataset. Our approach is based on a fast partitioning algorithm for identifying transcripts whose abundances may depend on the added or deleted isoforms, and on a fast follow-up approach to re-estimating abundances for all transcripts. We demonstrate the effectiveness of our methods by showing how to synchronize RNA-Seq abundance estimates with the daily RefSeq incremental updates. Thus, we provide a practical approach to maintaining relevant databases of RNA-Seq derived abundance estimates even as annotations are being constantly revised

A Synthetic Maternal-Effect Selfish Genetic Element Drives Population Replacement in Drosophila

One proposed strategy for controlling the transmission of insect-borne pathogens uses a drive mechanism to ensure the rapid spread of transgenes conferring disease refractoriness throughout wild populations. Here, we report the creation of maternal-effect selfish genetic elements in Drosophila that drive population replacement and are resistant to recombination-mediated dissociation of drive and disease refractoriness functions. These selfish elements use microRNA-mediated silencing of a maternally expressed gene essential for embryogenesis, which is coupled with early zygotic expression of a rescuing transgene

Human Intestinal Tissue with Adult Stem Cell Properties Derived from Pluripotent Stem Cells

Genetically engineered human pluripotent stem cells (hPSCs) have been proposed as a source for transplantation therapies and are rapidly becoming valuable tools for human disease modeling. However, many applications are limited due to the lack of robust differentiation paradigms that allow for the isolation of defined functional tissues. Here, using an endogenous LGR5-GFP reporter, we derived adult stem cells from hPSCs that gave rise to functional human intestinal tissue comprising all major cell types of the intestine. Histological and functional analyses revealed that such human organoid cultures could be derived with high purity and with a composition and morphology similar to those of cultures obtained from human biopsies. Importantly, hPSC-derived organoids responded to the canonical signaling pathways that control self-renewal and differentiation in the adult human intestinal stem cell compartment. This adult stem cell system provides a platform for studying human intestinal disease in vitro using genetically engineered hPSCs

Pseudoalignment for metagenomic and metatranscriptomic read assignment

The first step in many metagenomic and metatranscriptomic analysis workflows is assigning high-throughput sequencing reads to specific strains or transcripts, providing the basis for identification and later quantification. However, the high degree of similarity between the sequences of many strains and genes makes it difficult to assign reads at the lowest level of taxonomy, and reads are typically assigned to more general taxonomic levels where they are unambiguous. Recent developments in RNA-Seq analysis have found direct-match k-mer based methods to be extremely accurate and fast when comparing sequenced RNA-Seq reads to transcriptomes. While similar methods have been used in metagenomics before now, none are highly accurate at distinguishing similar strains, and none have been applied to metatranscriptomic data. We explore connections between metagenomic and metatranscriptomic read assignment and the quantification of transcripts from RNA-Seq data to develop novel methods for rapid and accurate quantification of microbiome strains and transcripts.We find that the recent idea of pseudoalignment introduced in the RNA-Seq context is highly applicable in the metagenomics and metatranscriptomics settings as well. When coupled with the Expectation-Maximization (EM) algorithm, reads can be assigned far more accurately and quickly than is currently possible with state of the art software, making it possible and practical for the first time to analyze abundances of individual genomes in metagenomics and metatranscriptomics projects

Pseudoalignment for metagenomic and metatranscriptomic read assignment

The first step in many metagenomic and metatranscriptomic analysis workflows is assigning high-throughput sequencing reads to specific strains or transcripts, providing the basis for identification and later quantification. However, the high degree of similarity between the sequences of many strains and genes makes it difficult to assign reads at the lowest level of taxonomy, and reads are typically assigned to more general taxonomic levels where they are unambiguous. Recent developments in RNA-Seq analysis have found direct-match k-mer based methods to be extremely accurate and fast when comparing sequenced RNA-Seq reads to transcriptomes. While similar methods have been used in metagenomics before now, none are highly accurate at distinguishing similar strains, and none have been applied to metatranscriptomic data. We explore connections between metagenomic and metatranscriptomic read assignment and the quantification of transcripts from RNA-Seq data to develop novel methods for rapid and accurate quantification of microbiome strains and transcripts.We find that the recent idea of pseudoalignment introduced in the RNA-Seq context is highly applicable in the metagenomics and metatranscriptomics settings as well. When coupled with the Expectation-Maximization (EM) algorithm, reads can be assigned far more accurately and quickly than is currently possible with state of the art software, making it possible and practical for the first time to analyze abundances of individual genomes in metagenomics and metatranscriptomics projects

Scaffolding a Caenorhabditis nematode genome with RNA-seq

Efficient sequencing of animal and plant genomes by next-generation technology should allow many neglected organisms of biological and medical importance to be better understood. As a test case, we have assembled a draft genome of Caenorhabditis sp. 3 PS1010 through a combination of direct sequencing and scaffolding with RNA-seq. We first sequenced genomic DNA and mixed-stage cDNA using paired 75-nt reads from an Illumina GAII. A set of 230 million genomic reads yielded an 80-Mb assembly, with a supercontig N50 of 5.0 kb, covering 90% of 429 kb from previously published genomic contigs. Mixed-stage poly(A)+ cDNA gave 47.3 million mappable 75-mers (including 5.1 million spliced reads), which separately assembled into 17.8 Mb of cDNA, with an N50 of 1.06 kb. By further scaffolding our genomic supercontigs with cDNA, we increased their N50 to 9.4 kb, nearly double the average gene size in C. elegans. We predicted 22,851 protein-coding genes, and detected expression in 78% of them. Multigenome alignment and data filtering identified 2672 DNA elements conserved between PS1010 and C. elegans that are likely to encode regulatory sequences or previously unknown ncRNAs. Genomic and cDNA sequencing followed by joint assembly is a rapid and useful strategy for biological analysis

Human intestinal tissue with adult stem cell properties derived from pluripotent stem cells.

Genetically engineered human pluripotent stem cells (hPSCs) have been proposed as a source for transplantation therapies and are rapidly becoming valuable tools for human disease modeling. However, many applications are limited due to the lack of robust differentiation paradigms that allow for the isolation of defined functional tissues. Here, using an endogenous LGR5-GFP reporter, we derived adult stem cells from hPSCs that gave rise to functional human intestinal tissue comprising all major cell types of the intestine. Histological and functional analyses revealed that such human organoid cultures could be derived with high purity and with a composition and morphology similar to those of cultures obtained from human biopsies. Importantly, hPSC-derived organoids responded to the canonical signaling pathways that control self-renewal and differentiation in the adult human intestinal stem cell compartment. This adult stem cell system provides a platform for studying human intestinal disease in vitro using genetically engineered hPSCs

Human Intestinal Tissue with Adult Stem Cell Properties Derived from Pluripotent Stem Cells

Genetically engineered human pluripotent stem cells (hPSCs) have been proposed as a source for transplantation therapies and are rapidly becoming valuable tools for human disease modeling. However, many applications are limited due to the lack of robust differentiation paradigms that allow for the isolation of defined functional tissues. Here, using an endogenous LGR5-GFP reporter, we derived adult stem cells from hPSCs that gave rise to functional human intestinal tissue comprising all major cell types of the intestine. Histological and functional analyses revealed that such human organoid cultures could be derived with high purity and with a composition and morphology similar to those of cultures obtained from human biopsies. Importantly, hPSC-derived organoids responded to the canonical signaling pathways that control self-renewal and differentiation in the adult human intestinal stem cell compartment. This adult stem cell system provides a platform for studying human intestinal disease in vitro using genetically engineered hPSCs.National Institutes of Health (U.S.) (Grant R37-CA084198)National Institutes of Health (U.S.). (Grant RO1-CA087869)National Institutes of Health (U.S.) (Grant RO1-HD045022)Howard Hughes Medical Institute (Grant