Dataset Information

Digital gene expression for non-model organisms.

ABSTRACT: Next-generation sequencing technologies offer new approaches for global measurements of gene expression but are mostly limited to organisms for which a high-quality assembled reference genome sequence is available. We present a method for gene expression profiling called EDGE, or EcoP15I-tagged Digital Gene Expression, based on ultra-high-throughput sequencing of 27-bp cDNA fragments that uniquely tag the corresponding gene, thereby allowing direct quantification of transcript abundance. We show that EDGE is capable of assaying for expression in >99% of genes in the genome and achieves saturation after 6-8 million reads. EDGE exhibits very little technical noise, reveals a large (10(6)) dynamic range of gene expression, and is particularly suited for quantification of transcript abundance in non-model organisms where a high-quality annotated genome is not available. In a direct comparison with RNA-seq, both methods provide similar assessments of relative transcript abundance, but EDGE does better at detecting gene expression differences for poorly expressed genes and does not exhibit transcript length bias. Applying EDGE to laboratory mice, we show that a loss-of-function mutation in the melanocortin 1 receptor (Mc1r), recognized as a Mendelian determinant of yellow hair color in many different mammals, also causes reduced expression of genes involved in the interferon response. To illustrate the application of EDGE to a non-model organism, we examine skin biopsy samples from a cheetah (Acinonyx jubatus) and identify genes likely to control differences in the color of spotted versus non-spotted regions.

SUBMITTER: Hong LZ

PROVIDER: S-EPMC3205575 | biostudies-literature | 2011 Nov

REPOSITORIES: biostudies-literature

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Digital gene expression for non-model organisms.

Hong Lewis Z LZ Li Jun J Schmidt-Küntzel Anne A Warren Wesley C WC Barsh Gregory S GS

Genome research 20110815 11

Next-generation sequencing technologies offer new approaches for global measurements of gene expression but are mostly limited to organisms for which a high-quality assembled reference genome sequence is available. We present a method for gene expression profiling called EDGE, or EcoP15I-tagged Digital Gene Expression, based on ultra-high-throughput sequencing of 27-bp cDNA fragments that uniquely tag the corresponding gene, thereby allowing direct quantification of transcript abundance. We show ...[more]

PMID: 21844123

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Project description:BackgroundRNA-seq is being increasingly adopted for gene expression studies in a panoply of non-model organisms, with applications spanning the fields of agriculture, aquaculture, ecology, and environment. For organisms that lack a well-annotated reference genome or transcriptome, a conventional RNA-seq data analysis workflow requires constructing a de-novo transcriptome assembly and annotating it against a high-confidence protein database. The assembly serves as a reference for read mapping, and the annotation is necessary for functional analysis of genes found to be differentially expressed. However, assembly is computationally expensive. It is also prone to errors that impact expression analysis, especially since sequencing depth is typically much lower for expression studies than for transcript discovery.ResultsWe propose a shortcut, in which we obtain counts for differential expression analysis by directly aligning RNA-seq reads to the high-confidence proteome that would have been otherwise used for annotation. By avoiding assembly, we drastically cut down computational costs - the running time on a typical dataset improves from the order of tens of hours to under half an hour, and the memory requirement is reduced from the order of tens of Gbytes to tens of Mbytes. We show through experiments on simulated and real data that our pipeline not only reduces computational costs, but has higher sensitivity and precision than a typical assembly-based pipeline. A Snakemake implementation of our workflow is available at: https://bitbucket.org/project_samar/samar .ConclusionsThe flip side of RNA-seq becoming accessible to even modestly resourced labs has been that the time, labor, and infrastructure cost of bioinformatics analysis has become a bottleneck. Assembly is one such resource-hungry process, and we show here that it can be avoided for quick and easy, yet more sensitive and precise, differential gene expression analysis in non-model organisms.

Project description:BACKGROUND: Molecular characterization of highly diverse gene families can be time consuming, expensive, and difficult, especially when considering the potential for relatively large numbers of paralogs and/or pseudogenes. Here we investigate the utility of Pacific Biosciences single molecule real-time (SMRT) circular consensus sequencing (CCS) as an alternative to traditional cloning and Sanger sequencing PCR amplicons for gene family characterization. We target vomeronasal gene receptors, one of the most diverse gene families in mammals, with the goal of better understanding intra-specific V1R diversity of the gray mouse lemur (Microcebus murinus). Our study compares intragenomic variation for two V1R subfamilies found in the mouse lemur. Specifically, we compare gene copy variation within and between two individuals of M. murinus as characterized by different methods for nucleotide sequencing. By including the same individual animal from which the M. murinus draft genome was derived, we are able to cross-validate gene copy estimates from Sanger sequencing versus CCS methods. RESULTS: We generated 34,088 high quality circular consensus sequences of two diverse V1R subfamilies (here referred to as V1RI and V1RIX) from two individuals of Microcebus murinus. Using a minimum threshold of 7× coverage, we recovered approximately 90% of V1RI sequences previously identified in the draft M. murinus genome (59% being identical at all nucleotide positions). When low coverage sequences were considered (i.e. < 7× coverage) 100% of V1RI sequences identified in the draft genome were recovered. At least 13 putatively novel V1R loci were also identified using CCS technology. CONCLUSIONS: Recent upgrades to the Pacific Biosciences RS instrument have improved the CCS technology and offer an alternative to traditional sequencing approaches. Our results suggest that the Microcebus murinus V1R repertoire has been underestimated in the draft genome. In addition to providing an improved understanding of V1R diversity in the mouse lemur, this study demonstrates the utility of CCS technology for characterizing complex regions of the genome. We anticipate that long-read sequencing technologies such as PacBio SMRT will allow for the assembly of multigene family clusters and serve to more accurately characterize patterns of gene copy variation in large gene families, thus revealing novel micro-evolutionary patterns within non-model organisms.

Project description:BackgroundThe advent of Next Generation Sequencing technologies and corresponding bioinformatics tools allows the definition of transcriptomes in non-model organisms. Non-model organisms are of great ecological and biotechnological significance, and consequently the understanding of their unique metabolic pathways is essential. Several methods that integrate de novo assembly with genome-based assembly have been proposed. Yet, there are many open challenges in defining genes, particularly where genomes are not available or incomplete. Despite the large numbers of transcriptome assemblies that have been performed, quality control of the transcript building process, particularly on the protein level, is rarely performed if ever. To test and improve the quality of the automated transcriptome reconstruction, we used manually defined and curated genes, several of them experimentally validated.ResultsSeveral approaches to transcript construction were utilized, based on the available data: a draft genome, high quality RNAseq reads, and ESTs. In order to maximize the contribution of the various data, we integrated methods including de novo and genome based assembly, as well as EST clustering. After each step a set of manually curated genes was used for quality assessment of the transcripts. The interplay between the automated pipeline and the quality control indicated which additional processes were required to improve the transcriptome reconstruction. We discovered that E. huxleyi has a very high percentage of non-canonical splice junctions, and relatively high rates of intron retention, which caused unique issues with the currently available tools. While individual tools missed genes and artificially joined overlapping transcripts, combining the results of several tools improved the completeness and quality considerably. The final collection, created from the integration of several quality control and improvement rounds, was compared to the manually defined set both on the DNA and protein levels, and resulted in an improvement of 20% versus any of the read-based approaches alone.ConclusionsTo the best of our knowledge, this is the first time that an automated transcript definition is subjected to quality control using manually defined and curated genes and thereafter the process is improved. We recommend using a set of manually curated genes to troubleshoot transcriptome reconstruction.

Project description:With the ease of gene sequencing and the technology available to study and manipulate non-model organisms, the extension of the methodological toolbox required to translate our understanding of model organisms to non-model organisms has become an urgent problem. For example, mining of large coral and their symbiont sequence data is a challenge, but also provides an opportunity for understanding functionality and evolution of these and other non-model organisms. Much more information than for any other eukaryotic species is available for humans, especially related to signal transduction and diseases. However, the coral cnidarian host and human have diverged over 700 million years ago and homologies between proteins in the two species are therefore often in the gray zone, or at least often undetectable with traditional BLAST searches. We introduce a two-stage approach to identifying putative coral homologues of human proteins. First, through remote homology detection using Hidden Markov Models, we identify candidate human homologues in the cnidarian genome. However, for many proteins, the human genome alone contains multiple family members with similar or even more divergence in sequence. In the second stage, therefore, we filter the remote homology results based on the functional and structural plausibility of each coral candidate, shortlisting the coral proteins likely to have conserved some of the functions of the human proteins. We demonstrate our approach with a pipeline for mapping membrane receptors in humans to membrane receptors in corals, with specific focus on the stony coral, P. damicornis. More than 1000 human membrane receptors mapped to 335 coral receptors, including 151 G protein coupled receptors (GPCRs). To validate specific sub-families, we chose opsin proteins, representative GPCRs that confer light sensitivity, and Toll-like receptors, representative non-GPCRs, which function in the immune response, and their ability to communicate with microorganisms. Through detailed structure-function analysis of their ligand-binding pockets and downstream signaling cascades, we selected those candidate remote homologues likely to carry out related functions in the corals. This pipeline may prove generally useful for other non-model organisms, such as to support the growing field of synthetic biology.

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Digital gene expression for non-model organisms.

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Digital gene expression for non-model organisms.

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