Dataset Information

SNP calling from RNA-seq data without a reference genome: identification, quantification, differential analysis and impact on the protein sequence.

ABSTRACT: SNPs (Single Nucleotide Polymorphisms) are genetic markers whose precise identification is a prerequisite for association studies. Methods to identify them are currently well developed for model species, but rely on the availability of a (good) reference genome, and therefore cannot be applied to non-model species. They are also mostly tailored for whole genome (re-)sequencing experiments, whereas in many cases, transcriptome sequencing can be used as a cheaper alternative which already enables to identify SNPs located in transcribed regions. In this paper, we propose a method that identifies, quantifies and annotates SNPs without any reference genome, using RNA-seq data only. Individuals can be pooled prior to sequencing, if not enough material is available from one individual. Using pooled human RNA-seq data, we clarify the precision and recall of our method and discuss them with respect to other methods which use a reference genome or an assembled transcriptome. We then validate experimentally the predictions of our method using RNA-seq data from two non-model species. The method can be used for any species to annotate SNPs and predict their impact on the protein sequence. We further enable to test for the association of the identified SNPs with a phenotype of interest.

SUBMITTER: Lopez-Maestre H

PROVIDER: S-EPMC5100560 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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SNP calling from RNA-seq data without a reference genome: identification, quantification, differential analysis and impact on the protein sequence.

Lopez-Maestre Hélène H Brinza Lilia L Marchet Camille C Kielbassa Janice J Bastien Sylvère S Boutigny Mathilde M Monnin David D Filali Adil El AE Carareto Claudia Marcia CM Vieira Cristina C Picard Franck F Kremer Natacha N Vavre Fabrice F Sagot Marie-France MF Lacroix Vincent V

Nucleic acids research 20160725 19

SNPs (Single Nucleotide Polymorphisms) are genetic markers whose precise identification is a prerequisite for association studies. Methods to identify them are currently well developed for model species, but rely on the availability of a (good) reference genome, and therefore cannot be applied to non-model species. They are also mostly tailored for whole genome (re-)sequencing experiments, whereas in many cases, transcriptome sequencing can be used as a cheaper alternative which already enables ...[more]

PMID: 27458203

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Project description:BackgroundRNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments.ResultsWe present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene.ConclusionsRSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.

Project description:Genomics research in mammals has produced reference genome sequences that are essential for identifying variation associated with disease. High quality reference genome sequences are now available for humans, model species, and economically important agricultural animals. Comparisons between these species have provided unique insights into mammalian gene function. However, the number of species with reference genomes is small compared to those needed for studying molecular evolutionary relationships in the tree of life. For example, among the even-toed ungulates there are approximately 300 species whose phylogenetic relationships have been calculated in the 10k trees project. Only six of these have reference genomes: cattle, swine, sheep, goat, water buffalo, and bison. Although reference sequences will eventually be developed for additional hoof stock, the resources in terms of time, money, infrastructure and expertise required to develop a quality reference genome may be unattainable for most species for at least another decade. In this work we mapped 35 Gb of next generation sequence data of a Katahdin sheep to its own species' reference genome ( Ovis aries Oar3.1) and to that of a species that diverged 15 to 30 million years ago ( Bos taurus UMD3.1). In total, 56% of reads covered 76% of UMD3.1 to an average depth of 6.8 reads per site, 83 million variants were identified, of which 78 million were homozygous and likely represent interspecies nucleotide differences. Excluding repeat regions and sex chromosomes, nearly 3.7 million heterozygous sites were identified in this animal vs. bovine UMD3.1, representing polymorphisms occurring in sheep. Of these, 41% could be readily mapped to orthologous positions in ovine Oar3.1 with 80% corroborated as heterozygous. These variant sites, identified via interspecies mapping could be used for comparative genomics, disease association studies, and ultimately to understand mammalian gene function.

Dataset Information

SNP calling from RNA-seq data without a reference genome: identification, quantification, differential analysis and impact on the protein sequence.

Publications

SNP calling from RNA-seq data without a reference genome: identification, quantification, differential analysis and impact on the protein sequence.

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