Download Fasta From Ensembl
Jorgelina Celli
Ensembl protein coding genes can be described as a set of splice variants resulting from alignments of cDNA and protein sequence to the genome and/or manual annotation by the Havana project. Human Ensembl genes are the GENCODE set. Read more about Ensembl genes in our help page or documentation, including noncoding genes such as ncRNA and pseudogenes.
The Ensembl Rapid Release website provides annotation for recently produced, publicly available vertebrate and non-vertebrate genomes from biodiversity initiatives such as Darwin Tree of Life, the Vertebrate Genomes Project and the Earth BioGenome Project.
For primary vs. toplevel, very few aligners can properly handle additional haplotypes. If you happen to be using BWA, then the toplevel assembly would benefit you, but only if you use a dedicated wrapper to handle the ALT information, see bwakit. If you use BWA (bwa-mem) right from the command line without this wrapper then do not use the toplevel assembly. For STAR/hisat2/bowtie2/BBmap/etc. the haplotypes will just cause you problems due to increasing multimapper rates incorrectly. Note that none of these actually use soft-masking.
npscharacter(0).(Your examples work fine, however). Also, is it possible to take this information from multiple databases? I understand this is the Homo sapiens package, however some of my proteins are from different species and I can't seem to find a streamlined, multi-species package. Thanks, J. Denton
There isn't a multi-species package, so you will either need to search using each species separately, or hypothetically you could use NCBI's Eutils to map things (there is the CRAN reutils package that will do things from within R). Although try as I might, I can never figure out how to use Eutils fluently.
The transcriptome and GTF files in iGenomes are vastly out of date with respect to current annotations from Ensembl e.g. human iGenomes annotations are from Ensembl release 75, while the current Ensembl release is 108. Please consider downloading and using a more updated version of your reference genome as outlined in the next section.
The GRCh38 iGenomes assembly is from the NCBI and not Ensembl and as such there are some discrepancies in the way that the annotation is defined that may cause problems when running certain pipelines e.g. nf-core/rnaseq#460. If you would like to use the latest soft-masked Ensembl assembly for GRCh38 instead please see the next section.
Most genomics nf-core pipelines are able to start from just a FASTA and GTF file and create any downstream reference assets as part of the pipeline execution e.g. genome indices, intervals files etc. To avoid having to recreate these assets every time you run the pipeline you can use the --save_reference parameter that will save the indices, interval files etc in the results directory for you to move and store in a more central location for re-use with future pipeline runs. Using nf-core/rnaseq as an example see docs:
A Refgenie server contains assets with established aliases, which can differ from the ones required by an nf-core pipeline.For example, the asset for an ensemble index on the default Refgenie server is called ensembl_gtf, while the same asset is called gtf in nf-core pipelines.
The maturing field of genomics is rapidly increasing the number of sequenced genomes and producing more information from those previously sequenced. Much of this additional information is variation data derived from sampling multiple individuals of a given species with the goal of discovering new variants and characterising the population frequencies of the variants that are already known. These data have immense value for many studies, including those designed to understand evolution and connect genotype to phenotype. Maximising the utility of the data requires that it be stored in an accessible manner that facilitates the integration of variation data with other genome resources such as gene annotation and comparative genomics.
The Ensembl project provides comprehensive and integrated variation resources for a wide variety of chordate genomes. This paper provides a detailed description of the sources of data and the methods for creating the Ensembl variation databases. It also explores the utility of the information by explaining the range of query options available, from using interactive web displays, to online data mining tools and connecting directly to the data servers programmatically. It gives a good overview of the variation resources and future plans for expanding the variation data within Ensembl.
Variation data is an important key to understanding the functional and phenotypic differences between individuals. The development of new sequencing and genotyping technologies is greatly increasing the amount of variation data known for almost all genomes. The Ensembl variation resources are integrated into the Ensembl genome browser and provide a comprehensive way to access this data in the context of a widely used genome bioinformatics system. All Ensembl data is freely available at and from the public MySQL database server at ensembldb.ensembl.org.
The amount of publicly available biological sequence data has been increasing exponentially over the last decade. In addition to the many reference genome sequences now available, variation data is being produced in significant quantities. These data fundamentally seek to extend our knowledge of the genome sequence from the concept of a single "reference" genome sequence, representing a single individual, to a more comprehensive understanding of the genomic diversity of entire species.
Today most variation data is produced in the context of large-scale genotyping assays or resequencing projects which focus either on the whole genome or selected functional regions of the genome such as protein coding regions, regulatory regions or sites of known disease mutations. One of the larger resources includes a comprehensive haplotype map of the human genome created by the International HapMap Project, based on DNA from 270 individuals from four populations [1]. The HapMap Project used array-based genotyping to assess markers with minor allele frequency (MAF) of greater than approximately 5%. Following on from this project, many of these same HapMap individuals are included with others in the 1000 Genomes Project [2], which seeks to assay variant sites including those with much lower MAFs. Previous efforts to map variation in other species include strain specific mouse resequencing [3, 4], a haplotype mapping in rat [5], as well as data mining of public domain resources such as NCBI's dbSNP [6]. Whole genome shotgun sequencing can be used for reliable variant discovery in a single sequenced individual by comparing the sequencing reads to the final consensus assembly as done with platypus [7]. When a reference assembly exists, it is more efficient to compare the sequenced individual to that assembly as this technique facilitates the discovery of both heterozygous variants within the individual and variants between the individual and the reference. Reference based variation discovery has been used for several species including human [8, 9], mouse [4], rat [5], and chicken [10].
These huge datasets including variation data are often available from the original sources in a variety of formats requiring the development of various methods to integrate, archive and display these data in a consistent fashion. Ensembl [11], University of California at Santa Cruz (UCSC) [12, 13] and the National Center for Biotechnology Information (NCBI) [14] have expertise in the storage and manipulation of biological data and have developed genome browsers and other methods to archive and display these data alongside their other large scale data resources. The variation data stored in Ensembl are discussed here.
The Ensembl project is a comprehensive bioinformatics resource for chordate genomes. Thousands of researchers from around the world access Ensembl data every day through the various portals provided by the project including the web interface at [15], the Ensembl API [16], and Ensembl BioMart [17]. In addition to the chordate genomes, selected model organisms (D. melanogaster, C. elegans, S. cerevisiae) are included to facilitate comparative analysis. More comparative analysis is available using species supported by Ensembl Genomes project, a sister project extending Ensembl analysis across a larger taxonomic space [18].
Ensembl is updated approximately every two months with newly sequenced genomes and newly available or processed data for existing genomes. The project specialises in integrating large-scale data from many different sources in a variety of formats with a high-quality annotation of the genome and gene set. In addition to comparative and functional genomics data resources, Ensembl provides variation data for a number of supported species.
Each year, Ensembl publishes a general update of all the project's resources [11, 19, 20]. In contrast to these high-level overviews, this is a more in-depth report specifically on the growing number of variation resources available within Ensembl. It describes, in detail, how data is extracted and combined from primary sources such as the Ensembl Trace Archive [21] and NCBI's dbSNP [6], how it is generated using data from resequencing information, how it is visualised and how to obtain the data via the website.
Ensembl produces variation databases for a subset of the genomes available in Ensembl (first two columns of Table 1). This allows integration and easy access to variation data from multiple sources as well as the effects of sequence variation on the genes. The databases incorporate four types of data: a complete polymorphism catalogue, genotype data from specific projects, phenotype data and selected resequencing data. The primary source of polymorphism and genotype data for SNPs and in-dels is from dbSNP, the major public archive of variation data, which is integrated with data from other sources as described below. Structural variants are imported from DGVa [22] and for some species, additional variants are generated from uniform processing and variation discovery using sequencing reads from the Ensembl Trace Archive (see column 4 of Table 1). Phenotype data comes from both a manually curated resource and a public archive, as explained below. Finally a series of quality-control stages are implemented as described below. In this way, Ensembl can begin to provide a comprehensive picture of variations, their effects, and their context.
760c119bf3