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---
title: "Sequence Retrieval"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Sequence Retrieval}
%\VignetteEngine{knitr::rmarkdown}
%\usepackage[utf8]{inputenc}
---
```{r, echo = FALSE, message = FALSE}
options(width = 750)
knitr::opts_chunk$set(
comment = "#>",
error = FALSE,
tidy = FALSE)
```
## Biological Sequence Retrieval
The `biomartr` package allows users to retrieve biological sequences in a very simple and intuitive way.
Using `biomartr`, users can retrieve either genomes, proteomes, CDS, RNA, GFF, and genome assembly statistics data using the specialized functions:
* [1. Genome retrieval with `getGenome()`](#genome-retrieval)
- [A. list total number of available genomes in each database](#listing-the-total-number-of-available-genomes)
- [B. dealing with kingdoms, groups, and subgroups ](#retrieving-kingdom-group-and-subgroup-information)
- [1.1 from NCBI RefSeq](#example-ncbi-refseq)
- [1.2 from NCBI Genbank](#example-ncbi-genbank)
- [1.3 from ENSEMBL](#example-ensembl)
* [1.B. Multiple genome retrieval with `getGenomeSet()`](#genomeset-retrieval)
* [2. Proteome retrieval with `getProteome()`](#proteome-retrieval)
- [2.1 from NCBI RefSeq](#example-ncbi-refseq-1)
- [2.2 from NCBI Genbank](#example-ncbi-genbank-1)
- [2.3 from ENSEMBL](#example-ensembl-1)
- [2.5 from UniProt](#example-retrieval-uniprot)
* [2.B. Multiple proteome retrieval with `getProteomeSet()`](#proteomeset-retrieval)
* [3. Coding sequence retrieval with `getCDS()`](#cds-retrieval)
- [3.1 from NCBI RefSeq](#example-ncbi-refseq-2)
- [3.2 from NCBI Genbank](#example-ncbi-genbank-2)
- [3.3 from ENSEMBL](#example-ensembl-2)
* [3.B. Multiple coding sequence retrieval with `getCDSSet()`](#cdsset-retrieval)
* [4. RNA retrieval with `getRNA()`](#rna-retrieval)
- [4.1 from NCBI RefSeq](#example-ncbi-refseq-3)
- [4.2 from NCBI Genbank](#example-ncbi-genbank-3)
- [4.3 from ENSEMBL](#example-ensembl-3)
* [4.B. Multiple RNA retrieval with `getRNASet()`](#rnaset-retrieval)
* [5. GFF retrieval with `getGFF()`](#retrieve-the-annotation-file-of-a-particular-genome)
- [5.1 from NCBI RefSeq](#example-ncbi-refseq-4)
- [5.2 from NCBI Genbank](#example-ncbi-genbank-4)
- [5.3 from ENSEMBL](#example-ensembl-4)
* [5.B. Multiple GFF retrieval with `getGFFSet()`](#gffset-retrieval)
* [6. GTF retrieval with `getGTF()`](#retrieve-the-annotation-file-of-a-particular-genome)
- [6.1 from NCBI RefSeq](#example-ncbi-refseq-5)
- [6.2 from NCBI Genbank](#example-ncbi-genbank-5)
- [6.3 from ENSEMBL](#example-ensembl-5)
* [7. Repeat Masker Annotation file retrieval with `getRepeatMasker()`](#repeat-masker-retrieval)
- [7.1 from NCBI RefSeq](#example-ncbi-refseq-6)
* [8. Genome Assembly Stats Retrieval with `getAssemblyStats()`](#genome-assembly-stats-retrieval)
- [8.1 from NCBI RefSeq](#example-ncbi-refseq-7)
- [8.2 from NCBI Genbank](#example-ncbi-genbank-6)
* [9. Collection (Genome, Proteome, CDS, RNA, GFF, Repeat Masker, AssemblyStats) retrieval with `getCollection()`](#collection-retrieval)
- [9.1 from NCBI RefSeq](#example-ncbi-refseq-8)
- [9.2 from NCBI Genbank](#example-ncbi-genbank-7)
- [9.3 from NCBI ENSEMBL](#example-ensembl-6)
## Getting Started with Sequence Retrieval
First users can check whether or not the genome, proteome, CDS, RNA, GFF, GTF, or genome assembly statistics of their interest is available for download.
Using the scientific name of the organism of interest, users can check whether the
corresponding genome is available via the `is.genome.available()` function.
Please note that the first time you run this command it might take a while, because
during the initial execution of this function all necessary information is retrieved from NCBI
and then stored locally. All further runs are then much faster.
### Example `NCBI RefSeq` (?is.genome.available):
```r
# checking whether or not the Homo sapiens
# genome is avaialable for download
is.genome.available(db = "refseq", organism = "Homo sapiens")
```
```
A reference or representative genome assembly is available for 'Homo sapiens'.
[1] TRUE
```
In the case of the human genome, there are more than one entry in the NCBI RefSeq
database (see message). By specifying the argument 'details = TRUE' users can
retrieve all information for 'Homo sapiens' that is stored in NCBI RefSeq.
```r
# checking whether or not the Homo sapiens
# genome is avaialable for download
is.genome.available(db = "refseq", organism = "Homo sapiens", details = TRUE)
```
```
assembly_accessi… bioproject biosample wgs_master refseq_category taxid
<chr> <chr> <chr> <chr> <chr> <int>
1 GCF_000001405.38 PRJNA168 NA NA reference geno… 9606
# ... with 15 more variables: species_taxid <int>, organism_name <chr>,
# infraspecific_name <chr>, isolate <chr>, version_status <chr>,
# assembly_level <chr>, release_type <chr>, genome_rep <chr>,
# seq_rel_date <date>, asm_name <chr>, submitter <chr>,
# gbrs_paired_asm <chr>, paired_asm_comp <chr>, ftp_path <chr>,
# excluded_from_refseq <chr>
```
Here, we find one possible versions of the human genome having the `assembly_accession`
ID `GCF_000001405.38`.
When retrieving a genome with e.g. `getGenome()` the `organism` argument can
also be specified using the `assembly_accession` ID instead of the scientific name of the
organism. This is true for all `get*()` functions. Hence, instead of writing
`getGenome(db = "refseq", organism = "Homo sapiens")`, users can specify
`getGenome(db = "refseq", organism = "GCF_000001405.38")`.
This is particularly useful when more than one entry is available for one organism.
__Please note that the `assembly_accession` id might change internally in external databases such as NCBI RefSeq. Thus when
writing automated scripts using the `assembly_accession` id they might stop working due to the
internal change of ids at RefSeq. Recently, I had the case where the `assembly_accession` id was changed
at RefSeq from `GCF_000001405.37` to `GCF_000001405.38`. Thus, scripts based on screening for entries with `is.genome.available()`
stopped working, because the id `GCF_000001405.37` couldn't be found anymore.__
In some cases users will find several entries for the same scientific name.
This might be due to the fact that ecotypes, strains, or other types of sub-species
are available in the respective databases.
For example. when we search for the bacterium `Mycobacterium tuberculosis` in NCBI RefSeq we get 5377 hits.
```r
is.genome.available(organism = "Mycobacterium tuberculosis", db = "refseq", details = TRUE)
```
```
A tibble: 5,377 x 21
assembly_accessi… bioproject biosample wgs_master refseq_category taxid
<chr> <chr> <chr> <chr> <chr> <int>
1 GCF_000008585.1 PRJNA2241… SAMN0260… NA na 83331
2 GCF_000016145.1 PRJNA2241… SAMN0260… NA na 419947
3 GCF_000016925.1 PRJNA2241… SAMN0010… NA na 336982
4 GCF_000023625.1 PRJNA2241… SAMN0076… NA na 478434
5 GCF_000152105.1 PRJNA2241… SAMN0259… ADHQ00000… na 675512
6 GCF_000153685.2 PRJNA2241… SAMN0308… NA na 395095
7 GCF_000154585.2 PRJNA2241… SAMN0308… NA na 478433
8 GCF_000154605.2 PRJNA2241… SAMN0010… NA na 478435
9 GCF_000155795.1 PRJNA2241… SAMN0259… ABVM00000… na 555461
10 GCF_000159755.1 PRJNA2241… SAMN0259… ACHP00000… na 611303
# ... with 5,367 more rows, and 15 more variables: species_taxid <int>,
# organism_name <chr>, infraspecific_name <chr>, isolate <chr>,
# version_status <chr>, assembly_level <chr>, release_type <chr>,
# genome_rep <chr>, seq_rel_date <date>, asm_name <chr>, submitter <chr>,
# gbrs_paired_asm <chr>, paired_asm_comp <chr>, ftp_path <chr>,
# excluded_from_refseq <chr>
```
When looking at the names of these organisms we see that they consist of different strains of `Mycobacterium tuberculosis`.
```r
head(is.genome.available(organism = "Mycobacterium tuberculosis",
db = "refseq",
details = TRUE)$organism_name)
```
```
[1] "Mycobacterium tuberculosis CDC1551"
[2] "Mycobacterium tuberculosis H37Ra"
[3] "Mycobacterium tuberculosis F11"
[4] "Mycobacterium tuberculosis KZN 1435"
[5] "Mycobacterium tuberculosis SUMu001"
[6] "Mycobacterium tuberculosis str. Haarlem"
```
Now we can use the `assembly_accession` id to retrieve the `Mycobacterium tuberculosis`
strain we are interested in, e.g. `Mycobacterium tuberculosis CDC1551`.
```r
MtbCDC1551 <- getGenome(db = "refseq",
organism = "GCF_000008585.1",
path = file.path("_ncbi_downloads","genomes"),
reference = FALSE)
MtbCDC1551_genome <- read_genome(MtbCDC1551)
MtbCDC1551_genome
```
```
A DNAStringSet instance of length 1
width seq names
[1] 4403837 TTGACCGATGACCC...AGGGAGATACGTCG NC_002755.2 Mycob...
```
#### Using the NCBI Taxonomy ID instead of the scientific name to screen for organism availability
Instead of specifying the `scientific name` of the organism of interest users can specify the [NCBI Taxonomy](https://www.ncbi.nlm.nih.gov/taxonomy) identifier (= `taxid`)
of the corresponding organism. For example, the `taxid` of `Homo sapiens` is `9606`.
Now users can specify `organism = "9606"` to retrieve entries for `Homo sapiens`:
```r
# checking availability for Homo sapiens using its taxid
is.genome.available(db = "refseq", organism = "9606", details = TRUE)
```
```
assembly_accession bioproject biosample wgs_master refseq_category taxid
<chr> <chr> <chr> <chr> <chr> <int>
1 GCF_000001405.38 PRJNA168 NA NA reference genome 9606
# ... with 15 more variables: species_taxid <int>, organism_name <chr>,
# infraspecific_name <chr>, isolate <chr>, version_status <chr>,
# assembly_level <chr>, release_type <chr>, genome_rep <chr>,
# seq_rel_date <date>, asm_name <chr>, submitter <chr>,
# gbrs_paired_asm <chr>, paired_asm_comp <chr>, ftp_path <chr>,
# excluded_from_refseq <chr>
```
#### Using the accession ID instead of the scientific name or taxid to screen for organism availability
Finally, instead of specifying either the `scientific name` of the organism of interest nor the `taxid`,
users can specify the accession ID of the organism of interest.
In the following example we use the accession ID of `Homo sapiens` (= `GCF_000001405.38`):
```r
# checking availability for Homo sapiens using its taxid
is.genome.available(db = "refseq", organism = "GCF_000001405.38", details = TRUE)
```
```
assembly_accession bioproject biosample wgs_master refseq_category taxid
<chr> <chr> <chr> <chr> <chr> <int>
1 GCF_000001405.38 PRJNA168 NA NA reference genome 9606
# ... with 15 more variables: species_taxid <int>, organism_name <chr>,
# infraspecific_name <chr>, isolate <chr>, version_status <chr>,
# assembly_level <chr>, release_type <chr>, genome_rep <chr>,
# seq_rel_date <date>, asm_name <chr>, submitter <chr>,
# gbrs_paired_asm <chr>, paired_asm_comp <chr>, ftp_path <chr>,
# excluded_from_refseq <chr>
```
#### A small negative example
In some cases your organism of interest will not be available in `NCBI RefSeq`.
Here an example:
```r
# check genome availability for Candida glabrata
is.genome.available(db = "refseq", organism = "Candida glabrata")
```
```r
Unfortunatey, no entry for 'Candida glabrata' was found in the 'refseq' database.
Please consider specifying 'db = genbank' or 'db = ensembl'
to check whether 'Candida glabrata' is availabe in these databases.
[1] FALSE
```
When now checking the availability in `NCBI Genbank` we find that indeed
'Candida glabrata' is availabe:
```r
# check genome availability for Candida glabrata
is.genome.available(db = "genbank", organism = "Candida glabrata")
```
```
Only a non-reference genome assembly is available for 'Candida glabrata'.
Please make sure to specify the argument 'reference = FALSE' when running any get*() function.
[1] TRUE
```
Although, an entry is available the `is.genome.available()` warns us that
only a non-reference genome is available for 'Candida glabrata'.
To then retrieve the e.g. genome etc. files users need to speficy the
`reference = FALSE` argument in the `get*()` functions.
For example:
```r
# retrieve non-reference genome
getGenome(db = "genbank", organism = "Candida glabrata", reference = FALSE)
```
```
Only a non-reference genome assembly is available for 'Candida glabrata'.
Please make sure to specify the argument 'reference = FALSE' when running any get*() function.
Genome download is completed!
Checking md5 hash of file: _ncbi_downloads/genomes/Candida_glabrata_md5checksums.txt ...
The md5 hash of file '_ncbi_downloads/genomes/Candida_glabrata_md5checksums.txt' matches!
The genome of 'Candida_glabrata' has been downloaded to '_ncbi_downloads/genomes'
and has been named 'Candida_glabrata_genomic_genbank.fna.gz'.
[1] "_ncbi_downloads/genomes/Candida_glabrata_genomic_genbank.fna.gz"
```
### Example `NCBI Genbank` (?is.genome.available):
Test whether or not the genome of `Homo sapiens` is present at NCBI Genbank.
```r
# checking whether or not the Homo sapiens
# genome is avaialable for download
is.genome.available(db = "genbank", organism = "Homo sapiens")
```
```
A reference or representative genome assembly is available for 'Homo sapiens'.
More than one entry was found for 'Homo sapiens'.
Please consider to re-run this funtion and specify 'details = TRUE'.
This will allow you to select the 'assembly_accession' identifier
that can then be specified in all get*() functions.
[1] TRUE
```
Now with `details = TRUE`.
```r
# checking whether or not the Homo sapiens
# genome is avaialable for download
is.genome.available(db = "genbank", organism = "Homo sapiens", details = TRUE)
```
```
assembly_accessi… bioproject biosample wgs_master refseq_category taxid
<chr> <chr> <chr> <chr> <chr> <int>
1 GCA_000001405.27 PRJNA31257 NA NA reference geno… 9606
2 GCA_000002115.2 PRJNA1431 SAMN029812… AADD000000… na 9606
3 GCA_000002125.2 PRJNA19621 SAMN029812… ABBA000000… na 9606
4 GCA_000002135.3 PRJNA10793 NA NA na 9606
5 GCA_000004845.2 PRJNA42199 SAMN000033… ADDF000000… na 9606
6 GCA_000005465.1 PRJNA42201 NA DAAB000000… na 9606
7 GCA_000181135.1 PRJNA28335 SAMN000016… ABKV000000… na 9606
8 GCA_000185165.1 PRJNA59877 SAMN029812… AEKP000000… na 9606
9 GCA_000212995.1 PRJNA19621 SAMN029812… ABSL000000… na 9606
10 GCA_000252825.1 PRJNA19621 SAMN029812… ABBA000000… na 9606
# ... with 70 more rows, and 15 more variables: species_taxid <int>,
# organism_name <chr>, infraspecific_name <chr>, isolate <chr>,
# version_status <chr>, assembly_level <chr>, release_type <chr>,
# genome_rep <chr>, seq_rel_date <date>, asm_name <chr>, submitter <chr>,
# gbrs_paired_asm <chr>, paired_asm_comp <chr>, ftp_path <chr>,
# excluded_from_refseq <chr>
```
As you can see there are several versions of the `Homo sapiens` genome
available for download from NCBI Genbank. Using the `assembly_accession`
id will now allow to specify which version shall be retrieved.
### Using `is.genome.available()` with ENSEMBL
Users can also specify `db = "ensembl"` to retrieve available organisms provided by ENSEMBL.
Again, users might experience a delay in the execution of this function when running it for the first time.
This is due to the download of ENSEMBL information which is then stored internally to enable a much faster
execution of this function in following runs. The corresponding information files are stored at `file.path(tempdir(), "ensembl_summary.txt")`.
### Example `ENSEMBL` (?is.genome.available):
```r
# cheking whether Homo sapiens is available in the ENSEMBL database
is.genome.available(db = "ensembl", organism = "Homo sapiens")
```
```
A reference or representative genome assembly is available for 'Homo sapiens'.
[1] TRUE
```
```r
# retrieve details for Homo sapiens
is.genome.available(db = "ensembl", organism = "Homo sapiens", details = TRUE)
```
```
division taxon_id name release display_name accession strain_collecti…
<chr> <int> <chr> <int> <chr> <chr> <chr>
1 Ensembl 9606 homo_s… 92 Human GCA_000001… NA
# ... with 3 more variables: common_name <chr>, strain <chr>, assembly <chr>
```
Again, users can either specify the `taxid` or `accession id` when searching for organism entries.
```r
# retrieve details for Homo sapiens using taxid
is.genome.available(db = "ensembl", organism = "9606", details = TRUE)
```
```
division taxon_id name release display_name accession strain_collecti…
<chr> <int> <chr> <int> <chr> <chr> <chr>
1 Ensembl 9606 homo_s… 92 Human GCA_000001… NA
# ... with 3 more variables: common_name <chr>, strain <chr>, assembly <chr>
>
```
```r
# retrieve details for Homo sapiens using accession id
is.genome.available(organism = "GCA_000001405.27", db = "ensembl", details = TRUE)
```
```
division taxon_id name release display_name accession strain_collecti…
<chr> <int> <chr> <int> <chr> <chr> <chr>
1 Ensembl 9606 homo_s… 92 Human GCA_00000… NA
# ... with 3 more variables: common_name <chr>, strain <chr>, assembly <chr>
```
__Please note that the `accession` id can change internally at ENSEMBL. E.g. in a recent case
the `accession` id changed from `GCA_000001405.25` to `GCA_000001405.27`. Hence, please
be careful and take this issue into account when you build automated retrieval scripts that are based
on `accession` ids.__
### Example `UniProt` (?is.genome.available):
Users can also check the availability of proteomes in the [UniProt database](http://www.uniprot.org)
by specifying:
```r
# retrieve information from UniProt
is.genome.available(db = "uniprot", "Homo sapiens", details = FALSE)
```
```
A reference or representative genome assembly is available for 'Homo sapiens'.
[1] TRUE
```
or with details:
```r
# retrieve information from UniProt
is.genome.available(db = "uniprot", "Homo sapiens", details = TRUE)
```
```
name description isReferenceProt… isRepresentativ… dbReference component
* <chr> <chr> <lgl> <lgl> <list> <list>
1 Homo … Homo sapie… TRUE TRUE <data.fram… <data.fr…
# ... with 7 more variables: reference <list>, upid <chr>, modified <dbl>,
# taxonomy <int>, source <chr>, sourceTaxonomy <int>, superregnum <chr>
```
Users can also search available species at UniProt via `taxid` or `upid` id.
Here `9606` defines the taxonomy id for `Homo sapiens`.
```r
# retrieve information from UniProt
is.genome.available(db = "uniprot", "9606", details = TRUE)
```
```
name description isReferenceProt… isRepresentativ… dbReference component
* <chr> <chr> <lgl> <lgl> <list> <list>
1 Homo … Homo sapie… TRUE TRUE <data.fram… <data.fr…
# ... with 7 more variables: reference <list>, upid <chr>, modified <dbl>,
# taxonomy <int>, source <chr>, sourceTaxonomy <int>, superregnum <chr>
```
Here `UP000005640` defines the `upid` for `Homo sapiens`.
```r
# retrieve information from UniProt
is.genome.available(db = "uniprot", "UP000005640", details = TRUE)
```
```
name description isReferenceProt… isRepresentativ… dbReference component
* <chr> <chr> <lgl> <lgl> <list> <list>
1 Homo … Homo sapie… TRUE TRUE <data.fram… <data.fr…
# ... with 7 more variables: reference <list>, upid <chr>, modified <dbl>,
# taxonomy <int>, source <chr>, sourceTaxonomy <int>, superregnum <chr>
```
In general, the argument `db` specifies from which database (`refseq`, `genbank`, `ensembl` or `uniprot`) organism
information shall be retrieved. Options are:
- `db = 'refseq'`
- `db = 'genbank'`
- `db = 'ensembl'`
- `db = 'uniprot'`
### Listing the total number of available genomes
In some cases it might be useful to check how many genomes (in total) are available in the different databases.
Users can determine the total number of available genomes using the `listGenomes()` function.
Example `refseq`:
```{r,eval=FALSE}
length(listGenomes(db = "refseq"))
```
```
[1] 14264
```
Example `genbank`:
```{r,eval=FALSE}
length(listGenomes(db = "genbank"))
```
```
[1] 21770
```
Example `ensembl`:
```{r,eval=FALSE}
length(listGenomes(db = "ensembl"))
```
```
[1] 113
```
Hence, currently 14264 genomes (including all kingdoms of life) are stored on NCBI RefSeq (as of 31/05/2018).
### Retrieving kingdom, group and subgroup information
Using this example users can retrieve the number of available species for each kingdom of life:
Example `refseq`:
```{r,eval=FALSE}
# the number of genomes available for each kingdom
listKingdoms(db = "refseq")
```
```
Archaea Bacteria Eukaryota Viroids Viruses
169 6321 561 47 7166
```
Example `genbank`:
```{r,eval=FALSE}
# the number of genomes available for each kingdom
listKingdoms(db = "genbank")
```
```
Archaea Bacteria Eukaryota
1249 18083 2438
```
Example `ENSEMBL`:
```{r,eval=FALSE}
# the number of genomes available for each kingdom
listKingdoms(db = "ensembl")
```
```
Ensembl
113
```
### Analogous computations can be performed for `groups` and `subgroups`
Unfortunately, `ENSEMBL` does not provide group or subgroup information.
Therefore, group and subgroup listings are limited to `refseq` and `genbank`.
Example `refseq`:
```{r,eval=FALSE}
# the number of genomes available for each group
listGroups(db = "refseq")
```
```
Abditibacteriota Acidithiobacillia
1 5
Acidobacteriia Ackermannviridae
16 21
Actinobacteria Adenoviridae
1226 62
Alloherpesviridae Alphaflexiviridae
8 56
Alphaproteobacteria Alphasatellitidae
857 67
Alphatetraviridae Alvernaviridae
3 1
Amalgaviridae Amphibians
10 3
Ampullaviridae Anelloviridae
3 61
Apicomplexans Apple fruit crinkle viroid
18 1
Apple hammerhead viroid-like circular RNA Apscaviroid
1 12
Aquificae Archaeoglobi
6 3
Arenaviridae Armatimonadetes
37 4
Arteriviridae Ascomycetes
13 45
Ascoviridae Asfarviridae
4 1
Aspiviridae Astroviridae
3 40
Avsunviroid Bacilladnaviridae
1 7
Bacteria candidate phyla Bacteroidetes/Chlorobi group
3 654
Baculoviridae Balneolia
66 4
Barnaviridae Basidiomycetes
1 5
Benyviridae Betaflexiviridae
3 92
Betaproteobacteria Bicaudaviridae
407 4
Birds Birnaviridae
54 8
Blastocatellia Bornaviridae
1 5
Bromoviridae Caliciviridae
35 26
Candidatus Kryptonia Candidatus Micrarchaeota
3 1
Candidatus Tectomicrobia Carmotetraviridae
1 1
Caulimoviridae Cherry leaf scorch small circular viroid-like RNA 1
67 1
Cherry small circular viroid-like RNA Chlamydiae
1 19
Chloroflexi Chrysoviridae
23 9
Circoviridae Closteroviridae
148 40
Cocadviroid Coleviroid
4 6
Coprothermobacterota Coronaviridae
1 47
Corticoviridae Crenarchaeota
1 9
Cyanobacteria/Melainabacteria group Cystoviridae
22 5
Deinococcus-Thermus delta/epsilon subdivisions
23 142
Deltaflexiviridae Dicistroviridae
1 27
Elaviroid Endomicrobia
1 2
Endornaviridae Fibrobacteres
29 2
Filoviridae Fimoviridae
7 6
Firmicutes Fishes
1378 52
Flatworms Flaviviridae
4 107
Fusariviridae Fuselloviridae
7 10
Fusobacteriia Gammaflexiviridae
20 1
Gammaproteobacteria Geminiviridae
1285 406
Gemmatimonadetes Genomoviridae
2 62
Globuloviridae Grapevine latent viroid
2 1
Green Algae Guttaviridae
8 1
Halobacteria Hantaviridae
80 24
Hepadnaviridae Hepeviridae
16 3
Herpesviridae Hostuviroid
72 2
Hydrogenophilalia Hypoviridae
1 15
Hytrosaviridae Iflaviridae
2 34
Inoviridae Insects
42 110
Iridoviridae Kinetoplasts
20 5
Kiritimatiellaeota Land Plants
1 80
Lavidaviridae Lentisphaerae
2 1
Leviviridae Lipothrixviridae
11 8
Luteoviridae Malacoherpesviridae
48 2
Mammals Marnaviridae
100 1
Marseilleviridae Megabirnaviridae
6 3
Mesoniviridae Methanobacteria
7 18
Methanococci Methanomicrobia
2 21
Methanonatronarchaeia Microviridae
1 42
Mimiviridae Mulberry small circular viroid-like RNA 1
5 1
Mymonaviridae Myoviridae
1 516
Nairoviridae Nanoviridae
13 8
Narnaviridae Nimaviridae
39 1
Nitrospira Nodaviridae
10 15
Nudiviridae Nyamiviridae
5 4
Oligoflexia Orthomyxoviridae
3 6
Other Other Animals
988 35
Other Fungi Other Protists
3 16
Papillomaviridae Paramyxoviridae
134 54
Partitiviridae Parvoviridae
55 90
Pelamoviroid Peribunyaviridae
2 32
Permutotetraviridae Persimmon viroid
1 1
Phasmaviridae Phenuiviridae
4 28
Phycodnaviridae Picobirnaviridae
26 5
Picornaviridae Pithoviridae
104 2
Planctomycetes Plasmaviridae
18 1
Pleolipoviridae Pneumoviridae
8 7
Podoviridae Polycipiviridae
343 6
Polydnaviridae Polyomaviridae
5 83
Pospiviroid Potyviridae
11 146
Poxviridae Quadriviridae
47 1
Reoviridae Reptiles
67 11
Retroviridae Rhabdoviridae
52 150
Rhodothermia Roniviridae
4 1
Roundworms Rubber viroid India/2009
8 1
Rudiviridae Secoviridae
15 53
Siphoviridae Solemoviridae
1061 20
Solibacteres Solinviviridae
3 2
Sphaerolipoviridae Spirochaetia
7 39
Sunviridae Synergistia
1 4
Tectiviridae Tenericutes
5 69
Thaumarchaeota Thermococci
8 19
Thermodesulfobacteria Thermoplasmata
4 7
Thermotogae Togaviridae
10 24
Tolecusatellitidae Tombusviridae
101 68
Tospoviridae Totiviridae
17 66
Tristromaviridae Turriviridae
1 2
Tymoviridae unclassified
37 606
unclassified Acidobacteria unclassified Bacteria (miscellaneous)
1 13
unclassified Nitrospirae unclassified Proteobacteria
1 2
Verrucomicrobia Virgaviridae
25 52
Zetaproteobacteria
5
```
Example `genbank`:
```{r,eval=FALSE}
# the number of genomes available for each group
listGroups(db = "genbank")
```
```
Abditibacteriota Acidithiobacillia
1 8
Acidobacteriia Actinobacteria
24 1596
Alphaproteobacteria Amphibians
1604 6
Apicomplexans Aquificae
47 7
Archaeoglobi Armatimonadetes
5 38
Ascomycetes Bacteria candidate phyla
689 3532
Bacteroidetes/Chlorobi group Balneolia
2247 44
Basidiomycetes Betaproteobacteria
204 751
Birds Blastocatellia
80 2
Caldisericia candidate division WS1
1 1
candidate division Zixibacteria Candidatus Aegiribacteria
17 1
Candidatus Aenigmarchaeota Candidatus Bathyarchaeota
14 42
Candidatus Cloacimonetes Candidatus Diapherotrites
88 11
Candidatus Fermentibacteria Candidatus Geothermarchaeota
8 3
Candidatus Heimdallarchaeota Candidatus Hydrogenedentes
4 10
Candidatus Korarchaeota Candidatus Kryptonia
1 4
Candidatus Lambdaproteobacteria Candidatus Latescibacteria
6 13
Candidatus Lokiarchaeota Candidatus Marinimicrobia
2 92
Candidatus Marsarchaeota Candidatus Micrarchaeota
15 35
Candidatus Moduliflexus Candidatus Muproteobacteria
1 14
Candidatus Nanohaloarchaeota Candidatus Odinarchaeota
16 1
Candidatus Omnitrophica Candidatus Pacearchaeota
126 41
Candidatus Parvarchaeota Candidatus Tectomicrobia
11 6
Candidatus Thorarchaeota Candidatus Vecturithrix
7 1
Candidatus Verstraetearchaeota Candidatus Woesearchaeota
5 36
Chlamydiae Chloroflexi
43 403
Chrysiogenetes Coprothermobacterota
1 1
Crenarchaeota Cyanobacteria/Melainabacteria group
54 172
Deferribacteres Deinococcus-Thermus
8 27
delta/epsilon subdivisions Dictyoglomia
615 1
Elusimicrobia Endomicrobia
1 2
environmental samples Fibrobacteres
3 15
Firmicutes Fishes
2663 168
Flatworms Fusobacteriia
35 29
Gammaproteobacteria Gemmatimonadetes
2041 82
Green Algae Hadesarchaea
30 5
Halobacteria Holophagae
162 9
Hydrogenophilalia Insects
32 280
Kinetoplasts Kiritimatiellaeota
33 3
Land Plants Lentisphaerae
282 32
Mammals Methanobacteria
145 31
Methanococci Methanomicrobia
2 77
Methanonatronarchaeia Methanopyri
2 1
Nanoarchaeota nitrifying bacterium enrichment culture
11 1
Nitrospinae Nitrospira
19 31
Oligoflexia Other
87 15
Other Animals Other Fungi
125 58
Other Plants Other Protists
1 128
Planctomycetes Reptiles
171 21
Rhodothermia Roundworms
4 91
Solibacteres Spirochaetia
7 115
Synergistia Tenericutes
43 141
Thaumarchaeota Theionarchaea
92 2
Thermococci Thermodesulfobacteria
21 7
Thermoplasmata Thermotogae
135 23
unclassified Acidobacteria unclassified Archaea (miscellaneous)
94 52
unclassified Bacteria (miscellaneous) unclassified Caldiserica
197 11
unclassified Calditrichaeota unclassified Elusimicrobia
2 101
unclassified Euryarchaeota unclassified Nitrospirae
348 90
unclassified Proteobacteria unclassified Rhodothermaeota
130 4
unclassified Spirochaetes unclassified Synergistetes
37 3
unclassified Thermotogae uncultured archaeon
1 1
uncultured archaeon A07HB70 uncultured archaeon A07HN63
1 1
uncultured archaeon A07HR60 uncultured archaeon A07HR67
1 1
uncultured bacterium Verrucomicrobia
1 296
Zetaproteobacteria
41
```
__Note__ that when running the `listGenomes()` function for the first time, it might take a while until
the function returns any results, because necessary information need to be downloaded from NCBI and ENSEMBL databases. All subsequent executions of `listGenomes()`
will then respond very fast, because they will access the corresponding files stored on your hard drive.
## Downloading Biological Sequences and Annotations
After checking for the availability of sequence information for an organism of interest,
the next step is to download the corresponding genome, proteome, CDS, or GFF file.
The following functions allow users to download proteomes, genomes, CDS and GFF files from several
database resources such as: [NCBI RefSeq](http://www.ncbi.nlm.nih.gov/refseq/about/), [NCBI Genbank](http://www.ncbi.nlm.nih.gov/genbank/about/), [ENSEMBL](http://www.ensembl.org/index.html). When a corresponding proteome, genome, CDS or GFF file was
loaded to your hard-drive, a documentation `*.txt` file is generated storing `File Name`, `Organism`,
`Database`, `URL`, `DATE`, `assembly_accession`, `bioproject`, `biosample`, `taxid`, `version_status`, `release_type`,
`seq_rel_date` etc. information of the retrieved file. This way a better reproducibility of proteome, genome, CDS and GFF versions
used for subsequent data analyses can be achieved.
### Genome Retrieval
The easiest way to download a genome is to use the `getGenome()` function.
In this example we will download the genome of `Homo sapiens`.
The `getGenome()` function is an interface function to the [NCBI RefSeq](http://www.ncbi.nlm.nih.gov/refseq/about/), [NCBI Genbank](http://www.ncbi.nlm.nih.gov/genbank/about/),
[ENSEMBL](http://www.ensembl.org/index.html) databases from
which corresponding genomes can be retrieved.
The `db` argument specifies from which database genome assemblies in `*.fasta` file format shall be retrieved.
Options are:
- `db = "refseq"` for retrieval from [NCBI RefSeq](http://www.ncbi.nlm.nih.gov/refseq/about/)
- `db = "genbank"` for retrieval from [NCBI Genbank](http://www.ncbi.nlm.nih.gov/genbank/about/)
- `db = "ensembl"` for retrieval from [ENSEMBL](http://www.ensembl.org/index.html)
Furthermore, users need to specify the `scientific name`, the `taxid` (= [NCBI Taxnonomy](https://www.ncbi.nlm.nih.gov/taxonomy) identifier), or the `accession identifier` of the organism of interest for which a genome assembly shall be downloaded, e.g. `organism = "Homo sapiens"` or `organism = "9606"` or `organism = "GCF_000001405.37"`.
Finally, the `path` argument specifies the folder path in which the corresponding assembly shall be locally stored. In case users would like to store the genome file at a different location,
they can specify the `path = file.path("put","your","path","here")` argument (e.g. `file.path("_ncbi_downloads","genomes")`).
### Example NCBI RefSeq:
```{r,eval=FALSE}
# download the genome of Homo sapiens from refseq
# and store the corresponding genome file in '_ncbi_downloads/genomes'
HS.genome.refseq <- getGenome( db = "refseq",
organism = "Homo sapiens",
path = file.path("_ncbi_downloads","genomes") )
```
In this example, `getGenome()` creates a directory named `'_ncbi_downloads/genomes'` into which the corresponding genome named `GCF_000001405.34_GRCh38.p8_genomic.fna.gz` is downloaded. The return value of `getGenome()` is the folder path to the downloaded genome file
that can then be used as input to the `read_genome()` function. The variable `HS.genome.refseq` stores the path to the downloaded genome.
Users can also omit the `path` argument if they wish to store the genome in their current working directory. E.g.:
```{r,eval=FALSE}
# download the genome of Homo sapiens from refseq
# and store the corresponding genome file in '_ncbi_downloads/genomes'
HS.genome.refseq <- getGenome( db = "refseq",
organism = "Homo sapiens")
```
Subsequently, users can use the `read_genome()` function to import the genome into the R session. Users can choose to work with the genome sequence in R either as [Biostrings](http://bioconductor.org/packages/release/bioc/html/Biostrings.html) object (`obj.type = "Biostrings"`) or [data.table](https://github.com/Rdatatable/data.table/wiki) object
(`obj.type = "data.table"`) by specifying the `obj.type` argument of the `read_genome()` function.
```{r,eval=FALSE}
# import downloaded genome as Biostrings object
Human_Genome <- read_genome(file = HS.genome.refseq)
```
```{r,eval=FALSE}
# look at the Biostrings object
Human_Genome
```
```
A DNAStringSet instance of length 551
width seq names
[1] 248956422 NNNNNNNNNNNNNNNNNNNNNNNN...NNNNNNNNNNNNNNNNNNNNNNN NC_000001.11 Homo...
[2] 175055 GAATTCAGCTGAGAAGAACAGGCA...TGTTTGTCAGTCACATAGAATTC NT_187361.1 Homo ...
[3] 32032 AGGGGTCTGCTTAGAGAGGGTCTC...TGACTTACGTTGACGTGGAATTC NT_187362.1 Homo ...
[4] 127682 GATCGAGACTATCCTGGCTAACAC...ATTGTCAATTGGGACCTTTGATC NT_187363.1 Homo ...
[5] 66860 GAATTCATTCGATGACGATTCCAT...AAAAAACTCTCAGCCACGAATTC NT_187364.1 Homo ...
... ... ...
[547] 170148 TTTCTTTCTTTTTTTTTTTTTTGT...GTCACAGGACTCATGGGGAATTC NT_187685.1 Homo ...
[548] 215732 TGTGGTGAGGACCCTTAAGATCTA...GTCACAGGACTCATGGGGAATTC NT_187686.1 Homo ...
[549] 170537 TCTACTCTCCCATGCTTGCCTCGG...GTCACAGGACTCATGGGGAATTC NT_187687.1 Homo ...
[550] 177381 GATCTATCTGTATCTCCACAGGTG...GTCACAGGACTCATGGGGAATTC NT_113949.2 Homo ...
[551] 16569 GATCACAGGTCTATCACCCTATTA...CCCTTAAATAAGACATCACGATG NC_012920.1 Homo ...
```
Internally, a text file named `doc_Homo_sapiens_db_refseq.txt` is generated. The information stored in this log file is structured as follows:
```
File Name: Homo_sapiens_genomic_refseq.fna.gz
Organism Name: Homo_sapiens
Database: NCBI refseq
URL: ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/001/405/
GCF_000001405.35_GRCh38.p9/GCF_000001405.35_GRCh38.p9_genomic.fna.gz
Download_Date: Sat Oct 22 12:41:07 2016
refseq_category: reference genome
assembly_accession: GCF_000001405.35
bioproject: PRJNA168
biosample: NA
taxid: 9606
infraspecific_name: NA
version_status: latest
release_type: Patch
genome_rep: Full
seq_rel_date: 2016-09-26
submitter: Genome Reference Consortium
```
In addition, the genome summary statistics for the retrieved species is stored locally (`doc_Homo_sapiens_db_refseq_summary_statistics.tsv`) to provide users with insights regarding the genome assembly quality (see `?summary_genome()` for details).
This file can be used as `Supplementary Information` file in publications to facilitate reproducible research.
Most comparative genomics studies do not consider differences in genome assembly qualities when comparing the genomes of diverse species.
This way, they expose themselves to technical artifacts that might generate patterns mistaken to be of biological relevance whereas
in reality they just reflect the difference in genome assembly quality. Considering the quality of genome assemblies when downloading
the genomic sequences will help researchers to avoid these pitfalls.
The summary statistics include:
- `genome_size_mbp`: Genome size in mega base pairs
- `n50_mbp`: The N50 contig size of the genome assembly in mega base pairs
- `n_seqs`: The number of chromosomes/scaffolds/contigs of the genome assembly file
- `n_nnn`: The absolute number of NNNs (over all chromosomes or scaffolds or contigs) in the genome assembly file
- `rel_nnn`: The percentage (relative frequency) of NNNs (over all chromosomes or scaffolds or contigs) compared to the total number of nucleotides in the genome assembly file
- `genome_entropy`: The Shannon Entropy of the genome assembly file (median entropy over all individual chromosome entropies)
- `n_gc`: The total number of GCs (over all chromosomes or scaffolds or contigs) in the genome assembly file
- `rel_gc`: The (relative frequency) of GCs (over all chromosomes or scaffolds or contigs) compared to the total number of nucleotides in the genome assembly file
In summary, the `getGenome()` and `read_genome()` functions allow users to retrieve genome assemblies by specifying
the scientific name of the organism of interest and allow them to import the retrieved genome assembly e.g. as `Biostrings` object.
Thus, users can then perform the `Biostrings notation` to work with downloaded genomes and can rely on the log
file generated by `getGenome()` to better document the source and version of genome assemblies used for subsequent studies.
Alternatively, users can perform the pipeline logic of the [magrittr](https://github.com/smbache/magrittr) package:
```{r,eval=FALSE}
# install.packages("magrittr")
library(magrittr)
# import genome as Biostrings object
Human_Genome <- getGenome( db = "refseq",
organism = "Homo sapiens",
path = file.path("_ncbi_downloads","genomes")) %>%
read_genome()
```
```{r,eval=FALSE}
Human_Genome
```
```
A DNAStringSet instance of length 551
width seq names
[1] 248956422 NNNNNNNNNNNNNNNNNNNNNNNN...NNNNNNNNNNNNNNNNNNNNNNN NC_000001.11 Homo...
[2] 175055 GAATTCAGCTGAGAAGAACAGGCA...TGTTTGTCAGTCACATAGAATTC NT_187361.1 Homo ...
[3] 32032 AGGGGTCTGCTTAGAGAGGGTCTC...TGACTTACGTTGACGTGGAATTC NT_187362.1 Homo ...
[4] 127682 GATCGAGACTATCCTGGCTAACAC...ATTGTCAATTGGGACCTTTGATC NT_187363.1 Homo ...
[5] 66860 GAATTCATTCGATGACGATTCCAT...AAAAAACTCTCAGCCACGAATTC NT_187364.1 Homo ...
... ... ...
[547] 170148 TTTCTTTCTTTTTTTTTTTTTTGT...GTCACAGGACTCATGGGGAATTC NT_187685.1 Homo ...
[548] 215732 TGTGGTGAGGACCCTTAAGATCTA...GTCACAGGACTCATGGGGAATTC NT_187686.1 Homo ...
[549] 170537 TCTACTCTCCCATGCTTGCCTCGG...GTCACAGGACTCATGGGGAATTC NT_187687.1 Homo ...
[550] 177381 GATCTATCTGTATCTCCACAGGTG...GTCACAGGACTCATGGGGAATTC NT_113949.2 Homo ...
[551] 16569 GATCACAGGTCTATCACCCTATTA...CCCTTAAATAAGACATCACGATG NC_012920.1 Homo ...
```
#### Use `taxid` id for genome retrieval
Alternatively, instead of specifying the scientific name in the argument `organism`
users can specify the `taxonomy` id of the corresponding organism. Here, we specify
the taxonomy id `559292` which encodes the species `Saccharomyces cerevisiae`.
```{r,eval=FALSE}
# install.packages("magrittr")
library(magrittr)
# import genome as Biostrings object
Scerevisiae_Genome <- getGenome(
db = "refseq",
organism = "559292") %>%
read_genome()
```
```{r,eval=FALSE}
Scerevisiae_Genome
```
```
A DNAStringSet instance of length 17
width seq names
[1] 230218 CCACACCACACCCACACAC...GGGTGTGGTGTGTGTGGG NC_001133.9 Sacch...
[2] 813184 AAATAGCCCTCATGTACGT...TGTGGTGTGTGGGTGTGT NC_001134.8 Sacch...
[3] 316620 CCCACACACCACACCCACA...GTGGGTGTGGTGTGTGTG NC_001135.5 Sacch...
[4] 1531933 ACACCACACCCACACCACA...GTAGTAAGTAGCTTTTGG NC_001136.10 Sacc...
[5] 576874 CGTCTCCTCCAAGCCCTGT...ATTTTCATTTTTTTTTTT NC_001137.3 Sacch...
... ... ...
[13] 924431 CCACACACACACCACACCC...TGTGGTGTGTGTGTGGGG NC_001145.3 Sacch...
[14] 784333 CCGGCTTTCTGACCGAAAT...TGTGGGTGTGGTGTGGGT NC_001146.8 Sacch...
[15] 1091291 ACACCACACCCACACCACA...TGTGTGGGTGTGGTGTGT NC_001147.6 Sacch...
[16] 948066 AAATAGCCCTCATGTACGT...TTTAATTTCGGTCAGAAA NC_001148.4 Sacch...
[17] 85779 TTCATAATTAATTTTTTAT...TATAATATAATATCCATA NC_001224.1 Sacch...
```
#### Use `assembly_accession` id for genome retrieval
Alternatively, instead of specifying the scientific name or taxonomy in the argument `organism`
users can specify the `assembly_accession` id of the corresponding organism. Here, we specify
the `assembly_accession` id `GCF_000146045.2` which encodes the species `Saccharomyces cerevisiae`.
```{r,eval=FALSE}
# install.packages("magrittr")
library(magrittr)
# import genome as Biostrings object
Scerevisiae_Genome <- getGenome(
db = "refseq",
organism = "GCF_000146045.2") %>%
read_genome()
```
```{r,eval=FALSE}
Scerevisiae_Genome
```
```
A DNAStringSet instance of length 17
width seq names
[1] 230218 CCACACCACACCCACACACCCACACA...GTGGTGTGGGTGTGGTGTGTGTGGG NC_001133.9 Sacch...
[2] 813184 AAATAGCCCTCATGTACGTCTCCTCC...GTGTGGGTGTGGTGTGTGGGTGTGT NC_001134.8 Sacch...
[3] 316620 CCCACACACCACACCCACACCACACC...GGGTGTGGTGGGTGTGGTGTGTGTG NC_001135.5 Sacch...
[4] 1531933 ACACCACACCCACACCACACCCACAC...AATAAAGGTAGTAAGTAGCTTTTGG NC_001136.10 Sacc...
[5] 576874 CGTCTCCTCCAAGCCCTGTTGTCTCT...GGGTTTCATTTTCATTTTTTTTTTT NC_001137.3 Sacch...
... ... ...
[13] 924431 CCACACACACACCACACCCACACCAC...GTGTGGGTGTGGTGTGTGTGTGGGG NC_001145.3 Sacch...
[14] 784333 CCGGCTTTCTGACCGAAATTAAAAAA...GGGTGTGTGTGGGTGTGGTGTGGGT NC_001146.8 Sacch...
[15] 1091291 ACACCACACCCACACCACACCCACAC...GAGAGAGTGTGTGGGTGTGGTGTGT NC_001147.6 Sacch...
[16] 948066 AAATAGCCCTCATGTACGTCTCCTCC...TTTTTTTTTTAATTTCGGTCAGAAA NC_001148.4 Sacch...
[17] 85779 TTCATAATTAATTTTTTATATATATA...GCTTAATTATAATATAATATCCATA NC_001224.1 Sacch...
```
### Example NCBI Genbank:
Genome retrieval from `NCBI Genbank`.
```{r,eval=FALSE}
# download the genome of Homo sapiens from Genbank
# and store the corresponding genome file in '_ncbi_downloads/genomes'
HS.genome.genbank <- getGenome( db = "genbank",
organism = "Homo sapiens",
path = file.path("_ncbi_downloads","genomes") )
```
```{r,eval=FALSE}
# import downloaded genome as Biostrings object
Human_Genome <- read_genome(file = HS.genome.genbank)
```
```{r,eval=FALSE}
# look at the Biostrings object
Human_Genome
```
```
A DNAStringSet instance of length 551
width seq names
[1] 248956422 NNNNNNNNNNNNNNNNNNNNNNNN...NNNNNNNNNNNNNNNNNNNNNNN CM000663.2 Homo s...
[2] 175055 GAATTCAGCTGAGAAGAACAGGCA...TGTTTGTCAGTCACATAGAATTC KI270706.1 Homo s...
[3] 32032 AGGGGTCTGCTTAGAGAGGGTCTC...TGACTTACGTTGACGTGGAATTC KI270707.1 Homo s...
[4] 127682 GATCGAGACTATCCTGGCTAACAC...ATTGTCAATTGGGACCTTTGATC KI270708.1 Homo s...
[5] 66860 GAATTCATTCGATGACGATTCCAT...AAAAAACTCTCAGCCACGAATTC KI270709.1 Homo s...
... ... ...
[547] 170148 TTTCTTTCTTTTTTTTTTTTTTGT...GTCACAGGACTCATGGGGAATTC KI270931.1 Homo s...
[548] 215732 TGTGGTGAGGACCCTTAAGATCTA...GTCACAGGACTCATGGGGAATTC KI270932.1 Homo s...
[549] 170537 TCTACTCTCCCATGCTTGCCTCGG...GTCACAGGACTCATGGGGAATTC KI270933.1 Homo s...
[550] 177381 GATCTATCTGTATCTCCACAGGTG...GTCACAGGACTCATGGGGAATTC GL000209.2 Homo s...
[551] 16569 GATCACAGGTCTATCACCCTATTA...CCCTTAAATAAGACATCACGATG J01415.2 Homo sap...
```
#### Use `taxonomy` id for genome retrieval
Alternatively, instead of specifying the scientific name in the argument `organism`
users can specify the `taxonomy` id of the corresponding organism. Here, we specify
the taxonomy id `559292` which encodes the species `Saccharomyces cerevisiae`.
```{r,eval=FALSE}
# install.packages("magrittr")
library(magrittr)
# import genome as Biostrings object
Scerevisiae_Genome <- getGenome(
db = "genbank",
organism = "559292") %>%
read_genome()
```
```{r,eval=FALSE}
Scerevisiae_Genome
```
```
A DNAStringSet instance of length 16
width seq names
[1] 230218 CCACACCACACCCACA...TGTGGTGTGTGTGGG BK006935.2 TPA_in...
[2] 813184 AAATAGCCCTCATGTA...GGTGTGTGGGTGTGT BK006936.2 TPA_in...
[3] 316620 CCCACACACCACACCC...GGTGTGGTGTGTGTG BK006937.2 TPA_in...
[4] 1531933 ACACCACACCCACACC...GTAAGTAGCTTTTGG BK006938.2 TPA_in...
[5] 576874 CGTCTCCTCCAAGCCC...TTCATTTTTTTTTTT BK006939.2 TPA_in...
... ... ...
[12] 1078177 CACACACACACACCAC...ACATGAGGGCTATTT BK006945.2 TPA_in...
[13] 924431 CCACACACACACCACA...GGTGTGTGTGTGGGG BK006946.2 TPA_in...
[14] 784333 CCGGCTTTCTGACCGA...GGGTGTGGTGTGGGT BK006947.3 TPA_in...
[15] 1091291 ACACCACACCCACACC...GTGGGTGTGGTGTGT BK006948.2 TPA_in...
[16] 948066 AAATAGCCCTCATGTA...AATTTCGGTCAGAAA BK006949.2 TPA_in...
```
#### Use `assembly_accession` id for genome retrieval
Alternatively, instead of specifying the scientific name or taxonomy in the argument `organism`
users can specify the `assembly_accession` id of the corresponding organism. Here, we specify
the `assembly_accession` id `GCA_000146045.2` which encodes the species `Saccharomyces cerevisiae`.
```{r,eval=FALSE}
# install.packages("magrittr")
library(magrittr)
# import genome as Biostrings object
Scerevisiae_Genome <- getGenome(
db = "genbank",
organism = "GCA_000146045.2") %>%
read_genome()
```
```{r,eval=FALSE}
Scerevisiae_Genome
```
```
A DNAStringSet instance of length 16
width seq names
[1] 230218 CCACACCACACCCACAC...GTGTGGTGTGTGTGGG BK006935.2 TPA_in...
[2] 813184 AAATAGCCCTCATGTAC...TGGTGTGTGGGTGTGT BK006936.2 TPA_in...
[3] 316620 CCCACACACCACACCCA...GGGTGTGGTGTGTGTG BK006937.2 TPA_in...
[4] 1531933 ACACCACACCCACACCA...AGTAAGTAGCTTTTGG BK006938.2 TPA_in...
[5] 576874 CGTCTCCTCCAAGCCCT...TTTCATTTTTTTTTTT BK006939.2 TPA_in...
... ... ...
[12] 1078177 CACACACACACACCACC...TACATGAGGGCTATTT BK006945.2 TPA_in...
[13] 924431 CCACACACACACCACAC...TGGTGTGTGTGTGGGG BK006946.2 TPA_in...
[14] 784333 CCGGCTTTCTGACCGAA...TGGGTGTGGTGTGGGT BK006947.3 TPA_in...
[15] 1091291 ACACCACACCCACACCA...TGTGGGTGTGGTGTGT BK006948.2 TPA_in...
[16] 948066 AAATAGCCCTCATGTAC...TAATTTCGGTCAGAAA BK006949.2 TPA_in...
```
In addition, the genome summary statistics for the retrieved species is stored locally (`doc_saccharomyces_cerevisiae_db_genbank_summary_statistics.tsv`) to provide users with insights regarding the genome assembly quality (see `?summary_genome()` for details).
This file can be used as `Supplementary Information` file in publications to facilitate reproducible research.
Most comparative genomics studies do not consider differences in genome assembly qualities when comparing the genomes of diverse species.
This way, they expose themselves to technical artifacts that might generate patterns mistaken to be of biological relevance whereas
in reality they just reflect the difference in genome assembly quality. Considering the quality of genome assemblies when downloading
the genomic sequences will help researchers to avoid these pitfalls.
The summary statistics include:
- `genome_size_mbp`: Genome size in mega base pairs
- `n50_mbp`: The N50 contig size of the genome assembly in mega base pairs
- `n_seqs`: The number of chromosomes/scaffolds/contigs of the genome assembly file
- `n_nnn`: The absolute number of NNNs (over all chromosomes or scaffolds or contigs) in the genome assembly file
- `rel_nnn`: The percentage (relative frequency) of NNNs (over all chromosomes or scaffolds or contigs) compared to the total number of nucleotides in the genome assembly file
- `genome_entropy`: The Shannon Entropy of the genome assembly file (median entropy over all individual chromosome entropies)
- `n_gc`: The total number of GCs (over all chromosomes or scaffolds or contigs) in the genome assembly file
- `rel_gc`: The (relative frequency) of GCs (over all chromosomes or scaffolds or contigs) compared to the total number of nucleotides in the genome assembly file
### Example ENSEMBL:
```{r,eval=FALSE}
# download the genome of Homo sapiens from ENSEMBL
# and store the corresponding genome file in '_ncbi_downloads/genomes'
HS.genome.ensembl <- getGenome( db = "ensembl",
organism = "Homo sapiens",
path = file.path("_ncbi_downloads","genomes") ,
assembly_type = "primary_assembly")
```
```{r,eval=FALSE}
# import downloaded genome as Biostrings object
Human_Genome <- read_genome(file = HS.genome.ensembl)
```
```{r,eval=FALSE}
# look at the Biostrings object
Human_Genome
```
```
A DNAStringSet instance of length 524
width seq names
[1] 248956422 NNNNNNNNNNNNNNNNNNNNNNNN...NNNNNNNNNNNNNNNNNNNNNNN 1 dna:chromosome ...
[2] 242193529 NNNNNNNNNNNNNNNNNNNNNNNN...NNNNNNNNNNNNNNNNNNNNNNN 2 dna:chromosome ...
[3] 198295559 NNNNNNNNNNNNNNNNNNNNNNNN...NNNNNNNNNNNNNNNNNNNNNNN 3 dna:chromosome ...
[4] 190214555 NNNNNNNNNNNNNNNNNNNNNNNN...NNNNNNNNNNNNNNNNNNNNNNN 4 dna:chromosome ...
[5] 181538259 NNNNNNNNNNNNNNNNNNNNNNNN...NNNNNNNNNNNNNNNNNNNNNNN 5 dna:chromosome ...
... ... ...
[520] 993 GCCCCACGTCCGGGAGGGAGGTGG...GAGGGAGGTGGGGGGTCAGCCCT KI270539.1 dna:sc...
[521] 990 TTTCATAGAGCATGTTTGAAACAC...TCAGAAACTTGTTGTGATGTGTG KI270385.1 dna:sc...
[522] 981 AGATTTCGTTGGAACGGGATAAAC...TCAGCTTTCAAACACTCTTTTTG KI270423.1 dna:sc...
[523] 971 ATTTGCGATGTGTGTTCTCAACTA...TTGGATAGCTTTGAAGTTTTCGT KI270392.1 dna:sc...
[524] 970 AAGTGGATATTTGGATAGCTTTGA...TCCTCAATAACAGAGTTGAACCT KI270394.1 dna:sc...
```
If you are using `db = "ensembl"` you can set `assembly_type = "primary_assembly"`. For the human genome the toplevel genome assembly size is > 70 GB uncompressed, while primary assembly is just a few GB.
Users can also specify the `release` argument which denotes the database release version of ENSEMBL (`db = "ensembl"`) in case they wish to download archived vgenome versions. Default is release = NULL meaning that the most recent database version is used.
In addition, the genome summary statistics for the retrieved species is stored locally (`doc_homo_sapiens_db_ensembl_summary_statistics.tsv`) to provide users with insights regarding the genome assembly quality (see `?summary_genome()` for details).
This file can be used as `Supplementary Information` file in publications to facilitate reproducible research.
Most comparative genomics studies do not consider differences in genome assembly qualities when comparing the genomes of diverse species.
This way, they expose themselves to technical artifacts that might generate patterns mistaken to be of biological relevance whereas
in reality they just reflect the difference in genome assembly quality. Considering the quality of genome assemblies when downloading
the genomic sequences will help researchers to avoid these pitfalls.
The summary statistics include:
- `genome_size_mbp`: Genome size in mega base pairs
- `n50_mbp`: The N50 contig size of the genome assembly in mega base pairs
- `n_seqs`: The number of chromosomes/scaffolds/contigs of the genome assembly file
- `n_nnn`: The absolute number of NNNs (over all chromosomes or scaffolds or contigs) in the genome assembly file
- `rel_nnn`: The percentage (relative frequency) of NNNs (over all chromosomes or scaffolds or contigs) compared to the total number of nucleotides in the genome assembly file
- `genome_entropy`: The Shannon Entropy of the genome assembly file (median entropy over all individual chromosome entropies)
- `n_gc`: The total number of GCs (over all chromosomes or scaffolds or contigs) in the genome assembly file
- `rel_gc`: The (relative frequency) of GCs (over all chromosomes or scaffolds or contigs) compared to the total number of nucleotides in the genome assembly file
#### Use `taxonomy` id for genome retrieval
Alternatively, instead of specifying the scientific name in the argument `organism`
users can specify the `taxonomy` id of the corresponding organism. Here, we specify
the taxonomy id `4932` which encodes the species `Saccharomyces cerevisiae`.
```{r,eval=FALSE}
# install.packages("magrittr")
library(magrittr)
# import genome as Biostrings object
Scerevisiae_Genome <- getGenome(
db = "ensembl",
organism = "4932") %>%
read_genome()
```
```{r,eval=FALSE}
Scerevisiae_Genome
```
```
A DNAStringSet instance of length 17
width seq names
[1] 230218 CCACACCACACCCA...TGGTGTGTGTGGG I dna:chromosome ...
[2] 813184 AAATAGCCCTCATG...TGTGTGGGTGTGT II dna:chromosome...
[3] 316620 CCCACACACCACAC...TGTGGTGTGTGTG III dna:chromosom...
[4] 1531933 ACACCACACCCACA...AAGTAGCTTTTGG IV dna:chromosome...
[5] 576874 CGTCTCCTCCAAGC...CATTTTTTTTTTT V dna:chromosome ...
... ... ...
[13] 924431 CCACACACACACCA...TGTGTGTGTGGGG XIII dna:chromoso...
[14] 784333 CCGGCTTTCTGACC...GTGTGGTGTGGGT XIV dna:chromosom...
[15] 1091291 ACACCACACCCACA...GGGTGTGGTGTGT XV dna:chromosome...
[16] 948066 AAATAGCCCTCATG...TTTCGGTCAGAAA XVI dna:chromosom...
[17] 85779 TTCATAATTAATTT...TATAATATCCATA Mito dna:chromoso...
```
#### Use `assembly_accession` id for genome retrieval
Alternatively, instead of specifying the scientific name or taxonomy in the argument `organism`
users can specify the `assembly_accession` id of the corresponding organism. Here, we specify
the `assembly_accession` id `GCA_000146045.2` which encodes the species `Saccharomyces cerevisiae`.
```{r,eval=FALSE}
# install.packages("magrittr")
library(magrittr)
# import genome as Biostrings object
Scerevisiae_Genome <- getGenome(
db = "ensembl",
organism = "GCA_000146045.2") %>%
read_genome()
```
```{r,eval=FALSE}
Scerevisiae_Genome
```
```
A DNAStringSet instance of length 17
width seq names
[1] 230218 CCACACCACACCCACA...TGTGGTGTGTGTGGG I dna:chromosome ...
[2] 813184 AAATAGCCCTCATGTA...GGTGTGTGGGTGTGT II dna:chromosome...
[3] 316620 CCCACACACCACACCC...GGTGTGGTGTGTGTG III dna:chromosom...
[4] 1531933 ACACCACACCCACACC...GTAAGTAGCTTTTGG IV dna:chromosome...
[5] 439888 CACACACACCACACCC...GTGTGGTGTGTGTGT IX dna:chromosome...
... ... ...
[13] 1078177 CACACACACACACCAC...ACATGAGGGCTATTT XII dna:chromosom...
[14] 924431 CCACACACACACCACA...GGTGTGTGTGTGGGG XIII dna:chromoso...
[15] 784333 CCGGCTTTCTGACCGA...GGGTGTGGTGTGGGT XIV dna:chromosom...
[16] 1091291 ACACCACACCCACACC...GTGGGTGTGGTGTGT XV dna:chromosome...
[17] 948066 AAATAGCCCTCATGTA...AATTTCGGTCAGAAA XVI dna:chromosom...
```
### GenomeSet Retrieval
The `getGenomeSet()` function enables users to retrieve genome files for
multiple species. This is convenient when users wish to bulk download a particular
set of species. Internally, a folder named `set_genomes` is generated in which
genomes and genome info files are stored. In addition, users can specify the arguments: `clean_retrieval` and `gunzip` (both are `TRUE` by default) to clean file names and automatically unzip downloaded files.
Example:
```r
# specify the species names
download_species <- c("Arabidopsis thaliana",
"Arabidopsis lyrata",
"Capsella rubella")
# retrieve these three species from NCBI RefSeq
biomartr::getGenomeSet("refseq", organisms = download_species, path = "set_genomes")
```
If the download process was interrupted, users can re-run the function
and it will only download missing genomes. In cases users wish
to download everything again and updating existing genomes, they may
specify the argument `update = TRUE`.
### Proteome Retrieval
The `getProteome()` function is an interface function to the [NCBI RefSeq](http://www.ncbi.nlm.nih.gov/refseq/about/), [NCBI Genbank](http://www.ncbi.nlm.nih.gov/genbank/about/),
[ENSEMBL](http://www.ensembl.org/index.html), and [UniProt](http://uniprot.org/) databases from
which corresponding proteomes can be retrieved. It works analogous to `getGenome()`.
The `db` argument specifies from which database proteomes in `*.fasta` file format shall be retrieved.
Options are:
- `db = "refseq"` for retrieval from [NCBI RefSeq](http://www.ncbi.nlm.nih.gov/refseq/about/)
- `db = "genbank"` for retrieval from [NCBI Genbank](http://www.ncbi.nlm.nih.gov/genbank/about/)
- `db = "ensembl"` for retrieval from [ENSEMBL](http://www.ensembl.org/index.html)
- `db = "uniprot" ` for retrieval from [UniProt](http://uniprot.org/)
Furthermore, again users need to specify the scientific name of the organism of interest for which a proteomes shall be downloaded, e.g. `organism = "Homo sapiens"`.
Finally, the `path` argument specifies the folder path in which the corresponding proteome shall be locally stored. In case users would like to store the proteome file at a different location,
they can specify the `path = file.path("put","your","path","here")` argument.
### Example `NCBI RefSeq`:
```{r,eval=FALSE}
# download the proteome of Homo sapiens from refseq
# and store the corresponding proteome file in '_ncbi_downloads/proteomes'
HS.proteome.refseq <- getProteome( db = "refseq",
organism = "Homo sapiens",
path = file.path("_ncbi_downloads","proteomes"))
```
In this example, `getProteome()` creates a directory named `'_ncbi_downloads/proteomes'` into which the corresponding genome named `GCF_000001405.34_GRCh38.p8_protein.faa.gz` is downloaded. The return value of `getProteome()` is the folder path to the downloaded proteome file
that can then be used as input to the `read_proteome()` function. The variable `HS.proteome.refseq` stores the path to the downloaded proteome.
Subsequently, users can use the `read_proteome()` function to import the proteome into the R session. Users can choose to work with the proteome sequence in R either as [Biostrings](http://bioconductor.org/packages/release/bioc/html/Biostrings.html) object (`obj.type = "Biostrings"`) or [data.table](https://github.com/Rdatatable/data.table/wiki) object
(`obj.type = "data.table"`) by specifying the `obj.type` argument of the `read_proteome()` function.
```{r,eval=FALSE}
# import proteome as Biostrings object
Human_Proteome <- read_proteome(file = HS.proteome.refseq)
```
```{r,eval=FALSE}
Human_Proteome
```
```
A AAStringSet instance of length 113620
width seq names
[1] 1474 MGKNKLLHPSLVL...YNAPCSKDLGNA NP_000005.2 alpha...
[2] 290 MDIEAYFERIGYK...LVPKPGDGSLTI NP_000006.2 aryla...
[3] 421 MAAGFGRCCRVLR...IVAREHIDKYKN NP_000007.1 mediu...
[4] 412 MAAALLARASGPA...VIAGHLLRSYRS NP_000008.1 short...
[5] 655 MQAARMAASLGRQ...RGGVVTSNPLGF NP_000009.1 very ...
... ... ...
[113616] 98 MPLIYMNIMLAFT...LDYVHNLNLLQC YP_003024034.1 NA...
[113617] 459 MLKLIVPTIMLLP...SLNPDIITGFSS YP_003024035.1 NA...
[113618] 603 MTMHTTMTTLTLT...FFPLILTLLLIT YP_003024036.1 NA...
[113619] 174 MMYALFLLSVGLV...GVYIVIEIARGN YP_003024037.1 NA...
[113620] 380 MTPMRKTNPLMKL...ISLIENKMLKWA YP_003024038.1 cy...
```
Alternatively, users can perform the pipeline logic of the [magrittr](https://github.com/smbache/magrittr) package:
```{r,eval=FALSE}
# install.packages("magrittr")
library(magrittr)
# import proteome as Biostrings object
Human_Proteome <- getProteome( db = "refseq",
organism = "Homo sapiens",
path = file.path("_ncbi_downloads","proteomes")) %>%
read_proteome()
```
```{r,eval=FALSE}
Human_Proteome
```
```
A AAStringSet instance of length 113620
width seq names
[1] 1474 MGKNKLLHPSLVL...YNAPCSKDLGNA NP_000005.2 alpha...
[2] 290 MDIEAYFERIGYK...LVPKPGDGSLTI NP_000006.2 aryla...
[3] 421 MAAGFGRCCRVLR...IVAREHIDKYKN NP_000007.1 mediu...
[4] 412 MAAALLARASGPA...VIAGHLLRSYRS NP_000008.1 short...
[5] 655 MQAARMAASLGRQ...RGGVVTSNPLGF NP_000009.1 very ...
... ... ...
[113616] 98 MPLIYMNIMLAFT...LDYVHNLNLLQC YP_003024034.1 NA...
[113617] 459 MLKLIVPTIMLLP...SLNPDIITGFSS YP_003024035.1 NA...
[113618] 603 MTMHTTMTTLTLT...FFPLILTLLLIT YP_003024036.1 NA...
[113619] 174 MMYALFLLSVGLV...GVYIVIEIARGN YP_003024037.1 NA...
[113620] 380 MTPMRKTNPLMKL...ISLIENKMLKWA YP_003024038.1 cy...
```
### Example `NCBI Genbank`:
```{r,eval=FALSE}
# download the proteome of Homo sapiens from genbank
# and store the corresponding proteome file in '_ncbi_downloads/proteomes'
HS.proteome.genbank <- getProteome( db = "genbank",
organism = "Homo sapiens",
path = file.path("_ncbi_downloads","proteomes"))
```
```{r,eval=FALSE}
# import proteome as Biostrings object
Human_Proteome <- read_proteome(file = HS.proteome.genbank)
```
```{r,eval=FALSE}
Human_Proteome
```
```
A AAStringSet instance of length 13
width seq names
[1] 318 MPMANLLLLIVPILI...VSMPITISSIPPQT AAB58943.1 NADH d...
[2] 347 MNPLAQPVIYSTIFA...TLLLPISPFMLMIL AAB58944.1 NADH d...
[3] 513 MFADRWLFSTNHKDI...PPYHTFEEPVYMKS AAB58945.1 cytoch...
[4] 227 MAHAAQVGLQDATSP...IPLKIFEMGPVFTL AAB58946.1 cytoch...
[5] 68 MPQLNTTVWPTMITP...WTKICSLHSLPPQS AAB58947.1 ATPase...
... ... ...
[9] 98 MPLIYMNIMLAFTIS...YGLDYVHNLNLLQC AAB58951.1 NADH d...
[10] 459 MLKLIVPTIMLLPLT...LLSLNPDIITGFSS AAB58952.1 NADH d...
[11] 603 MTMHTTMTTLTLTSL...SFFFPLILTLLLIT AAB58953.1 NADH d...
[12] 174 MMYALFLLSVGLVMG...FVGVYIVIEIARGN AAB58954.1 NADH d...
[13] 380 MTPMRKTNPLMKLIN...PTISLIENKMLKWA AAB58955.3 cytoch...
```
### Example `ENSEMBL`:
```{r,eval=FALSE}
# download the proteome of Homo sapiens from ENSEMBL
# and store the corresponding proteome file in '_ncbi_downloads/proteomes'
HS.proteome.ensembl <- getProteome( db = "ensembl",
organism = "Homo sapiens",
path = file.path("_ncbi_downloads","proteomes"))
```
```{r,eval=FALSE}
# import proteome as Biostrings object
Human_Proteome <- read_proteome(file = HS.proteome.ensembl)
```
```{r,eval=FALSE}
Human_Proteome
```
```
A AAStringSet instance of length 107844
width seq names
[1] 3 PSY ENSP00000451515.1...
[2] 4 TGGY ENSP00000452494.1...
[3] 2 EI ENSP00000451042.1...
[4] 4 GTGG ENSP00000487941.1...
[5] 4 GTGG ENSP00000488240.1...
... ... ...
[107840] 135 MLQKKIEEKDLKV...LNHICKVPLAIK ENSP00000495237.1...
[107841] 166 MEHAFTPLEPLLS...IIQEESLIYLLQ ENSP00000496198.1...
[107842] 42 MEVPTAYMISPKE...GLKSENTMLLRC ENSP00000495723.1...
[107843] 508 MPSMLERISKNLV...GTLSLLQQLAEA ENSP00000496548.1...
[107844] 508 MPSMLERISKNLV...GTLSLLQQLAEA ENSP00000494855.1...
```
Users can also specify the `release` argument which denotes the database release version of ENSEMBL (`db = "ensembl"`) in case they wish to download archived vgenome versions. Default is release = NULL meaning that the most recent database version is used.
### Example Retrieval `Uniprot`:
Another way of retrieving proteome sequences is from the [UniProt](http://www.uniprot.org/) database.
```{r,eval=FALSE}
# download the proteome of Mus musculus from UniProt
# and store the corresponding proteome file in '_uniprot_downloads/proteomes'
Mm.proteome.uniprot<- getProteome( db = "uniprot",
organism = "Mus musculus",
path = file.path("_uniprot_downloads","proteomes"))
```
```{r,eval=FALSE}
# import proteome as Biostrings object
Mouse_Proteome <- read_proteome(file = Mm.proteome.uniprot)
```
```{r,eval=FALSE}
Mouse_Proteome
```
```
A AAStringSet instance of length 84522
width seq names
[1] 781 MATQADLMELDMAMEPD...LPPGDSNQLAWFDTDL sp|Q02248|CTNB1_M...
[2] 2531 MPRLLTPLLCLTLLPAL...PTTMPSQITHIPEAFK sp|Q01705|NOTC1_M...
[3] 437 MLLLLARCFLVILASSL...LDSETMHPLGMAVKSS sp|Q62226|SHH_MOU...
[4] 380 MKKPIGILSPGVALGTA...VKCKKCTEIVDQFVCK sp|P22725|WNT5A_M...
[5] 387 MEESQSDISLELPLSQE...SRHKKTMVKKVGPDSD sp|P02340|P53_MOU...
... ... ...
[84518] 459 MLKIILPSLMLLPLTWL...LILLTTSPKLITGLTM tr|A0A0F6PZ84|A0A...
[84519] 380 MTNMRKTHPLFKIINHS...LMPISGIIEDKMLKLY tr|U3TEV9|U3TEV9_...
[84520] 381 MTNMRKTHPLFKIINHS...MPISGIIEDKMLKLYP tr|A0A0F6PXN8|A0A...
[84521] 172 MNNYIFVLSSLFLVGCL...WSLFAGIFIIIEITRD tr|A0A0F6PXK9|A0A...
[84522] 381 MTNMRKTHPLFKIINHS...MPISGIIEDKMLKLYP tr|A0A0F6PYR5|A0A...
```
### ProteomeSet Retrieval
The `getProteomeSet()` function enables users to retrieve proteome files for
multiple species. This is convenient when users wish to bulk download a particular
set of species. Internally, a folder named `set_proteomes` is generated in which
proteomes and proteome info files are stored. In addition, users can specify the arguments: `clean_retrieval` and `gunzip` (both are `TRUE` by default) to clean file names and automatically unzip downloaded files.
Example:
```r
# specify the species names
download_species <- c("Arabidopsis thaliana",
"Arabidopsis lyrata",
"Capsella rubella")
# retrieve these three species from NCBI RefSeq
biomartr::getProteomeSet("refseq", organisms = download_species, path = "set_proteomes")
```
If the download process was interrupted, users can re-run the function
and it will only download missing genomes. In cases users wish
to download everything again and updating existing genomes, they may
specify the argument `update = TRUE`.
### CDS Retrieval
The `getCDS()` function is an interface function to the [NCBI RefSeq](http://www.ncbi.nlm.nih.gov/refseq/about/), [NCBI Genbank](http://www.ncbi.nlm.nih.gov/genbank/about/),
[ENSEMBL](http://www.ensembl.org/index.html) databases from
which corresponding CDS files can be retrieved. It works analogous to `getGenome()` and `getProteome()`.
The `db` argument specifies from which database proteomes in `*.fasta` file format shall be retrieved.
Options are:
- `db = "refseq"` for retrieval from [NCBI RefSeq](http://www.ncbi.nlm.nih.gov/refseq/about/)
- `db = "genbank"` for retrieval from [NCBI Genbank](http://www.ncbi.nlm.nih.gov/genbank/about/)
- `db = "ensembl"` for retrieval from [ENSEMBL](http://www.ensembl.org/index.html)
Furthermore, again users need to specify the scientific name of the organism of interest for which a proteomes shall be downloaded, e.g. `organism = "Homo sapiens"`.
Finally, the `path` argument specifies the folder path in which the corresponding CDS file shall be locally stored. In case users would like to store the CDS file at a different location,
they can specify the `path = file.path("put","your","path","here")` argument.
### Example `NCBI RefSeq`:
```{r,eval=FALSE}
# download the genome of Homo sapiens from refseq
# and store the corresponding genome CDS file in '_ncbi_downloads/CDS'
HS.cds.refseq <- getCDS( db = "refseq",
organism = "Homo sapiens",
path = file.path("_ncbi_downloads","CDS"))
```
In this example, `getCDS()` creates a directory named `'_ncbi_downloads/CDS'` into which the corresponding genome named `Homo_sapiens_cds_from_genomic_refseq.fna.gz` is downloaded. The return value of `getCDS()` is the folder path to the downloaded genome file
that can then be used as input to the `read_cds()` function. The variable `HS.cds.refseq` stores the path to the downloaded CDS file.
Subsequently, users can use the `read_cds()` function to import the genome into the R session. Users can choose to work with the genome sequence in R either as [Biostrings](http://bioconductor.org/packages/release/bioc/html/Biostrings.html) object (`obj.type = "Biostrings"`) or [data.table](https://github.com/Rdatatable/data.table/wiki) object
(`obj.type = "data.table"`) by specifying the `obj.type` argument of the `read_cds()` function.
```{r,eval=FALSE}
# import downloaded CDS as Biostrings object
Human_CDS <- read_cds(file = HS.cds.refseq,
obj.type = "Biostrings")
```
```{r,eval=FALSE}
# look at the Biostrings object
Human_CDS
```
```
A BStringSet instance of length 114967
width seq names
[1] 918 ATGGTGACTGAATTCATTTTTCTG...CACATTCTAGTGTAAAGTTTTAG lcl|NC_000001.11_...
[2] 402 ATGAGTGACAGCATCAACTTCTCT...CAGGACCCAGGCACAGGCATTAG lcl|NC_000001.11_...
[3] 402 ATGAGTGACAGCATCAACTTCTCT...CAGGACCCAGGCACAGGCATTAG lcl|NC_000001.11_...
[4] 402 ATGAGTGACAGCATCAACTTCTCT...CAGGACCCAGGCACAGGCATTAG lcl|NC_000001.11_...
[5] 402 ATGAGTGACAGCATCAACTTCTCT...CAGGACCCAGGCACAGGCATTAG lcl|NC_000001.11_...
... ... ...
[114963] 297 ATGCCCCTCATTTACATAAATATT...ACCTAAACCTACTCCAATGCTAA lcl|NC_012920.1_c...
[114964] 1378 ATGCTAAAACTAATCGTCCCAACA...CATCATTACCGGGTTTTCCTCTT lcl|NC_012920.1_c...
[114965] 1812 ATAACCATGCACACTACTATAACC...TAACCCTACTCCTAATCACATAA lcl|NC_012920.1_c...
[114966] 525 ATGATGTATGCTTTGTTTCTGTTG...TTGAGATTGCTCGGGGGAATAGG lcl|NC_012920.1_c...
[114967] 1141 ATGACCCCAATACGCAAAACTAAC...AAACAAAATACTCAAATGGGCCT lcl|NC_012920.1_c...
```
Internally, a text file named `doc_Homo_sapiens_db_refseq.txt` is generated. The information stored in this log file is structured as follows:
```
File Name: Homo_sapiens_cds_from_genomic_refseq.fna.gz
Organism Name: Homo_sapiens
Database: NCBI refseq
URL: ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/001/405/
GCF_000001405.35_GRCh38.p9/GCF_000001405.35_GRCh38.p9_cds_from_genomic.fna.gz
Download_Date: Sun Oct 23 17:19:05 2016
refseq_category: reference genome
assembly_accession: GCF_000001405.35
bioproject: PRJNA168
biosample: NA
taxid: 9606
infraspecific_name: NA
version_status: latest
release_type: Patch
genome_rep: Full
seq_rel_date: 2016-09-26
submitter: Genome Reference Consortium
```
In summary, the `getCDS()` and `read_cds()` functions allow users to retrieve CDS files by specifying
the scientific name of the organism of interest and allow them to import the retrieved CDS file e.g. as `Biostrings` object.
Thus, users can then perform the `Biostrings notation` to work with downloaded CDS and can rely on the log
file generated by `getCDS()` to better document the source and version of CDS used for subsequent studies.
Alternatively, users can perform the pipeline logic of the [magrittr](https://github.com/smbache/magrittr) package:
```{r,eval=FALSE}
# install.packages("magrittr")
library(magrittr)
# import CDS as Biostrings object
Human_CDS <- getCDS( db = "refseq",
organism = "Homo sapiens",
path = file.path("_ncbi_downloads","CDS")) %>%
read_cds(obj.type = "Biostrings")
```
```{r,eval=FALSE}
Human_CDS
```
```
A BStringSet instance of length 114967
width seq names
[1] 918 ATGGTGACTGAATTCATTTTTCTG...CACATTCTAGTGTAAAGTTTTAG lcl|NC_000001.11_...
[2] 402 ATGAGTGACAGCATCAACTTCTCT...CAGGACCCAGGCACAGGCATTAG lcl|NC_000001.11_...
[3] 402 ATGAGTGACAGCATCAACTTCTCT...CAGGACCCAGGCACAGGCATTAG lcl|NC_000001.11_...
[4] 402 ATGAGTGACAGCATCAACTTCTCT...CAGGACCCAGGCACAGGCATTAG lcl|NC_000001.11_...
[5] 402 ATGAGTGACAGCATCAACTTCTCT...CAGGACCCAGGCACAGGCATTAG lcl|NC_000001.11_...
... ... ...
[114963] 297 ATGCCCCTCATTTACATAAATATT...ACCTAAACCTACTCCAATGCTAA lcl|NC_012920.1_c...
[114964] 1378 ATGCTAAAACTAATCGTCCCAACA...CATCATTACCGGGTTTTCCTCTT lcl|NC_012920.1_c...
[114965] 1812 ATAACCATGCACACTACTATAACC...TAACCCTACTCCTAATCACATAA lcl|NC_012920.1_c...
[114966] 525 ATGATGTATGCTTTGTTTCTGTTG...TTGAGATTGCTCGGGGGAATAGG lcl|NC_012920.1_c...
[114967] 1141 ATGACCCCAATACGCAAAACTAAC...AAACAAAATACTCAAATGGGCCT lcl|NC_012920.1_c...
```
### Example `NCBI Genbank`:
```{r,eval=FALSE}
# download the genome of Homo sapiens from genbank
# and store the corresponding genome CDS file in '_ncbi_downloads/CDS'
HS.cds.genbank <- getCDS( db = "genbank",
organism = "Homo sapiens",
path = file.path("_ncbi_downloads","CDS"))
```
```{r,eval=FALSE}
# import downloaded CDS as Biostrings object
Human_CDS <- read_cds(file = HS.cds.genbank,
obj.type = "Biostrings")
```
```{r,eval=FALSE}
# look at the Biostrings object
Human_CDS
```
```
A BStringSet instance of length 13
width seq names
[1] 956 ATACCCATGGCCAACCTCCTACTCCT...ATCTCCAGCATTCCCCCTCAAACCTA lcl|J01415.2_cds_...
[2] 1042 ATTAATCCCCTGGCCCAACCCGTCAT...CTCCCCTTTTATACTAATAATCTTAT lcl|J01415.2_cds_...
[3] 1542 ATGTTCGCCGACCGTTGACTATTCTC...AAGAACCCGTATACATAAAATCTAGA lcl|J01415.2_cds_...
[4] 684 ATGGCACATGCAGCGCAAGTAGGTCT...AAATAGGGCCCGTATTTACCCTATAG lcl|J01415.2_cds_...
[5] 207 ATGCCCCAACTAAATACTACCGTATG...TTCATTCATTGCCCCCACAATCCTAG lcl|J01415.2_cds_...
... ... ...
[9] 297 ATGCCCCTCATTTACATAAATATTAT...ATAACCTAAACCTACTCCAATGCTAA lcl|J01415.2_cds_...
[10] 1378 ATGCTAAAACTAATCGTCCCAACAAT...CGACATCATTACCGGGTTTTCCTCTT lcl|J01415.2_cds_...
[11] 1812 ATAACCATGCACACTACTATAACCAC...TCCTAACCCTACTCCTAATCACATAA lcl|J01415.2_cds_...
[12] 525 ATGATGTATGCTTTGTTTCTGTTGAG...TAATTGAGATTGCTCGGGGGAATAGG lcl|J01415.2_cds_...
[13] 1141 ATGACCCCAATACGCAAAACTAACCC...TGAAAACAAAATACTCAAATGGGCCT lcl|J01415.2_cds_...
```
### Example `ENSEMBL`:
```{r,eval=FALSE}
# download the genome of Homo sapiens from ensembl
# and store the corresponding genome CDS file in '_ncbi_downloads/CDS'
HS.cds.ensembl <- getCDS( db = "ensembl",
organism = "Homo sapiens",
path = file.path("_ncbi_downloads","CDS"))
```
```{r,eval=FALSE}
# import downloaded CDS as Biostrings object
Human_CDS <- read_cds(file = HS.cds.ensembl,
obj.type = "Biostrings")
```
```{r,eval=FALSE}
# look at the Biostrings object
Human_CDS
```
```
A BStringSet instance of length 102915
width seq names
[1] 13 ACTGGGGGATACG ENST00000448914.1...
[2] 12 GGGACAGGGGGC ENST00000631435.1...
[3] 12 GGGACAGGGGGC ENST00000632684.1...
[4] 9 CCTTCCTAC ENST00000434970.2...
[5] 8 GAAATAGT ENST00000415118.1...
... ... ...
[102911] 1665 ATGCTACTGCCACTGCTGCTGTCC...ATGCAGAAGTCAAGTTCCAATGA ENST00000436984.6...
[102912] 1920 ATGCTACTGCCACTGCTGCTGTCC...ATGCAGAAGTCAAGTTCCAATGA ENST00000439889.6...
[102913] 2094 ATGCTACTGCCACTGCTGCTGTCC...ATGCAGAAGTCAAGTTCCAATGA ENST00000339313.9...
[102914] 466 ATGCTACTGCCACTGCTGCTGTCC...AGCCCTGGACCTCTCTGTGCAGT ENST00000529627.1...
[102915] 559 ATGCGGAGATGCTACTGCCACTGC...CCTCACCTGCCATGTGGACTTCT ENST00000530476.1...
```
Users can also specify the `release` argument which denotes the database release version of ENSEMBL (`db = "ensembl"`) in case they wish to download archived vgenome versions. Default is release = NULL meaning that the most recent database version is used.
### CDSSet Retrieval
The `getCDSSet()` function enables users to retrieve CDS files for
multiple species. This is convenient when users wish to bulk download a particular
set of species. Internally, a folder named `set_cds` is generated in which
CDS and CDS info files are stored. In addition, users can specify the arguments: `clean_retrieval` and `gunzip` (both are `TRUE` by default) to clean file names and automatically unzip downloaded files.
Example:
```r
# specify the species names
download_species <- c("Arabidopsis thaliana",
"Arabidopsis lyrata",
"Capsella rubella")
# retrieve these three species from NCBI RefSeq
biomartr::getCDSSet("refseq", organisms = download_species, path = "set_cds")
```
If the download process was interrupted, users can re-run the function
and it will only download missing genomes. In cases users wish
to download everything again and updating existing genomes, they may
specify the argument `update = TRUE`.
### RNA Retrieval
The `getRNA()` function is an interface function to the [NCBI RefSeq](http://www.ncbi.nlm.nih.gov/refseq/about/), [NCBI Genbank](http://www.ncbi.nlm.nih.gov/genbank/about/),
[ENSEMBL](http://www.ensembl.org/index.html) databases from
which corresponding RNA files can be retrieved. It works analogous to `getGenome()`, `getProteome()`, and `getCDS()`.
The `db` argument specifies from which database proteomes in `*.fasta` file format shall be retrieved.
Options are:
- `db = "refseq"` for retrieval from [NCBI RefSeq](http://www.ncbi.nlm.nih.gov/refseq/about/)
- `db = "genbank"` for retrieval from [NCBI Genbank](http://www.ncbi.nlm.nih.gov/genbank/about/)
- `db = "ensembl"` for retrieval from [ENSEMBL](http://www.ensembl.org/index.html)
Furthermore, again users need to specify the scientific name of the organism of interest for which a proteomes shall be downloaded, e.g. `organism = "Homo sapiens"`.
Finally, the `path` argument specifies the folder path in which the corresponding RNA file shall be locally stored. In case users would like to store the RNA file at a different location,
they can specify the `path = file.path("put","your","path","here")` argument.
### Example `NCBI RefSeq`:
```{r,eval=FALSE}
# download the RNA of Homo sapiens from refseq
# and store the corresponding RNA file in '_ncbi_downloads/RNA'
HS.rna.refseq <- getRNA( db = "refseq",
organism = "Homo sapiens",
path = file.path("_ncbi_downloads","RNA"))
```
In this example, `getRNA()` creates a directory named `'_ncbi_downloads/RNA'` into which the corresponding RNA file named `Homo_sapiens_rna_from_genomic_refseq.fna.gz` is downloaded. The return value of `getRNA()` is the folder path to the downloaded genome file
that can then be used as input to the `read_rna()` function. The variable `HS.rna.refseq` stores the path to the downloaded RNA file.
Subsequently, users can use the `read_cds()` function to import the genome into the R session. Users can choose to work with the genome sequence in R either as [Biostrings](http://bioconductor.org/packages/release/bioc/html/Biostrings.html) object (`obj.type = "Biostrings"`) or [data.table](https://github.com/Rdatatable/data.table/wiki) object
(`obj.type = "data.table"`) by specifying the `obj.type` argument of the `read_rna()` function.
```{r,eval=FALSE}
# import downloaded RNA as Biostrings object
Human_rna <- read_rna(file = HS.rna.refseq,
obj.type = "Biostrings")
```
```{r,eval=FALSE}
# look at the Biostrings object
Human_rna
```
```
A BStringSet instance of length 164136
width seq names
[1] 1652 CTTGCCGTCAGCCTTTTCTTTGACCTCTTCTTTCTGTTCATGT...CACAGCTAGAGATCCTTTATTAAAAGCACACTGTTGGTTTCTG lcl|NC_000001.11_...
[2] 1769 TCCGGCAGAGCGGAAGCGGCGGCGGGAGCTTCCGGGAGGGCGG...ACCAACAGTGTGCTTTTAATAAAGGATCTCTAGCTGTGCAGGA lcl|NC_000001.11_...
[3] 68 TGTGGGAGAGGAACATGGGCTCAGGACAGCGGGTGTCAGCTTGCCTGACCCCCATGTCGCCTCTGTAG lcl|NC_000001.11_...
[4] 23 TGACCCCCATGTCGCCTCTGTAG lcl|NC_000001.11_...
[5] 23 GAGAGGAACATGGGCTCAGGACA lcl|NC_000001.11_...
... ... ...
[164132] 59 GAGAAAGCTCACAAGAACTGCTAACTCATGCCCCCATGTCTAACAACATGGCTTTCTCA lcl|NC_012920.1_t...
[164133] 71 ACTTTTAAAGGATAACAGCTATCCATTGGTCTTAGGCCCCAAAAATTTTGGTGCAACTCCAAATAAAAGTA lcl|NC_012920.1_t...
[164134] 69 GTTCTTGTAGTTGAAATACAACGATGGTTTTTCATATCATTGGTCGTGGTTGTAGTCCGTGCGAGAATA lcl|NC_012920.1_t...
[164135] 66 GTCCTTGTAGTATAAACTAATACACCAGTCTTGTAAACCGGAGATGAAAACCTTTTTCCAAGGACA lcl|NC_012920.1_t...
[164136] 68 CAGAGAATAGTTTAAATTAGAATCTTAGCTTTGGGTGCTAATGGTGGAGTTAAAGACTTTTTCTCTGA lcl|NC_012920.1_t...
```
Internally, a text file named `doc_Homo_sapiens_db_refseq.txt` is generated. The information stored in this log file is structured as follows:
```
File Name: Homo_sapiens_rna_from_genomic_refseq.fna.gz
Organism Name: Homo_sapiens
Database: NCBI refseq
URL: ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/001/405/GCF_000001405.36_GRCh38.p10/GCF_000001405.36_GRCh38.p10_rna_from_genomic.fna.gz
Download_Date: Wed Mar 15 16:46:45 2017
refseq_category: reference genome
assembly_accession: GCF_000001405.36
bioproject: PRJNA168
biosample: NA
taxid: 9606
infraspecific_name: NA
version_status: latest
release_type: Patch
genome_rep: Full
seq_rel_date: 2017-01-06
submitter: Genome Reference Consortium
```
In summary, the `getRNA()` and `read_rna()` functions allow users to retrieve RNA files by specifying
the scientific name of the organism of interest and allow them to import the retrieved RNA file e.g. as `Biostrings` object.
Thus, users can then perform the `Biostrings notation` to work with downloaded RNA and can rely on the log
file generated by `getRNA()` to better document the source and version of RNA used for subsequent studies.
Alternatively, users can perform the pipeline logic of the [magrittr](https://github.com/smbache/magrittr) package:
```{r,eval=FALSE}
# install.packages("magrittr")
library(magrittr)
# import RNA as Biostrings object
Human_rna <- getRNA( db = "refseq",
organism = "Homo sapiens",
path = file.path("_ncbi_downloads","RNA")) %>%
read_cds(obj.type = "Biostrings")
```
```{r,eval=FALSE}
Human_rna
```
```
A BStringSet instance of length 164136
width seq names
[1] 1652 CTTGCCGTCAGCCTTTTCTTTGACCTCTTCTTTCTGTTCATGT...CACAGCTAGAGATCCTTTATTAAAAGCACACTGTTGGTTTCTG lcl|NC_000001.11_...
[2] 1769 TCCGGCAGAGCGGAAGCGGCGGCGGGAGCTTCCGGGAGGGCGG...ACCAACAGTGTGCTTTTAATAAAGGATCTCTAGCTGTGCAGGA lcl|NC_000001.11_...
[3] 68 TGTGGGAGAGGAACATGGGCTCAGGACAGCGGGTGTCAGCTTGCCTGACCCCCATGTCGCCTCTGTAG lcl|NC_000001.11_...
[4] 23 TGACCCCCATGTCGCCTCTGTAG lcl|NC_000001.11_...
[5] 23 GAGAGGAACATGGGCTCAGGACA lcl|NC_000001.11_...
... ... ...
[164132] 59 GAGAAAGCTCACAAGAACTGCTAACTCATGCCCCCATGTCTAACAACATGGCTTTCTCA lcl|NC_012920.1_t...
[164133] 71 ACTTTTAAAGGATAACAGCTATCCATTGGTCTTAGGCCCCAAAAATTTTGGTGCAACTCCAAATAAAAGTA lcl|NC_012920.1_t...
[164134] 69 GTTCTTGTAGTTGAAATACAACGATGGTTTTTCATATCATTGGTCGTGGTTGTAGTCCGTGCGAGAATA lcl|NC_012920.1_t...
[164135] 66 GTCCTTGTAGTATAAACTAATACACCAGTCTTGTAAACCGGAGATGAAAACCTTTTTCCAAGGACA lcl|NC_012920.1_t...
[164136] 68 CAGAGAATAGTTTAAATTAGAATCTTAGCTTTGGGTGCTAATGGTGGAGTTAAAGACTTTTTCTCTGA lcl|NC_012920.1_t...
```
### Example `NCBI Genbank`:
```{r,eval=FALSE}
# download the RNA of Homo sapiens from genbank
# and store the corresponding genome RNA file in '_ncbi_downloads/RNA'
HS.rna.genbank <- getRNA( db = "genbank",
organism = "Homo sapiens",
path = file.path("_ncbi_downloads","RNA"))
```
```{r,eval=FALSE}
# import downloaded RNA as Biostrings object
Human_rna <- read_cds(file = HS.rna.genbank,
obj.type = "Biostrings")
```
```{r,eval=FALSE}
# look at the Biostrings object
Human_rna
```
```
A BStringSet instance of length 24
width seq names
[1] 71 GTTTATGTAGCTTACCTCCTCAAAGCAATACACTGAAAATGTTTAGACGGGCTCACATCACCCCATAAACA lcl|J01415.2_trna...
[2] 954 AATAGGTTTGGTCCTAGCCTTTCTATTAGCTCTTAGTAAGATTACA...AAGTCGTAACATGGTAAGTGTACTGGAAAGTGCACTTGGACGAAC lcl|J01415.2_rrna...
[3] 69 CAGAGTGTAGCTTAACACAAAGCACCCAACTTACACTTAGGAGATTTCAACTTAACTTGACCGCTCTGA lcl|J01415.2_trna...
[4] 1559 GCTAAACCTAGCCCCAAACCCACTCCACCTTACTACCAGACAACCT...ATCTCAACTTAGTATTATACCCACACCCACCCAAGAACAGGGTTT lcl|J01415.2_rrna...
[5] 75 GTTAAGATGGCAGAGCCCGGTAATCGCATAAAACTTAAAACTTTACAGTCAGAGGTTCAATTCCTCTTCTTAACA lcl|J01415.2_trna...
... ... ...
[20] 59 GAGAAAGCTCACAAGAACTGCTAACTCATGCCCCCATGTCTAACAACATGGCTTTCTCA lcl|J01415.2_trna...
[21] 71 ACTTTTAAAGGATAACAGCTATCCATTGGTCTTAGGCCCCAAAAATTTTGGTGCAACTCCAAATAAAAGTA lcl|J01415.2_trna...
[22] 69 GTTCTTGTAGTTGAAATACAACGATGGTTTTTCATATCATTGGTCGTGGTTGTAGTCCGTGCGAGAATA lcl|J01415.2_trna...
[23] 66 GTCCTTGTAGTATAAACTAATACACCAGTCTTGTAAACCGGAGATGAAAACCTTTTTCCAAGGACA lcl|J01415.2_trna...
[24] 68 CAGAGAATAGTTTAAATTAGAATCTTAGCTTTGGGTGCTAATGGTGGAGTTAAAGACTTTTTCTCTGA lcl|J01415.2_trna...
```
### Example `ENSEMBL`:
```{r,eval=FALSE}
# download the RNA of Homo sapiens from ensembl
# and store the corresponding genome RNA file in '_ncbi_downloads/RNA'
HS.rna.ensembl <- getRNA( db = "ensembl",
organism = "Homo sapiens",
path = file.path("_ncbi_downloads","RNA"))
```
```{r,eval=FALSE}
# import downloaded RNA as Biostrings object
Human_rna <- read_cds(file = HS.rna.ensembl,
obj.type = "Biostrings")
```
```{r,eval=FALSE}
# look at the Biostrings object
Human_rna
```
```
A BStringSet instance of length 36701
width seq names
[1] 104 GTGCTCACTTTGGCAACATACATACTAAAATTGGACGGATACAG...GCACAAGGATGACATGCAAATTCATGAAGCATTCCATATTTTT ENST00000516494.2...
[2] 164 TTAACTACCTGACAGAGGAGATACTGTGATCATGAAAGTGGTTT...CATAATTTCTGGTGGTAGGGGACTGCGTTCATGTTCTCCCCTA ENST00000627793.1...
[3] 114 ACACTGGTTTCTCTTCAGATCGAATAAATCTTTCGCCTTTTACT...AGTTATAAGCTAATTTTTTGTAAGCCTTGCCCTGGGGAGGCAG ENST00000629478.1...
[4] 64 CAGTGTTACACCTCTTTTAGAATTTATCTATCAGGTTTTCCAGTGTTCACTGAAATTTGTCTCT ENST00000629187.1...
[5] 107 GTGTTGGCCTGGGCAGCACGTATACTAAAGTTGGAATGACACAG...GCGCAAGGATGATGTGCAAATTCGTGACAAGTTCCATATTTTT ENST00000631612.1...
... ... ...
[36697] 90 GGCTAGTCCAAATGTAGTGGTGTTCCAAACTAATTAATCACAACCAGTTACAGATTTCTTGTTTTCTTTTCCACTCACACTTAGCCTTAA ENST00000410951.1...
[36698] 109 GGCTGATCTGAAGATGATGAGTTATCTCAATTGATTGTTCAGCC...TCTATTCTTTCCTCTCTTCTCACTACTGCACTTGGCTAGGAAA ENST00000410462.2...
[36699] 109 GGTGGGTCCAAAGGTAGCAAGTTATCTCAATTGATCACAGTCAG...CATTCTATCACCCCTTCTCATTACTGTACTTGACTAGTCTTTT ENST00000364501.1...
[36700] 293 GGATATGAGGGCGATCTGGCAGGGACATCTGTCACCCCACTGAT...AAAATTAGCTGGGCATAGTGGCGTGCACCTGTCGTCCTAGCTA ENST00000365097.1...
[36701] 172 TTGAAGGCGTGGAGACTGAAGTCCTCTCTATATCCACAGAACAG...AAGAGGGCTGTTCAGTCTCCATGCCCTTCAATCCTTGGCTACT ENST00000615087.1...
```
Users can also specify the `release` argument which denotes the database release version of ENSEMBL (`db = "ensembl"`) in case they wish to download archived vgenome versions. Default is release = NULL meaning that the most recent database version is used.
### RNASet Retrieval
The `getRNASet()` function enables users to retrieve RNA files for
multiple species. This is convenient when users wish to bulk download a particular
set of species. Internally, a folder named `set_rna` is generated in which
RNA and RNA info files are stored. In addition, users can specify the arguments: `clean_retrieval` and `gunzip` (both are `TRUE` by default) to clean file names and automatically unzip downloaded files.
Example:
```r
# specify the species names
download_species <- c("Arabidopsis thaliana",
"Arabidopsis lyrata",
"Capsella rubella")
# retrieve these three species from NCBI RefSeq
biomartr::getRNASet("refseq", organisms = download_species, path = "set_rna")
```
If the download process was interrupted, users can re-run the function
and it will only download missing genomes. In cases users wish
to download everything again and updating existing genomes, they may
specify the argument `update = TRUE`.
### Retrieve the annotation file of a particular genome
Finally, users can download the corresponding annotation `.gff` files for particular genomes of interest using the `getGFF()` or alternatively for `ensembl` the `getGTF()` function.
### Example `NCBI RefSeq`:
```{r,eval=FALSE}
# download the GFF file of Homo sapiens from refseq
# and store the corresponding file in '_ncbi_downloads/annotation'
HS.gff.refseq <- getGFF( db = "refseq",
organism = "Homo sapiens",
path = file.path("_ncbi_downloads","annotation"))
```
After downloading the `.gff` file, users can import the `.gff` file with `read_gff()`.
```{r,eval=FALSE}
# import downloaded GFF file
Human_GFF <- read_gff(file = HS.gff.refseq)
```
```{r,eval=FALSE}
Human_GFF
```
```
seqid source type start end score strand phase
<chr> <chr> <chr> <int> <int> <dbl> <chr> <dbl>
1 NC_000001.11 RefSeq region 1 248956422 0 + 0
2 NC_000001.11 BestRefSeq gene 11874 14409 0 + 0
3 NC_000001.11 BestRefSeq transcript 11874 14409 0 + 0
4 NC_000001.11 BestRefSeq exon 11874 12227 0 + 0
5 NC_000001.11 BestRefSeq exon 12613 12721 0 + 0
6 NC_000001.11 BestRefSeq exon 13221 14409 0 + 0
7 NC_000001.11 BestRefSeq gene 14362 29370 0 - 0
8 NC_000001.11 BestRefSeq transcript 14362 29370 0 - 0
9 NC_000001.11 BestRefSeq exon 29321 29370 0 - 0
10 NC_000001.11 BestRefSeq exon 24738 24891 0 - 0
```
#### Removing corrupt lines from downloaded GFF files
In some cases, `GFF` files stored at NCBI databases include corrupt lines that have more than 65000 characters. This [leads to problems](https://github.com/lawremi/rtracklayer/issues/15)
when trying to import such annotation files into R. To overcome this issue users can specify the `remove_annotation_outliers = TRUE`
argument to remove such outlier lines and overwrite the downloaded annotation file. This will make any downstream analysis with this annotation file much more reliable.
Example:
```r
Ath_path <- biomartr::getGFF(organism = "Arabidopsis thaliana", remove_annotation_outliers = TRUE)
```
```
Starting GFF retrieval of 'Arabidopsis thaliana' from refseq ...
|============================================| 100% 52 MB
File _ncbi_downloads/annotation/Arabidopsis_thaliana_genomic_refseq.gff.gz exists already. Thus, download has been skipped.
Importing '_ncbi_downloads/annotation/Arabidopsis_thaliana_genomic_refseq.gff.gz' ...
|============================================| 100% 434 MB
Reading annotation file '_ncbi_downloads/annotation/Arabidopsis_thaliana_genomic_refseq.gff.gz' and removing all outlier lines with number of characters greater 65000 ...
Overwriting '_ncbi_downloads/annotation/Arabidopsis_thaliana_genomic_refseq.gff.gz' with removed outlier lines ...
Unzipping file _ncbi_downloads/annotation/Arabidopsis_thaliana_genomic_refseq.gff.gz' ...
The new annotation file was created and has been stored at '_ncbi_downloads/annotation/Arabidopsis_thaliana_genomic_refseq.gff'.
The outlier lines were stored in a separate file to explore at '/var/folders/3x/6bbw6ds1039gpwny1m0hn8r80000gp/T//RtmpuVjnsC/Arabidopsis_thaliana_genomic_refseq.gff.gz_outlier_lines.txt'.
```
### Example `NCBI Genbank`:
```{r,eval=FALSE}
# download the GFF file of Homo sapiens from genbank
# and store the corresponding file in '_ncbi_downloads/annotation'
HS.gff.genbank <- getGFF( db = "genbank",
organism = "Homo sapiens",
path = file.path("_ncbi_downloads","annotation"))
```
After downloading the `.gff` file, users can import the `.gff` file with `read_gff()`.
```{r,eval=FALSE}
# import downloaded GFF file
Human_GFF <- read_gff(file = HS.gff.genbank)
```
```{r,eval=FALSE}
# show all elements of the data.frame
# options(tibble.print_max = Inf)
Human_GFF
```
```
seqid source type start end score strand phase
<chr> <chr> <chr> <int> <int> <dbl> <chr> <dbl>
1 CM000663.2 Genbank region 1 248956422 0 + 0
2 CM000663.2 Genbank centromere 122026460 125184587 0 + 0
3 KI270706.1 Genbank region 1 175055 0 + 0
4 KI270707.1 Genbank region 1 32032 0 + 0
5 KI270708.1 Genbank region 1 127682 0 + 0
6 KI270709.1 Genbank region 1 66860 0 + 0
7 KI270710.1 Genbank region 1 40176 0 + 0
8 KI270711.1 Genbank region 1 42210 0 + 0
9 KI270712.1 Genbank region 1 176043 0 + 0
10 KI270713.1 Genbank region 1 40745 0 + 0
```
#### Removing corrupt lines from downloaded GFF files
In some cases, `GFF` files stored at NCBI databases include corrupt lines that have more than 65000 characters. This [leads to problems](https://github.com/lawremi/rtracklayer/issues/15)
when trying to import such annotation files into R. To overcome this issue users can specify the `remove_annotation_outliers = TRUE`
argument to remove such outlier lines and overwrite the downloaded annotation file. This will make any downstream analysis with this annotation file much more reliable.
Example:
```r
Ath_path <- biomartr::getGFF(db = "genbank",
organism = "Arabidopsis thaliana",
remove_annotation_outliers = TRUE)
```
```
Starting GFF retrieval of 'Arabidopsis thaliana' from genbank ...
Completed!
Now continue with species download ...
GFF download of Arabidopsis_thaliana is completed!
Checking md5 hash of file: _ncbi_downloads/annotation/Arabidopsis_thaliana_md5checksums.txt ...
The md5 hash of file '_ncbi_downloads/annotation/Arabidopsis_thaliana_md5checksums.txt' matches!
Importing '_ncbi_downloads/annotation/Arabidopsis_thaliana_genomic_genbank.gff.gz' ...
Reading annotation file '_ncbi_downloads/annotation/Arabidopsis_thaliana_genomic_genbank.gff.gz' and removing all outlier lines with number of characters greater 65000 ...
Overwriting '_ncbi_downloads/annotation/Arabidopsis_thaliana_genomic_genbank.gff.gz' with removed outlier lines ...
Unzipping file _ncbi_downloads/annotation/Arabidopsis_thaliana_genomic_genbank.gff.gz' ...
The new annotation file was created and has been stored at '_ncbi_downloads/annotation/Arabidopsis_thaliana_genomic_genbank.gff'.
The outlier lines were stored in a separate file to explore at '/var/folders/3x/6bbw6ds1039gpwny1m0hn8r80000gp/T//RtmpuVjnsC/Arabidopsis_thaliana_genomic_genbank.gff.gz_outlier_lines.txt'.
```
### Example `ENSEMBL`:
```{r,eval=FALSE}
# download the GFF file of Homo sapiens from ENSEMBL
# and store the corresponding file in 'ensembl/annotation'
HS.gff.ensembl <- getGFF( db = "ensembl",
organism = "Homo sapiens",
path = file.path("ensembl","annotation"))
```
After downloading the `.gff` file, users can import the `.gff` file with `read_gff()`.
```{r,eval=FALSE}
# import downloaded GFF file
Human_GFF <- read_gff(file = HS.gff.ensembl)
```
```{r,eval=FALSE}
# show all elements of the data.frame
# options(tibble.print_max = Inf)
Human_GFF
```
```
seqid source type start end score strand phase
<int> <chr> <chr> <int> <int> <chr> <chr> <dbl>
1 1 GRCh38 chromosome 1 248956422 . . 0
2 1 . biological_region 10469 11240 1.3e+03 . 0
3 1 . biological_region 10650 10657 0.999 + 0
4 1 . biological_region 10655 10657 0.999 - 0
5 1 . biological_region 10678 10687 0.999 + 0
6 1 . biological_region 10681 10688 0.999 - 0
7 1 . biological_region 10707 10716 0.999 + 0
8 1 . biological_region 10708 10718 0.999 - 0
9 1 . biological_region 10735 10747 0.999 - 0
10 1 . biological_region 10737 10744 0.999 + 0
```
Users can also specify the `release` argument which denotes the database release version of ENSEMBL (`db = "ensembl"`) in case they wish to download archived vgenome versions. Default is release = NULL meaning that the most recent database version is used.
#### Removing corrupt lines from downloaded GFF files
In some cases, `GFF` files stored at NCBI databases include corrupt lines that have more than 65000 characters. This [leads to problems](https://github.com/lawremi/rtracklayer/issues/15)
when trying to import such annotation files into R. To overcome this issue users can specify the `remove_annotation_outliers = TRUE`
argument to remove such outlier lines and overwrite the downloaded annotation file. This will make any downstream analysis with this annotation file much more reliable.
Alternatively for `getGTF()`:
```{r,eval=FALSE}
# download the GTF file of Homo sapiens from ENSEMBL
# and store the corresponding file in 'ensembl/annotation'
HS.gtf.ensembl <- getGTF( db = "ensembl",
organism = "Homo sapiens",
path = file.path("ensembl","annotation"),
assembly_type = "primary_assembly")
```
Taken together, `getGFF()` or `getGTF()` in combination with `getGenome()`, `getProteome()`, `getRNA()` and `getCDS()` allows users to retrieve the genome information together with the
corresponding `.gff` or `gtf` annotation file to make sure that they both have the same version and origin.
### GFFSet Retrieval
The `getGFFSet()` function enables users to retrieve GFF files for
multiple species. This is convenient when users wish to bulk download a particular
set of species. Internally, a folder named `set_gff` is generated in which
GFF and GFF info files are stored. In addition, users can specify the arguments: `clean_retrieval` and `gunzip` (both are `TRUE` by default) to clean file names and automatically unzip downloaded files.
Example:
```r
# specify the species names
download_species <- c("Arabidopsis thaliana",
"Arabidopsis lyrata",
"Capsella rubella")
# retrieve these three species from NCBI RefSeq
biomartr::getGFFSet("refseq", organisms = download_species, path = "set_gff")
```
If the download process was interrupted, users can re-run the function
and it will only download missing genomes. In cases users wish
to download everything again and updating existing genomes, they may
specify the argument `update = TRUE`.
In some cases, `GFF` files stored at NCBI databases include corrupt lines that have more than 65000 characters. This [leads to problems](https://github.com/lawremi/rtracklayer/issues/15)
when trying to import such annotation files into R. To overcome this issue users can specify the `remove_annotation_outliers = TRUE`
argument to remove such outlier lines and overwrite the downloaded annotation file. This will make any downstream analysis with this annotation file much more reliable.
## Repeat Masker Retrieval
[Repeat Masker](http://www.repeatmasker.org) is a tool for screening DNA sequences for interspersed repeats and low complexity DNA sequences.
NCBI stores the `Repeat Masker` for sevel species in their databases and can be retrieved using `getRepeatMasker()` and imported
via `read_rm()`.
### Example `NCBI RefSeq`:
```{r,eval=FALSE}
# download repeat masker annotation file for Homo sapiens
Hsapiens_rm <- getRepeatMasker( db = "refseq",
organism = "Homo sapiens",
path = file.path("refseq","TEannotation"))
```
Now users can import the TE annotation file using `read_rm()`.
```{r,eval=FALSE}
# import TE annotation file
Hsapiens_rm_import <- read_rm("refseq/TEannotation/Homo_sapiens_rm_refseq.out.gz")
# look at data
Hsapiens_rm_import
```
## Genome Assembly Stats Retrieval
By specifying the scientific name of an organism of interest the corresponding genome assembly stats file storing the assembly statistics of the organism of interest can be downloaded and stored locally.
### Example `NCBI RefSeq`:
```{r,eval=FALSE}
# download genome assembly stats file for Homo sapiens
Hsapiens_stats <- getAssemblyStats( db = "refseq",
organism = "Homo sapiens",
path = file.path("refseq","AssemblyStats"))
```
Now users can import the TE annotation file using `read_rm()`.
```{r,eval=FALSE}
# import TE annotation file
Hsapiens_stats_import <- read_assemblystats(Hsapiens_stats)
# look at data
Hsapiens_stats_import
```
```
A tibble: 1,196 x 6
unit_name molecule_name molecule_type sequence_type statistic value
<chr> <chr> <chr> <chr> <chr> <int>
1 all all all all total-length NA
2 all all all all spanned-gaps 661
3 all all all all unspanned-gaps 349
4 all all all all region-count 317
5 all all all all scaffold-count 875
6 all all all all scaffold-N50 59364414
7 all all all all scaffold-L50 17
8 all all all all scaffold-N75 31699399
9 all all all all scaffold-N90 7557127
10 all all all all contig-count 1536
```
### Example `NCBI Genbank`:
```{r,eval=FALSE}
# download genome assembly stats file for Homo sapiens
Hsapiens_stats <- getAssemblyStats( db = "genbank",
organism = "Homo sapiens",
path = file.path("genbank","AssemblyStats"))
```
Now users can import the TE annotation file using `read_rm()`.
```{r,eval=FALSE}
# import TE annotation file
Hsapiens_stats_import <- read_assemblystats(Hsapiens_stats)
# look at data
Hsapiens_stats_import
```
```
A tibble: 1,196 x 6
unit_name molecule_name molecule_type sequence_type statistic value
<chr> <chr> <chr> <chr> <chr> <int>
1 all all all all total-length NA
2 all all all all spanned-gaps 661
3 all all all all unspanned-gaps 349
4 all all all all region-count 317
5 all all all all scaffold-count 875
6 all all all all scaffold-N50 59364414
7 all all all all scaffold-L50 17
8 all all all all scaffold-N75 31699399
9 all all all all scaffold-N90 7557127
10 all all all all contig-count 1536
```
## Collection Retrieval
The automated retrieval of collections (= Genome, Proteome, CDS, RNA, GFF, Repeat Masker, AssemblyStats)
will make sure that the genome file of an organism will match the CDS, proteome, RNA, GFF, etc file
and was generated using the same genome assembly version. One aspect of why genomics studies
fail in computational and biological reproducibility is that it is not clear whether CDS, proteome, RNA, GFF, etc files
used in a proposed analysis were generated using the same genome assembly file denoting the same genome assembly version.
To avoid this seemingly trivial mistake we encourage users to retrieve
genome file collections using the `biomartr` function `getCollection()`
and attach the corresponding output as Supplementary Data
to the respective genomics study to ensure computational and biological reproducibility.
By specifying the scientific name of an organism of interest a collection consisting of the genome file, proteome file, CDS file, RNA file, GFF file, Repeat Masker file, AssemblyStats file of the organism of interest can be downloaded and stored locally.
### Example `NCBI RefSeq`:
```{r,eval=FALSE}
# download collection for Saccharomyces cerevisiae
getCollection( db = "refseq",
organism = "Saccharomyces cerevisiae",
path = file.path("refseq","Collections"))
```
Internally, the `getCollection()` function will now generate a folder named `refseq/Collection/Saccharomyces_erevisiae`
and will store all genome and annotation files for `Saccharomyces cerevisiae` in the same folder.
In addition, the exact genoem and annotation version will be logged in the `doc` folder.
```
Genome download is completed!
Checking md5 hash of file: refseq/Collections/Saccharomyces_cerevisiae_md5checksums.txt ...
The md5 hash of file 'refseq/Collections/Saccharomyces_cerevisiae_md5checksums.txt' matches!
The genome of 'Saccharomyces_cerevisiae' has been downloaded to 'refseq/Collections' and has been named 'Saccharomyces_cerevisiae_genomic_refseq.fna.gz'.
Starting proteome retrieval of 'Saccharomyces cerevisiae' from refseq ...
Proteome download is completed!
Checking md5 hash of file: refseq/Collections/Saccharomyces_cerevisiae_md5checksums.txt ...
The md5 hash of file 'refseq/Collections/Saccharomyces_cerevisiae_md5checksums.txt' matches!
The proteome of 'Saccharomyces_cerevisiae' has been downloaded to 'refseq/Collections' and has been named 'Saccharomyces_cerevisiae_protein_refseq.faa.gz' .
Starting CDS retrieval of 'Saccharomyces cerevisiae' from refseq ...
CDS download is completed!
Checking md5 hash of file: refseq/Collections/Saccharomyces_cerevisiae_md5checksums.txt ...
The md5 hash of file 'refseq/Collections/Saccharomyces_cerevisiae_md5checksums.txt' matches!
The genomic CDS of 'Saccharomyces_cerevisiae' has been downloaded to 'refseq/Collections' and has been named 'Saccharomyces_cerevisiae_cds_from_genomic_refseq.fna.gz' .
Starting GFF retrieval of 'Saccharomyces cerevisiae' from refseq ...
GFF download is completed!
Checking md5 hash of file: refseq/Collections/Saccharomyces_cerevisiae_md5checksums.txt ...
The md5 hash of file 'refseq/Collections/Saccharomyces_cerevisiae_md5checksums.txt' matches!
The *.gff annotation file of 'Saccharomyces_cerevisiae' has been downloaded to 'refseq/Collections' and has been named 'Saccharomyces_cerevisiae_genomic_refseq.gff.gz'.
Starting RNA retrieval of 'Saccharomyces cerevisiae' from refseq ...
RNA download is completed!
Checking md5 hash of file: refseq/Collections/Saccharomyces_cerevisiae_md5checksums.txt ...
The md5 hash of file 'refseq/Collections/Saccharomyces_cerevisiae_md5checksums.txt' matches!
The genomic RNA of 'Saccharomyces_cerevisiae' has been downloaded to 'refseq/Collections' and has been named 'Saccharomyces_cerevisiae_rna_from_genomic_refseq.fna.gz' .
Starting Repeat Masker retrieval of 'Saccharomyces cerevisiae' from refseq ...
RepeatMasker download is completed!
Checking md5 hash of file: refseq/Collections/Saccharomyces_cerevisiae_md5checksums.txt ...
The md5 hash of file 'refseq/Collections/Saccharomyces_cerevisiae_md5checksums.txt' matches!
The Repeat Masker output file of 'Saccharomyces_cerevisiae' has been downloaded to 'refseq/Collections' and has been named 'Saccharomyces_cerevisiae_rm_refseq.out.gz'.
Starting assembly quality stats retrieval of 'Saccharomyces cerevisiae' from refseq ...
Genome assembly quality stats file download completed!
The assembly statistics file of 'Saccharomyces_cerevisiae' has been downloaded to 'refseq/Collections' and has been named 'Saccharomyces_cerevisiae_assembly_stats_refseq.txt'.
Collection retrieval finished successfully!
We retrieved the genome assembly and checked the annotation for 'Saccharomyces cerevisiae' (taxid: 559292, strain=S288C) from 'ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/146/045/GCF_000146045.2_R64/GCF_000146045.2_R64_assembly_stats.txt' (accession: GCF_000146045.2, bioproject: PRJNA128, biosample: NA) using the biomartr R package (Drost and Paszkowski, 2017).
```
Users can now simply attach the output folder `` as supplementary data in their study and state in the materials sections. This way, computational and biological reproducibility can be standardized and indeed will become trivial
in the context of genome version and annotation version.
You can’t perform that action at this time.