Working with biological sequences

Last updated on 2024-11-19 | Edit this page

Estimated time: NA minutes

Overview

Questions

What is the recommended way to represent biological sequences in Bioconductor?
What Bioconductor packages provides methods to efficiently process biological sequences?

Objectives

Explain how biological sequences are represented in the Bioconductor project.
Identify Bioconductor packages and methods available to process biological sequences.

Install packages

Before we can proceed into the following sections, we install some Bioconductor packages that we will need. First, we check that the BiocManager package is installed before trying to use it; otherwise we install it. Then we use the BiocManager::install() function to install the necessary packages.

R

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("Biostrings")

The Biostrings package and classes

Why do we need classes for biological sequences?

Biological sequences are arguably some of the simplest biological entities to represent computationally. Examples include nucleic acid sequences (e.g., DNA, RNA) and protein sequences composed of amino acids.

That is because alphabets have been designed and agreed upon to represent individual monomers using character symbols.

For instance, using the alphabet for amino acids, the reference protein sequence for the Actin, alpha skeletal muscle protein sequence is represented as follows.

OUTPUT

[1] "MCDEDETTALVCDNGSGLVKAGFAGDDAPRAVFPSIVGRPRHQGVMVGMGQKDSYVGDEAQSKRGILTLKYPIEHGIITNWDDMEKIWHHTFYNELRVAPEEHPTLLTEAPLNPKANREKMTQIMFETFNVPAMYVAIQAVLSLYASGRTTGIVLDSGDGVTHNVPIYEGYALPHAIMRLDLAGRDLTDYLMKILTERGYSFVTTAEREIVRDIKEKLCYVALDFENEMATAASSSSLEKSYELPDGQVITIGNERFRCPETLFQPSFIGMESAGIHETTYNSIMKCDIDIRKDLYANNVMSGGTTMYPGIADRMQKEITALAPSTMKIKIIAPPERKYSVWIGGSILASLSTFQQMWITKQEYDEAGPSIVHRKCF"

However, a major limitation of regular character vectors is that they do not check the validity of the sequences that they contain. Practically, it is possible to store meaningless sequences of symbols in character strings, including symbols that are not part of the official alphabet for the relevant type of polymer. In those cases, the burden of checking the validity of sequences falls on the programs that process them, or causing those programs to run into errors when they unexpectedly encounter invalid symbols in a sequence.

Instead, S4 classes – demonstrated in the earlier episode The S4 class system – provide a way to label objects as distinct “DNA”, “RNA”, or “protein” varieties of biological sequences. This label is an extremely powerful way to inform programs on the set of character symbols they can expect in the sequence, but also the range of computational operations that can be applied to those sequences. For instance, a function designed to translate nucleic acid sequences into the corresponding amino acid sequence should only be allowed to run on sequences that represent nucleic acids.

Challenge

Can you tell whether this character string is a valid DNA sequence?

AATTGGCCRGGCCAATT

Show me the solution

Yes, this is a valid DNA sequence using ambiguity codes defined in the IUPAC notation. In this case, A, T, C, and G represents the four standard types of nucleotides, while the R symbol acts as a regular expression representing either of the two purine nucleotide bases, A and G.

The Biostrings package

Overview

In the Bioconductor project, the Biostrings package implements S4 classes to represent biological sequences as S4 objects, e.g. DNAString for sequences of nucleotides in deoxyribonucleic acid polymers, and AAString for sequences of amino acids in protein polymers. Those S4 classes provide memory-efficient containers for character strings, automatic validity-checking functionality for each class of biological molecules, and methods implementing various string matching algorithms and other utilities for fast manipulation and processing of large biological sequences or sets of sequences.

A short presentation of the basic classes defined in the Biostrings package is available in one of the package vignettes, accessible as vignette("Biostrings2Classes"), while more detailed information is provided in the other package vignettes, accessible as browseVignettes("Biostrings").

First steps

To get started, we load the package.

R

library(Biostrings)

With the package loaded and attached to the session, we have access to the package functions. Those include functions that let us create new objects of the classes defined in the package. For instance, we can create an object that represents a DNA sequence, using the DNAString() constructor function. Without assigning the output to an object, we let the resulting object be printed in the console.

R

DNAString("ATCG")

OUTPUT

4-letter DNAString object
seq: ATCG

Notably, DNA sequences may only contain the symbols A, T, C, and G, to represent the four DNA nucleotide bases, the symbol N as a placeholder for an unknown or unspecified base, and a restricted set of additional symbols with special meaning defined in the IUPAC Extended Genetic Alphabet. Notice that the constructor function does not let us create objects that contain invalid characters, e.g. Z.

R

DNAString("ATCGZ")

ERROR

Error in .Call2("new_XString_from_CHARACTER", class(x0), string, start, : key 90 (char 'Z') not in lookup table

Specifically, the IUPAC Extended Genetic Alphabet defines ambiguity codes that represent sets of nucleotides, in a way similar to regular expressions. The IUPAC_CODE_MAP named character vector contains the mapping from the IUPAC nucleotide ambiguity codes to their meaning.

R

IUPAC_CODE_MAP

OUTPUT

     A      C      G      T      M      R      W      S      Y      K      V
   "A"    "C"    "G"    "T"   "AC"   "AG"   "AT"   "CG"   "CT"   "GT"  "ACG"
     H      D      B      N
 "ACT"  "AGT"  "CGT" "ACGT"

Any of those nucleotide codes are allowed in the sequence of a DNAString object. For instance, the symbol M represents either of the two nucleotides A or C at a given position in a nucleic acid sequence.

R

DNAString("ATCGM")

OUTPUT

5-letter DNAString object
seq: ATCGM

In particular, pattern matching methods implemented in the Biostrings package recognize the meaning of ambiguity codes for each class of biological sequence, allowing them to efficiently match motifs queried by users without the need to design elaborate regular expressions. For instance, the method matchPattern() takes a pattern= and a subject= argument, and returns a Views object that reports and displays any match of the pattern expression at any position in the subject sequence.

Note that the default option fixed = TRUE instructs the method to match the query exactly – i.e., ignore ambiguity codes – which in this case does not report any exact match.

R

dna1 <- DNAString("ATCGCTTTGA")
matchPattern("GM", dna1, fixed = TRUE)

OUTPUT

Views on a 10-letter DNAString subject
subject: ATCGCTTTGA
views: NONE

Instead, to indicate that the pattern includes some ambiguity code, the argument fixed must be set to FALSE.

R

matchPattern("GM", dna1, fixed = FALSE)

OUTPUT

Views on a 10-letter DNAString subject
subject: ATCGCTTTGA
views:
      start end width
  [1]     4   5     2 [GC]
  [2]     9  10     2 [GA]

In this particular example, two views describe matches of the pattern in the subject sequence. Specifically, the pattern GM first matched the sequence GC spanning positions 4 to 5 in the subject sequence, and then also matched the sequence GA from positions 9 to 10.

Similarly to the method matchPattern(), the method countPattern() can be applied to simply count the number of matches of the pattern in the subject sequence. And again, the option fixed controls whether to respect ambiguity codes, or match them exactly.

Challenge

How many hits does the following code return? Why?

dna2 <- DNAString("TGATTGCTTGGTTGMTT")
countPattern("GM", dna2, fixed = FALSE)

Show me the solution

The method countPattern() reports 3 hits, because the option fixed = FALSE allows the pattern GM to match GA, GC, and GM sequences, due to the use of the ambiguity code M in the pattern.

Importing biological strings from files

In practice, users rarely type the strings representing biological sequences themselves. Most of the time, biological strings are imported from files, either downloaded from public repositories or generated locally using bioinformatics programs.

For instance, we can load the set of adapter sequences for the TruSeq™ DNA PCR-Free whole-genome sequencing library preparation kit from a file that we downloaded during the lesson setup. Since adapter sequences are nucleic acid sequences, we must use the function readDNAStringSet().

R

truseq_adapters <- readDNAStringSet(filepath = "data/TruSeq3-PE-2.fa")
truseq_adapters

OUTPUT

DNAStringSet object of length 6:
    width seq                                               names
[1]    34 TACACTCTTTCCCTACACGACGCTCTTCCGATCT                PrefixPE/1
[2]    34 GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT                PrefixPE/2
[3]    34 TACACTCTTTCCCTACACGACGCTCTTCCGATCT                PE1
[4]    34 AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTA                PE1_rc
[5]    34 GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT                PE2
[6]    34 AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC                PE2_rc

Going further

The help page of the function readDNAStringSet() – accessible using help(readDNAStringSet) – documents related functions designed to import other types of biological sequences, e.g readRNAStringSet(), readAAStringSet().

Operations on biological strings

Computing the frequency of symbols

The Biostrings package provides several functions to process and manipulate classes of biological strings. For example, we have come across matchPattern() and countPattern() earlier in this episode.

Another example of a method that can be applied to biological strings is letterFrequency(), to compute the frequency of letters in a biological sequence.

R

letterFrequency(truseq_adapters, letters = DNA_ALPHABET)

OUTPUT

      A  C  G  T M R W S Y K V H D B N - + .
[1,]  6 14  3 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[2,]  5  8 10 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[3,]  6 14  3 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[4,] 11  3 14  6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[5,]  5  8 10 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[6,] 11 10  8  5 0 0 0 0 0 0 0 0 0 0 0 0 0 0

The output is a matrix with one row for each sequence in the DNAStringSet object, and one column for each symbol in the alphabet of deoxyribonucleic acids, provided by the Biostrings package in a built-in object called DNA_ALPHABET.

Amino acid sequences

Similarly to the DNAString and DNAStringSet classes, the classes AAString and AAStringSet allow efficient storage and manipulation of a long amino acid sequence, or a set thereof.

Similarly to built-in objects for the DNA alphabet, the built-in objects AA_ALPHABET, AA_STANDARD and AA_PROTEINOGENIC describe different subsets of the alphabet of valid symbols for amino acid sequences.

For instance, the AA_ALPHABET object describes the set of symbols in the full amino acid alphabet.

R

AA_ALPHABET

OUTPUT

 [1] "A" "R" "N" "D" "C" "Q" "E" "G" "H" "I" "L" "K" "M" "F" "P" "S" "T" "W" "Y"
[20] "V" "U" "O" "B" "J" "Z" "X" "*" "-" "+" "."

Challenge

Use base R code to identify the two symbols present in the AA_PROTEINOGENIC alphabet object that are absent from the AA_STANDARD alphabet object. What do those two symbols represent?

Show me the solution

> setdiff(AA_PROTEINOGENIC, AA_STANDARD)
[1] "U" "O"

The symbols U and O represent selenocysteine and pyrrolysine, respectively. Those two amino acids are in some species coded for by codons that are usually interpreted as stop codons. As such, they are not included in the alphabet of “standard” amino acids, and an alphabet of “proteinogenic” amino acids was defined to acknowledge the special biology of those amino acids. Either of those alphabets may be used to determine the validity of an amino acid sequence, depending on its biological nature.

Translating nucleotide sequences

One of the key motivations for the use of S4 classes and the object-oriented programming (OOP) model relies on the infrastructure of S4 generics and methods. As described in the earlier episode The S4 class system, generics provide a mechanism for defining and applying distinct implementations of the same generic function name, according to the nature of the input object(s) provided to the function call.

For instance, the Biostrings package provides multiple implementations of a generic called translate(), for translating DNA or RNA sequences into amino acid sequences. The set of input objects supported by the generic translate() can be listed using the function showMethods(), from the CRAN package methods.

R

showMethods("translate")

OUTPUT

Function: translate (package Biostrings)
x="DNAString"
x="DNAStringSet"
x="MaskedDNAString"
x="MaskedRNAString"
x="RNAString"
x="RNAStringSet"

In the output above, we see that that the generic function translate() includes methods capable of handling objects representing DNA and RNA sequences in the DNAString and RNAString classes, respectively; but also lists of DNA and RNA sequences in objects of class DNAStringSet and RNAStringSet, as well as other classes capable of storing DNA and RNA sequences.

To demonstrate the use of the translate() method, we first load a set of open reading frames (ORFs) identified by the NIH Open Reading Frame Finder for the Homo sapiens actin beta (ACTB) mRNA (RefSeq: NM_001101), using the standard genetic code, a minimal ORF length of 75 nucleotides, and starting with the “ATG” start codon only.

R

actb_orf_nih <- readDNAStringSet("data/actb_orfs.fasta")
actb_orf_nih

OUTPUT

DNAStringSet object of length 13:
     width seq                                              names
 [1]   222 ATGCCCACCATCACGCCCTGGTG...CGGGGCGGACGCGGTCTCGGCG gi|1519311456|ref...
 [2]  1128 ATGGATGATGATATCGCCGCGCT...CGTCCACCGCAAATGCTTCTAG gi|1519311456|ref...
 [3]   126 ATGATGATATCGCCGCGCTCGTC...CGCCCCAGGCACCAGGGCGTGA gi|1519311456|ref...
 [4]    90 ATGTCGTCCCAGTTGGTGACGAT...CTGGGCCTCGTCGCCCACATAG gi|1519311456|ref...
 [5]   225 ATGGGCACAGTGTGGGTGACCCC...AGCCACACGCAGCTCATTGTAG gi|1519311456|ref...
 ...   ... ...
 [9]   342 ATGAGATTGGCATGGCTTTATTT...ATGTAATGCAAAATTTTTTTAA gi|1519311456|ref...
[10]   168 ATGGCTTTATTTGTTTTTTTTGT...TTGCACATTGTTGTTTTTTTAA gi|1519311456|ref...
[11]   111 ATGACTATTAAAAAAACAACAAT...CCTTCACCGTTCCAGTTTTTAA gi|1519311456|ref...
[12]   105 ATGCAAAATTTTTTTAATCTTCG...CCTTTTTTGTCCCCCAACTTGA gi|1519311456|ref...
[13]   135 ATGATGAGCCTTCGTGCCCCCCC...TGACTTGAGACCAGTTGAATAA gi|1519311456|ref...

Having imported the nucleotide sequences as a DNAStringSet object, we can apply the translate() method to that object to produce the amino acid sequence that results from the translation process for each nucleotide sequence.

R

actb_aa <- translate(actb_orf_nih)
actb_aa

OUTPUT

AAStringSet object of length 13:
     width seq                                              names
 [1]    74 MPTITPWCLGRPTMEGKTARGAS...VWTGGGSAKARLCARGADAVSA gi|1519311456|ref...
 [2]   376 MDDDIAALVVDNGSGMCKAGFAG...MWISKQEYDESGPSIVHRKCF* gi|1519311456|ref...
 [3]    42 MMISPRSSSTTAPACARPASRATMPPGPSSPPSWGAPGTRA*       gi|1519311456|ref...
 [4]    30 MSSQLVTMPCSMGYFRVRMPLLLWASSPT*                   gi|1519311456|ref...
 [5]    75 MGTVWVTPSPESITMPVVRPEAY...GFRGASVSSTGCSSGATRSSL* gi|1519311456|ref...
 ...   ... ...
 [9]   114 MRLAWLYLFFLFCFGFFFFFGLT...QVHTGEVIALLSCKLCNAKFF* gi|1519311456|ref...
[10]    56 MALFVFFVLFWFFFFFWLDSGFK...ERASPKVHNVAEDFDCTLLFF* gi|1519311456|ref...
[11]    37 MTIKKTTMCNQSPRPHCELWGMLAPTDCCHLHRSSF*            gi|1519311456|ref...
[12]    35 MQNFFNLRLNTFLFCFILNDEPSCPPFPLFCPPT*              gi|1519311456|ref...
[13]    45 MMSLRAPPSPFFVPQLEMYEGFWSPWEWVEAARAYLYTDLRPVE*    gi|1519311456|ref...

In the example above, all amino acid sequences visible start with the typical methionin amino acid encoded by the “ATG” start codon. We also see that all but one of the amino acid sequences visible end with the * symbol, which indicates that the translation process ended on a stop codon. In contrast, the first open reading frame above reached the end of the nucleotide sequence without encoutering a stop codon.

Conveniently, the number of amino acids in each sequence is stated under the header width.

Challenge

Extract the length of each amino acid sequence above as an integer vector. What is the length of the longest amino acid sequence translated from any of those open reading frames?

Compare your result with the sequence information on the UniPro page for ACTB (https://www.uniprot.org/uniprot/P60709#sequences).

Show me the solution

width(actb_aa)
# or
max(width(actb_aa))

The longest translated sequence contains 376 amino acids.

The Uniprot page reports a sequence of 375 amino acids. However, the UniProt amino acid sequence does not comprise any symbol to represent the stop codon. That difference aside, the UniPro amino acid sequence is identical to the sequence that was produced by the translate() method.

The BSgenome package

Overview

In the Bioconductor project, the BSgenome package provides software infrastructure for efficient representation of full genome and their single-nucleotide polymorphisms.

The BSgenome package itself does not contain any genome sequence itself, but provides functionality to access genome sequences available in other Bioconductor packages, as we demonstrate in the next section.

First steps

To get started, we load the package.

R

library(BSgenome)

With the package loaded and attached to the session, we have access to the package functions.

In particular, the function BSgenome::available.genomes() can be used to display the names of Bioconductor packages that contain genome sequences.

R

available.genomes()

OUTPUT

'getOption("repos")' replaces Bioconductor standard repositories, see
'help("repositories", package = "BiocManager")' for details.
Replacement repositories:
    BioCsoft: https://bioconductor.org/packages/3.19/bioc
    BioCann: https://bioconductor.org/packages/3.19/data/annotation
    BioCexp: https://bioconductor.org/packages/3.19/data/experiment
    BioCworkflows: https://bioconductor.org/packages/3.19/workflows
    BioCbooks: https://bioconductor.org/packages/3.19/books
    CRAN: https://cran.rstudio.com

OUTPUT

  [1] "BSgenome.Alyrata.JGI.v1"
  [2] "BSgenome.Amellifera.BeeBase.assembly4"
  [3] "BSgenome.Amellifera.NCBI.AmelHAv3.1"
  [4] "BSgenome.Amellifera.UCSC.apiMel2"
  [5] "BSgenome.Amellifera.UCSC.apiMel2.masked"
  [6] "BSgenome.Aofficinalis.NCBI.V1"
  [7] "BSgenome.Athaliana.TAIR.04232008"
  [8] "BSgenome.Athaliana.TAIR.TAIR9"
  [9] "BSgenome.Btaurus.UCSC.bosTau3"
 [10] "BSgenome.Btaurus.UCSC.bosTau3.masked"
 [11] "BSgenome.Btaurus.UCSC.bosTau4"
 [12] "BSgenome.Btaurus.UCSC.bosTau4.masked"
 [13] "BSgenome.Btaurus.UCSC.bosTau6"
 [14] "BSgenome.Btaurus.UCSC.bosTau6.masked"
 [15] "BSgenome.Btaurus.UCSC.bosTau8"
 [16] "BSgenome.Btaurus.UCSC.bosTau9"
 [17] "BSgenome.Btaurus.UCSC.bosTau9.masked"
 [18] "BSgenome.Carietinum.NCBI.v1"
 [19] "BSgenome.Celegans.UCSC.ce10"
 [20] "BSgenome.Celegans.UCSC.ce11"
 [21] "BSgenome.Celegans.UCSC.ce2"
 [22] "BSgenome.Celegans.UCSC.ce6"
 [23] "BSgenome.Cfamiliaris.UCSC.canFam2"
 [24] "BSgenome.Cfamiliaris.UCSC.canFam2.masked"
 [25] "BSgenome.Cfamiliaris.UCSC.canFam3"
 [26] "BSgenome.Cfamiliaris.UCSC.canFam3.masked"
 [27] "BSgenome.Cjacchus.UCSC.calJac3"
 [28] "BSgenome.Cjacchus.UCSC.calJac4"
 [29] "BSgenome.CneoformansVarGrubiiKN99.NCBI.ASM221672v1"
 [30] "BSgenome.Creinhardtii.JGI.v5.6"
 [31] "BSgenome.Dmelanogaster.UCSC.dm2"
 [32] "BSgenome.Dmelanogaster.UCSC.dm2.masked"
 [33] "BSgenome.Dmelanogaster.UCSC.dm3"
 [34] "BSgenome.Dmelanogaster.UCSC.dm3.masked"
 [35] "BSgenome.Dmelanogaster.UCSC.dm6"
 [36] "BSgenome.Drerio.UCSC.danRer10"
 [37] "BSgenome.Drerio.UCSC.danRer11"
 [38] "BSgenome.Drerio.UCSC.danRer5"
 [39] "BSgenome.Drerio.UCSC.danRer5.masked"
 [40] "BSgenome.Drerio.UCSC.danRer6"
 [41] "BSgenome.Drerio.UCSC.danRer6.masked"
 [42] "BSgenome.Drerio.UCSC.danRer7"
 [43] "BSgenome.Drerio.UCSC.danRer7.masked"
 [44] "BSgenome.Dvirilis.Ensembl.dvircaf1"
 [45] "BSgenome.Ecoli.NCBI.20080805"
 [46] "BSgenome.Gaculeatus.UCSC.gasAcu1"
 [47] "BSgenome.Gaculeatus.UCSC.gasAcu1.masked"
 [48] "BSgenome.Ggallus.UCSC.galGal3"
 [49] "BSgenome.Ggallus.UCSC.galGal3.masked"
 [50] "BSgenome.Ggallus.UCSC.galGal4"
 [51] "BSgenome.Ggallus.UCSC.galGal4.masked"
 [52] "BSgenome.Ggallus.UCSC.galGal5"
 [53] "BSgenome.Ggallus.UCSC.galGal6"
 [54] "BSgenome.Gmax.NCBI.Gmv40"
 [55] "BSgenome.Hsapiens.1000genomes.hs37d5"
 [56] "BSgenome.Hsapiens.NCBI.GRCh38"
 [57] "BSgenome.Hsapiens.NCBI.T2T.CHM13v2.0"
 [58] "BSgenome.Hsapiens.UCSC.hg17"
 [59] "BSgenome.Hsapiens.UCSC.hg17.masked"
 [60] "BSgenome.Hsapiens.UCSC.hg18"
 [61] "BSgenome.Hsapiens.UCSC.hg18.masked"
 [62] "BSgenome.Hsapiens.UCSC.hg19"
 [63] "BSgenome.Hsapiens.UCSC.hg19.masked"
 [64] "BSgenome.Hsapiens.UCSC.hg38"
 [65] "BSgenome.Hsapiens.UCSC.hg38.dbSNP151.major"
 [66] "BSgenome.Hsapiens.UCSC.hg38.dbSNP151.minor"
 [67] "BSgenome.Hsapiens.UCSC.hg38.masked"
 [68] "BSgenome.Hsapiens.UCSC.hs1"
 [69] "BSgenome.Mdomestica.UCSC.monDom5"
 [70] "BSgenome.Mfascicularis.NCBI.5.0"
 [71] "BSgenome.Mfascicularis.NCBI.6.0"
 [72] "BSgenome.Mfuro.UCSC.musFur1"
 [73] "BSgenome.Mmulatta.UCSC.rheMac10"
 [74] "BSgenome.Mmulatta.UCSC.rheMac2"
 [75] "BSgenome.Mmulatta.UCSC.rheMac2.masked"
 [76] "BSgenome.Mmulatta.UCSC.rheMac3"
 [77] "BSgenome.Mmulatta.UCSC.rheMac3.masked"
 [78] "BSgenome.Mmulatta.UCSC.rheMac8"
 [79] "BSgenome.Mmusculus.UCSC.mm10"
 [80] "BSgenome.Mmusculus.UCSC.mm10.masked"
 [81] "BSgenome.Mmusculus.UCSC.mm39"
 [82] "BSgenome.Mmusculus.UCSC.mm8"
 [83] "BSgenome.Mmusculus.UCSC.mm8.masked"
 [84] "BSgenome.Mmusculus.UCSC.mm9"
 [85] "BSgenome.Mmusculus.UCSC.mm9.masked"
 [86] "BSgenome.Osativa.MSU.MSU7"
 [87] "BSgenome.Ppaniscus.UCSC.panPan1"
 [88] "BSgenome.Ppaniscus.UCSC.panPan2"
 [89] "BSgenome.Ptroglodytes.UCSC.panTro2"
 [90] "BSgenome.Ptroglodytes.UCSC.panTro2.masked"
 [91] "BSgenome.Ptroglodytes.UCSC.panTro3"
 [92] "BSgenome.Ptroglodytes.UCSC.panTro3.masked"
 [93] "BSgenome.Ptroglodytes.UCSC.panTro5"
 [94] "BSgenome.Ptroglodytes.UCSC.panTro6"
 [95] "BSgenome.Rnorvegicus.UCSC.rn4"
 [96] "BSgenome.Rnorvegicus.UCSC.rn4.masked"
 [97] "BSgenome.Rnorvegicus.UCSC.rn5"
 [98] "BSgenome.Rnorvegicus.UCSC.rn5.masked"
 [99] "BSgenome.Rnorvegicus.UCSC.rn6"
[100] "BSgenome.Rnorvegicus.UCSC.rn7"
[101] "BSgenome.Scerevisiae.UCSC.sacCer1"
[102] "BSgenome.Scerevisiae.UCSC.sacCer2"
[103] "BSgenome.Scerevisiae.UCSC.sacCer3"
[104] "BSgenome.Sscrofa.UCSC.susScr11"
[105] "BSgenome.Sscrofa.UCSC.susScr3"
[106] "BSgenome.Sscrofa.UCSC.susScr3.masked"
[107] "BSgenome.Tgondii.ToxoDB.7.0"
[108] "BSgenome.Tguttata.UCSC.taeGut1"
[109] "BSgenome.Tguttata.UCSC.taeGut1.masked"
[110] "BSgenome.Tguttata.UCSC.taeGut2"
[111] "BSgenome.Vvinifera.URGI.IGGP12Xv0"
[112] "BSgenome.Vvinifera.URGI.IGGP12Xv2"
[113] "BSgenome.Vvinifera.URGI.IGGP8X"

Installing BSgenome packages

To use one of the available genomes, the corresponding package must be installed first. For instance, the example below demonstrates how the data package BSgenome.Hsapiens.UCSC.hg38.masked can be installed using the function BiocManager::install() that we have seen before.

R

BiocManager::install("BSgenome.Hsapiens.UCSC.hg38.masked")

Using BSgenome packages

Once installed, BSgenome packages can be loaded like any other R package, using the library() function.

R

library(BSgenome.Hsapiens.UCSC.hg38.masked)

Each BSgenome package contains an object that is named identically to the package and contains the genome sequence.

Having loaded the package BSgenome.Hsapiens.UCSC.hg38.masked above, we can display the BSgenome object as follows.

R

BSgenome.Hsapiens.UCSC.hg38.masked

OUTPUT

| BSgenome object for Human
| - organism: Homo sapiens
| - provider: UCSC
| - genome: hg38
| - release date: 2023/01/31
| - 711 sequence(s):
|     chr1                    chr2                    chr3
|     chr4                    chr5                    chr6
|     chr7                    chr8                    chr9
|     chr10                   chr11                   chr12
|     chr13                   chr14                   chr15
|     ...                     ...                     ...
|     chr19_KV575256v1_alt    chr19_KV575257v1_alt    chr19_KV575258v1_alt
|     chr19_KV575259v1_alt    chr19_KV575260v1_alt    chr19_MU273387v1_alt
|     chr22_KN196485v1_alt    chr22_KN196486v1_alt    chr22_KQ458387v1_alt
|     chr22_KQ458388v1_alt    chr22_KQ759761v1_alt    chrX_KV766199v1_alt
|     chrX_MU273395v1_alt     chrX_MU273396v1_alt     chrX_MU273397v1_alt
|
| Tips: call 'seqnames()' on the object to get all the sequence names, call
| 'seqinfo()' to get the full sequence info, use the '$' or '[[' operator to
| access a given sequence, see '?BSgenome' for more information.

Given the length and the complexity of the object name, it is common practice to assign a copy of BSgenome objects to a new object simply called genome.

R

genome <- BSgenome.Hsapiens.UCSC.hg38.masked

Using BSgenome objects

When printing BSgenome objects in the console (see above), some helpful tips are displayed under the object itself, hinting at functions commonly used to access information in the object.

For instance, the function seqnames() can be used get the list of sequence names (i.e., chromosomes and contigs) present in the object.

R

seqnames(genome)

OUTPUT

  [1] "chr1"                    "chr2"
  [3] "chr3"                    "chr4"
  [5] "chr5"                    "chr6"
  [7] "chr7"                    "chr8"
  [9] "chr9"                    "chr10"
 [11] "chr11"                   "chr12"
 [13] "chr13"                   "chr14"
 [15] "chr15"                   "chr16"
 [17] "chr17"                   "chr18"
 [19] "chr19"                   "chr20"
 [21] "chr21"                   "chr22"
 [23] "chrX"                    "chrY"
 [25] "chrM"                    "chr1_GL383518v1_alt"
 [27] "chr1_GL383519v1_alt"     "chr1_GL383520v2_alt"
 [29] "chr1_KI270759v1_alt"     "chr1_KI270760v1_alt"
 [31] "chr1_KI270761v1_alt"     "chr1_KI270762v1_alt"
 [33] "chr1_KI270763v1_alt"     "chr1_KI270764v1_alt"
 [35] "chr1_KI270765v1_alt"     "chr1_KI270766v1_alt"
 [37] "chr1_KI270892v1_alt"     "chr2_GL383521v1_alt"
 [39] "chr2_GL383522v1_alt"     "chr2_GL582966v2_alt"
 [41] "chr2_KI270767v1_alt"     "chr2_KI270768v1_alt"
 [43] "chr2_KI270769v1_alt"     "chr2_KI270770v1_alt"
 [45] "chr2_KI270771v1_alt"     "chr2_KI270772v1_alt"
 [47] "chr2_KI270773v1_alt"     "chr2_KI270774v1_alt"
 [49] "chr2_KI270775v1_alt"     "chr2_KI270776v1_alt"
 [51] "chr2_KI270893v1_alt"     "chr2_KI270894v1_alt"
 [53] "chr3_GL383526v1_alt"     "chr3_JH636055v2_alt"
 [55] "chr3_KI270777v1_alt"     "chr3_KI270778v1_alt"
 [57] "chr3_KI270779v1_alt"     "chr3_KI270780v1_alt"
 [59] "chr3_KI270781v1_alt"     "chr3_KI270782v1_alt"
 [61] "chr3_KI270783v1_alt"     "chr3_KI270784v1_alt"
 [63] "chr3_KI270895v1_alt"     "chr3_KI270924v1_alt"
 [65] "chr3_KI270934v1_alt"     "chr3_KI270935v1_alt"
 [67] "chr3_KI270936v1_alt"     "chr3_KI270937v1_alt"
 [69] "chr4_GL000257v2_alt"     "chr4_GL383527v1_alt"
 [71] "chr4_GL383528v1_alt"     "chr4_KI270785v1_alt"
 [73] "chr4_KI270786v1_alt"     "chr4_KI270787v1_alt"
 [75] "chr4_KI270788v1_alt"     "chr4_KI270789v1_alt"
 [77] "chr4_KI270790v1_alt"     "chr4_KI270896v1_alt"
 [79] "chr4_KI270925v1_alt"     "chr5_GL339449v2_alt"
 [81] "chr5_GL383530v1_alt"     "chr5_GL383531v1_alt"
 [83] "chr5_GL383532v1_alt"     "chr5_GL949742v1_alt"
 [85] "chr5_KI270791v1_alt"     "chr5_KI270792v1_alt"
 [87] "chr5_KI270793v1_alt"     "chr5_KI270794v1_alt"
 [89] "chr5_KI270795v1_alt"     "chr5_KI270796v1_alt"
 [91] "chr5_KI270897v1_alt"     "chr5_KI270898v1_alt"
 [93] "chr6_GL000250v2_alt"     "chr6_GL000251v2_alt"
 [95] "chr6_GL000252v2_alt"     "chr6_GL000253v2_alt"
 [97] "chr6_GL000254v2_alt"     "chr6_GL000255v2_alt"
 [99] "chr6_GL000256v2_alt"     "chr6_GL383533v1_alt"
[101] "chr6_KB021644v2_alt"     "chr6_KI270758v1_alt"
[103] "chr6_KI270797v1_alt"     "chr6_KI270798v1_alt"
[105] "chr6_KI270799v1_alt"     "chr6_KI270800v1_alt"
[107] "chr6_KI270801v1_alt"     "chr6_KI270802v1_alt"
[109] "chr7_GL383534v2_alt"     "chr7_KI270803v1_alt"
[111] "chr7_KI270804v1_alt"     "chr7_KI270805v1_alt"
[113] "chr7_KI270806v1_alt"     "chr7_KI270807v1_alt"
[115] "chr7_KI270808v1_alt"     "chr7_KI270809v1_alt"
[117] "chr7_KI270899v1_alt"     "chr8_KI270810v1_alt"
[119] "chr8_KI270811v1_alt"     "chr8_KI270812v1_alt"
[121] "chr8_KI270813v1_alt"     "chr8_KI270814v1_alt"
[123] "chr8_KI270815v1_alt"     "chr8_KI270816v1_alt"
[125] "chr8_KI270817v1_alt"     "chr8_KI270818v1_alt"
[127] "chr8_KI270819v1_alt"     "chr8_KI270820v1_alt"
[129] "chr8_KI270821v1_alt"     "chr8_KI270822v1_alt"
[131] "chr8_KI270900v1_alt"     "chr8_KI270901v1_alt"
[133] "chr8_KI270926v1_alt"     "chr9_GL383539v1_alt"
[135] "chr9_GL383540v1_alt"     "chr9_GL383541v1_alt"
[137] "chr9_GL383542v1_alt"     "chr9_KI270823v1_alt"
[139] "chr10_GL383545v1_alt"    "chr10_GL383546v1_alt"
[141] "chr10_KI270824v1_alt"    "chr10_KI270825v1_alt"
[143] "chr11_GL383547v1_alt"    "chr11_JH159136v1_alt"
[145] "chr11_JH159137v1_alt"    "chr11_KI270826v1_alt"
[147] "chr11_KI270827v1_alt"    "chr11_KI270829v1_alt"
[149] "chr11_KI270830v1_alt"    "chr11_KI270831v1_alt"
[151] "chr11_KI270832v1_alt"    "chr11_KI270902v1_alt"
[153] "chr11_KI270903v1_alt"    "chr11_KI270927v1_alt"
[155] "chr12_GL383549v1_alt"    "chr12_GL383550v2_alt"
[157] "chr12_GL383551v1_alt"    "chr12_GL383552v1_alt"
[159] "chr12_GL383553v2_alt"    "chr12_GL877875v1_alt"
[161] "chr12_GL877876v1_alt"    "chr12_KI270833v1_alt"
[163] "chr12_KI270834v1_alt"    "chr12_KI270835v1_alt"
[165] "chr12_KI270836v1_alt"    "chr12_KI270837v1_alt"
[167] "chr12_KI270904v1_alt"    "chr13_KI270838v1_alt"
[169] "chr13_KI270839v1_alt"    "chr13_KI270840v1_alt"
[171] "chr13_KI270841v1_alt"    "chr13_KI270842v1_alt"
[173] "chr13_KI270843v1_alt"    "chr14_KI270844v1_alt"
[175] "chr14_KI270845v1_alt"    "chr14_KI270846v1_alt"
[177] "chr14_KI270847v1_alt"    "chr15_GL383554v1_alt"
[179] "chr15_GL383555v2_alt"    "chr15_KI270848v1_alt"
[181] "chr15_KI270849v1_alt"    "chr15_KI270850v1_alt"
[183] "chr15_KI270851v1_alt"    "chr15_KI270852v1_alt"
[185] "chr15_KI270905v1_alt"    "chr15_KI270906v1_alt"
[187] "chr16_GL383556v1_alt"    "chr16_GL383557v1_alt"
[189] "chr16_KI270853v1_alt"    "chr16_KI270854v1_alt"
[191] "chr16_KI270855v1_alt"    "chr16_KI270856v1_alt"
[193] "chr17_GL000258v2_alt"    "chr17_GL383563v3_alt"
[195] "chr17_GL383564v2_alt"    "chr17_GL383565v1_alt"
[197] "chr17_GL383566v1_alt"    "chr17_JH159146v1_alt"
[199] "chr17_JH159147v1_alt"    "chr17_JH159148v1_alt"
[201] "chr17_KI270857v1_alt"    "chr17_KI270858v1_alt"
[203] "chr17_KI270859v1_alt"    "chr17_KI270860v1_alt"
[205] "chr17_KI270861v1_alt"    "chr17_KI270862v1_alt"
[207] "chr17_KI270907v1_alt"    "chr17_KI270908v1_alt"
[209] "chr17_KI270909v1_alt"    "chr17_KI270910v1_alt"
[211] "chr18_GL383567v1_alt"    "chr18_GL383568v1_alt"
[213] "chr18_GL383569v1_alt"    "chr18_GL383570v1_alt"
[215] "chr18_GL383571v1_alt"    "chr18_GL383572v1_alt"
[217] "chr18_KI270863v1_alt"    "chr18_KI270864v1_alt"
[219] "chr18_KI270911v1_alt"    "chr18_KI270912v1_alt"
[221] "chr19_GL000209v2_alt"    "chr19_GL383573v1_alt"
[223] "chr19_GL383574v1_alt"    "chr19_GL383575v2_alt"
[225] "chr19_GL383576v1_alt"    "chr19_GL949746v1_alt"
[227] "chr19_GL949747v2_alt"    "chr19_GL949748v2_alt"
[229] "chr19_GL949749v2_alt"    "chr19_GL949750v2_alt"
[231] "chr19_GL949751v2_alt"    "chr19_GL949752v1_alt"
[233] "chr19_GL949753v2_alt"    "chr19_KI270865v1_alt"
[235] "chr19_KI270866v1_alt"    "chr19_KI270867v1_alt"
[237] "chr19_KI270868v1_alt"    "chr19_KI270882v1_alt"
[239] "chr19_KI270883v1_alt"    "chr19_KI270884v1_alt"
[241] "chr19_KI270885v1_alt"    "chr19_KI270886v1_alt"
[243] "chr19_KI270887v1_alt"    "chr19_KI270888v1_alt"
[245] "chr19_KI270889v1_alt"    "chr19_KI270890v1_alt"
[247] "chr19_KI270891v1_alt"    "chr19_KI270914v1_alt"
[249] "chr19_KI270915v1_alt"    "chr19_KI270916v1_alt"
[251] "chr19_KI270917v1_alt"    "chr19_KI270918v1_alt"
[253] "chr19_KI270919v1_alt"    "chr19_KI270920v1_alt"
[255] "chr19_KI270921v1_alt"    "chr19_KI270922v1_alt"
[257] "chr19_KI270923v1_alt"    "chr19_KI270929v1_alt"
[259] "chr19_KI270930v1_alt"    "chr19_KI270931v1_alt"
[261] "chr19_KI270932v1_alt"    "chr19_KI270933v1_alt"
[263] "chr19_KI270938v1_alt"    "chr20_GL383577v2_alt"
[265] "chr20_KI270869v1_alt"    "chr20_KI270870v1_alt"
[267] "chr20_KI270871v1_alt"    "chr21_GL383578v2_alt"
[269] "chr21_GL383579v2_alt"    "chr21_GL383580v2_alt"
[271] "chr21_GL383581v2_alt"    "chr21_KI270872v1_alt"
[273] "chr21_KI270873v1_alt"    "chr21_KI270874v1_alt"
[275] "chr22_GL383582v2_alt"    "chr22_GL383583v2_alt"
[277] "chr22_KB663609v1_alt"    "chr22_KI270875v1_alt"
[279] "chr22_KI270876v1_alt"    "chr22_KI270877v1_alt"
[281] "chr22_KI270878v1_alt"    "chr22_KI270879v1_alt"
[283] "chr22_KI270928v1_alt"    "chrX_KI270880v1_alt"
[285] "chrX_KI270881v1_alt"     "chrX_KI270913v1_alt"
[287] "chr1_KI270706v1_random"  "chr1_KI270707v1_random"
[289] "chr1_KI270708v1_random"  "chr1_KI270709v1_random"
[291] "chr1_KI270710v1_random"  "chr1_KI270711v1_random"
[293] "chr1_KI270712v1_random"  "chr1_KI270713v1_random"
[295] "chr1_KI270714v1_random"  "chr2_KI270715v1_random"
[297] "chr2_KI270716v1_random"  "chr3_GL000221v1_random"
[299] "chr4_GL000008v2_random"  "chr5_GL000208v1_random"
[301] "chr9_KI270717v1_random"  "chr9_KI270718v1_random"
[303] "chr9_KI270719v1_random"  "chr9_KI270720v1_random"
[305] "chr11_KI270721v1_random" "chr14_GL000009v2_random"
[307] "chr14_GL000194v1_random" "chr14_GL000225v1_random"
[309] "chr14_KI270722v1_random" "chr14_KI270723v1_random"
[311] "chr14_KI270724v1_random" "chr14_KI270725v1_random"
[313] "chr14_KI270726v1_random" "chr15_KI270727v1_random"
[315] "chr16_KI270728v1_random" "chr17_GL000205v2_random"
[317] "chr17_KI270729v1_random" "chr17_KI270730v1_random"
[319] "chr22_KI270731v1_random" "chr22_KI270732v1_random"
[321] "chr22_KI270733v1_random" "chr22_KI270734v1_random"
[323] "chr22_KI270735v1_random" "chr22_KI270736v1_random"
[325] "chr22_KI270737v1_random" "chr22_KI270738v1_random"
[327] "chr22_KI270739v1_random" "chrY_KI270740v1_random"
[329] "chrUn_GL000195v1"        "chrUn_GL000213v1"
[331] "chrUn_GL000214v1"        "chrUn_GL000216v2"
[333] "chrUn_GL000218v1"        "chrUn_GL000219v1"
[335] "chrUn_GL000220v1"        "chrUn_GL000224v1"
[337] "chrUn_GL000226v1"        "chrUn_KI270302v1"
[339] "chrUn_KI270303v1"        "chrUn_KI270304v1"
[341] "chrUn_KI270305v1"        "chrUn_KI270310v1"
[343] "chrUn_KI270311v1"        "chrUn_KI270312v1"
[345] "chrUn_KI270315v1"        "chrUn_KI270316v1"
[347] "chrUn_KI270317v1"        "chrUn_KI270320v1"
[349] "chrUn_KI270322v1"        "chrUn_KI270329v1"
[351] "chrUn_KI270330v1"        "chrUn_KI270333v1"
[353] "chrUn_KI270334v1"        "chrUn_KI270335v1"
[355] "chrUn_KI270336v1"        "chrUn_KI270337v1"
[357] "chrUn_KI270338v1"        "chrUn_KI270340v1"
[359] "chrUn_KI270362v1"        "chrUn_KI270363v1"
[361] "chrUn_KI270364v1"        "chrUn_KI270366v1"
[363] "chrUn_KI270371v1"        "chrUn_KI270372v1"
[365] "chrUn_KI270373v1"        "chrUn_KI270374v1"
[367] "chrUn_KI270375v1"        "chrUn_KI270376v1"
[369] "chrUn_KI270378v1"        "chrUn_KI270379v1"
[371] "chrUn_KI270381v1"        "chrUn_KI270382v1"
[373] "chrUn_KI270383v1"        "chrUn_KI270384v1"
[375] "chrUn_KI270385v1"        "chrUn_KI270386v1"
[377] "chrUn_KI270387v1"        "chrUn_KI270388v1"
[379] "chrUn_KI270389v1"        "chrUn_KI270390v1"
[381] "chrUn_KI270391v1"        "chrUn_KI270392v1"
[383] "chrUn_KI270393v1"        "chrUn_KI270394v1"
[385] "chrUn_KI270395v1"        "chrUn_KI270396v1"
[387] "chrUn_KI270411v1"        "chrUn_KI270412v1"
[389] "chrUn_KI270414v1"        "chrUn_KI270417v1"
[391] "chrUn_KI270418v1"        "chrUn_KI270419v1"
[393] "chrUn_KI270420v1"        "chrUn_KI270422v1"
[395] "chrUn_KI270423v1"        "chrUn_KI270424v1"
[397] "chrUn_KI270425v1"        "chrUn_KI270429v1"
[399] "chrUn_KI270435v1"        "chrUn_KI270438v1"
[401] "chrUn_KI270442v1"        "chrUn_KI270448v1"
[403] "chrUn_KI270465v1"        "chrUn_KI270466v1"
[405] "chrUn_KI270467v1"        "chrUn_KI270468v1"
[407] "chrUn_KI270507v1"        "chrUn_KI270508v1"
[409] "chrUn_KI270509v1"        "chrUn_KI270510v1"
[411] "chrUn_KI270511v1"        "chrUn_KI270512v1"
[413] "chrUn_KI270515v1"        "chrUn_KI270516v1"
[415] "chrUn_KI270517v1"        "chrUn_KI270518v1"
[417] "chrUn_KI270519v1"        "chrUn_KI270521v1"
[419] "chrUn_KI270522v1"        "chrUn_KI270528v1"
[421] "chrUn_KI270529v1"        "chrUn_KI270530v1"
[423] "chrUn_KI270538v1"        "chrUn_KI270539v1"
[425] "chrUn_KI270544v1"        "chrUn_KI270548v1"
[427] "chrUn_KI270579v1"        "chrUn_KI270580v1"
[429] "chrUn_KI270581v1"        "chrUn_KI270582v1"
[431] "chrUn_KI270583v1"        "chrUn_KI270584v1"
[433] "chrUn_KI270587v1"        "chrUn_KI270588v1"
[435] "chrUn_KI270589v1"        "chrUn_KI270590v1"
[437] "chrUn_KI270591v1"        "chrUn_KI270593v1"
[439] "chrUn_KI270741v1"        "chrUn_KI270742v1"
[441] "chrUn_KI270743v1"        "chrUn_KI270744v1"
[443] "chrUn_KI270745v1"        "chrUn_KI270746v1"
[445] "chrUn_KI270747v1"        "chrUn_KI270748v1"
[447] "chrUn_KI270749v1"        "chrUn_KI270750v1"
[449] "chrUn_KI270751v1"        "chrUn_KI270752v1"
[451] "chrUn_KI270753v1"        "chrUn_KI270754v1"
[453] "chrUn_KI270755v1"        "chrUn_KI270756v1"
[455] "chrUn_KI270757v1"        "chr1_KN196472v1_fix"
[457] "chr1_KN196473v1_fix"     "chr1_KN196474v1_fix"
[459] "chr1_KN538360v1_fix"     "chr1_KN538361v1_fix"
[461] "chr1_KQ031383v1_fix"     "chr1_KZ208906v1_fix"
[463] "chr1_KZ559100v1_fix"     "chr1_MU273333v1_fix"
[465] "chr1_MU273334v1_fix"     "chr1_MU273335v1_fix"
[467] "chr1_MU273336v1_fix"     "chr2_KN538362v1_fix"
[469] "chr2_KN538363v1_fix"     "chr2_KQ031384v1_fix"
[471] "chr2_ML143341v1_fix"     "chr2_ML143342v1_fix"
[473] "chr2_MU273341v1_fix"     "chr2_MU273342v1_fix"
[475] "chr2_MU273343v1_fix"     "chr2_MU273344v1_fix"
[477] "chr2_MU273345v1_fix"     "chr3_KN196475v1_fix"
[479] "chr3_KN196476v1_fix"     "chr3_KN538364v1_fix"
[481] "chr3_KQ031385v1_fix"     "chr3_KQ031386v1_fix"
[483] "chr3_KV766192v1_fix"     "chr3_KZ559104v1_fix"
[485] "chr3_MU273346v1_fix"     "chr3_MU273347v1_fix"
[487] "chr3_MU273348v1_fix"     "chr4_KQ983257v1_fix"
[489] "chr4_ML143344v1_fix"     "chr4_ML143345v1_fix"
[491] "chr4_ML143346v1_fix"     "chr4_ML143347v1_fix"
[493] "chr4_ML143348v1_fix"     "chr4_ML143349v1_fix"
[495] "chr4_MU273350v1_fix"     "chr4_MU273351v1_fix"
[497] "chr5_KV575244v1_fix"     "chr5_ML143350v1_fix"
[499] "chr5_MU273352v1_fix"     "chr5_MU273353v1_fix"
[501] "chr5_MU273354v1_fix"     "chr5_MU273355v1_fix"
[503] "chr6_KN196478v1_fix"     "chr6_KQ031387v1_fix"
[505] "chr6_KQ090016v1_fix"     "chr6_KV766194v1_fix"
[507] "chr6_KZ208911v1_fix"     "chr6_ML143351v1_fix"
[509] "chr7_KQ031388v1_fix"     "chr7_KV880764v1_fix"
[511] "chr7_KV880765v1_fix"     "chr7_KZ208912v1_fix"
[513] "chr7_ML143352v1_fix"     "chr8_KV880766v1_fix"
[515] "chr8_KV880767v1_fix"     "chr8_KZ208914v1_fix"
[517] "chr8_KZ208915v1_fix"     "chr8_MU273359v1_fix"
[519] "chr8_MU273360v1_fix"     "chr8_MU273361v1_fix"
[521] "chr8_MU273362v1_fix"     "chr8_MU273363v1_fix"
[523] "chr9_KN196479v1_fix"     "chr9_ML143353v1_fix"
[525] "chr9_MU273364v1_fix"     "chr9_MU273365v1_fix"
[527] "chr9_MU273366v1_fix"     "chr10_KN196480v1_fix"
[529] "chr10_KN538365v1_fix"    "chr10_KN538366v1_fix"
[531] "chr10_KN538367v1_fix"    "chr10_KQ090021v1_fix"
[533] "chr10_ML143354v1_fix"    "chr10_ML143355v1_fix"
[535] "chr10_MU273367v1_fix"    "chr11_KN196481v1_fix"
[537] "chr11_KQ090022v1_fix"    "chr11_KQ759759v1_fix"
[539] "chr11_KQ759759v2_fix"    "chr11_KV766195v1_fix"
[541] "chr11_KZ559108v1_fix"    "chr11_KZ559109v1_fix"
[543] "chr11_ML143356v1_fix"    "chr11_ML143357v1_fix"
[545] "chr11_ML143358v1_fix"    "chr11_ML143359v1_fix"
[547] "chr11_ML143360v1_fix"    "chr11_MU273369v1_fix"
[549] "chr11_MU273370v1_fix"    "chr11_MU273371v1_fix"
[551] "chr12_KN196482v1_fix"    "chr12_KN538369v1_fix"
[553] "chr12_KN538370v1_fix"    "chr12_KQ759760v1_fix"
[555] "chr12_KZ208916v1_fix"    "chr12_KZ208917v1_fix"
[557] "chr12_ML143361v1_fix"    "chr12_ML143362v1_fix"
[559] "chr12_MU273372v1_fix"    "chr13_KN196483v1_fix"
[561] "chr13_KN538371v1_fix"    "chr13_KN538372v1_fix"
[563] "chr13_KN538373v1_fix"    "chr13_ML143363v1_fix"
[565] "chr13_ML143364v1_fix"    "chr13_ML143365v1_fix"
[567] "chr13_ML143366v1_fix"    "chr14_KZ208920v1_fix"
[569] "chr14_ML143367v1_fix"    "chr14_MU273373v1_fix"
[571] "chr15_KN538374v1_fix"    "chr15_ML143369v1_fix"
[573] "chr15_ML143370v1_fix"    "chr15_ML143371v1_fix"
[575] "chr15_ML143372v1_fix"    "chr15_MU273374v1_fix"
[577] "chr16_KV880768v1_fix"    "chr16_KZ559113v1_fix"
[579] "chr16_ML143373v1_fix"    "chr16_MU273376v1_fix"
[581] "chr16_MU273377v1_fix"    "chr17_KV575245v1_fix"
[583] "chr17_KV766196v1_fix"    "chr17_ML143374v1_fix"
[585] "chr17_ML143375v1_fix"    "chr17_MU273379v1_fix"
[587] "chr17_MU273380v1_fix"    "chr17_MU273381v1_fix"
[589] "chr17_MU273382v1_fix"    "chr17_MU273383v1_fix"
[591] "chr18_KQ090028v1_fix"    "chr18_KZ208922v1_fix"
[593] "chr18_KZ559115v1_fix"    "chr19_KN196484v1_fix"
[595] "chr19_KQ458386v1_fix"    "chr19_ML143376v1_fix"
[597] "chr19_MU273384v1_fix"    "chr19_MU273385v1_fix"
[599] "chr19_MU273386v1_fix"    "chr20_MU273388v1_fix"
[601] "chr20_MU273389v1_fix"    "chr21_ML143377v1_fix"
[603] "chr21_MU273390v1_fix"    "chr21_MU273391v1_fix"
[605] "chr21_MU273392v1_fix"    "chr22_KQ759762v1_fix"
[607] "chr22_KQ759762v2_fix"    "chr22_ML143378v1_fix"
[609] "chr22_ML143379v1_fix"    "chr22_ML143380v1_fix"
[611] "chrX_ML143381v1_fix"     "chrX_ML143382v1_fix"
[613] "chrX_ML143383v1_fix"     "chrX_ML143384v1_fix"
[615] "chrX_ML143385v1_fix"     "chrX_MU273393v1_fix"
[617] "chrX_MU273394v1_fix"     "chrY_KN196487v1_fix"
[619] "chrY_KZ208923v1_fix"     "chrY_KZ208924v1_fix"
[621] "chrY_MU273398v1_fix"     "chr1_KQ458382v1_alt"
[623] "chr1_KQ458383v1_alt"     "chr1_KQ458384v1_alt"
[625] "chr1_KQ983255v1_alt"     "chr1_KV880763v1_alt"
[627] "chr1_KZ208904v1_alt"     "chr1_KZ208905v1_alt"
[629] "chr1_MU273330v1_alt"     "chr1_MU273331v1_alt"
[631] "chr1_MU273332v1_alt"     "chr2_KQ983256v1_alt"
[633] "chr2_KZ208907v1_alt"     "chr2_KZ208908v1_alt"
[635] "chr2_MU273337v1_alt"     "chr2_MU273338v1_alt"
[637] "chr2_MU273339v1_alt"     "chr2_MU273340v1_alt"
[639] "chr3_KZ208909v1_alt"     "chr3_KZ559101v1_alt"
[641] "chr3_KZ559102v1_alt"     "chr3_KZ559103v1_alt"
[643] "chr3_KZ559105v1_alt"     "chr3_ML143343v1_alt"
[645] "chr4_KQ090013v1_alt"     "chr4_KQ090014v1_alt"
[647] "chr4_KQ090015v1_alt"     "chr4_KQ983258v1_alt"
[649] "chr4_KV766193v1_alt"     "chr4_MU273349v1_alt"
[651] "chr5_KN196477v1_alt"     "chr5_KV575243v1_alt"
[653] "chr5_KZ208910v1_alt"     "chr5_MU273356v1_alt"
[655] "chr6_KQ090017v1_alt"     "chr6_MU273357v1_alt"
[657] "chr7_KZ208913v1_alt"     "chr7_KZ559106v1_alt"
[659] "chr7_MU273358v1_alt"     "chr8_KZ559107v1_alt"
[661] "chr9_KQ090018v1_alt"     "chr9_KQ090019v1_alt"
[663] "chr10_KQ090020v1_alt"    "chr11_KN538368v1_alt"
[665] "chr11_KZ559110v1_alt"    "chr11_KZ559111v1_alt"
[667] "chr11_MU273368v1_alt"    "chr12_KQ090023v1_alt"
[669] "chr12_KZ208918v1_alt"    "chr12_KZ559112v1_alt"
[671] "chr13_KQ090024v1_alt"    "chr13_KQ090025v1_alt"
[673] "chr14_KZ208919v1_alt"    "chr14_ML143368v1_alt"
[675] "chr15_KQ031389v1_alt"    "chr15_MU273375v1_alt"
[677] "chr16_KQ031390v1_alt"    "chr16_KQ090026v1_alt"
[679] "chr16_KQ090027v1_alt"    "chr16_KZ208921v1_alt"
[681] "chr17_KV766197v1_alt"    "chr17_KV766198v1_alt"
[683] "chr17_KZ559114v1_alt"    "chr17_MU273378v1_alt"
[685] "chr18_KQ458385v1_alt"    "chr18_KZ559116v1_alt"
[687] "chr19_KV575246v1_alt"    "chr19_KV575247v1_alt"
[689] "chr19_KV575248v1_alt"    "chr19_KV575249v1_alt"
[691] "chr19_KV575250v1_alt"    "chr19_KV575251v1_alt"
[693] "chr19_KV575252v1_alt"    "chr19_KV575253v1_alt"
[695] "chr19_KV575254v1_alt"    "chr19_KV575255v1_alt"
[697] "chr19_KV575256v1_alt"    "chr19_KV575257v1_alt"
[699] "chr19_KV575258v1_alt"    "chr19_KV575259v1_alt"
[701] "chr19_KV575260v1_alt"    "chr19_MU273387v1_alt"
[703] "chr22_KN196485v1_alt"    "chr22_KN196486v1_alt"
[705] "chr22_KQ458387v1_alt"    "chr22_KQ458388v1_alt"
[707] "chr22_KQ759761v1_alt"    "chrX_KV766199v1_alt"
[709] "chrX_MU273395v1_alt"     "chrX_MU273396v1_alt"
[711] "chrX_MU273397v1_alt"

Similarly, the function seqinfo() can be used to get the full sequence information stored in the object.

R

seqinfo(genome)

OUTPUT

Seqinfo object with 711 sequences (1 circular) from hg38 genome:
  seqnames             seqlengths isCircular genome
  chr1                  248956422      FALSE   hg38
  chr2                  242193529      FALSE   hg38
  chr3                  198295559      FALSE   hg38
  chr4                  190214555      FALSE   hg38
  chr5                  181538259      FALSE   hg38
  ...                         ...        ...    ...
  chr22_KQ759761v1_alt     145162      FALSE   hg38
  chrX_KV766199v1_alt      188004      FALSE   hg38
  chrX_MU273395v1_alt      619716      FALSE   hg38
  chrX_MU273396v1_alt      294119      FALSE   hg38
  chrX_MU273397v1_alt      330493      FALSE   hg38

Finally, the nature of BSgenome objects being akin to a list of sequences, the operators $ and [[]] can both be used to extract individual sequences from the BSgenome object.

R

genome$chr1

OUTPUT

248956422-letter MaskedDNAString object (# for masking)
seq: ####################################...####################################
masks:
  maskedwidth  maskedratio active names                               desc
1    18470101 7.419010e-02   TRUE AGAPS                      assembly gaps
2        5309 2.132502e-05   TRUE   AMB           intra-contig ambiguities
3   119060341 4.782377e-01  FALSE    RM                       RepeatMasker
4     1647959 6.619468e-03  FALSE   TRF Tandem Repeats Finder [period<=12]
all masks together:
  maskedwidth maskedratio
    137685771   0.5530517
all active masks together:
  maskedwidth maskedratio
     18475410  0.07421142

For instance, we can extract the sequence of the Y chromosome and assign it to a new object chrY.

R

chrY <- genome[["chrY"]]

Using genome sequences

From this point, genome sequences can be treated very much like biological strings (e.g. DNAString) described earlier, in the Biostrings package.

For instance, the function countPattern() can be used to count the number of occurences of a given pattern in a given genome sequence.

R

countPattern(pattern = "CANNTG", subject = chrY, fixed = FALSE)

OUTPUT

[1] 141609

Note

In the example above, the argument fixed = FALSE is used to indicate that the pattern contain IUPAC ambiguity codes.

Key Points

The Biostrings package defines classes to represent sequences of nucleotides and amino acids.
The Biostrings package also defines methods to efficiently process biological sequences.
The BSgenome package provides genome sequences for a range of model organisms immediately available as Bioconductor objects.