Loading in Genome Data with DECIPHER

Reading in Sequencing Data

In order to work with sequencing data, we first have to get it into R in a format that allows us to work with it. The most commonly used formats for genomic data are the XString and XStringSet classes, which are available through the Biostrings package.

XStrings come in four distinct flavors, depending on the characters allowed:

DNAString, for DNA data (ATGC, plus gaps and ambiguity codes)
RNAString, for RNA data (AUGC, plus gaps and ambiguity codes)
AAString, for amino acid data (20 amino acids plus U, O, ambiguity codes, and unknown/gaps)
BString, for any combination of any letters

When XString objects of the same type are combined, they become an XStringSet. This provides an easy way to store and work with genomics data. Below is an example of manually creating an XStringSet:

library(DECIPHER) # Auto-imports Biostrings

# Making some toy sequences
sequences <- c('AGACTCGCA',
               'AGACGGTCA',
               'TCATTAGTT',
               'TGCACAAAA',
               'AGCTGTTGC')

sequenceSet <- DNAStringSet(sequences)
sequenceSet

## DNAStringSet object of length 5:
##     width seq
## [1]     9 AGACTCGCA
## [2]     9 AGACGGTCA
## [3]     9 TCATTAGTT
## [4]     9 TGCACAAAA
## [5]     9 AGCTGTTGC

We can also translate DNA sequences to their amino acid counterparts with translate().

translate(sequenceSet)

## AAStringSet object of length 5:
##     width seq
## [1]     3 RLA
## [2]     3 RRS
## [3]     3 SLV
## [4]     3 CTK
## [5]     3 SCC

Manually typing in sequences obviously isn’t a great system. Modern sequencing data are typically in .fasta or .fastq file format, so let’s look at a more realistic use-case that reads in a data from a .fasta. We’re going to be using an example from Micrococcus genomes obtained from NCBI GenBank.

For purposes of the workshop, I’ve reduced the dataset to a single Micrococcus gene. This expedites examples to allow for quick processing. These sequences have been packaged into a single .FASTA file (compressed into a .fa.gz file), which you can download with the below button. Each gene is uniquely identified with a three number code detailed on the Finding COGs page.

# MicroFASTAtrimmed.fa.gz is loaded from the above button
# also available at extdata/LoadingData/ within this package

exampleSeqs <- '/path/to/MicroFASTAtrimmed.fa.gz'

exStringSet <- readDNAStringSet(exampleSeqs, format="fasta")
# we could also use format='fastq' for FASTQ datasets

exStringSet

## DNAStringSet object of length 20:
##      width seq                                              names               
##  [1]   756 TCATCCTGCCAGCGCGGCGCGGG...CAATCGCTGAGGTGTTCGCCAT 13_8_653
##  [2]   756 ATGGCGAACACCTCAGCGATTGA...CCGCGCCGCGCTGGCAGGATGA 18_1_1772
##  [3]   756 TCATCCTGCCAGCGCGGCGCGGG...CAATCGCTGAGGTGTTCGCCAT 27_52_1555
##  [4]   756 ATGGCGAACACCTCAGCGATTGA...CCGCGCCGCGCTGGCAGGATGA 29_16_724
##  [5]   756 TCATCCTGCCAGCGCGGCGCGGG...CAATCGCTGAGGTGTTCGCCAT 30_15_973
##  ...   ... ...
## [16]   756 ATGGCGAACACCTCAGCGATTGA...CCGCGCCGCGCTGGCAGGATGA 78_1_1914
## [17]   756 TCATCCTGCCAGCGCGGCGCGGG...CAATCGCTGAGGTGTTCGCCAT 81_48_2187
## [18]   756 TCATCCTGCCAGCGCGGCGCGGG...CAATCGCTGAGGTGTTCGCCAT 82_2_146
## [19]   756 ATGGCGAACACCTCAGCGATTGA...CCGCGCCGCGCTGGCAGGATGA 90_1_931
## [20]   756 ATGGCGAACACCTCAGCGATTGA...CCGCGCCGCGCTGGCAGGATGA 91_1_936

Success! Now we have a large example dataset to work with.

If we wanted to visualize these sequences, we can open them in a web browser using BrowseSeqs() from DECIPHER:

BrowseSeqs(exStringSet)

Aligning Sequences

Now that we have some sequences, let’s explore some of the ways we can manipulate them. A complete demo of Biostrings is outside the scope of this workshop, so we’ll just focus on functionality added via DECIPHER.

A common analysis in bioinformatics is aligning sequences. This is easily achievable with either the AlignSeqs() function or the AlignTranslation() functions from DECIPHER. AlignTranslation() aligns sequences based on their translated amino acid sequences, and is significantly more accurate for coding sequences (like the ones we’re working with).

# Align the sequences
aliNoTranslate <- AlignSeqs(exStringSet, verbose=FALSE)

# Aligning using translated amino acid sequences
# These sequences are coding sequences, so this function will be more accurate
aliTranslate <- AlignTranslation(exStringSet, verbose=FALSE)

# Visualize through R 
aliTranslate

## DNAStringSet object of length 20:
##      width seq                                              names               
##  [1]   954 -----------------------...---------------------- 13_8_653
##  [2]   954 ATGGCGAACACCTCAGCGATTGA...CCGCGCCGCGCTGGCAGGATGA 18_1_1772
##  [3]   954 -----------------------...---------------------- 27_52_1555
##  [4]   954 ATGGCGAACACCTCAGCGATTGA...CCGCGCCGCGCTGGCAGGATGA 29_16_724
##  [5]   954 -----------------------...---------------------- 30_15_973
##  ...   ... ...
## [16]   954 ATGGCGAACACCTCAGCGATTGA...CCGCGCCGCGCTGGCAGGATGA 78_1_1914
## [17]   954 -----------------------...---------------------- 81_48_2187
## [18]   954 -----------------------...---------------------- 82_2_146
## [19]   954 ATGGCGAACACCTCAGCGATTGA...CCGCGCCGCGCTGGCAGGATGA 90_1_931
## [20]   954 ATGGCGAACACCTCAGCGATTGA...CCGCGCCGCGCTGGCAGGATGA 91_1_936

Alignments tend to start with a lot of gaps, and as a result viewing them through R isn’t always super informative. As before, we can visualize this alignment in a much better way using BrowseSeqs():

BrowseSeqs(aliTranslate)

The output should resemble the following:

Big Data with DECIPHER

One of DECIPHER’s unique features is the ability to work with massive amounts of data. DECIPHER incorporates a rich API for working with sequencing data stored in SQLite files. These allow users to work with enormous amounts of data (ex. hundreds of thousands of sequences) while providing the following benefits:

Space optimization
Fast random access
Concurrent use from multiple queries/users
Reliable and cross-platform storage

For more information on all the benefits of using databases with DECIPHER, check out the DECIPHER publication.

Setting up a DECIPHER database takes a few extra lines of code, but it’s worth it! Let’s start by creating a connection to a SQLite database.

# You can set this to any path, use a non-tempfile for long term storage
DBPATH <- tempfile()
dbConn <- dbConnect(SQLite(), DBPATH)

Now we have a connection to a database! I used tempfile() for a temporary file, but you could have also added a filepath to a non-temporary file. Note that if the file does not exist, it will be automatically created.

Now it’s time to start populating our database with sequences! This is done with Seqs2DB() with the following syntax:

# All sequences must be identified with a unique quantifier
# This has to be a character vector; we can make it 1:length(exStringSet)
identifiers = as.character(seq_along(exStringSet))
Seqs2DB(seqs=exStringSet, type='XStringSet', 
        dbFile=dbConn, identifier=identifiers)

## Adding 20 sequences to the database.
## 
## 20 total sequences in table Seqs.
## Time difference of 0.11 secs

We can also write sequences directly from a FASTA file to our database. In this code, we’re using replaceTbl=TRUE so that we replace our table with the sequences (rather than add the same set twice).

# We can also directly import from a FASTA file 
# FASTA files 
Seqs2DB(seqs=exampleSeqs, type='FASTA', 
        dbFile=dbConn, identifier=identifiers,
        replaceTbl=TRUE)

## 
Reading FASTA file chunk 1
## 
## 20 total sequences in table Seqs.
## Time difference of 0.06 secs

Now that we have our database, what do we do with it?

To start, we can run BrowseDB(), a function analogous to BrowseSeqs() but for DECIPHER databases. Instead of showing sequences, this visualization shows entries in the database that can be queried.

BrowseDB(dbConn)

If we want to read the sequences back into R as an XStringSet, we use SearchDB().

dbStringSet <- SearchDB(dbConn)

## Search Expression:
## select row_names, sequence from _Seqs where row_names in (select row_names
## from Seqs)
## 
## DNAStringSet of length: 20
## Time difference of 0.01 secs

dbStringSet

## DNAStringSet object of length 20:
##      width seq                                              names               
##  [1]   756 TCATCCTGCCAGCGCGGCGCGGG...CAATCGCTGAGGTGTTCGCCAT 1
##  [2]   756 ATGGCGAACACCTCAGCGATTGA...CCGCGCCGCGCTGGCAGGATGA 2
##  [3]   756 TCATCCTGCCAGCGCGGCGCGGG...CAATCGCTGAGGTGTTCGCCAT 3
##  [4]   756 ATGGCGAACACCTCAGCGATTGA...CCGCGCCGCGCTGGCAGGATGA 4
##  [5]   756 TCATCCTGCCAGCGCGGCGCGGG...CAATCGCTGAGGTGTTCGCCAT 5
##  ...   ... ...
## [16]   756 ATGGCGAACACCTCAGCGATTGA...CCGCGCCGCGCTGGCAGGATGA 16
## [17]   756 TCATCCTGCCAGCGCGGCGCGGG...CAATCGCTGAGGTGTTCGCCAT 17
## [18]   756 TCATCCTGCCAGCGCGGCGCGGG...CAATCGCTGAGGTGTTCGCCAT 18
## [19]   756 ATGGCGAACACCTCAGCGATTGA...CCGCGCCGCGCTGGCAGGATGA 19
## [20]   756 ATGGCGAACACCTCAGCGATTGA...CCGCGCCGCGCTGGCAGGATGA 20

SearchDB() incorporates far more options than just returning all the sequences in the database. If we had a database of a million sequences, we would likely crash our R environment trying to load them all in. Instead, we can use the following options:

# Limit amount returned to first 10
SearchDB(dbConn, limit=10)

# Look for a specific identifier
# BrowseDB() is useful here for determining what the identifiers are
SearchDB(dbConn, identifier = 'example identifier')

# Run SQL queries
#     This clause is appended to the end of a `WHERE ... ` call
SearchDB(dbConn, clause = 'identifier in ("1", "2", "3")')

Once we’re done working with the database, it’s important to disconnect any open SQLite connections.

dbDisconnect(dbConn)

Many DECIPHER functions use databases rather than XStringSet objects for speed and efficiency. As an example, let’s look at the FindSynteny() function.

FindSynteny() allows us to look at syntenic hits between sequences without aligning them. The result allows us to easily visualize differences between two sequences without the computational cost of sequence alignment. Let’s look at an example of finding syntenic hits between the first 6 sequences in our set.

to_select <- c(1,3,5,8)
synData <- exStringSet[to_select]

names(synData) <- as.character(to_select)

# Initialize our database
DBPATH <- tempfile()
dbConn <- dbConnect(SQLite(), DBPATH)
Seqs2DB(synData, "XStringSet", dbConn, identifier=as.character(to_select))

syn <- FindSynteny(dbConn)
dbDisconnect(dbConn)

pairs(syn)

What are we looking at with this plot? Each plot compares two genomes, and each point is a syntenic hit between the two genomes. The X position of the point is its location on the first genome, and the Y position is its position on the second genome. If we had identical genomes, we would expect to see a diagonal line \(y=x\), indicating that all bases occur at the same place in both genomes. Gaps indicate areas that do not match, and points off the diagonal indicate matching areas in different places on each genomes. These particular examples are highly syntenic, so they all resemble the line \(y=x\).

We’ll see a more in-depth example of using Synteny objects returned by FindSynteny() later on.

Runtime Considerations

Loading genomes is fairly fast; each sequence takes on the order of 1-2 seconds. The runtime of these operations are rarely significant in the overall runtime of the entire pipeline.

When implementing at scale on a compute cluster, it may be more efficient to use NCBI’s Entrez Search to download genomes on the fly; this adds some runtime overhead but improves memory efficiency on individual compute nodes.

Conclusion

Now we’ve managed to read in sequence data and perform some basic analyses. With this knowledge, we can proceed to the next step of the pipeline: finding and annotating genes.

Aidan Lakshman1

Reading in Sequencing Data

Aligning Sequences

Big Data with DECIPHER

Runtime Considerations

Conclusion

Aidan Lakshman¹