While next generation sequencing technology can produce sequences covering the entire genome, assembly and annotation are still prohibitive steps for many phylogenomics applications.  Here we describe a method of Target Restricted Assembly (TRAM) of a single lane of Illumina sequences for genes of relevance to phylogeny reconstruction, i.e. single copy protein-coding genes.  This method has the potential to produce a data set of hundreds of genes using only one Illumina lane per taxon

Hugh M. Robertson

Kevin P. Johnson

Kimberly K. O. Walden

English

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A Target Restricted Assembly Method (TRAM) for Phylogenomics 
Kevin P. Johnson1,*, Kimberly K. O. Walden2, and Hugh M. Robertson2 
1Illinois Natural History Survey, University of Illinois, 1816 S. Oak Street, Champaign, IL  68120  USA 
2Department of Entomology, University of Illinois, 505 S. Goodwin Ave., Urbana, IL  61801  USA 
 
 
ABSTRACT 
While next generation sequencing technology can produce 
sequences covering the entire genome, assembly and 
annotation are still prohibitive steps for many 
phylogenomics applications.  Here we describe a method 
of Target Restricted Assembly (TRAM) of a single lane of 
Illumina sequences for genes of relevance to phylogeny 
reconstruction, i.e. single copy protein-coding genes.  This 
method has the potential to produce a data set of 
hundreds of genes using only one Illumina lane per taxon.  
INTRODUCTION  
Next generation sequencing technologies are 
revolutionizing the field of genomics.  New DNA 
sequencing methods such as 454 and Illumina 
pyrosequencing can produce billions of base pairs of 
DNA sequence in a highly automated fashion for a 
fraction of the costs of traditional targeted Sanger 
sequencing (Hudson, 2008). For many phylogenetic 
questions, targeted sequencing of one or a few genes has 
proved insufficient to provide phylogenetic resolution 
with good support.  Even "phylogenomic" studies that 
have included a large number of genes sequenced using 
PCR targeted sequencing have not resolved trees with 
good branch support at all nodes (Hackett et al., 2008; 
Regier et al., 2010).  For difficult phylogenetic problems, 
DNA sequence datasets with many additional genes 
would be desirable in such cases, but the costs and labor 
of traditional targeted sequencing become prohibitive. 
Completely assembled genome sequences are also not 
a panacea for solving difficult phylogenetic problems.  
Genome assembly and annotation is still a time and 
labor-intensive process.  For most phylogenetic 
problems, complete genome sequences will not be 
necessary to provide well supported and resolved 
phylogenetic trees.  One restricted genome approach used 
with success recently is to sequence expressed sequence 
tags (ESTs), which are cDNA copies of mRNA 
transcripts (Philippe and Telford, 2006).  This restricts 
the proportion of the genome examined to the 
"transcriptome", which represents only the protein coding 
portions of the genome, and may potentially be more 
ideal for phylogenetic analysis. However, generation of 
very large EST datasets requires a large quantity of 
mRNA, which typically must be extracted from specially 
preserved or frozen very fresh starting material.  For 
uncommon or small-bodied organisms, this might be 
prohibitive. 
                                                           
*To whom correspondence should be addressed.  
e-mail: kjohnson@inhs.uiuc.edu 
Here we extend and generalize a hybrid technique to 
identify protein-coding targets of interest in DNA 
sequences generated from next-generation sequencing 
technologies, a Target Restricted Assembly Method 
(TRAM).  A version of this method, using human 
promoter regions as targets for mammalian low coverage 
sequence reads, was previously developed by Bainbridge 
et al. (2007).  Using sequence reads from single lanes of 
Illumina sequencing runs, we first show that using 
targeted tblastn searches we can recover protein-coding 
sequence reads of interest from a single Illumina lane.  
These sequences can then be assembled locally, to 
provide a longer sequence (contig) of the gene of interest.  
By targeting genes that have been shown to be 1:1 
orthologs across genomes for a relevant group 
(Kriventseva et al., 2008), a dataset of a very large 
number (>500) of single copy nuclear protein coding 
genes can be produced for a fraction of the cost of 
producing such a dataset using targeted sequencing 
techniques.  In addition, for most phylogenetic studies 
that might make use of this method, nuclear protein 
coding genes are likely to be the ideal genes to resolve 
phylogenetic relationships (i.e. species level and higher).  
Here we first outline the principles and procedures 
involved and then illustrate the method when 1) the target 
sequence is known and 2) the target sequence is 
unknown. 
METHODS AND RESULTS 
The Target Restricted Assembly Method 
The Target Restricted Assembly Method (TRAM) 
consists of a series of steps to identify and assemble 
sequences of genes of interest from raw sequence reads 
generated by next generation sequencing technologies.  
These steps may require modification for particular 
applications, but outline the basic procedure. 
 
Compile sequences of genes/proteins of interest from a 
species closely related to focal species.  For most 
applications, the sequences of the target genes in the 
focal species will be unknown.  Thus, it will be necessary 
to use sequences of such genes from closely related 
species.  Sequence similarity between the two species 
will determine, in part, the success rate of this method 
(see Examples below).  For protein coding genes, the 
amino acid sequences will generally be preferred to DNA 
sequences because they are more conserved.  For most 
higher level phylogenetic problems, nuclear protein 
coding genes are the preferred data source because of 
their relatively high level of sequence conservation 
relative to non-coding regions and the relative ease with 
which they can be aligned.  These sequences could either 
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be sequences from EST data sets or genome sequences in 
which protein coding genes have been annotated. 
 
Use the target sequence(s) in a BLAST search of the raw 
read data to identify matches.  This step can be done via 
local blast of Illumina data consisting of the raw 
unassembled reads.  For protein coding sequences a 
tblastn search of the raw reads will be appropriate.  These 
BLAST searches should identify potential matches to the 
target sequence among the raw reads.  
 
Assemble the potential matches using relevant sequence 
assembly software.  Once the list of matching reads from 
the BLAST search is obtained, these reads should be 
retrieved and imported into a relevant sequence assembly 
software.  For the purposes of our examples (below), we 
retrieved the reads as a FASTA file and imported these 
sequences into Sequencher (GeneCodes), which we used 
to perform the assemblies.  For protein coding sequences, 
typically this will result in subassemblies for each exon.  
 
Examples 
Target Sequence Known.  The DNA sequence for the 
odorant receptor 1 (Or1) gene (1440 bp) for the 
Argentine ant (Linepithema humile) has been previously 
determined (Robertson, unpublished data).  We use the 
inferred protein sequences for this species, another ant 
(Pogonomyrmex barbatus), and the honey bee (Apis 
melifera) as tblastn queries against a single lane of 
Illumina reads from this Argentine ant.  These reads 
averaged around 75 bp and approximately 20 million 
reads were available as blast targets.  We assembled the 
all the blast matches into contigs using Sequencher with 
default settings.  These local assemblies were then 
assembled against the actual DNA sequence for the gene, 
which included both exons and introns. 
 In general, we recovered at least partial 
coverage for all eight exons independent of the 
divergence of the starting sequence: Linepithema (94% 
coverage), Pogonomyrmex (90%), Apis (90%).  However, 
the number of reads recovered as tblastn matches were 
higher when more closely related species were used as 
the query target: 111 (Linepithema), 108 
(Pogonomyrmex), and 89 (Apis) reads.  Only 3 to 5 sites 
from the consensus assembly were a mismatch from the 
known sequence, and we cannot rule out native allelic 
variation in this case, given the relatively low coverage. 
 
Target Sequence Unknown.  Our next example involves 
Target Restricted Assembly in a more realistic 
application, where the target sequence is unknown in the 
species of interest (in this case the bumble bee, Bombus 
impatiens).  Here we used a single lane of an Illumina 
sequencing run, with new technology where the average 
read length was around 125bp and which produced 
approximately 35 million reads.  As targets we used 
genes that were strict 1:1 orthologs across all insects and 
crustacean genomes as identified in the Ortholog 
Database (http://cegg.unige.ch/orthodb).  This database 
contains 898 such genes, so for illustrative purposes we 
used the first 10 genes recovered from the database.
  
Protein sequences of these genes from the honey bee 
(Apis melifera) were used as queries in tblastn searches 
against the Bombus impatiens Illumina raw reads.  All the 
reads that were tblastn matches for each protein query 
were then assembled in Sequencher with default 
parameters.  The length of these assemblies and number 
of contigs was highly variable, depending on the 
existence and size of introns.  In general coverage ranged 
from 5-20X.  In several cases, even though only the 
amino acid (i.e. exon) sequence was used as the query we 
were able to assemble these reads completely through 
intron sequences (see Table 1). 
To assemble these sequences into complete protein 
coding DNA sequences, we downloaded the DNA 
sequences for Apis melifera for the 1:1 ortholog proteins 
from BeeBase (http://genomes.arc.georgetown.edu/drupal 
/beebase).  We assembled the consensus sequence from 
each contig in the initial Bombus assemblies in 
Sequencher with the complete protein coding DNA 
sequence from Apis.  Because the Bombus assemblies 
included at least partial intron sequences, we set the 
parameters to allow for large gaps and set the minimum 
match to 70%.  We further aligned these sequences by 
removing unnecessary gaps, adjusting intron positions 
and removing overlap between contigs in non-
homologous intron sequence (i.e. when the intron 
sequence was only partially represented on each end).  In 
nearly all cases complete protein coding sequences were 
recovered from these assemblies (see Table 1).  
Reciprocal best blastn of these sequences against 
sequences in BeeBase recovered the DNA sequence from 
the original query protein in all 10 cases.  Unlike the 
example with ants, some contigs were not included in the 
final assembly and it is likely that these represent other 
genes that are low level blast matches.  Further 
optimization of BLAST cutoffs might be necessary, so 
that such sequences are not recovered.  However, we 
found that these null matches do not interfere with the 
final assembly because they do not contain a high match 
for the DNA sequences themselves, and the entire protein 
coding DNA sequences were already represented in other 
contigs. 
Table 1. Results of Target Restricted Assemblies for Bombus 
Genes  
Gene bp in Apis # exons # contigs protein coverage 
GB15017 1932 10 3 97% 
GB11672 2043 11 3 100% 
GB15983 1575 10 3 100% 
GB13777 1062 6 2 100% 
GB12202 1695 8 7 97% 
GB19115 3930 19 12 98% 
GB13857 891 5 4 100% 
GB11458 1113 1 1 100% 
GB17705 2316 1 1 100% 
GB16928 1485 10 2 100% 
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DISCUSSION 
We have shown complete or nearly complete protein 
coding DNA sequences can be recovered by a Target 
Restricted Assembly Method of raw sequences from a 
single Illumina lane.  This extends the procedure outlined 
for human promoter regions (Bainbridge et al. 2007) to 
protein coding sequences in insects.  Strictly orthologous 
genes, such as these, avoid the problems of paralogy 
associated with using genomic data for phylogenetics.  In 
addition, because these are nuclear protein coding genes, 
alignment is relatively straightforward, and such data can 
be directly combined with inferred protein coding 
sequences from EST data sets generated from cDNA 
sequences, reversed transcribed from mRNA.  For a 
modest scale phylogenetic project (20-30 species), the 
costs of data production would likely not be prohibitive, 
making phylogenomic data sets of hundreds of single-
copy protein-coding genes with current technology very 
feasible. 
 
Funding: KPJ was supported by NSF DEB-0612938.  
Gene Robinson provided the Bombus impatiens Illumina 
reads. 
REFERENCES 
Bainbridge,M.N. et al.  (2007)  THOR: targeted high-throughput ortholog 
reconstructor.  Bioinformatics  23, 2622-2624. 
Hackett,S.J. et al.  (2008)  A phylogenomic study of birds reveals their 
evolutionary history.  Science  320, 1763-1768. 
Hudson,M.E.  (2008)  Sequencing breakthroughs for genomic ecology 
and evolutionary biology.  Molecular Ecology Resources  8, 3-17. 
Kriventseva,E.V.  et al.  (2008)  OrthoDB: the hierarchical catalog of 
eukaryotic orthologs.  Nucleic Acids Research  36, D271-D275. 
Philippe,H. and Telford,M.J.  (2006) Large-scale sequencing and the new 
animal phylogeny.  Trends in Ecology and Evolution  21, 614-620. 
Regier,J.C. et al.  (2010)  Arthropod relationships revealed by 
phylogenomic analysis of nuclear protein-coding sequences.  Nature  
463, 1079-1083.  
 


A Target Restricted Assembly Method (TRAM) for Phylogenomics

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