Supplementary materials
Background
The Puerto Rican parrot (Amazona vittata) is the only surviving native parrot species in
the United States [1]. This endangered bird was once abundant throughout the island of
Puerto Rico, but the population declined drastically during the 19th century with the
decimation of the old growth forest habitat [2]. One of the first people to call attention to
the need for its conservation was President Theodore Roosevelt, who formed the Luquillo
Forest Reserve for the purpose of protecting the parrots’ nesting grounds [1]. El Yunque
National Forest and a few other remote locations were spared from deforestation,
allowing a small population of parrots to survive [2-4]. At the end of the 20th century, the
species came very close to extinction: only 16 birds remained alive in 1975. The Puerto
Rican parrot has been included on the Endangered Species List since 1967, and listed as
critically endangered by the World Conservation Union since 1994. A recovery effort has
been initiated by two captive breeding programs, one at the Luquillo Aviary close to El
Yunque and another at the Rio Abajo State Forest. This effort has proven to be a success;
the captive population is steadily increasing, and several captive-bred birds have been
released into the wild.
Genome-wide consequences of the population decline and their effects on the species
recovery have never been assessed. Similarly to most birds, the A. vittata genome is
relatively small (1.58 Gb) – less than half the size of the human genome [5]. It is also
highly invariable due to the recent population bottleneck [6]. These two aspects made the
Puerto Rican parrot an ideal candidate for genome sequencing and assembly: the small
genome size allows high sequencing coverage, while the low amount of genetic variation
permits accurate alignments between sequenced fragments resulting in fewer assembly
errors.
Methods
Development of the local community involvement
The Puerto Rico National Parks Company recently started exhibiting Puerto Rican
parrots and related species at the Puerto Rico Zoo Juan A. Rivero. This exhibition will
bring attention to the conservation effort for this endangered species. In conjunction with
the comparative genomics study to be conducted at the University of Puerto Rico, the
parrot exhibition will become a valuable research and educational resource for the local
and international scientific community. The sequence of the Puerto Rican parrot will be
used as a reference to study differences between species and to find genomic regions
responsible for adaptation and survival.
Sample collection and genome sequencing
All sampling procedures were carried out as approved by the University of Puerto Rico at
Mayaguez Institutional Animal Care and Use Committee (IACUC#201109.1), and in
accordance with the guidance for the Endangered Species Act. A veterinarian designated
by the Puerto Rico Department of Natural and Environmental Resources (DNER)
collected blood from two non-reproductive captive female birds at the Rio Abajo aviary
during a routine health check procedure. Syringes were heparinized before samples were
taken. Approximately 1.5 cc of blood was extracted from each bird using a 3 cc syringe
with a 27-gauge needle. Birds were placed into their cages after the procedure and
remained under observation for the rest of the day to make sure that no harm had been
done. Samples were aliquoted and extracted multiple times (23 times for one bird and 3
times for the other) to produce a total of 2 ug of purified DNA with the QIAamp® DNA
Blood Midi kit (Qiagen, Venlo, The Netherlands) according to the manufacturer's
instructions and using the following modifications. Blood samples were diluted in 1x
PBS in a 1:10 ratio. The samples were incubated at 70ºC in the lysis buffer and protease
for 2–12 hours. After cellular lysis, DNA was precipitated with 100% ethanol and the
solution was passed twice through the Midi column. The DNA was eluded two times
with 300 µl of pre-warmed elution buffer, incubated for 10 minutes at room temperature
and then centrifuged for two minutes. After the DNA isolation, DNA purity was assessed
with a BioTek® microplate reader (BioTek, Winooski, VT, USA (A260/A280) compared
to a blank control after correcting for the baseline (A320). The remainder of the tissue
material has been refrigerated at -80°C. The concentrations and quality of DNA were reassessed using a fluorometer and the Quant-iT™ PicoGreen® ds DNA binding assay
(Invitrogen, Carlsbad, CA, USA from which four of the best DNA samples were chosen.
These samples were shipped to Axeq Technologies (Rockville, MD, USA; and Seoul,
South Korea) to be used for next generation sequencing, and the DNA concentrations
were validated once more upon arrival (Table S1). The DNA concentrations did not
deviate between different methods of measurement. One sample (Pa9a) has been finally
selected and sequenced on Illumina HiSeq 2000 platform (Illumina, San Diego, CA,
USA) with both fragment and paired-end sequencing approaches, resulting in a total of
42,479,499,706 bases (Table S2). The sequencing was initiated with the construction of
two genome libraries: a short fragment library (~300 bp inserts) for sequencing the
majority of the genome, and a long fragment library (~2.5 kb inserts) to generate
scaffolds to be used to order and assemble contigs derived from the short fragment
library. Raw Illumina HiSeq reads were processed and filtered using the Genome
Analyzer Pipeline software provided by the manufacturer set to the default parameters.
As much as 86.48% of the 309,060,168 paired-end reads, and 85.14% of the 180,079,956
mate-pair reads generated passed Illumina quality control (QC). If one read from a pair
failed the QC, the whole pair was filtered out.
Assembly
We did two different de novo assemblies: we used Ray [7] software on 256 CPUs for 8
hours for the first assembly, and SOAPdenovo [8] for the second (Table S3). Ultimately,
only the Ray assembly was used in this study, but the contigs and scaffold FASTA files
for both assemblies and the associated parameter files have been deposited to our locally
managed genome database [9] and in the GigaScience database [10].
For Assembly 1 with Ray: The Illumina paired-end and mate-pair reads were assembled
together, with the k-mer defined iteratively. Here, we cautiously selected a k-mer value of
31 to ensure that the N50 parameter was not over-optimized (i.e., over-optimizing N50s
may lead to chimeric contigs [11], as these values are indicative of the length of the
contigs, but do not yield any information on their veracity). Furthermore, to assess the
overall quality of the assembly, reads were subsequently re-mapped to the 10 largest
scaffolds in order to detect regions harboring unusually high/low coverage, and potential
assembly errors were manually reviewed. Of these, only one (scaffold 74754) contained a
single chimera (Figure S2), while the other nine did not (i.e., 1 error per 1,930,389 bp).
To test for the possibility of bacterial contamination, we queried all scaffolds against the
entire GenBank nucleotide database [12], and filtered matches with more than 95%
identity. Most of the scaffolds did not match any of the fragments in the database (87%).
The rest of these seem to belong to sequences from either avian (6%) or mammalian (5%)
DNA (Figure S3 and Table S6A). This indicates that contamination, if it exists, is most
likely minimal. The database resulting from this query contains highly conserved
elements and can be used for the subsequent annotation effort (Table S6B). In total, given
that the genome size is predicted to be 1.58 Gb, with the total scaffold length of 1,184,
594,388 bp, we infer the overall coverage of the genome to be around 76%, a value that
might be slightly overestimated given that some of the scaffolds may be overlapping but
could not be assembled.
Sequence data was manipulated with custom Python scripts, and with the MUSCLE
algorithm [13,14] within Geneious [15] that has been used for the local alignment. Local
BLAST and BLAT were used for the similarity search. All statistical analyses in this
study were performed with SAS 9.2 (1996-2012) software (SAS Institute, Cary NC,
USA).
Annotation
To evaluate the current assembly, we compared the entire collection of transcripts listed
for G. gallus in the NCBI Entrez Gene database using local BLAST [16]. Among the
28,846 queries, 20,138 (70%) have been found on 245,947 scaffolds, resulting in 27,431
matches (Figure S1 and Table 3). As much as 11% percent of scaffolds shared similarity
with at least one G. gallus sequence at average density of 1.39 genes per kbp. The
smallest number of matches was found on unmapped scaffolds (2%), but they were found
there to be at the highest density (3.72 3 genes per kbp). Mapped Entrez Gene sequences
made up 4% of the scaffold length on average, but in the scaffolds that were not mapped
to either of the two avian genomes, the proportion was much higher (22%) (Table 3).
While the unmapped scaffolds were the shortest (Figure S4A), they contained the highest
density and percentage of the gene sequences (Figure S4B and C; Table S4A).
A database containing G. gallus gene sequences that were mapped to the parrot scaffolds
and their locations is available from Table S7A and B. We used RepeatMasker software
[17] to search scaffolds for the presence of the known repeat classes. Overall, 59% of the
scaffolds contained at least one repeat, and the proportion was much higher in those that
matched to another genome (Table 3). Even though a relative proportion of unmatched
category of sequences that contained repeats was smaller than that in the other four
categories, the percentage of its length classified as a repetitive sequence was much
higher than in other classes (22%) (Figure S4D). The most common class of repeats
found on scaffolds were the LINES/SINES, low complexity regions, and simple repeats,
as they add up to 96% of the length of all repetitive sequences we found (Figure S5). A
representative database of different repetitive elements and their locations on scaffolds is
also hosted as supplementary information from our homepage and the GigaScience
database [10]. There were no observable differences in the distribution of different cases
of repetitive elements among different classes of scaffolds listed in Table 3 and Table
S8).
Genome annotation and education
In addition to the preliminary annotation analysis, and with the goal of using the current
genome sequence as an educational tool for training the next five generations of
genomics and bioinformatics students at the University Puerto Rico at Mayaguez, we
developed a strategy of manual annotation of the genome. Manual annotation is also used
as a method to validate high-throughput annotation, and to canalize the desire of the local
community to contribute to the project. Two annotation strategies are used: (1) annotation
of scaffolds for gene and repeat elements, and (2) annotating known genes from other
species.
According to the first strategy, each student in the Genome Annotation class (20
Undergraduate students) is given five from the list of 100 longest scaffolds ranging from
120 to 206 kbp in length and learns to apply a variety of bioinformatics tools (Table S9).
Students divide each scaffold into 25 kbp segments and use an online BLAT tool to
search against the chicken genome in the UCSC Genome Browser. When one segment
would not align to the same chromosome as the other segments in the scaffold, the result
is confirmed using an online UCSC BLAT query [18] against the zebra finch genome, as
well as by the NCBI nucleotide BLAST [16] query against the chicken genome. As an
output for each scaffold, a student receives an additive score of its segments, the leftmost
and rightmost coordinates of the matches, the partial or complete RefSeq genes, and the
number of conserved elements in at least four of the following six vertebrate species:
human, mouse, rat, opossum, Xenopus tropicalis, and the zebrafish. To investigate the
level of evolutionary conservation of each RefSeq gene, students also score the
taxonomic groups where orthologs in NCBI’s Homologene or Gene are found. In
addition, they look for the gene ontology in UniProtKB, always scoring the major
biological processes in which the gene is involved, when known. Finally, students use an
online version of RepeatMasker to score the number, type and extension of repetitive
elements in their scaffolds. They also store the RepeatMasker detailed outputs with the
coordinates and identification of each repetitive element. An example of the annotation
output produced by a student is presented in Table S10.
The other strategy of annotation is to search for the genes known from other species in
the assembled scaffolds. Students in the Genome Annotation class receive either one or
two complete genes, depending on gene structure complexity. To annotate the
coordinates where coding regions start and end, as well as those of the splice sites within
coding regions, students first identify these sites in the chicken genome from Ensembl,
and then localize them in the parrot genome using BLASTx against the chicken proteins
and identifying the results of this query in the UCSC Genome Browser. All isoforms
identified in UniprotKB for each gene are annotated.
Comparative analysis
Scaffolds were compared to the chicken (Gallus gallus) [19] and zebra finch
(Taeniopygia guttata) [20] genomes using local BLAST [16]. First, the entire database of
A. vittata scaffolds was queried against both reference avian genomes to assess coverage,
mapping concordance and the long-range contiguity of the constructed scaffolds. Only
the top alignment was taken into consideration for each scaffold. The alignment with two
other avian genomes (G. gallus and T. guttata) resulted in 93.4 Mbp of total length of
alignments to the chicken genome with 82.7% identity on average (average bit score
577.3), and 41.7 Mbp of total length of alignments to the zebra finch genome with 84.5%
identity on average (average bit score 431.1). Despite the overall better alignment, gap
lengths in chicken were much larger (43 vs. 16 per scaffold alignment in zebra finch
genome). There was no relationship between the quality scores of alignment of the parrot
scaffolds to chicken and zebra finch genomes (Figure S6A), but there was a positive
correlation between the length of the scaffolds and the alignment score in zebra finch,
which was not observed for the chicken scaffolds (Figure S6B). However, the longer
scaffolds have better alignment scores in T. guttata, but not in G. gallus (Figure S7). On
average, chicken genes constitute 4% of our scaffolds, and as much as 20% in the shorter
unmatched fragments (Table 3). This may be the consequence of a simple tendency that
gene rich-regions are the easiest to assemble: longer scaffolds contain less repetitive
sequence (Figure S8.A), matched to more of these coding sequences (Figure S8.B).
References
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19. International Chicken Genome Sequencing C: Sequence and comparative
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Supplementary Figures:
Figure S1. Venn diagram of the overlap between the number of A. vittata scaffolds and
the G. gallus transcripts from GenBank that were mapped to them by BLAST
Figure S2. A single example of chimera detected on scaffold-74754 after visual
inspection of reads mapped to 100 largest scaffolds
Figure S3. Percentage of scaffolds containing fragments with > 95% similarity to
GenBank sequences
Figure S4. Comparison between categories of A. guttata scaffolds (described earlier in
Figure 2): The box plots show the medians, Q1, Q3 and the extreme values. The means
are shown in Table 3. A. Distribution of scaffold lengths; B. Distribution of densities of
genes mapped per kbp of scaffold length. C. Differences in the distribution of proportion
of the length of the scaffold mapped to a G. gallus transcript from NCBI Entrez Gene
database. D. Differences in the distribution of proportion of the length of the scaffold
mapped to a known repeat class using RepeatMasker software [5].
Figure S5. Distribution of major classes of repetitive sequences found on A. vittata
scaffolds
Figure S6. Relationship between the quality scores of the alignments between the parrot
scaffolds to the chicken and zebra finch genomes: A. All scaffolds. B. Mismatched
scaffolds only (those scaffolds that shared similarity with sequences of G. gallus and T.
guttata genomes but mapped to different chromosomes in the two species; see
classification in Figure 2). C. Matched sequences only (those that mapped to the same
chromosome in reference genomes of the two avian species).
Figure S7. Relationship between the size of a scaffold and the quality of its alignment to
T. guttata and/or G. gallus genome sequence: A. All scaffolds aligned to the T. guttata
genome. B. All scaffolds aligned to the G. gallus genome. C. Scaffolds from T. guttata
that Mismatched scaffolds mapped to different chromosomes in G. gallus; see
classification in Figure 2). D. Scaffolds from G. gallus that Mismatched scaffolds
mapped to different chromosomes in T. guttata). E. Matched sequences from T. guttata
only (those that mapped to the same chromosome in reference genomes of the two avian
species), F. Matched sequences from G. gallus only (those that mapped to the same
chromosome in reference genomes of the two avian species).
Figure S8. Small fragments are repeat- rich and gene-rich: A. Relationship between the
length of the scaffolds and the proportion of it length matched to the G. gallus sequences
from NCBI Entrez Gene database. B. Relationship between the length of the scaffolds
and the proportion of it length designated by RepeatMasker as repetitive sequence.
Supplementary Tables
Table S1 Quality and volume of four DNA samples extracted from whole blood of
two Amazona vittata parrots selected for the genome sequencing
Sample
#
1
2
3
4
Sample
name
Pa1a
Pa9a
Pa15a
Pa16a
Concentration
(ng/ul) picogreen
method
180.68
171.15
242.57
258.89
Purity
(A260/A28
0)
1.79
1.79
1.87
1.87
Volume (ul)
550
550
550
260
Total DNA
amount (ug)
99.37
94.13
133.41
67.31
Table S2. Results of the genome sequencing (Illumina HiSeq, Axeq Technologies).
Pa9a_1 and Pa9a_2 represent the opposite ends of the 300 bp short reads, and the Pa9aMP_1 and Pa9a-MP_2 are the 2,500 bp mate pairs (MP). All sequences were 101 bp
long.
Bases
Sequence
name
A
C
G
T
N
Pa9a_1
3,848,744,755
2,897,323,917
2,900,927,647
3,825,967,145
23,781,474
Pa9a_2
3,868,006,366
2,890,106,491
2,911,110,520
3,826,704,806
816,755
Pa9a-MP_1
2,174,635,550
1,720,812,735
1,688,621,488
2,158,688,549
246,593
Pa9a-MP_2
2,171,866,496
1,674,935,595
1,748,437,549
2,147,171,234
594,041
Table S3. Results of the genome assembly by SOAPdenovo [8]
Statistics Category
paired-end
(27.0Gb)
PE+ MP 5Gb
PE+ MP All
(27.0Gb + 5.2Gb)
(27.0Gb + 15.5Gb)
# Contigs
Total Length
Largest Contig
Mean Length
12,764,879
1,560,663,735
15,182
122.3
12,887,828
1,772,155,983
18,359
137.5
12,887,828
1,772,155,983
18,359
137.5
Of All,
Contigs
≥ 100bp
N50
# Contigs
Total Length
Mean Length
N50
673
1,884,625
1,099,661,342
583.5
1,188
636
4,450,396
1,396,126,299
313.7
1,123
636
4,450,396
1,396,126,299
313.7
1,123
Scaffolds
Largest Scaffold
# Scaffolds
Total Length
Mean Length
N50
N/A
N/A
N/A
N/A
N/A
2,014,591
3,410,722
1,530,900,674
448.8
74,348
3,309,686
3,384,799
1,590,552,602
470
126,952
All
Contigs
These results are not used in the study, but have been deposited at
http://genomes.uprm.edu/parrot/Assembly-2011/
Table S4A. Summary of the alignment of A. vittata sequences to the G. gallus genome
sequence containing only the top alignment for each scaffold, its chromosomal position
and quality scores.
Table S4B. Summary of the alignment of A. vittata sequences to the T. guttata genome
sequence containing only the top alignment for each scaffold, its chromosomal position
and quality scores
Table S4C. The database of the alignment information of A. vittata sequences to G.
gallus and T. guttata genome sequence by BLAST
Table S5. Proportions of sequences with some similarity that mapped to chromosomes of
two reference avian genomes (G. gallus and T. guttata)
Table S6A The summary of the database of GenBank sequences with more than 95%
similarity with the parrot scaffolds
Table S6B. The database of GenBank sequences with more than 95% similarity with the
parrot scaffolds found by BLAST
Table S7A. A map of G. gallus transcripts from NCBI Entrez Gene database that mapped
to one of the A. guttata scaffolds
Table S7B. The database of alignments between of G. gallus transcripts from NCBI
Entrez Gene database and A. guttata scaffolds by BLAST
Table S8. Distribution of different cases of repetitive elements among different classes of
A. guttata scaffolds
Table S9. Bioinformatics tools and outputs for scaffold and gene annotation
Elements Annotated
Tools
Outputs per Scaffold or Gene
Scaffolds
BLAT
n-BLAST
Homologene
Gene,
UniProtKB
RepeatMasker
- Chromosome number and end
coordinates of BLAT matches
- Match scores
- # of elements conserved
- # and identity of RefSeq genes
- Gene orthology and ontology
Genes
UniProtKB
- Coordinates for start and end of each
Ensembl
coding region plus splice sites within
x-BLAST
UCSC Genome Browser
Table S10. An example of annotation output produced by a student in the Genome
annotation class using A. vittata genome