A practical, bioinformatic workflow system for large data sets generated by next-generation sequencing.

Abstract

Transcriptomics (at the level of single cells, tissues and/or whole organisms) underpins many fields of biomedical science, from understanding the basic cellular function in model organisms, to the elucidation of the biological events that govern the development and progression of human diseases, and the exploration of the mechanisms of survival, drug-resistance and virulence of pathogens. Next-generation sequencing (NGS) technologies are contributing to a massive expansion of transcriptomics in all fields and are reducing the cost, time and performance barriers presented by conventional approaches. However, bioinformatic tools for the analysis of the sequence data sets produced by these technologies can be daunting to researchers with limited or no expertise in bioinformatics. Here, we constructed a semi-automated, bioinformatic workflow system, and critically evaluated it for the analysis and annotation of large-scale sequence data sets generated by NGS. We demonstrated its utility for the exploration of differences in the transcriptomes among various stages and both sexes of an economically important parasitic worm (Oesophagostomum dentatum) as well as the prediction and prioritization of essential molecules (including GTPases, protein kinases and phosphatases) as novel drug target candidates. This workflow system provides a practical tool for the assembly, annotation and analysis of NGS data sets, also to researchers with a limited bioinformatic expertise. The custom-written Perl, Python and Unix shell computer scripts used can be readily modified or adapted to suit many different applications. This system is now utilized routinely for the analysis of data sets from pathogens of major socio-economic importance and can, in principle, be applied to transcriptomics data sets from any organism.

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A practical, bioinformatic workflow system for large
data sets generated by next-generation sequencing
Cinzia Cantacessi1,*, Aaron R. Jex1, Ross S. Hall1, Neil D. Young1,
Bronwyn E. Campbell1, Anja Joachim2, Matthew J. Nolan1, Sahar Abubucker3,
Paul W. Sternberg4, Shoba Ranganathan5, Makedonka Mitreva3,* and Robin B. Gasser1,*
1Department of Veterinary Science, The University of Melbourne, 250 Princes Highway, Werribee, Victoria 3030,
Australia,2Institute of Parasitology, Department of Pathobiology, University of Veterinary Medicine Vienna,
A-1210 Vienna, Austria,3Genome Sequencing Center, Department of Genetics, Washington University School
of Medicine, MO 63108,4Biology Division, California Institute of Technology, CA 91125, USA and5Department
of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, New South Wales 2109, Australia
Received June 2, 2010; Revised July 11, 2010; Accepted July 15, 2010
ABSTRACT
Transcriptomics (at the level of single cells, tissues
and/or whole organisms) underpins many fields of
biomedical science, from understanding the basic
cellular function in model organisms, to the elucida-
tion of the biological events that govern the devel-
opment and progression of human diseases, and
the exploration of the mechanisms of survival,
drug-resistance andvirulence
Next-generation sequencing (NGS) technologies
are contributing to a massive expansion of trans-
criptomics in all fields and are reducing the cost,
time and performance barriers presented by con-
ventionalapproaches.
tools for the analysis of the sequence data sets
produced by these technologies can be daunting
to researchers with limited or no expertise in bio-
informatics.Here,we
automated, bioinformatic workflow system, and
critically evaluated it for the analysis and annotation
of large-scale sequence data sets generated by
NGS. We demonstrated its utility for the exploration
of differences in the transcriptomes among various
stages and both sexes of an economically important
parasitic worm (Oesophagostomum dentatum) as
well as the prediction and prioritization of essential
molecules (including GTPases, protein kinases and
phosphatases) as novel drug target candidates. This
workflow system provides a practical tool for the
assembly, annotation and analysis of NGS data
ofpathogens.
However, bioinformatic
constructedasemi-
sets, also to researchers with a limited bioinformatic
expertise. The custom-written Perl, Python and Unix
shell computer scripts used can be readily modified
or adapted to suit many different applications. This
system is now utilized routinely for the analysis of
data sets from pathogens of major socio-economic
importance and can, in principle, be applied to
transcriptomics data sets from any organism.
INTRODUCTION
Transcriptomics is the molecular science of examining,
simultaneously, the transcription of all genes at the level
of the cell, tissue and/or whole organism, allowing infer-
ences regarding cellular functions and mechanisms. The
ability to measure the transcription of thousands of
genes simultaneously has led to major advances in all bio-
medical fields, from understanding the basic function in
model organisms, such as the free-living nematode
Caenorhabditiselegans(1–3)
Drosophila melanogaster (4–6), to studying molecular
events associated with the development and progression
ofhumandiseases,including
neurodegenerative disorders (10–12), to the exploration
of the mechanisms of survival, drug-resistance and viru-
lence/pathogenicityofbacteria
socioeconomically important pathogens, such as parasites
(15–20). For more than a decade, transcriptomes have
been determined by sequencing expressed sequence tags
(ESTs) using the conventional Sanger method (21,22),
whereas levels of transcription have been established
quantitativelyor semi-quantitatively
orthe vinegarfly,
cancer(7–9)and
(13,14) andother
byreal-time
*To whom correspondence should be addressed. Tel: +61 3 9731 2294; Fax: +61 3 9731 2366; Email: robinbg@unimelb.edu.au
Correspondence may also be addressed to Cinzia Cantacessi. Tel: +61 3 9731 2294; Fax: +61 3 9731 2366; Email: cinziacantacessi@gmail.com
Correspondence may also be addressed to Makedonka Mitreva. Tel: +1 314 286 1118; Fax: +1 314 286 1810; Email: mmitreva@watson.wustl.edu
Nucleic Acids Research, 2010, 1–12
doi:10.1093/nar/gkq667
? The Author(s) 2010. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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polymerase chain reaction (PCR) (23) and/or cDNA
microarrays (24). The use of these technologies has been
accompanied by an increasing demand for analytical tools
for the efficient annotation of nucleotide sequence data
sets, particularly within the framework of large-scale
EST projects (25). With a substantial expansion of EST
sequencing has come the development of algorithms for
sequence assembly, analysis and annotation, in the form
of individual programs (26–28) and integrated pipelines
(29,30), some of which have been made available on the
worldwide web (29,31,32). However, the cost and time
associated with large-scale sequencing using a convention-
al (Sanger) method and/or the design of customized
analytical tools (e.g. cDNA microarray) have driven
the search for alternative methods for transcriptomic
studies (33).
In the last few years, there has been a massive expansion
in the demand for and access to low cost, high-throughput
sequencing, attributable mainly to the development of
next-generation sequencing (NGS) technologies, which
allow massively parallelized sequencing of millions of
nucleic acids (33,34). These sequencing platforms, such
as 454/Roche (35; http://www.454.com/) and Illumina/
Solexa (36; http://www.illumina.com/), have transformed
transcriptomics by decreasing the cost, time and perform-
ance limitations presented by previous approaches. This
situation has resulted in an explosion of the number of
EST sequences deposited in databases worldwide, the
majority of which is still awaiting detailed functional
annotation. However, the high-throughput analysis of
such large data sets has necessitated significant advances
in computing capacity and performance, and in the avail-
ability of bioinformatic tools to distil biologically mean-
ingful information from raw sequence data.
Sequences generated by NGS are significantly shorter
(454/Roche: ?400 bases; Illumina/ABI-SOLiD: ?60
bases) than those determined by Sanger sequencing (0.8–
1kb), which poses a challenge for assembly. In addition,
the data files generated by these technologies are often
gigabytes to terabytes (1?109to 1?1012bytes) in size,
substantially increasing the demands placed on data
transfer and storage, such that many web-based interfaces
are not suited for large-scale analyses. The bioinformatic
processing of large data sets usually requires access to
powerful computers and support from bioinformaticians
with significant expertise in a range of programming
languages (e.g. Perl and Python). This situation has
limited the accessibility of high-throughput sequencing
technologies to some (smaller) research groups, and has
thus restricted somewhat the ‘democratization’ of large-
scale genomic and/or transcriptomic sequencing. Clearly,
user-friendly and flexible bioinformatic pipelines are
needed to assist researchers from different disciplines
and backgrounds in accessing and taking full advantage
of the advances heralded by NGS. Increasing the accessi-
bility to high-throughput sequencing will have major
benefits in a range of areas, including the investigation
of pathogens. The exploration of the transcriptomes of
pathogens has major implications in improving our under-
standing of their development and reproduction, survival
in and interactions with the host, virulence, pathogenicity,
the
(17–20,37–39), and has the potential to pave the way to
novel approaches for treatment, diagnosis and control. In
the present study, we (i) constructed a semi-automated,
bioinformatic workflow system for the analysis and anno-
tation of large-scale sequence data sets generated by NGS,
(ii) demonstrated its utility by profiling differences in the
transcriptome of an economically important parasite,
Oesophagostomum dentatum (Strongylida), throughout
its development, and (iii) indicated the broader applicabil-
ity of this system to different types of transcriptomic
data sets.
diseases thattheycause and drugresistance
METHODS
Sequence data sets
For this study, original cDNA sequence data sets repre-
senting four distinct developmental stages of O. dentatum
[i.e. third-stage (L3) and fourth-stage (L4) larvae as well as
adult female and male worms] were produced and stored
as described previously (40). Total RNA (10mg) from
each stage and/or sex was used to construct a normalised
cDNA library; each library was sequenced using a
Genome SequencerTM
(GS) Titanium FLX (Roche
Diagnostics) as described previously (18). FASTA- and
associated files, with short-read sequence quality scores
of each data set, were extracted from each SFF-file;
sequence adaptors were clipped using the ‘sff_extract’
software (http://bioinf.comav.upv.es/sff_extract/index
.html).
Bioinformatic components for the construction of the
workflow system
Five components (1–5), documented in a series of peer-
reviewed, international publications, were selected based
on the parameters of general applicability, ease of use,
versatilityandefficiency.
workflow system was applied to the analysis of the
O. dentatum data sets.
Onceconstructed,the
Assembly. The Contig Assembly Program (CAP3 v.3; 31)
was used to cluster sequences (with quality scores) into
contigs and singletons from individual or combined
(i.e. pooled) data sets, employing a minimum sequence
overlap of 40 nucleotides and an identity threshold of
90%. This program was selected to enable the assembly
of relatively long sequences and to remove redundant
short-reads (41).
Similarity searching. BLASTn and BLASTx algorithms
(42) were used to compare contigs and singletons with
sequences available in public databases [i.e. NCBI (www
.ncbi.nlm.nih.gov) and EMBL-EBI Parasite Genome
Blast Server (www.ebi.ac.uk); April 2010], to identify
putative homologuesinrange
(cut-off: <1E-05). For nematodes, WormBase (release
WS200; www.wormbase.org) was interrogated extensively
for relevant information on C. elegans orthologues/
homologues, including transcriptomic, proteomic, RNA
interference (RNAi) phenotypes and interactomic data.
ofotherorganisms
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Prediction and annotation of peptides. The program
ESTScan (32) was used to conceptually translate peptides
from assembled contigs and singletons. InterProScan
(available at http://www.ebi.ac.uk/InterProScan/; 27) and
gene ontology (GO; 43) were used to classify peptides
(based on their putative function/s). Biological pathways
were inferred from C. elegans for each peptide using the
KEGG Orthology-Based Annotation System software
(KOBAS; 44) and displayed using the iPath tool
(http://pathways.embl.de/data_mapping.html; 45).
In silico subtraction. A BLASTn algorithm, employing a
stringent cut-off (cut-off: <1E-15; 17), was used to
examine differential transcription between data sets by
subtraction in silico. Peptides corresponding to transcripts
that were unique to a particular data set were assigned
parental (i.e. level 1) InterPro terms and compared,
using a BLASTp algorithm (cut-off: <1E-15), with
peptides inferred from the assembly of sequences from
combined data sets. The subtraction approach allows
qualitative (not quantitative) differences between or
among samples to be established.
Probabilistic functional networking of protein-encoding
genes, and drug target prediction. Interaction networks
among C. elegans orthologues of differentially transcribed
molecules were inferred using an established approach
(46). The druggability of C. elegans homologues of mol-
ecules unique to a particular O. dentatum data set or
common to all data sets was inferred using a published
method (18). Briefly, the InterPro domains of predicted
proteins were compared with those linked to known,
small molecular drugs, which follow the ‘Lipinsky rule
of50
regarding bioavailability
were mapped to Enzyme Commission (EC) numbers,
and a list of enzyme-targeting drugs was compiled based
on data available in the BRENDA database (www
.brenda-enzymes.info;49,50).
orthologues/homologues included in this list were ranked
according to the ‘severity’ of non-wild-type RNAi pheno-
types (including lethality or sterility of different develop-
mental stages; see www.wormbase.org; release WS200).
(47,48).GO terms
TheC. elegans
RESULTS
A
(Figure 1), incorporating five key bioinformatic compo-
nents, was constructed and linked using customized Perl,
Python and Unix shell computer scripts (listed in
Supplementary File S1 and accessible via http://research
.vet.unimelb.edu.au/gasserlab/index.html).
was then assessed for the assembly, analysis and functional
annotation of each or all of the four sequence data sets for
O. dentatum. The specificity of the in silico subtraction step
was verified using independent experimental evidence.
semi-automatedbioinformatic workflowsystem
Thissystem
Assembly and detailed annotation and analyses of the
O. dentatum data sets
A total of 1826367 sequences (244±32 bases; i.e. mean
length ± standard deviation) were determined for L3, L4
as well as adult female and male of O. dentatum.
Following the clipping of adapter sequences, only se-
quences of >100 bases (n=1800874; 98.6%) were
included in further analyses. The numbers of contigs
assembled for each of the four data sets are listed in
Table 1. The assembly of the sequences of all four data
Individual data sets (xn) Combined data set
[1] Assembly
[4] Conceptual translation
[2] Removal of contaminants
[3] Similarity searches
[5] Annotation of predicted peptides
[6] In silico subtraction – nucleotide
[6] In silico subtraction – protein
[8] Probabilistic genetic
interaction networks
[7] Prediction of drug
target candidates
Figure 1. Bioinformatic analyses of the Oesophagostomum dentatum
data sets. Stars indicate analyses performed using custom-written
Perl, Python and/or Unix shell computer scripts, accessible via http://
research.vet.unimelb.edu.au/gasserlab/index.html. [1] Individual and
combined expressed sequence tags (EST) data sets are assembled
using CAP3 (compiled Linux 64-bit executable) to generate consensus
sequences. [2] Assembled contigs with high similarity (cut-off: <1E-15)
to nucleotide sequences of the vertebrate host (Sus scrofa) are
eliminated.[3]Databasesimilarity
combined data sets) are carried out using BLASTn and BLASTx
(compiled Linux 64-bit executable; 42), embedded in custom-built
Unix shell scripts. [4] Sequences (from the individually and combined
assembled data sets) are conceptually translated into peptide sequences
using ESTScan (compiled Linux 64-bit executable with a Perl wrapper).
[5] Domains/motifs within translated peptides are identified via
InterProScan (Perl wrapper) and linked to biological pathways in
C. elegans using KOBAS (stand-alone Python application; 44).
Functional annotation of the predicted peptides is performed by gene
ontology (Perl wrapper; 27). [6] The individually assembled data sets
are subtracted from one another (in both directions) using a BLASTn
algorithm (42) embedded in a custom-built Unix shell script; proteins
inferred from subtracted transcripts are assigned parental (i.e. level 1)
InterPro terms and subtracted from one another using a BLASTp al-
gorithm, embedded in a custom-built Unix shell script. [7] Potential
drug target candidates for each of the individually assembled and/or
in silico subtracted data sets are predicted and ranked according to the
‘severity’ of the non-wild-type RNAi phenotypes observed for the cor-
responding C. elegans orthologues/homologues (custom-built Unix shell
scripts). [8] Probabilistic interaction networks among C. elegans
orthologues of subtracted molecules are predicted (command lines).
searches(for individual or
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sets yielded 36233 contigs (516±316 bases in length) and
452528 singletons (Table 1); sequences (n=115) with
similarity (cut-off: <1E-15) to potential host molecules
were excluded. The L3 data set had the largest number
of sequence clusters with orthologues/homologues in C.
elegans (n=32904; Table 1) and in organisms other
than nematodes (n=14731; Table 1), whereas the L4
data set included the largest number of clusters with
orthologues/homologues in other parasitic nematodes
(n=38634; Table 1).
Of the four assembled data sets, the L3 set included the
largest number of sequence clusters with predicted open
reading frames (ORFs; n=57818; Table 1), of which
27297 (47.2%) could be annotated functionally using
InterPro terms and 12763 (22.1%) could be assigned
GO terms, including 19705 ‘biological process’, 10926
‘cellular component’ and 34904 ‘molecular function’.
The numbers of peptides inferred from sequence clusters
in the adult female, adult male and/or L4 data sets, which
could be assigned InterPro and/or GO terms, are given in
Table 1. In total, 85395 peptides were predicted for all
sequences from all four data sets, representing 17.5% of
clusters (Table 1); 56940 (66.7%) of them could be
mapped to known proteins defined by 31982 different
domains, the most represented being ‘SCP-like extracellu-
lar’ (IPR014044; 1.2% of the peptides mapping to a
conserved protein motif), ‘NAD(P)-binding’ (IPR016040;
1.1%) and ‘proteinase inhibitor I2, Kunitz metazoa’
(IPR002223; 1%) (Table 2). GO annotation allowed
56940 (66.7%) inferred proteins to be assigned to 19346
‘biological process’, 11007 ‘cellular component’ and
35182 ‘molecular function’ terms (Table 1). The predom-
inant terms were ‘metabolic process’ (GO:0008152;
10.9%), ‘proteolysis’ (GO:0006508; 7%) and ‘translation’
(GO:0006412; 5.4%) for ‘biological process’; ‘intracellu-
lar’ (GO:0005622; 17.5%), ‘membrane’ (GO:0016020;
15.6%) and ‘nucleus’ (GO:0005634; 11.6%) for ‘cellular
component’ and ‘ATP binding’ (GO:0005524; 7.5%);
‘catalytic activity’ (GO:0003824; 7%) and ‘binding’
(GO:0005488; 4.6%) for ‘molecular function’ (Table 3).
Proteins inferred from the combined assembly were
predicted to be involved in 262 different biological
pathways, defined by 64 unique KEGG terms, of which
‘peptidases’ (12%), ‘other enzymes’ (8%) and ‘antigen
processing and presentation’ (5.5%) were predominant
(see Supplementary File S2). A display of biological
pathways, defined by KEGG terms, inferred from
predicted peptides and mapped to the complement of
knownpathways in C.
Supplementary Figure S1.
Using BLASTn algorithms, subsets of 3451, 10344,
14380 and 7520 nucleotide sequences were identified as
being uniquely transcribed in adult female, adult male,
L3 and L4, respectively (Table 1). The accuracy of the in
silico subtraction process was verified using independent
evidence from a previous analysis of differential transcrip-
tion between adult females and males of O. dentatum using
a microarray-based approach (51). This verification
elegans, isshownin
Table 1. Summary of the nucleotide sequence data for the adult female, adult male, and third (L3) and fourth (L4) larval stages of
Oesophagostomum dentatum prior to and following in silico subtraction as well as detailed bioinformatic annotation and analyses
FemaleMaleL3 L4 Combined
Expressed sequence tags (ESTs)
Number of unassembled ESTs
Contigs (average length ± SD)
Singletons
Total
Containing an open reading frame (%)
Returning InterProScan results (%)
Gene ontology (%)
Number of biological process terms
Cellular component
Molecular function
With orthologues in C. elegans
Other parasitic nematodes (%)
Other organisms (%)
KOBAS (number of biological
pathways predicted)
In silico subtracted data sets
Number of ESTs (contigs+singletons)
Containing an open reading frame (%)
Predicted peptides
Returning InterProScan results (%)
Gene ontology (%)
Number of biological process terms
Cellular component
Molecular function
With homologues in C. elegans (%)
Other parasitic nematodes (%)
Other organisms (%)
KOBAS (number of biological pathways predicted)
336131
23807 (483±290)
23303 (233±50)
47110
38504 (81.7)
20229 (43)
9970 (25.9)
17031
8864
30482
23485 (50)
17533 (37.2)
12011 (25.5)
256
490645
29043 (484±289)
37248 (243±45)
66291
52787 (80)
26496 (40)
12386 (23.5)
19510
10091
35934
28643 (43.2)
21553 (32.5)
13843 (21)
254
503566
30176 (465±281)
49341 (227±57)
79517
57818 (73)
27297 (47.2)
12763 (22.1)
19705
10926
34904
32904 (41.4)
23748 (29.9)
14731 (18.5)
249
496025
26349 (498±308)
36875 (242±40)
63224
50533 (80)
26121 (51.7)
12735 (25.2)
19645
10649
35241
30000 (47.4)
38634 (61)
14332 (22.7)
255
1826367
36233 (516±316)
452528 (244±37)
488761
85395 (17.5)
56940 (66.7)
25216 (30)
19346
11007
35182
3451 (671+2780)
2397 (70)
10344 (2902+7442)
7117 (69)
14380 (2752+11628)
7222 (50.2)
7520 (1280+6240)
4789 (63.7)
521 (21.7)
376 (15.7)
314
177
563
824 (23.9)
558 (16.1)
159 (4.6)
7
1179 (16.6)
840 (11.8)
625
355
1259
1834 (17.7)
1212 (11.7)
123 (1.2)
16
1224 (17)
760 (10.5)
684
412
1073
2252 (15.6)
1384 (9.6)
176 (1.2)
18
989 (20.7)
652 (13.6)
527
359
948
1589 (21.1)
1052 (14)
137 (1.8)
23
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Table 2. The 20 most represented (InterPro) protein domains inferred from peptides conceptually translated from individual
contigs for Oesophagostomum dentatum [combined assembly of data for adult female, adult male, and the third (L3) and
fourth (L4) larval stages] and InterPro protein domains (level 1) assigned to predicted peptides unique to each stage or sex
following in silico subtraction
InterPro descriptionInterPro codeNumber of predicted
peptides (%)
Combined assembly (31982)a
SCP-like extracellular
NAD(P)-binding domain
Proteinase inhibitor I2, Kunitz metazoa
Zinc finger, LIM-type
WD40 repeat
Ankyrin
EF-HAND 2
WD40 repeat, subgroup
Allergen V5/Tpx-1 related
Protein kinase-like
RNA recognition motif, RNP-1
WD40 repeat 2
Protease inhibitor I4, serpin
Src homology-3 domain
Peptidase C1A, papain C-terminal
C-type lectin
Kelch repeat type 1
Annexin repeat
Protein kinase, core
EF-HAND 1
Female (139)a
Chitin binding protein, peritrophin-A
Basic-leucine zipper (bZIP) transcription factor
DNA primase, small subunit
p53-like transcription factor, DNA-binding
DNA-binding HORMA
Acyl-CoA dehydrogenase/oxidase
Frizzled-like domain
Lipid transport protein
PreATP-grasp-like fold
UbiA prenyltransferase
Male (243)a
PapD-like
Major sperm protein
C-type lectin
Phosphoenolpyruvate carboxykinase
Protein of unknown function DUF236
Scramblase
ClpX, ATPase regulatory subunit
Galactose oxidase/kelch
Ribosomal protein S2
Amidinotransferase
L3 (220)a
RmlC-like jelly roll fold
Six-bladed beta-propeller, TolB-like
Protein of unknown function DUF590
7TM GPCR, serpentine receptor class r (Str), Nematode
Acyltransferase ChoActase/COT/CPT
Putative DNA binding
7TM GPCR, serpentine receptor class e (Sre), Nematode
Nuclear hormone receptor, ligand-binding, core
Coenzyme A transferase
Ion transport
L4 (249)a
Peptidase M24, methionine aminopeptidase
FAD-binding, type 2
Oxysterol-binding protein
Translation protein SH3-like
Tubulin/FtsZ, GTPase domain
6-phosphogluconate dehydrogenase
Peptidase C13, legumain
Aminoacyl-tRNA synthetase
Adenosylcobalamin biosynthesis, ATP
Aspartate/other aminotransferase
IPR014044
IPR016040
IPR002223
IPR001781
IPR001680
IPR002110
IPR018249
IPR019781
IPR001283
IPR011009
IPR000504
IPR019782
IPR000215
IPR001452
IPR000668
IPR001304
IPR006652
IPR018502
IPR000719
IPR018247
377 (1.2)
365 (1.1)
339 (1)
332 (1)
312 (0.9)
257 (0.8)
247 (0.7)
242 (0.7)
236 (0.7)
220 (0.6)
216 (0.6)
215 (0.6)
207 (0.6)
201 (0.6)
194 (0.6)
183 (0.5)
183 (0.5)
183 (0.5)
172 (0.5)
168 (0.5)
IPR002557
IPR004827
IPR002755
IPR008967
IPR003511
IPR013786
IPR020067
IPR001747
IPR016185
IPR000537
18 (8.6)
10 (4.8)
6 (2.9)
5 (2.4)
4 (2)
3 (1.4)
3 (1.4)
3 (1.4)
3 (1.4)
3 (1.4)
IPR008962
IPR000535
IPR018378
IPR008209
IPR004296
IPR005552
IPR004487
IPR011043
IPR001865
IPR003198
16 (4)
15 (3.7)
6 (1.5)
6 (1.5)
6 (1.5)
6 (1.5)
5 (1.3)
5 (1.3)
5 (1.3)
4 (1)
IPR014710
IPR011042
IPR007632
IPR019428
IPR000542
IPR009061
IPR004151
IPR000536
IPR004165
IPR005821
17 (4.5)
10 (2.7)
9 (2.4)
8 (2.1)
7 (1.9)
7 (1.9)
6 (1.6)
6 (1.6)
5 (1.3)
5 (1.3)
IPR001714
IPR016166
IPR000648
IPR008991
IPR003008
IPR008927
IPR001096
IPR015413
IPR016030
IPR000796
7 (2.2)
4 (1.3)
4 (1.3)
4 (1.3)
4 (1.3)
3 (1)
3 (1)
3 (1)
3 (1)
2 (0.6)
aNumber of unique InterPro domains assigned to predicted peptides in each data set
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showed that all 220 female- and 171 male-enriched mol-
ecules characterized previously (51; GenBank accession
numbers AM157797-AM158083) were contained exclu-
sively within the female and male data sets, respectively,
following in silico subtraction (data available upon
request). Based on these findings, the specificity of the
subtraction process, calculated using the Wilson score
(52) at a confidence interval of 95%, ranged from 98%
to 100%. Of the 139 parental functional domains assigned
to predicted peptides unique to the adult female data set,
‘chitin-binding protein, peritrophin-A’ (IPR002557; 8.6%)
and ‘basic-leucine zipper (bZIP) transcription factor’
(IPR004827; 4.8%) were highly represented. Of the 243
protein motifs identified amongst the predicted peptides
that were unique to the adult male data set, ‘PapD-like’
(IPR008962; 4%) and ‘major-sperm protein’ (IPR000535;
3.7%) were most represented. For the L3 data set, 220
unique proteinmotifswere
‘RmlC-like jelly roll fold’ (IPR014710; 4.5%) and
‘six-bladed beta-propeller’ (IPR011042; 2.7%) had the
highest representation. In contrast, of the 249 protein
motifs unique to L4 data set, ‘peptidase M24, methionine
aminopeptidase’ (IPR0011714; 2.2%) and ‘FAD-binding’
(IPR016166; 1.3%) were the predominant domains
(Table 2). The number of ‘biological process’, ‘cellular
component’ and ‘molecular function’ terms assigned to
peptides unique to each of the individually assembled
identified, of which
data sets is given in Table 1. The KOBAS analysis
assigned 7, 16, 18 and 23 KEGG terms to inferred
peptides exclusive to the adult female, adult male, L3
and L4 data sets, respectively; of the 23 KEGG terms
assigned to L4, 20 could be mapped to known pathways
in C. elegans (Supplementary Figure S2).
Probabilistic genetic interaction networking predicted
215C. elegans orthologues, representing sequence clusters
unique to the adult female of O. dentatum, to interact
directly with a total of 1729 other genes (range: 1–277),
including some (e.g. lin-12, mom-5, glp-1, ppk-1, tbx-2 and
rnr-1; Supplementary Figure S3, and Supplementary File
S3) that are essential to embryogenesis and reproduction
(see www.wormbase.org). The 373C. elegans orthologues
of sequence clusters unique to the adult male of
O. dentatum were predicted to interact directly with a
total of 1710 other genes (range: 1–117; Supplementary
File S3). Amongst them were genes involved in sperm
development(i.e. ima-3)
(Supplementary Figure S3, and Supplementary File S3;
www.wormbase.org). A total number of 387 and 323C.
elegans orthologues of L3- and L4-unique molecules,
respectively, were predicted to interact with 790 (range:
1–122; Supplementary File S3) and 1058 (range: 1–59;
Supplementary FileS3)
including some involved in embryonic and/or larval via-
bility (i.e. scc-1, tba-4, cct-3, pfd-3 and mcm-4) and larval
development (i.e. let-711) (Supplementary Figure S3 and
Supplementary File S3; www.wormbase.org).
The 2397 predicted peptides unique to the adult female
of O. dentatum had significant homology (cut-off: >1E-05)
to 261C. elegans orthologues/homologues (data not
shown), of which 151 were associated with EC numbers
linked to ‘druggable’ enzymes and/or InterPro domains
(Table 4); of these, 92 were associated with non-wild-type
RNAi phenotypes, including adult lethality (n=3), em-
bryonic and/or larval lethality (n=44) and/or adult ster-
ility (n=65). Of the 541C. elegans homologues of the
7117 predicted peptides unique to the adult male of
O. dentatum, 375 were associated with EC numbers
linked to ‘druggable’ enzymes and/or InterPro domains
(Table 4). Of these, 205 were associated with the RNAi
phenotypes ‘embryonic and/or larval lethality’ and 196 to
‘sterility’ (Table 4). Of the 565 unique C. elegans homo-
logues of predicted peptides unique to the L3 of
O. dentatum, 344 were associated with EC numbers
linked to ‘druggable’ enzymes and/or InterPro domains
(Table 4); 121 of these were linked to RNAi phenotypes
‘embryonic and/or larval lethality’ and 165 to ‘sterility’
(Table 4). Amongst the 416C. elegans homologues of pre-
dicted peptides unique to the L4 stage of O. dentatum, 283
couldbe associated with
‘druggable’ enzymes and/or InterPro domains (Table 4).
Sixty-three of these homologues were associated with
RNAi phenotypes ‘embryonic and/or larval lethality’
and 72 to ‘sterility’ (Table 4). Examples of ‘druggable’
molecules unique to each of the data sets, together with
examples of effective BRENDA compounds, are given in
Table 4 and Supplementary Figure S4; the complete lists,
together with the list of ‘druggable’ molecules common
andmotility (i.e.act-2)
othergenes,respectively,
EC numbers linked to
Table 3. Functions predicted for proteins encoded in the transcrip-
tome of Oesophagostomum dentatum (combined assembly), based on
gene ontology (GO)
GO description (GO code)Number of
predicted
peptides (%)
Biological process (19346)a
Metabolic process (GO:0008152)
Proteolysis (GO:0006508)
Translation (GO:0006412)
Transport (GO:0006810)
Protein amino acid phosphorylation (GO:0006468)
Cellular component (11007)
Intracellular (GO:0005622)
Membrane (GO:0016020)
Nucleus (GO:0005634)
Integral to membrane (GO:0016021)
Ribosome (GO:0005840)
Molecular function (35182)
ATP binding (GO:0005524)
Catalytic activity (GO:0003824)
Binding (GO:0005488)
Zinc ion binding (GO:0008270)
Oxidoreductase activity (GO:0016491)
Protein binding (GO:0005515)
Nucleic acid binding (GO:0003676)
DNA binding (GO:0003677)
Structural constituent of ribosome (GO:0003735)
Nucleotide binding (GO:0000166)
2102 (10.9)
1361 (7)
1033 (5.4)
816 (4.2)
763 (4)
1925 (17.5)
1717 (15.6)
1279 (11.6)
1159 (10.5)
736 (6.7)
2645 (7.5)
2449 (7)
1622 (4.6)
1229 (3.5)
1226 (3.5)
1206 (3.4)
919 (2.6)
788 (2.2)
755 (2.1)
717 (2)
aTotal number of unique GO terms assigned to predicted peptides.
The parental (=level 2) GO categories were assigned according to
(InterPro) domains inferred from proteins with homology to function-
ally annotated molecules.
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Table 4. Examples of C. elegans orthologues of contigs unique to each Oesophagostomum dentatum adult female, adult male and the third (L3) and fourth (L4) larval stages, following
in silico subtraction, ranked according to the ‘severity’ of the RNAi phenotype/s observed, and for which inferred peptides were associated with ‘druggable’ (InterPro) domains and/or
Enzyme Commission (EC) numbers as well as examples of candidate compounds linked to these domains, predicted using the BRENDA database. The number of the C. elegans ortologues
predicted to interact with each of the molecules listed is also indicated
Contig code
C. elegans
gene ID
Gene
name
RNAi
phenotypes
Protein
description
Druggable IPR
domain (description)
Examples ofBRENDA
compounds
No. of predicted
interacting genes
Female (151)
Contig722
T23G5.1
rnr-1
Embryonic lethal, embryonic
defects, larval lethal, larval
arrest, sterile
Ribonucleotide reductase
IPR000788 (ribonucleotide)
D-phosphoserine
35
Contig18241
F44F4.2
egg-3
Embryonic lethal, maternal
sterile, sterile progeny
Protein tyrosine
phosphatase
IPR000242 (protein tyrosine)
4-nitrophenyl phosphate
–
Contig15526
T21E3.1
egg-4
Embryonic lethal, maternal
sterile
Protein tyrosine
phosphatase
IPR000242 (protein tyrosine)
4-nitrophenyl phosphate
–
Contig10671
Y110A7A.4
Embryonic lethal, reduced
brood size
Thymidylate synthase
IPR000398 (thymidylate)
5,10-methylenetetrahydrofolate+deoxyuridine
phosphate
26
E6SSEER01EX2TA
F17C8.1
acy-1
Embryonic defects, larval arrest
Adenylyl cyclase
IPR001054 (denylyl)
30,50-cAMP+diphosphate
2
Male (375)
Contig12350
W03A5.1
Embryonic lethal, embryonic
defects
Fibroblast/platelet-derived
growth factor receptor and related receptor
tyrosine kinase
IPR001254 (serine proteases)
Cleaved azocasein
–
Contig10801
T04B2.2
frk-1
Embryonic lethal, embryonic
defects
Protein tyrosine kinase
IPR001245 (tyrosine protein
kinase)
ADP+a phosphoprotein
–
Contig13376
T04B2.2
frk-1
Embryonic lethal, embryonic
defects
Protein tyrosine kinase
IPR001245 (tyrosine protein
kinase)
ADP+a phosphoprotein
–
Contig10782
ZK354.6
Embryonic defects
Casein kinase
IPR001245 (tyrosine protein
kinase)
ADP+a phosphoprotein
–
Contig13084
C25A8.5
Aldicarb resistant
Protein tyrosine kinase
IPR001254 (serine proteases
Cleaved azocasein
–
L3 (344)
Contig10987
T04D3.4
gcy-35
Embryonic lethal, larval arrest
Adenylate/guanylate kinase
IPR001054 (guanylate cyclase)
30,50-cAMP+diphosphate
1
Contig17117
B0240.3
daf-11
Embryonic lethal, slow growth
Transmembrane guanylate
cyclase
IPR001054 (guanylate cyclase)
30,50-cAMP+diphosphate
27
Contig10518
R01E6.1b
odr-1
Slow growth
Guanylate cyclase
IPR001054 (guanylate cyclase)
30,50-cAMP+diphosphate
–
Contig10600
C24G6.2b
Fibroblast/platelet-derived
growth factor receptor and related receptor
tyrosine kinase
IPR11009 (protein kinase)
Cleaved azocasein
–
Contig1406
R134.2
gcy-2
Slow growth
Guanylyl cyclase
IPR001054 (guanylate cyclase)
30,50-cAMP+diphosphate
–
Contig11765
Y46H3A.1
srt-42
Extended life span
7-transmembrane receptor
IPR11009 (protein kinase)
ADP+a phosphoprotein
–
L4 (283)
Contig23920
T05G5.3
Embryonic lethal, embryonic
defects, maternal sterile
Protein kinase PCTAIRE
and related kinases
IPR000719 (protein kinase)
ADP+a phosphoprotein
139
Contig1501
K12D12.1
top-2
Embryonic lethal, embryonic
defects, larval arrest
DNA topoisomerase type
II
IPR002205 (DNA girase)
Catenated DNA networks+ADP+phosphate
39
Contig2892
C46A5.4
Protruding vulva
IPR002007 (animal haem
peroxidase
2-Amino-9,10a-dihydro-3H-phenoxazin-3-one
–
Contig20741
C46A5.4
Protruding vulva
IPR002007 (animal haem
peroxidase)
2-Amino-9,10a-dihydro-3H-phenoxazin-3-one
–
Contig25779
R11A5.7
Dumpy
Zinc carboxypeptidase
IPR000834 (Zinc
carboxypeptidases)
4-chlorocinnamic acid+L- b-phenyllactate
5
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between two or among more data sets, are available from
the primary author upon request.
DISCUSSION
Technical considerations
We demonstrated the utility of an integrated bioinformat-
ic workflow system for the analysis and annotation of
large sequence data sets produced by NGS. This system
is considered useful for researchers with basic expertise in
computer programming but without the means for de-
veloping bioinformatic pipelines or purchasing expensive
soft- or hardware packages. The system constructed here
was appraised according to: (i) computational time
required to perform the analyses, (ii) ease of use, (iii) com-
patibility with different computer operating systems, (iv)
ability to focus the analyses on answering relevant bio-
logical questions and (v) general applicability.
The majority of the software incorporated in the bio-
informatic workflow was derived from existing application
tools (e.g. CAP3=maximum length of 50kb) available as
web-based interfaces, and originally designed for the
analysis and annotation of a relatively small number of
sequences. These applications were adapted here to face
the challenges presented by the need to analyse large
sequence data sets in a time-efficient manner. Indeed, the
original sequence data sets described herein, which
included a total of ?2 million sequences (244±32
bases), could be analysed and annotated using a 2 CPU
Linux computer with 8 processor cores, within ?2000
computing hours corresponding to ?240 man-hours (one
computing hour = 1 hour of computing time on one pro-
cessor core). Based on our experience, the same analyses,
conducted using web-based interfaces, require several
months tocomplete. However,
web-based software tools with extensive graphical inter-
faces isthat noknowledge
programming is required (29). The process of developing,
trouble-shooting, maintaining and updating scripts can be
involved and challenging, laborious and time-consuming.
On the other hand, the use of a command line (which
consists of a series of standardized commands) to
execute pre-existing scripts, such as the Perl, Python and
Unix shell, which have been written and made available
here, overcomes this limitation. Furthermore, although
these scripts have been written and optimized using the
Linux operational system, the output files (generated in
the form of text or tab delimited files) can be readily
viewed, analysed and modified in a range of different
operating systems, such as Microsoft Windows and Mac
OS, thus being broadly applicable.
A key goal for scientists focusing on the analyses of
large NGS data sets is to distil, from large amounts of
raw data, biologically meaningful information about the
organism under investigation. For example, some patho-
gens, such as parasitic worms, have complex life cycles and
thus represent a challenging group of organisms for
genomic and transcriptomic studies, because different
life stages can express various sets of genes which are
involved in development, reproduction, host–parasite
anadvantage of
of computingand/or
interactions and/or disease (17,37–39). Understanding
these aspects should have important implications for
finding new ways of disrupting biological processes and
pathways, and thus could facilitate the prediction and pri-
oritization of new drug and/or vaccine targets. In
addition,comparedwith
C. elegans, there is a paucity of knowledge on the funda-
mental molecular biology of parasitic worms (17,39,53).
However, extensive information is available on the func-
tions of C. elegans genes through the use of gene silencing
and/or transgenesis (see www.wormbase.org). This know-
ledge, together with the results of comparative analyses of
genetic data sets, revealed that parasitic nematodes usually
share ?50–70% of genes with C. elegans (54,55),
indicating the utility of this free-living nematode as a
model to explore molecular aspects of development,
survival and reproduction in some parasitic nematodes
(18,38,51,56,57).
thefree-livingnematode
Biological interpretations from the annotated data set
The bioinformatic workflow system constructed here was
utilized to exploredifferential
O. dentatum. Several reports indicate that this nematode
provides a unique model system for studying fundamental
aspects of the molecular biology of gastrointestinal
strongylid nematodes (58). The in silico subtraction
approach identified 139 and 243 protein motifs specific
to the adult female and male of O. dentatum, respectively.
Most of these molecules could be linked, using KOBAS
analyses and genetic interaction networking, to pathways
associated with reproductive processes. For instance, a
large numberof female-specific
proteins containing a ‘chitin-binding protein, peritrophin
A’ domain (i.e. n=18; Table 2). This domain was also
found to be highly represented amongst the molecules
enriched in the female of the pig roundworm, Ascaris
suum (59). These proteins are hypothesized to have
crucial roles in pathways linked to developmental and re-
productive processes, based on the knowledge that the
corresponding C. elegans homologues (containing one or
more peritrophin-A domains) CPG-1/CEJ-1 and CPG-2
are essential for the synthesis of the eggshell as well as for
early embryonic development (60). The production and
maturation of oocyteshas
C. elegans, to be regulated by nematode-specific bipartite
signalling molecules, the major-sperm proteins (MSPs)
(61,62). Numerous sequences unique to the adult male
of O. dentatum represented MSPs (n=15; c.f. Table 2),
in accordance with previous studies of male-enriched data
sets of other species of strongylid nematodes, including
Trichostrongylus vitrinus (63), Haemonchus contortus
(38), as well as the filarioid Brugia malayi (64–66), and
A. suum (59). Based on the observation that MSPs from
various nematodes, including C. elegans, are characterized
by a significant amino acid sequence conservation (i.e.
?64%) (67), a similar role has been proposed for these
proteins in processes linked to the maturation of oocytes
in the uterus of female nematodes (61,62).
In addition to molecules unique to adult female and
male of O. dentatum, the predicted proteins exclusive to
transcription in
moleculesencoded
alsobeenshown, in
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the larval stages of this parasite could be linked, using
InterPro and/or GO classification and/or probabilistic
genetic interaction networking, to biological pathways
associated with larval development and/or interactions
with the vertebrate host (see Table 2). For example, a
large number of molecules unique to the L4 stage
(n=10) were inferred to represent proteases. In parasitic
nematodes, proteases have been proposed to facilitate the
survival of the parasite by mediating, for instance, tissue
penetration, feeding and/or immune evasion (68–70).
Indeed, O. dentatum L4s are known to evoke immuno-
logical reactions that result in the encapsulation of the
larvae in nodules with aggregations of neutrophils and
eosinophils (58,71). In addition, somatic extracts of and
supernatants from in vitro maintenance cultures of
O. dentatum L4s have been shown to induce the prolifer-
ation of porcine mononuclear cells in vitro (72). These
observations suggest an active role for L4-specific prote-
ases in the modulation of the host’s immune response,
which (as proposed for other biological systems) could
consist of: (i) the direct digestion of antibodies (68);
(ii) cleavage of cell-surface receptors for cytokines (73)
and/or (iii) direct lysis of immune cells (74). In parasitic
nematodes, other molecules have been proposed to play
immuno-modulatory roles during the invasion of the host,
the migration through tissues as well as feeding. Amongst
them, proteins containing a ‘sperm-coating protein
(SCP)-like extracellular domain’ (InterPro: IPR014044),
also called SCP/Tpx-1/Ag5/PR-1/Sc7 (SCP/TAPS; Pfam
accession number no. PF00188), were highly represented
in the transcriptome of O. dentatum (see Table 2).
Members of the SCP/TAPS protein family have been
identified in various eukaryotes, including plants, arthro-
pods, snakes, mammals as well as free-living and parasitic
helminths (75). These molecules have been studied mainly
in the hookworms Ancylostoma caninum and Necator
americanus, andare commonly
Ancylostoma secreted proteins (i.e. ASPs; 75). Due to
their abundance in the excretory/secretory (ES) products
from serum-activated L3s (=aL3s) of A. caninum and
to the high levels of mRNAs encoding ASPs in aL3s
compared with non-activated, ensheathed L3s (L3s),
these molecules have been hypothesized to play a major
role in the transition from the free-living to the parasitic
stage of this species (39,76). Other ASP homologues have
been characterized for the adult stage of hookworms, and
suggested to play a role in the initiation, establishment
and/or maintenance of the host-parasite relationship
(39,77,78). Although a male-biased transcription of ASP
homologues had been reported for O. dentatum (51),
results from the present study show that the transcription
of SCP/TAPS molecules occurs in all developmental
stages studied herein. As the sequences analysed were
generated from normalized cDNA libraries, the differ-
ences in levels of transcription of genes encoding SCP/
TAPS throughout the life cycle of O. dentatum could not
be inferred. Future work could involve, for instance, the
application of the present bioinformatic workflow tool to
theanalysis ofdatagenerated
sequencing) from non-normalized cDNA libraries of
O. dentatum, which would allow quantitative rather than
referredas to
(e.g. by Illumina
qualitative differences in transcription to be determined
for genes encoding SCP/TAPS, to assist in the study of
the biological function(s) of these molecules (75). The
O. dentatum-pig model could also provide a useful
means of exploring the biological role/s of these molecules
in the development and reproduction of this nematode as
well as its interactions with the host. Several features of
O. dentatum, including its short life-cycle, its ability to
survive and grow in culture in vitro for weeks through
several moults, and the possibility of rectally transplanting
worms (e.g. from in vitro culture) into the host without the
need for surgical intervention (58,79), offer an opportun-
ity to experimentally test hypotheses formulated based
on the interpretation of results from bioinformatic
analyses. Bioinformatically guided interpretations of
NGS data sets are also increasingly playing an important
role in the identification of putative drug targets (80), due
to the possibility of using predictive algorithms to priori-
tize and select sets of molecules for experimental studies
both in vitro and in vivo (81–83), potentially leading to a
significant reduction in the cost associated with drug
discovery and development (84). For instance, in the
present study, subsets of molecules without known host
(pig) homologues were identified and predicted to repre-
sent targets for intervention. Amongst them, protein
kinases and phosphatases were the most abundantly rep-
resented (Table 4). Previously, in O. dentatum, a catalytic
subunit of a serine/threonine protein phosphatase (PP1)
was characterized (Od-mpp1); gene silencing by RNAi
of the corresponding C. elegans homologue resulted in a
significantreduction(30–40%)
F2-progeny produced (56). Based on these findings, it is
tempting to speculate that some pathways, involving
phosphatases/kinases, represent key targets for nemato-
cidal drugs.
inthe numbersof
Concluding remarks
Here, we demonstrated, using a large test data set derived
from differentstages/sexes
(O. dentatum), that our bioinformatic workflow system
provides a practical tool for the assembly, annotation
and analysis of NGS data. The custom-written Perl,
Python and Unix shell computer scripts, accessible via
the web, can be readily adapted to suit the requirements
of researchers conducting transcriptomic studies in their
particular discipline. This workflow system is now
routinely used by our research group for the analysis of
data setsfroma range
socio-economic importance and has been applied more
broadly to data sets representing other organisms,
including mammals. Thus, this integrated system should
be a user-friendly and efficient tool for biologists involved
in transcriptomic studies in any field on any organism.
ofa parasiticworm
ofpathogensof major
SUPPLEMENTARY DATA
Supplementary Data are available at NAR Online.
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ACKNOWLEDGEMENTS
Staff at WormBase are gratefully acknowledged. The
Austrian Ministry for Science and Research approved
the animal experimentation (BMWF-68.205/0103-II/10b/
2008) and is also acknowledged. C.C. is in receipt of an
International Postgraduate Research Scholarship from the
Australian Government and a fee-remission scholarship
from The University of Melbourne as well as the
Clunies Ross (2008) and Sue Newton (2009) awards
from the School of Veterinary Science of the same
university.
FUNDING
The Australian Research Council; Australian Academy of
Science; the Australian-American Fulbright Commission
(to R.B.G.); National Human Genome Research Institute
and National Institutes of Health (to M.M.).
Conflict of interest statement. None declared.
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