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Al-Shahib et al. BMC Bioinformatics 2010, 11:437
http://www.biomedcentral.com/1471-2105/11/437

RESEARCH ARTICLE

Open Access

Coherent pipeline for biomarker discovery using
mass spectrometry and bioinformatics
Ali Al-Shahib*, Raju Misra, Nadia Ahmod, Min Fang, Haroun Shah, Saheer Gharbia

Abstract
Background: Robust biomarkers are needed to improve microbial identification and diagnostics. Proteomics
methods based on mass spectrometry can be used for the discovery of novel biomarkers through their high
sensitivity and specificity. However, there has been a lack of a coherent pipeline connecting biomarker discovery
with established approaches for evaluation and validation. We propose such a pipeline that uses in silico methods
for refined biomarker discovery and confirmation.
Results: The pipeline has four main stages: Sample preparation, mass spectrometry analysis, database searching
and biomarker validation. Using the pathogen Clostridium botulinum as a model, we show that the robustness of
candidate biomarkers increases with each stage of the pipeline. This is enhanced by the concordance shown
between various database search algorithms for peptide identification. Further validation was done by focusing on
the peptides that are unique to C. botulinum strains and absent in phylogenetically related Clostridium species.
From a list of 143 peptides, 8 candidate biomarkers were reliably identified as conserved across C. botulinum
strains. To avoid discarding other unique peptides, a confidence scale has been implemented in the pipeline
giving priority to unique peptides that are identified by a union of algorithms.
Conclusions: This study demonstrates that implementing a coherent pipeline which includes intensive
bioinformatics validation steps is vital for discovery of robust biomarkers. It also emphasises the importance of
proteomics based methods in biomarker discovery.

Background
Within the last decade, mass spectrometry has been
widely used for identifying and characterising proteins
within complex mixtures. This is mainly due to its high
sensitivity, specificity, mass accuracy and good dynamic
range. Some of the identified proteins can be characterised as unique to the organism thus qualifying them
as potential biomarkers. Such biomarkers can be used to
monitor the pathological changes within cells and lead
to a better understanding of disease processes while
improving diagnostics. Furthermore, they can also be
used to detect biological agents thus providing a fast
and accurate identification system [1,2]
In a typical bottom up mass spectrometry proteomic
approach, a complex protein mixture is first digested
with a proteolytic enzyme such as trypsin. The resulting
peptides are separated by liquid chromatography and
* Correspondence: ali.al-shahib@hpa.org.uk
Health Protection Agency, Centre for Infections, 61 Colindale Avenue,
London, NW9 5EQ, UK

subjected to analysis by mass spectrometry. The peptides are then ionized (in MS mode) and selected ions
are further fragmented (in MS/MS mode). The resulting
MS/MS spectra displaying the ion fragments derived
from the selected peptide ions are submitted to search
algorithms for peptide identification [3]. The most efficient method to identify the amino acid sequence from
the MS/MS spectra is based on searching the spectra
against a protein sequence database. This is done using
various algorithms such as Sequest [4], Mascot [5] and
X!Tandem [6]. Generally, all the algorithms follow the
same principle: since peptide fragmentation can be predicted, the algorithms compare an observed MS/MS
fragmentation pattern with theoretical (predicted) fragmentation patterns from peptides (in proteins) contained in the selected protein database. The output will
be a list of peptide sequences that have the closest
match to the observed spectrum.
Database search algorithms apply various constrained search parameters when implementing

© 2010 Al-Shahib et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.

Al-Shahib et al. BMC Bioinformatics 2010, 11:437
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comparisons. These include the mass tolerance, proteolytic enzyme constraints (only peptides that conform to the digestion rules of the proteolytic enzyme
are included), number of missed cleavage sites and
amino acid modifications. However, algorithms differ
in their implementation of the scoring system when
comparing the fragment ion spectrum against the theoretical fragmentation patterns in the database. This
score, which can be more than one for a single algorithm, ultimately differentiates between the true and
false peptide identifications (i.e. the likelihood that the
peptide identified has occurred by chance). Typically
those peptides that have a score higher than the
threshold (assigned manually by the user) are accepted
and presented.
The popular Sequest algorithm applies the spectral
correlation functions scoring system where it calculates
the cross correlation score (Xcorr) for each fragment
ion spectrum for all the candidate peptides searched in
the database. Mascot on the other hand applies a probability-based score instead of a score reporting the number of matched peaks. Finally, X!Tandem uses a
statistical scoring system for its peptide assignment as it
converts the database search score into an expectation
value which provides an estimate of whether or not the
observed match is random.
Discovering biomarkers in proteomics has been done
using database search algorithms [7] mass spectral peak
finding [8] and binning [9]. In this study we go beyond
that and evaluate the peptides identified by the database
search algorithms using intensive bioinformatics validation methods. To achieve this we have implemented a
pipeline that identifies peptide biomarkers based on
mass spectrometry and in silico data analysis (Figure 1).
The in silico approaches involve selecting candidate
markers from BLAST [10], verifying the candidates by
comparison with a control, and further validating the
markers by using a consensus approach of database
search algorithms. Studies have shown that accuracy in
protein identification increases by using a concordance
of database search algorithm scores [11-14]. Together
with increasing peptide coverage and thus biomarker
coverage, there are two other reasons for applying this
approach in our pipeline. First, as the algorithm scores
differ between each algorithm, the number of peptides
identified by each algorithm will be different. Therefore
if one was to consider the results from a single algorithm, the likeliness of selecting the ‘correct’ biomarker
will be minimal. Second, as the search databases are
often incomplete and the correct match may not be
available, the MS/MS spectrum may be matched to an
incorrect peptide sequence thus resulting in false positive identification. Two search engines can provide very
different results from the same MS/MS spectrum [11].

Page 2 of 9

The model bacterium used in this study was Clostridium botulinum, a lethal pathogen that causes a rare
form of food poisoning known as botulism. It can be
used as a potential bioterrorism agent because of its
high lethality and mode of transmission.
A crucial step in any study that focuses on the discovery of species specific biomarkers is to eliminate markers that are also present in other species. This ensures
that any biomarkers discovered are only unique to those
strains of interest. C. botulinum consists of a collection
of phenotypically diverse strains that do not cluster
within a single phylogenetically coherent group [15].
Due to these properties, a panel of species that belong
to the genus were selected so that the specificity of any
potential peptide biomarker can be tested. This pipeline
may provide a standard for biomarker discovery using
mass spectrometry and provide a useful tool for microbial identification and disease diagnosis. The pipeline is
described in detail in Figure 1.

Results and Discussion
Proteomic analysis provides tools for examining gene
products and their features such as protein-protein
interactions, post-translational modifications, protein
isoforms and subcellular localization. As proteins are
the functional component of cells, the rapid and sensitive identification of proteins is essential for organism
identification and disease diagnosis. This can be
achieved by discovering robust protein markers through
the use of tandem mass spectrometry based proteomics
which is characterised by its high sensitivity, specificity,
mass accuracy and good dynamic range.
Various database search algorithms have been used to
identify peptides by searching the MS/MS spectra
against a protein sequence database. Each algorithm
adapts a particular scoring system for peptide identification. To generate a biomarker discovery system therefore, there is a need to outline a comprehensive
workflow of mass spectrometry data analysis that
includes intensive validated steps in achieving a robust
biomarker list. Here, we have developed such a pipeline.
Six Clostridium botulinum strains and an additional
eight Clostridium species that range from species that
are phylogenetically closely related to C. botulinum (e.g.
Clostridium Sporogenes) to species that are distantly
related (e.g.Clostridium baratii ) were used to identify
robust biomarkers that can delineate Clostridium botulinum from any other pathogen.
Peptide Identification

From each algorithm, the number of peptides identified
for each replicate ranged between ± 300. This variation
between biological replicates is expected and allows for
the comparison between the replicates in the later phase

Al-Shahib et al. BMC Bioinformatics 2010, 11:437
http://www.biomedcentral.com/1471-2105/11/437

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Figure 1 Proteomics pipeline for biomarker discovery. IP is Identified Peptides and UP is Unique Peptides. This process is applied to each
search algorithm and the refined list of markers from each algorithm is compared and a consensus of the peptides in all algorithms is selected
for a final validated list of markers.

of the pipeline. As shown in Table 1, more peptides
were identified by Sequest followed by X!Tandem and
Mascot. These results agree with Kapp et al [12] who
performed a comparison of the algorithms using blood
specimen data. It is interesting, however, to note the
large difference between Mascot and the other two algorithms. Sequest identified about double the number of
peptides identified by Mascot while X!Tandem identified
around 2500 more peptides than Mascot. One reason
for this could be the fairly stringent Mascot scores
applied (Mascot ion score greater than 20).

Measuring the performance of algorithms

One consequence of the scoring system used by database search algorithms is the possibility for false positive
peptide identifications. The peptides returned by each
algorithm are not necessarily the result of a perfect
match in the database but may be due to coincidental
similarity in MS/MS fragmentation patterns [16]. This
can lead to a very large number of incorrect peptide
sequence assignments in large scale proteomics experiments. One way to measure the portion of incorrect
peptide assignments is to apply a target-decoy search

Table 1 Number of peptides identified by each algorithm
Algorithm(s)

Number of peptides from raw files

Number of biomarkers

X!Tandem

6920

34

Mascot

4465

23

Sequest

7923

12

X!Tandem ∩ Mascot

2324

17

X!Tandem ∩ Sequest
Sequest ∩ Mascot

3342
2241

12
9

X!Tandem ∩ Sequest ∩ Mascot

2081

8

∩ indicates the overlap between the algorithms specified.

Al-Shahib et al. BMC Bioinformatics 2010, 11:437
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strategy [17,18]. This is done by reversing the target
proteins of the search database to generate a decoy database. After the algorithm has identified the peptides, the
correct (True Positive) and incorrect (False Positive) hits
are counted and the false discovery rate (FDR) is calculated by FP/(FP+TP).
Mascot uses the target-decoy strategy and calculates
the FDR for each raw spectral file (.RAW file). There,
the performance was satisfactory as the overall FDR was
around 3%. For Sequest, the FDR had to be calculated
manually by concatenating the decoy with the target
database [18]. Results show the expected number of
incorrectly identified peptides were 988, while the total
number of peptides identified was 24939. Thus FP =
988, TP = 23951 and FDR = 988/24939 = 3.9%.
Unique peptide identification

Using WU BLAST [19] and in-house scripts, unique
peptides from each replicate of Clostridium botulinum
were identified. Uniqueness was measured by selecting
the peptides that had an exact match with an amino
acid sequence in the NCBI non redundant database
(Figure 2). The selected peptides equate to 5% of the
overall peptides identified by X!Tandem and Mascot and
4% for Sequest. The lists of peptides from each replicate
for every algorithm were compared to eliminate the

Page 4 of 9

non-conserved peptides amongst the three replicates.
For X!Tandem, 43% of the peptides were conserved in
all the three replicates, followed by 38% and 30% for
Mascot and Sequest respectively.
Comparison with other Clostridia species

Peptides were identified from eight non-botulinum Clostridial species searched against the Swiss-Prot Protein
database [20]. For all eight species, we see a similar
trend with the C. botulinum strains where Sequest identified 16318 peptides followed by X!Tandem with 16116
and finally Mascot with 14222 peptides (Figure 3). This
could be because of Mascot’s fairly strict score statistic
that limits the algorithm for identifying peptides. The
non-Clostridium botulinum peptides were compared
with peptides identified from Clostridium botulinum to
ensure the peptides identified are only present in C.
botulinum and not any other Clostridium species. Peptides identified by Mascot were refined by 25%, X!Tandem 25% and Sequest 20% which highlight the
importance of experimental controls.
To examine whether the unique peptides selected are
conserved amongst all C. botulinum strains, a comparison of these peptides across all C. botulinum strains was
performed. For X!Tandem, 357 peptides were present in
at least one of C. botulinum strains and 33 were

Figure 2 Refinement of peptides. Unique Peptides (UP) were identified by selecting the peptides that had an exact hit in BLAST against the
NCBI non-redundant database. These peptides were refined by selecting the consensus of peptides in all biological triplicates (Rep ∩ 1 ∩ 2 ∩ 3)
followed by selecting the peptides that are not present in non-Clostridium botulinum species (Non-CBOT). The final phase selects only those
peptides that are present in all six C. botulinum strains (All strains). We have labelled these peptides as ‘Biomarkers’.

Al-Shahib et al. BMC Bioinformatics 2010, 11:437
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Figure 3 non-Clostridium botulinum species union. Venn
diagram showing the intersection between all peptides identified
from non-Clostridium botulinum species by X!Tandem, Mascot and
Sequest.

conserved across all strains. As for Mascot, 22 unique
peptides were conserved from a possible 224 and 11
from a possible 316 were conserved for Sequest.
Conserved unique peptides

To add further statistical significance to the peptides of
interest, we decided to select the peptides that are conserved across the list of unique peptides in all C. botulinum strains. This is important for the identification of
microorganisms as the biomarkers selected in this study
must be present in all C. botulinum strains for the
detection of the pathogen in an unknown sample. However, this should not be the case for all studies involved
in biomarker discovery. In some cases, unique peptides
that are present in at least one but not all strains can
also qualify as potential biomarkers as it could be specific to certain strains only. It is therefore important to
study whether non-conserved peptides should be
dismissed prior to the implementation of the pipeline.

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by 8 peptides while Sequest only overlaps by 3 peptides
with X!Tandem and none with Mascot. However, the
level of overlap seems to increase when a third algorithm is introduced. This achieves a final list of 8 candidate biomarkers that are identified as a consensus of the
three algorithms, which ultimately gives higher confidence to each of the peptides identified by all
algorithms.
There are three reasons for this. First, each algorithm
implements a different scoring system that identifies the
peptides according to a particular score. By utilizing the
complementary score strength of each algorithm, we
can maximise biomarker coverage and achieve higher
confidence in the identification. Second, by combining
the results from different search engines, we expect a
reduction in the noise level obtained from the MS/MS
spectra. This is important because the discriminatory
effectiveness of the algorithm is reduced by the presence
of noise or low quality MS/MS spectra. Thirdly, the
search databases are often incomplete which can result
in incorrect peptide identification. This is the case as
the algorithm will return a match for all the spectra,
regardless of whether the correct peptide/protein is present in the database [11]. Therefore, to increase the
chances of selecting a robust biomarker, it is good practice to apply a union of multiple algorithms that can
reduce the number of incorrect peptide identifications
and provide more robust markers.
For these reasons, we have assigned a confidence scale
for biomarker identification with algorithm intersection.
Table 2 shows the confidence assignment of the candidate biomarkers discovered after the implementation of
the pipeline using C. botulinum MS/MS data. Highest
to lowest confidence (Rank 1-3) was assigned respectively to the unique peptides that were identified by all
three algorithms followed by peptides identified by two
algorithms and finally peptides that were exclusively
identified by a single algorithm. The need for this confidence scale provides the researcher or the clinician with
three sets of candidate biomarkers that can be given
priorities for further experimental validation. From this,
we avoid discarding peptides that can potentially be
important signatures in species identification and clinical diagnosis.

Concordance between database search algorithms

One possible way of validating the performance of a
database search algorithm and to maximise peptide coverage is to have other algorithms corroborate the results
from a single algorithm. Here, the refined list of unique
peptides from each algorithm were collected and compared to achieve a final list of candidate biomarkers that
have been recognised by all algorithms (Figure 4). Some
overlap of the result is evident when comparing two
algorithms. For example, X!Tandem and Mascot overlap

Candidate biomarker functions

Candidate biomarkers were classified into functional
categories using the KEGG Automatic Annotation Server [21]. Proteins assigned to these categories belong to
ancient conserved domains and six broad functional
categories exist. The results show that candidate biomarkers fall into 4 different broad functional categories
(Table 2). This highlights that the peptides identified
belong to a diverse range of proteins and the pipeline is

Al-Shahib et al. BMC Bioinformatics 2010, 11:437
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Figure 4 Overlap and concordance between database search algorithms. Both diagrams show the intersection between the three
algorithms in peptide identification. The diagram on the left shows the intersection of the algorithms at identifying peptides prior to in silico
validation (first step in the pipeline). The diagram on the right shows the overlap of the different algorithms in biomarker discovery after the
complete implementation of the pipeline. The arrow represents the flow of the pipeline to achieve a final refined list of biomarkers.

not bias towards any specific functional group. Peptide
candidate biomarkers were mainly found to belong to
the metabolism and genetic information processing categories. The genetic information-processing category
includes genes such as ribosomal proteins, initiation factors and elongation factors. This category contains
many genes that encode for proteins that have housekeeping functions essential for the living cell. One such
example is a candidate peptide present within the prolyl-tRNA synthetase. This protein is essential in bacteria
and expressed when a microbial cell is actively growing.
Biomarkers derived from such proteins are crucial in
the development of diagnostic assays as they represent
markers from proteins that are constitutively expressed
independent of growth phase. The method described in
this paper relies on the detection of peptides from
organisms grown under standardised condition but the
discovery of biomarkers that originate from essential
proteins allows for less stringent growth conditions to
be applied and also allows for the detection of biomarkers directly from environmental and clinical samples.

Conclusions
There is has been a lack of a comprehensive and sensitive pipeline to discover novel biomarkers for microorganism identification and disease diagnosis. Proteomics
methods based on mass spectrometry hold special value
for the detection of biomarkers due to their high sensitivity and specificity. In this study, we have designed
and implemented a proteomic pipeline that uses mass
spectral data together with intensive bioinformatics
approaches for biomarker discovery. As the peptides are
refined in the pipeline, we show that the robustness of
the unique peptides increases with every stage of the
pipeline. We conclude that 8 C. botulinum candidate

biomarkers have been selected with high confidence and
31 others selected with less confidence for species identification. Further optimisation of the pipeline can be
implemented by the verification of the biomarkers at the
DNA level. This is important as the proteomic approach
applied here is dependent on protein expression and the
genomic validation of these biomarkers at the DNA
level ensures specificity in strains where genome data is
not available. We envisage the marriage of proteomics
and genomics and how this will develop to be a valuable
tool for biomarker discovery.

Methods
Sample Preparation

Six Clostridium botulinum strains, (C. botulinum toxin
type A NCTC 7272 and clinical isolate AHO6506,C.
botulinum toxin type B NCTC 7273, NCTC 3807 and
NCTC 751, C. botulinum toxin type F NCTC 10281)
and eight strains representing pathogenic and nonpathogenic phylogenetically related Clostridial species
were cultured and protein extractions performed in triplicate. Non-Clostridium botulinum strains included
four strains of Clostridium sporogenes, two strains of
Clostridium perfringens, one strain of Clostridium butyricum and one strain of Clostridium tetanii. Clostridium
botulinum strains were cultured on Clostridium botulinum isolation agar under anaerobic conditions at 37°C
for 48 hours while non-C. botulinum strains were grown
under the same conditions but on fastidious anaerobic
agar. From each culture, a starting inoculum was transferred using a loopful of cells in 20 ml of TrypticasePeptone-Glucose-Yeast Extract Broth. The turbidity of
the starting culture was adjusted to give an optical density at 600 nm (OD600) of approximately 0.3 to ensure
broths contained equivalent cell mass before incubation

Al-Shahib et al. BMC Bioinformatics 2010, 11:437
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Table 2 Sequence and functions of C. botulinum
candidate biomarkers
Rank Biomarker Sequence Algorithm Protein Annotation
(s)
EAEYIFGNFGK

X ∩ M ∩ S Unknown

EDLTNVFDLSER

X ∩ M ∩ S Unknown

ENLITAPENTTIGEAK

X ∩ M ∩ S Metabolism Multiple
pathways

GMDTLLESFIK

1

X ∩ M ∩ S Metabolism Multiple
pathways

INEFPEILEYK

X ∩ M ∩ S Enzyme Families

MNEIMQDDTLER

X ∩ M ∩ S Carbohydrate Metabolism

TIVSLDEIEIK
YANIADYLSLGGK

X ∩ M ∩ S Translation
X ∩ M ∩ S Unknown

AAGLEIGETGAIK

X∩M
X∩M

Replication and Repair

GAYVLNKEEIEK

X∩M

Metabolism Multiple
pathways

GEPIVLDNGAVAGQAFR X ∩ M

Replication and Repair/
Cell motility

IMNDFSMLASK

X∩M

Unknown

NDIVVVSPDLGSVTR

X∩M

Metabolism Multiple
pathways

QAATAIDEYLSK

X∩M

Metabolism Multiple
pathways

TSAEGIEIVAK

X∩M

Amino Acid Metabolism

DLSTNSWTMIR

X∩S

Replication and Repair

IIDMNNAAIDEGVNAIVK X ∩ S

Unknown

TTTIPSMVEALSR
3

Table 2 Sequence and functions of C. botulinum candidate biomarkers (Continued)
SMPALITAISELNQPR

M

Unknown

TRFETNLAVANHLVDK

M

Translation

X = X!Tandem, M = Mascot, S = Sequest and ∩ = intersection. The first
column indicates the rank of the biomarkers. Rank 1 markers have higher
confidence than the other markers due to the concordance of the search
algorithms in their identification. The second column shows the amino acid
sequence of the candidate biomarker. Column 3 shows the algorithm(s) used
to identify the candidate biomarker. Where there is no algorithm intersection,
the candidate biomarker was exclusively identified by the single algorithm.
The final column indicates the functional classification of the proteins (of the
listed peptides) obtained from KEGG [21]. The biomarkers fall into 4 different
categories: Metabolism, Genetic Information Processing (Replication and
Repair, Cell motility and Translation), Enviromental Information Processing
(Membrane Transport) and Unknown.

Unknown

AFIESVEEALEGGEK

2

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X∩S

Translation

AMESILVVIPAEK

X

Unknown

CLVAEEETGLTTR

X

Metabolism Multiple
pathways

EALTEYLLNMSTR

X

EGSFIYVIGPK

X

Metabolism Multiple
pathways
Energy Metabolism

GIENVNVFTVR

X

Unknown

GLEVQEEVLNK

X

Nucleotide Metabolism

GTNIVNIIPIENNEK

X

Replication and Repair

GTNIVNLIPIENNEK

X

Replication and Repair

IGNIVEHEETPQISGMIK

X

Translation

ISTVGDVVDYIK

X

Unknown

KWDEDKFEEVMK
TFGVELEDEPSGK

X
X

Unknown
Amino Acid Metabolism

VGIAHNVTPETVEK

X

Amino Acid Metabolism

WYIVDAADKPLGR

X

Translation

LGLAEDEAIESK

M

Membrane Transport

MIGYDIFENEEAK

M

Carbohydrate Metabolism

MIGYDLFENEEAK

M

Carbohydrate Metabolism

QVLSFVTEETVVQR

M

Unknown

under anaerobic conditions at 37°C for 24 hours to an
OD 600 of 2-2.7. Liquid cultures were centrifuged at
10000 × g for 10 minutes at 4°C to harvest the cells.
Broth supernatant was removed and the cell pellet
washed with 1 ml 1× TE buffer (Sigma-Aldrich) containing a 1× protease inhibitor cocktail (Roche). The
cells were centrifuged at 10000 × g for 10 minutes at
4°C to re-pellet the cells and the supernatant removed.
The wash step was repeated before the cell pellet from
each sample was used for protein extraction.
The cell pellet was treated with 1.5 ml of 1× TE buffer
containing 6 μg/μl lysozyme (Sigma-aldrich) and a 1×
protease inhibitor cocktail (Roche) and incubated for 1
hour at 37°C on a heating block. Cells were collected by
centrifugation at 8000 × g for 10 minutes at 4°C and the
supernatant removed. The pellet was re-suspended in
100 μl of a solubilisation cocktail (30 mM Tris-Cl pH
8.5, 7 M Urea, 2 M Thiorurea, 4% CHAPS and 70 mM
DTT) and solubilisation allowed to occur for 30 minutes
at room temperature. The suspension was clarified by
centrifugation at 21000 × g for 30 minutes at 21°C.
Extracts were filtered through 0.2 μm anopore vectra
spin filters (Whatman) by centrifugation at 8000 × g
and 4°C and the filtrate transferred to fresh 1.5 ml
tubes.
The protein concentration of each preparation was
measured using a Bradford assay. Proteins (10 μg) were
then separated by one-dimensional SDS-PAGE using
NuPAGER® Novex 1.0 mm 4-12% Bis-Tris gels (Invitrogen). Each gel lane was excised into 12 bands that were
in-gel digested with trypsin (Promega) for 16 h at 37°C.
Peptides were extracted from gel bands with 0.1% TFA.

Al-Shahib et al. BMC Bioinformatics 2010, 11:437
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Liquid Chromatography and Mass Spectrometry

Peptide digests were analysed using online nano liquid
chromatography and tandem mass spectrometry (nano
LC-MS/MS) on an Ultimate 3000 Dionex nano/capillary
HPLC system (Dionex) coupled to a LTQ Orbitrap mass
spectrometer (Thermo Electron). The separations were
performed on a nano analytical C18 column (75 μm id ×
15 cm, 3 μm) (Dionex) using a 45-minute linear gradient
of 5 to 45% solvent B (90% CH 3 CN/0.1% formic acid)
versus solvent A (2% CH3CN/0.1% formic acid), then to
90% B for an additional 5 minutes. The mass spectrometer was operated in a standard data-dependent mode
to automatically switch between MS and MS/MS acquisition. The full survey scan (m/z 440-2000) was acquired
in the Orbitrap with a resolution of 60,000 at m/z 400,
which was followed by six MS/MS scans in which the
most abundant peptide precursor ions detected in the
preceding survey scan were dynamically selected and
subjected for collision-induced dissociation (CID) in the
linear ion trap to generate MS/MS spectra.
Database searching

MS data were generated in the form of .RAW files
(Thermo Finnigan file formats), which contain all the spectra detected from the LC-MS/MS analysis for each sample.
Each replicate was examined separately. Three database
search algorithms were used: X!Tandem (freeware distributed by Global Proteome Machine Organization), Mascot
(Matrix Science, UK) and Sequest Bioworks version 3.3
(Thermo Finnigan, San Jose, CA). The same parameters
were applied for all three algorithms for a fair assessment
of peptide identification. They were: Enzyme: trypsin; Fixed
(or static) Modifications: carbamidomethylation of cystine;
Variable Modifications: oxidation of methionine; Missed
Cleavage Sites: 2; peptide mass tolerance ± 10 ppm. Specific X!Tandem parameters were: refinement was disabled;
maximum valid E-value for reported peptides was 0.1.
An in-house Perl script (Additional file 1) was written
to export peptide sequences from Mascot .dat files.
Sequences that had the specific Mascot score of > 20
were reported. The protein sequence database used by all
algorithms was the Swiss-Prot Protein database [20] containing 498110 non-redundant proteins. For false positive
discovery assessment, a decoy database was used. Mascot
generates this automatically and uses the results to calculate the false discovery rate. A decoy database was used
in Bioworks for Sequest using its .reverse function. We
decided to concatenate the decoy and target databases as
recommended by Elias et al [18]. FDR was then calculated based on the true and false positive identifications.
Data Analysis

The output from each algorithm was analysed and
scripts were used to remove duplicate peptides from

Page 8 of 9

each replicate and the data was converted to FASTA.
For each replicate, BLASTP [10] in WU-BLAST 2.0 [19]
was adjusted for short sequences and used to identify
specific markers. The command line argument used to
run blastp was -v = 500 -b = 40 -filter = none -e = 5000
-mformat = 2 -matrix = pam30 -nogap -hspmax = 40
-cpus = 8. To identify the biomarkers from the BLAST
output, an in-house script written in Perl (Additional
file 2) was implemented. The script works as follows:
For each alignment, if the description states Clostridium
botulinum, the script looks for 100% sequence identity
to the query sequence. If this is satisfied, then the length
of the query sequence is determined and searches for an
exact match in the alignment. A search for any match
in that block of alignments is then made, and if there
are no conflicts, the script will label the match as a biomarker. Peptides identified from each replicate were
compared to find the peptides that are conserved
amongst all three replicates. To ensure that these peptides are unique to C. botulinum and not any other species, a comparison of peptides were made from C.
botulinum to the closest Clostridium species as these
are genetically the most likely biological systems to have
common sequence similarity. The peptides that were
shared with these strains were eliminated and a refined
list of markers was produced. In the next phase of the
pipeline, for each algorithm, only those unique peptides
that are present in six C. botulinum strains were
selected. Finally the three refined lists of markers from
each algorithm were compared and the consensus
between the three lists was selected as the final marker
list. The in silico pipeline is summarised in Figure 1.

Additional material
Additional file 1: Extracting peptide sequences with a user set cuttoff score from Mascot .dat files. This Perl script parses out MASCOT .
dat files and outputs the peptide sequence with a score cut-off set
below. Input: Mascot .dat files. Output: Peptide sequences identified by
Mascot in FASTA format.
Additional file 2: Unique Peptide Identification from WU BLAST
output. This is script written in Perl and will identify potential biomarkers
from a WU BLAST output. Input: Peptide sequences identified from MS/
MS experiment and WU BLAST output file. Output: Unique peptides in
FASTA format. The script works as follows: For each alignment, if the
description states e.g. Clostridium botulinum, the script looks for 100%
sequence identity to the query sequence. If this is satisfied, then the
length of the query sequence is determined and searches for an exact
match in the alignment. A search for any match in that block of
alignments is then made, and if there are no conflicts, the script will
label the match as a biomarker.

Acknowledgements
This work has been supported by a grant from the United Kingdom Home
Office. We thank Philip Ashton for his contribution to this work. We also
thank Noor Ahmad for his input.

Al-Shahib et al. BMC Bioinformatics 2010, 11:437
http://www.biomedcentral.com/1471-2105/11/437

Authors’ contributions
AA performed the bioinformatics analysis and prepared the manuscript. RM
devised and overlooked the study and checked the manuscript. NA
performed the sample preparation and contributed to the manuscript. MF
performed the mass spectrometry and contributed to the manuscript. HS
and SG supervised the study. All authors have read and approved the final
manuscript.
Received: 13 May 2010 Accepted: 26 August 2010
Published: 26 August 2010
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doi:10.1186/1471-2105-11-437
Cite this article as: Al-Shahib et al.: Coherent pipeline for biomarker
discovery using mass spectrometry and bioinformatics. BMC
Bioinformatics 2010 11:437.

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