Transcript Quality Control In Biological Databases

Data Quality Issues:
Traps & Pitfalls
Ashok Kolaskar
Vice-Chancellor
University of Pune, Pune 411 007. India
[email protected]
Cancer cell growth appears to
be related to evolutionary
development of plump fruits
and vegetables
• Large tomatoes can evolve from wild, blueberry-size
tomatoes. The genetic mechanism responsible for this is
similar to the one that proliferates cancer cells in
mammalians.
• This is a case where we found a connection between
agricultural research, in how plants make edible fruit
and how humans become susceptible to cancer. That's a
connection nobody could have made in the past.
Cornell University News, July 2000
Size of Tomato Fruit
Single gene, ORFX, that is
responsible for QTL has a
sequence
and
structural
similarity to the human
oncogene c-H-ras p21.
Fruit size alterations, imparted
by fw2.2 alleles, are most likely
to be due to the changes in
regulation rather than in
sequence/structure of protein.
•fw2.2: A Quantitative Trait Locus (QTL) key to the Evolution of Tomato Fruit
Size. Anne Frary (2000) Science, 289: 85-88
Genome Update: Public
domain
• Published Complete Genomes: 59
– Archaeal
9
– Bacterial
36
– Eukaryal
14
• Ongoing Genomes: 335
– Prokaryotic 203
– Eukaryotic 132
Private sector holds
data of more than 100
finished & unfinished
genomes.
Challenges in Post-Genomic era: Unlocking
Secretes of quantitative variation
• For even after genomes have been sequenced
and the functions of most genes revealed, we
will have no better understanding of the
naturally occurring variation that determines
why one person is more disease prone than
another, or why one variety of tomato yields
more fruit than the next.
• Identifying genes like fw2.2 is a critical first
step toward attaining this understanding.
Value of Genome Sequence Data
• Genome sequence data provides, in a rapid
and cost effective manner, the primary
information used by each organism to carry
on all of its life functions.
• This data set constitutes a stable, primary
resource for both basic and applied research.
• This resource is the essential link required to
efficiently utilize the vast amounts of
potentially applicable data and expertise
available in other segments of the biomedical
research community.
Challenges
• Genome databases have individual genes
with
relatively
limited
functional
annotation
(enzymatic
reaction,
structural role)
• Molecular reactions need to be placed in
the context of higher level cellular
functions
Nature of Biological data
• Biomolecular Sequence Data
– Nucleic acids
– Protein
– Carbohydrates
– Genes and Genome
• Biomolecular structure data
• Pathways/wire diagrams
• DNA array data
• Protein array data
Bioinformatics Databases
•
•
•
•
Usually organised in flat files
Huge collection of Data
Include alpha-numeric and pictorial data
Latest databases have gene/protein expression data
(images)
Demand
• High quality curated data
• Interconnectivity between data sets
• Fast and accurate data retrieval tools
– queries using fussy logic
• Excellent Data mining tools
– For sequence and structural patters
What is CODATA?
• CODATA is the committee on Data for Science
and Technology of the International Council of
Scientific Unions.
• It was established in to improve the quality,
reliability,
processing,
management
and
accessibility of data for science and technology.
• CODATA
Task
Group
on
Biological
Macromolecules has recently surveyed quality
control issues of archival databanks in in
molecular biology
Task Group on Biological Macromolecules
E. E. Abola
A. Bairoch
W. C. Barker
S. Beck
D. A. Benson
H. Berman
G. Cameron
C. Cantor
S. Doubet
T.J.P. Hubbard
T. A. Jones
G.J. Kleywegt
A.S. Kolaskar
A. Van Kuik
A. M. Lesk
H. –W. Mewes
D. Neuhaus
F. Pfeiffer
L.F. TenEyck
R.J. Simpson
G. Stoesser
J. L. Sussman
Y. Tateno
A. Tsugita
E. L. Ulrich
J. F. G. Vliegenthart
Quality Control Issues
• The quality of archived data can, of course, be no
better
than the data determined in the
contributing laboratories.
• Nevertheless, careful curation of the data can
help to identify errors.
• Disagreement between duplicate determinations
is as always, a clear warning of an error in one or
the other.
• Similarly, results that disagree with established
principles may contain errors.
• It is useful, for instance, to flag deviations from
expected stereochemistry in protein structures,
but such ``outliers'' are not necessarily wrong.
QCI contd..
• The state of the experimental art is the most
important determinant of data quality.
• Quality control procedures provide the
second level of protection. Indices of
quality, even if they do not permit error
correction, can help scientists avoid basing
conclusions on questionable data.
Typical Databank record
Journey from Entry to Distribution
A. Sequence in journal publication; nucleic
acid sequence not found in EMBL data
library
• Data input: sequence and journal
information keyboarded three times
• Data verification: different keyboardings
compared
• Release of data: directly after verification
sequences were added to the public dataset
Typical Databank record
Journey from Entry to Distribution
B. Nucleic acid sequence submitted to EMBL
Data Library with no associated
publication.
• Data input: nucleic acid sequence
translated into protein sequence
• Data verification: none
• Release of data: directly after data input
sequences were added to the public dataset.
Typical Databank record
Journey from Entry to Distribution
C. Nucleic acid sequence submitted to EMBL
Data Library with associated publication:
protein sequence displayed in paper.
• Data input: nucleic acid sequence translated
into protein sequence
• Data verification: sequence and journal
information keyboarded once; comparison
of translation with published sequence.
• Release of data: directly after verification
sequences were added to the public dataset
Typical Databank record
Journey from Entry to Distribution
D. Nucleic acid sequence submitted to EMBL Data
Library with no
associated
publication:
protein sequence
NOT displayed in paper:
• Data input: nucleic acid sequence translated
into protein sequence
• Data verification: journal information
keyboarded once; comparison of journal
information
• Release of data: directly after verification
sequence were added to the pubic dataset.
Errors in DNA sequence and Data Annotation
• Current technology should reduce error rates to as
low as 1 base in 10000 as every base is sequenced
between 6-10 times and at least one reading per
strand.
• Therefore, in a procaryote, error of 1 isolated wrong
base would result to one amino acid error in ~10-15
proteins.
• In human genome gene-dense regions contain about
1 gene per 10000 bases, with average estimated at 1
gene per 30000bases.
• Therefore, corresponding error rate would be
roughly one amino acid substitution in 100 proteins.
• But large scale error in sequence assembly can also
occur. Missing a nucleotide can cause a frameshift
error.
DNA data …
• The DNA databases (EMBL/ GenBank/ DDBJ)
carry out quality checks on every sequence
submitted.
• No general quality control algorithm is yet in
widespread use.
• Some annotations are hypothetical because they
are inferences derived from the sequences.
– Ex. Identification of coding regions. These inferences
have error rates of their own.
Policies of PIR
• Entries in the PIR database are subject to continual
update, correction, and modification. At least 2025 % of entries are updated during each quarterly
release cycle.
• Every entry added or revised is run through a
battery of checking programs. Some fields have
controlled vocabulary and others are linked in way
that can be checked. For example, enzymes that
are identified by EC number are required to have
certain appropriate keywords; scientific and
common names for an organism are required to be
consistent.
Policies of PIR contd..
• Features are checked for the identity of the amino
acids involved, e.g., disulfide bonds must involve
only Cys residues.
• Standards list and auxiliary database used in the
checking
procedures include database for
enzymes, human genes, taxonomy, and residue
modifications and standard lists for journal
abbreviations, keywords, super family names, and
some other fields.
Indices of quality maintained by the
databank
When data from different sources are
‘merged’ into a single entry, any difference in
the reported sequences are explicitly shown
unless they are too extensive.
Policies of SWISS-PROT
• An annotated protein sequence database
established
in
1986
and
maintained
collaboratively, since 1987, by the Department of
Medical Biochemistry of the University of Geneva
and the EMBL Data Library.
• SWISS-PROT is curated protein sequence
database which strives to provide a high level of
annotations, a minimal level of redundancy,
integration with other databases, and extensive
external documentation.
SWISS-PROT
• Contributors provide: Sequence (99.9% translated
from the DNA database), bibliographic references.
Cross reference to DNA database.
• Databank staff adds: Annotations, keywords,
feature table, cross-reference to DNA database.
• Processing of an entry from point of arrival
through to distribution:
– Sequence, References, Annotations.
Yeast genome data
Different Centre announce different
numbers on the same day!
• MIPS:
• SGD:
• YPD:
• MIPS:
• YPD:
• MIPS:
http://www.mips.biochem.mpg.de
http://genome-www.stanford.edu
http://www.proteome.com
Total proteins
4344 ORFs
6149 out of these 4270 were reported to be
characterized experimentally.
6368 ORFs
» Out of these about 178 correspond to small
proteins of length < 100.
Yeast genome ..
• In brief, because of different definitions of
‘unknown’ or ‘hypothetical’ and ‘uncoding’
or ‘questionable’ ORFs, the number of yeast
proteins of which the function remains to be
identified is estimated to be 300 (the Cebrat
‘uncoding’)
or
1568
(the
MIPS
‘hypothetical’) or 1879 (the YPD
‘unknown’).
Annotation of Genome Data
•In general, annotation of bacterial genomes is
more complete and accurate than that of
eukaryotes.
•The types of errors that tend to appear are
entries with frame shift sequencing errors, which
lead to truncation of predicted reading frames or
even double errors leading to a mistranslated
internal fragment.
•Small genes, indeed any small functionally
important sequences, are likely to be missed, as
they may fall below statistically significant limits.
•In higher organisms, identifying genes is harder
and, in consequence, database annotation is
more dubious. Experimental studies can improve
the annotations more dubious.
•Alternative splicing patterns present a particular
Annotation of Human Genome
• In contrast, the sequence of the human genome is
being determined in many labs and its annotation
varies from nothing, for certain regions, to gene
predictions that are based on different methods
and that reflect different thresholds of accepted
significance.
• Therefore the annotation of DNA sequences must
be frequently updated and not frozen. It is a
challenge for databanks to find ways to link
primary sequence data to new and updated
annotations.
Quantitating the signals from DNA arrays
• A linear response that covers two or three orders
of magnitude is often needed to detect low and
high copy number transcripts on the same array.
• In cases where this is not possible it may be
necessary to scan the chip at different wave
lengths, or to amplify the signal with an immune
sandwich on top of the bound sample.
Standardization of DNA microarrays
• Comparison of data obtained from independent arrays
and from different laboratories requires standardization.
Both the Affymetrix chips and the custom made cDNA
chips use different methods for standardization.
• The Affymetrix chips have approximately 20 probes per
gene and standardization is either based on the
expression level of selected genes, like actin and
GAPDH, or on a setting of the global chip intensity to
approximately 150 units per gene on the chip.
• In this way, chip data from different experiments can be
compared to each other.
• In out hands, the data obtained with the two
standardization methods differ only by approximately
10 % (unpublished observations).
Samples for expression monitoring
• The analysis of relatively homogenous cell populations
(cloned cell lines, yeast, etc.) has proven much simpler
than the analysis of tissue biopsies as the latter often
contain many cell types (epithelial, endothelial,
inflammatory, nerve, muscle, and connective tissue
cells) that are present in variable amounts.
• Standardization may require microdissection of the
tissue to isolate specific cell types, although the number
of cells needed for the assay is well above a million.
Sampling of specific cell types using laser capture
microdissection (LCM) can be a time-consuming task,
and given that mRNA is prone to degradation the
processing time must be kept to a minimum.
Quantitation of Protein array data
• Even though there are several tools available for the
quantitation of protein spots, there is at present no available
procedure for quantitating all of the proteins resolved in a
complex mixture.
• Part of the problem lies in the large dynamic range of
protein expression, lack of resolution, post-translational
modifications, staining behavior of the protein as well as in
the fact that many abundant proteins streak over less
abundant components interfering with the measurements.
• At present, fluorescent technology seems to be way ahead;
as with the fluorescence stain Sypro Ruby there is a linear
response with respect to the sample amount over a wide
range of abundance.
• Quantitative fluorescence measurements can be performed
with CCD-Camera based systems as well as with laser
scanner systems.
Gene expression profiling techniques
Challenges & Perspectives
• A major challenge in the near future will be
to define a base line for the normal gene
expression phenotype of a given cell type,
tissue or body fluid.
• This is not a trivial task, however, as it will
require the analysis of hundreds or even
thousands of samples.
Current Limitations of Gene expression
profiling techniques
• Technical problems associated with the analysis of
expression profiles derived from tissues that are
composed of different cell types
• Lack of procedures for identifying targets that lie
in the pathway of disease
• Need for Bioinformatics tools for rapidly
assessing the function of the putative targets,
• The latter is so for paramount importance to the
pharmaceutical industry as the identification of
disease deregulated targets alone is not sufficient
to start costly drug screening process.
Protein Arrays
Statistical issues in data collection phase
Within labs
Signal to noise ratio
• quantifying and making as high as possible
• identifying and controlling sources of variability
• reproducibility
Between Labs
• Inter lab variability and biases
• Reproducibility
Tends to have been ignored in the excitement ? Cost ?
• really obvious/big effects
Becomes important when dealing with more subtle effects
Lab effects and scanning effects
• Needs systematic designed experiments to quantify sources of variation.
• Strategies for optimizing and monitoring processes
Protein Arrays
Statistical issues-data analysis phase
• What’s being done now
- visualization of data as an image
- Clustering of rows and columns to interpret
arrays
• Some limitations
- Visualizations tend to be of raw expression data
- Methods tend to ignore structure on rows-genes
and columns-samples
- Methods involove rectangular clusters
- Genes usually restricted to lie in one cluster
Protein Arrays
Statistical issues-data analysis phase contd..
• What's needed?
- Other ways of visualizing the data which can also use
-
-
information about rows and columns
Local clustering which is not restricted to “rectangles”
Genes in more than one cluster
Clustering with prior information
Analysis of experimental designs where the response is
a vector of microarray data
Dimension reduction
Methods for finding associations between large number
of predictor and response variables
Quality Control Issues related to 3-D
structure data determined using X-rays
• The reported parameter called the `B-factor'
of each atom describes its effective size, and
for proteins it should be treated as an
empirical value.
• Because every atom contributes to every
observation, it is difficult to estimate errors
in individual atomic positions.
Resolution of structures in PDB
Low resolution . . .
High
Resolution in Å
4.0 3.5 3.0 2.5 2.0 1.5
Ratio of observations to Parameters
0.3 0.4 0.6 1.1
2.2 3.8
The median resolution of structures in the Protein
Data Bank is about 2.0 Å .
R-factor contd..
• The R-factor measures how well the model fits the
data. If the set of observed X-ray intensities is Fo,
and the corresponding predicted intensities
calculated from the model are Fc, the R-factor is
defined as |Fo – Fc| /|Fo|. (The set of F's may
contain a list of tens of thousands of numbers.)
• For high resolution models values around
0.18±0.22 are good. For low resolution studies,
however, `good' R-factor values may be obtained
even for models that are largely or entirely wrong.
A more sophisticated quality measure is the crossvalidation R factor, Rfree.
R-factor, R-free contd..
• Murshudov and Dodson estimate overall
uncertainties
of
atomic
positions
in
macromolecules from the Rfree values, giving in a
typical case values of about ~ 0.05 Å at 1.5 Å
resolution and ~ 0.15 Å at ~ 2 Å resolution.
• They approximate uncertainties of individual
atomic positions from B-factors, giving values of
about 0.16 Å for an atom with B=20 Å and 0.3 Å
for an atom with B=60 Å.
Methods to detect the outliers
Type I
• Nomenclature and convention-related checks:
• Examples include incorrect chirality, and the
naming of chemically equivalent side-chain atoms
(e.g., in phenylalanine and tyrosine rings).
• Such errors can be corrected confidently without
reference to experimental data and current
submissions can be fixed at the time of
deposition.Checking of old datasets is in progress.
Methods to detect the outliers
Type II
• Self-consistency tests:
• Many stereochemical features of macromolecular
models are restrained during refinement. Bond
lengths and angles are restrained to ideal values,
planarity is imposed on aromatic rings and
carboxylate groups, non-bonded atoms are
prevented from clashing, temperature factors of
atoms bonded to each other are forced to be
similar, etc. Methods that assess how well these
restraints are satisfied are an important part of the
arsenal of structure verification tools.
• Nevertheless, their inadequacy in detecting
genuine shortcomings in models has been
demonstrated.
Type II continue…
Proper assessment of outliers (as features or
errors) requires access to the experimental
data. Sometimes,outliers warn of more serious
problems and may require careful inspection of
the electron-density maps and even model
rebuilding
by
an
experienced
crystallographer.Unfortunately, not all errors can
be fixed, even by appeal to structure factors
and maps; some regions are fatally disordered.
Methods to detect the outliers
Type III
• Orthogonal tests:
• Most revealing and useful are verification methods
independent of the restraints used during model
refinement. Such methods use database derived
information to assess how usual or unusual an
atom, residue, or entire molecule is.
• Examples include the analysis of torsion angles of
the protein main-chain (Ramachandran analysis)
and side-chain atoms (rotamer analysis), the
orientation of the peptide plane (peptide-flip
analysis), atomic volumes, geometry of the Cabackbone, nonbonded contacts, and the use of
sequence-structure profiles.
Quality of NMR structure determination
• NMR is the second major technique for
determining macromolecular structure.
• The experiments determine approximate values of
a set of inter-atomic distances and conformational
angles.
• These distances, derived from the Nuclear
Overhauser Effect (NOE), identify pairs of atoms
close together in space, including those from
residues distant in the sequence which are
essential for assembling the overall folding
pattern.
• Calculations then produce sets of structures that
are consistent–as far as possible–with the
experimental constraints on distances and angles,
and that have proper stereochemistry.
Q.C.I of NMR data
• None of these measures really relates to accuracy,
i.e. the similarity of the calculated structure to the
``true'' structure.
• One can determine, however, whether a calculated
structure is consistent with experimental data not
used to constrain it.
• One such approach is cross-validation. A
proportion of constraints is omitted from the
structure calculation, and the consistency of the
resulting structure with the unused constraints is
taken as a measure of accuracy. (This is analogous
to the procedures used by crystallographers in
measuring Rfree).
Conclusions
Two factors dominate current developments in
Bioinformatics:
(1) The amount of raw data is increasing in quantity,
spectacularly so, and in quality. Methods for
annotation are improving but by no means at a
comparable rate. Tools for identification of errors
are
improving
both
through
enhanced
understanding of what to expect and from a better
statistical base from which to flag outliers.
(2) A proliferation of web sites provides different
views or slices or means of access to these data;
and an increasingly dense reticulation of these
sites provides links among databanks and
information-retrieval engines. These links provide
useful avenues to applications; but they also
provide routes for propagation of errors in raw or
immature data. subsequently corrected in the
databanks but the corrections not passed on, and
in annotation.
Conclusions contd../
• Annotation is a weak component of the enterprise.
• Automation of annotation is possible only to a
limited extent and getting annotation right remains
labor-intensive.
• But the importance of proper annotation, however,
cannot be underestimated.
• P. Bork has commented that for people interested
in analysing the protein sequences implicit in
genome sequence information, errors in gene
assignment vitiate the high quality of the sequence
data.
• The only possible solution is a distributed and
dynamic error-correction and annotation
Contd../
• The workload must be distributed because
databank staff have neither the time nor the
expertise for the job; specialists will have to act as
curators.
• The process must be dynamic, in that progress in
automation of annotation and error identification
/correction will permit re-annotation of databanks.
• As a result, we will have to give up the ``safe''
idea of a stable databank composed of entries that
are correct when they are first distributed in
mature form and stay fixed thereafter.
• Databanks will become a seething broth of
information both growing in size, and maturing–
we must hope–in quality.
Contd../
• This will create problems, however, in organizing
applications.
• Many institutions maintain local copies of databanks: At
present, ``maintain'' means ``top up;'' yet this will no
longer be sufficient.
• In the face of dynamically changing databanks, how can
we avoid proliferation of various copies in various states?
• How will it be possible to reproduce a scientific
investigation based on a database search?
• One possible solution is to maintain adequate history
records in each databank itself in order to be able to
reconstruct its form at any time.
• This is analogous to the information in the Oxford English
Dictionary, which permits reconstruction of a English
dictionary appropriate for 1616 or 1756.