Introduction to bioinformatics - National Genetics Reference ...

Shedding some light on
Bioinformatics
Dr. Georgina Moulton
BioHealth Informatics Education and Development Fellow
(georgina.moulton@manchester.ac.uk)

Outline
• Bioinformatics definition and history
• Introduction to computers
• Challenges of Bioinformatics for Bioinformaticians
• Challenges and issues of Bioinformatics for end-users –
using some examples of bioinformatics databases and
tools.
• Ground rules!

What is Bioinformatics?
“Bioinformatics is the field of science in which biology,
computer science, and information technology merge into
a single discipline. The ultimate goal of the field is to
enable the discovery of new biological insights and to
create a global perspective from which unifying principles
in biology can be discerned.”
http://www.ncbi.nlm.nih.gov/About/primer/bioinformatics.html

What is Bioinformatics?
Wet-lab
In-silico
Analysis
Sequence
Structure
Function
Evolution
Pathway
Interaction
Mutation
Expression
Hypothesis

GENE EXPRESSION ANALYSIS
COMPARATIVE GENOMICS
HTGS
DNA sequencing
Genotyping
Taq-Man
TEXT MINING
Genomics
PHENOME
PHYSIOME
Physiomics
Information
Extraction
GENOME
Natural Language Processing
Tiling arrays
SNP chips
Transcriptomics
TRANSCRIPTOME
BIOINFORMATICS
Bioinformaticians
BIBLIOME
Bibliomics
Text Categorisation
Microarrays
PROTEOME
Proteomics
INTERACTOME
Interactomics
2D electrophoresis
MALDI-MS
NMR
METABOLOME
Metabolomics
Metabolic profiling
2-hybrid system
PROTEIN EXPRESSION
ANALYSIS
Protein Chips
Mass
Spectrometry
SYSTEMS BIOLOGY

Bioinformatics Questions
• What genes are in chromosomal region X and are linked
to disease?
• What genes cause the condition?
• What is the normal function of gene Y?
• What mutations have been linked to diseases A and B?
• How does the mutation M alter gene function F?
• What is the 3D structure of gene Y?
• Is gene Y expressed in condition C?
• Are there any known variants of gene G?

Growth of Sequences and Annotations
(since 1982)

Challenges in Bioinformatics
• Data storage
– What is the most efficient way of storing it?
• Data interoperability
– How can we get bioinformatics resources be linked?
• Data analysis
– Automated pipelines? Reliability?
– How can we analyse all data from High Throughput Sequencing
efforts?
Impact on how you (as end users )
extract, use, analyse and visualise
bioinformatics data

Computers – they are your
workbench

Computers – they are everywhere!
• A Computer is a machine that manipulates data according
to a list of instructions
• A Personal Computer (PC) is any general purpose
computer that is used by individuals
Purchase request
Tune download
iTunes interface
iTunes database

Computer Software
On a computer there is:
1. Operating System
– Manages the resources and provides an interface for programs
– Windows, MS-DOS, Unix, Linux
2. Programs
– A set of instructions for the computer [wikipedia, 2009]
– Executable
– Manipulate data
• Bioinformatics software (tools) is developed by software
developers using an iterative process with users

Biological Databases
Purpose
1. To disseminate biological data and information
2. To provide biological data in computer-readable form
3. To allow analysis of biological data
A database needs to have at minimum a specific tool for
searching and data extraction.
– Web pages, books, journal articles, tables, text files, and
spreadsheet files cannot be considered as databases

Major Bioinformatics Databases
• Growing steadily in number and in size
– In Nucleic Acids Research journal, they reported 179 databases, 95 were new
– In total there are 1170 molecular biology databases
• Specialisations
– Which genome they contain (mouse, human, all of them)
– Which types of information about the genome they contain
• Contain information such as
– Sequences: of bases and of residues
– Structure: 3d conformations of known proteins
– Families: Which sets of genes are known to be homologous
– Annotations: which processes each gene is involved in
• And lots of other information
– Conceptual structure
• How concepts in biology/genetics are related to each other
• Here is a list of some of the different ones

Primary sequence resources
Nucleic
EMBL
GenBank
DDBJ
Protein
SWISSPROT
TrEMBL
PIR
NRL-3D
•They have long-term national or international funding
•The resources are:
– Archival: they take the data as produced with little or no addition of
information
– Horizontal: they gather all of a type of information from all sources

Nucleic Acid Sequence Databases
• General purpose databases focusing on DNA sequences
and their properties
• Three types of database:
– Genbank (hosted at NCBI)
– EMBL (hosted at EBI)
– DDBJ (hosted in Japan)
• GenBank, EMBL and DDBJ exchange data to ensure
comprehensive worldwide coverage and accession
numbers are managed consistently between the three
centers.

Important identifiers for database entries
LOCUS NM_000492 6132 bp mRNA linear PRI 07-OCT-2007
DEFINITION Homo sapiens cystic fibrosis transmembrane conductance regulator
(ATP-binding cassette sub-family C, member 7) (CFTR), mRNA.
ACCESSION NM_000492
VERSION NM_000492.3 GI:90421312
KEYWORDS .
SOURCE Homo sapiens (human)
ORGANISM Homo sapiens
Eukaryota; Metazoa; Chordata; Craniata; Vertebrata; Euteleostomi;
Mammalia; Eutheria; Euarchontoglires; Primates; Haplorrhini;
Catarrhini; Hominidae; Homo.
REFERENCE 1 (bases 1 to 6132)
AUTHORS Pall,H., Zielenski,J., Jonas,M.M., DaSilva,D.A., Potvin,K.M.,
Yuan,X.., Huang,Q. and Freedman,S.D.
Modification date of entry
TITLE Primary sclerosing cholangitis in childhood is associated with
abnormalities Accession in cystic numbers fibrosis-mediated identify the chloride database channel entry function
JOURNAL J. Pediatr. 151 (3), 255-259 (2007)
PUBMED 17719933 Accession number is a unique identifier
REMARK GeneRIF: There is a high prevalence of CFTR-mediated ion transport
dysfunction Sequence in subjects Identifiers/GI with childhood numbers primary usually sclerosing are NOT unique.
cholangitis.
.........

Issue 1: Why so many IDs? Biology!
Gene Symbol (CFTR)
GeneID – Unique to organism (1080)
Transcript ID -1
•Transcript 1 version1
•Transcript 1 version2
•Transcript 1 version3
Transcript ID -2 etc
•Transcript 2 version1
•Transcript 2 version2
When publishing always refer to sequence
accession number (unique), version and date
of database, and the gene symbol.

Issue 2: Data Quality
• Is the sequence and associated data up-to-date?
• Genbank/EMBL allow direct submissions of nucleotide
sequences by the scientist who actually conducted the
sequencing
• It is the responsibility of the author to keep the database
entry up-to-date – sequence may change, features found
etc.
• There is no or little data quality check carried out by the
hosting institutions.

Issue 3: Data redundancy
COMMENT REVIEWED REFSEQ: This record has been curated by NCBI staff. The
reference sequence was derived from M28668.1, AC000111.1 and
AC000061.1.
On Mar 24, 2006 this sequence version replaced gi:6995995.
Summary: This gene encodes a member of the ATP-binding cassette
(ABC) transporter superfamily. ABC proteins transport various
molecules…..[removed]. [provided by RefSeq].
COMPLETENESS: full length.
PRIMARY REFSEQ_SPAN PRIMARY_IDENTIFIER PRIMARY_SPAN COMP
1-1539 M28668.1 1-1539
1540-1558 AC000111.1 99186-99204
1559-1972 M28668.1 1559-1972
Data Redundancy
1973-1990 AC000111.1 131715-131732
It is often that there are more than one database entry
that corresponds to the same gene or entity
1991-2628 M28668.1 1991-2628
2629-2650 AC000111.1 134643-134664
In some cases these get merged – like here
2651-5731 M28668.1 2651-5731
5732-6132 AC000061.1 58618-59018

5732-6132 AC000061.1 58618-59018
FEATURES Location/Qualifiers
source 1..6132
/organism="Homo sapiens"
/mol_type="mRNA"
/db_xref="taxon:9606"
/chromosome="7"
/map="7q31.2"
gene 1..6132
/gene="CFTR"
/gene_synonym="ABC35; ABCC7; CF; CFTR/MRP; dJ760C5.1;
MRP7; TNR-CFTR"
/note="cystic fibrosis transmembrane conductance regulator
(ATP-binding cassette sub-family C, member 7)"
/db_xref="GeneID:1080"
/db_xref="HGNC:1884"
/db_xref="HPRD:03883"
/db_xref="MIM:602421"
Gene names – very useful for humans! Not unique – look at the number
of synonyms!

CDS 133..4575
Database entries usually cross-reference to other databases
/gene="CFTR"
Navigation: you can click on them and go to those entries!
/gene_synonym="ABC35; ABCC7; CF; CFTR/MRP; dJ760C5.1;
MRP7; TNR-CFTR"
Remember /note="ATP-binding this is a gene entry: cassette if there sub-family is a translation, C, member you can 7"
navigate to the corresponding protein entry.
/codon_start=1
/product="cystic fibrosis transmembrane conductance
regulator"
/protein_id="NP_000483.3"
/db_xref="GI:90421313"
/db_xref="CCDS:CCDS5773.1"
/db_xref="GeneID:1080"
/db_xref="HGNC:1884"
/db_xref="HPRD:03883"
/db_xref="MIM:602421"
/translation="MQRSPLEKASVVSKLFFSWTRPILRKGYRQRLELSDIYQIPSVD
SADNLSEKLEREWDRELASKKNPKLINALRRCFFWRFMFYGIFLYLGEVTKAVQPLLL
GRIIASYDPDNKEERSIAIYLGIGLCLLFIVRTLLLHPAIFGLHHIGMQMRIAMFSLI

A protein entry!
LOCUS NP_000483 1480 aa
linear PRI 15-MAR-2009
DEFINITION cystic fibrosis transmembrane conductance regulator [Homo sapiens].
ACCESSION NP_000483
VERSION NP_000483.3 GI:90421313
DBSOURCE REFSEQ: accession NM_000492.3
KEYWORDS .
SOURCE Homo sapiens (human)
ORGANISM Homo sapiens
Eukaryota; Metazoa; Chordata; Craniata; Vertebrata; Euteleostomi;
Mammalia; Eutheria; Euarchontoglires; Primates; Haplorrhini;
Catarrhini; Hominidae; Homo.
REFERENCE 1 (residues 1 to 1480)
AUTHORS Santos,R.P., Prestidge,C.B., Brown,M.E., Urbancyzk,B.,
Murphey,D.K., Salvatore,C.M., Jafri,H.S., McCracken,G.H. Jr.,

FEATURES Location/Qualifiers
source 1..1480
/organism="Homo sapiens"
/db_xref="taxon:9606"
/chromosome="7"
/map="7q31.2"
Protein 1..1480
/product="cystic fibrosis transmembrane conductance
regulator"
/note="ATP-binding cassette sub-family C, member 7"
/calculated_mol_wt=168011
Region 78..641
/region_name="MdlB"
/note="ABC-type multidrug transport system, ATPase and
permease components [Defense mechanisms]; COG1132"
/db_xref="CDD:31327"
Region 84..350
/region_name="ABC_membrane"
/note="ABC transporter transmembrane region; cl00549"
/db_xref="CDD:119924"

Issue 4: Navigation of database entries
using database cross-references
references
• It is certainly a lot smoother than what it; however, you
still have to be careful
• Problems:
– Database updates are out of synchronisation
– Databases has merged entries, thus using a different accession
number

NCBI Map of links
between Entrez nodes
and types of databases
However, there are links
between databases that
are NOT hosted at the
NCBI

• UniProt is a collaboration between the European Bioinformatics
Institute (EBI), the Swiss Institute of Bioinformatics (SIB), and the
Protein Information Resource (PIR).
– The SIB used to put out “Swiss-Prot”, which was a curated database of
protein sequences.
– EBI used to put out TrEMBL, an uncurated, automated database of
nucleotide sequences translated into proteins.
– PIR also had a protein database, PSD, along with a set of curated protein
families.
– They pooled their resources, reducing 3 websites to one.
• The main product is UniProtKB (Uniprot Knowledge Base, i.e a
database).

An accession number and identifer
Note: this shows many different types of
protein functional/structural and
interaction data

Issue 5: Annotation quality for biological
databases
• Annotation is additional information to the raw
sequence; for example, references, gene position,
cross – links to other databases
• Most genes/proteins are identified and annotated
based on the similarity to other genes with known
functions using automated bioinformatics tools
(pipeline)
• Comparison was made when data was less complete
• If sequence is incorrectly annotated, the error
propagates in the database

Issue 5 cntd.
• Most databases state how the data was generated
– for example, in Ensembl, genes are
automatically predicted and then manually
checked.
• Another example, SWISSPROT is manually
curated, TrEMBL contains automatics translations
from nucleotide databases

Issue 6: Describing the gene (or
anything) in standard terms
• The Gene Ontology
– http://www.geneontology.org/
– Knowledge about gene function (the ontology itself)
• There are other examples:
– The MGED Ontology (arising from MIAME)
• http://mged.sourceforge.net/
• Annotation of microarray experiments for public repositories
– Clinical Bioinformatics Ontology:
• Annotation of gene tests in electronic medical records
• http://www.cerner.com/cbo

Issue 6 cntd: : Standard vocabulary
• Human Phenotype Ontology
– standardized vocabulary of phenotypic abnormalities encountered in
human disease
– http://www.human-phenotype-ontology.org/index.php/hpo_home.html
• Uses include:
– clinical diagnostics in human genetics (Phenomizer)
– bioinformatics research on the relationships between human
phenotypic abnormalities and cellular and biochemical networks,
– mapping between human and model organism phenotypes
– providing a standardized vocabulary for clinical databases

polyA_site 6132
ORIGIN
/gene="CFTR"
/gene_synonym="ABC35; ABCC7; CF; CFTR/MRP; dJ760C5.1;
MRP7; TNR-CFTR"
The Sequence
1 aattggaagc aaatgacatc acagcaggtc agagaaaaag ggttgagcgg caggcaccca
61 gagtagtagg tctttggcat taggagcttg agcccagacg gccctagcag ggaccccagc
121 gcccgagaga ccatgcagag gtcgcctctg gaaaaggcca gcgttgtctc caaacttttt
181 ttcagctgga ccagaccaat tttgaggaaa ggatacagac agcgcctgga attgtcagac
241 atataccaaa tcccttctgt tgattctgct gacaatctat ctgaaaaatt ggaaagagaa
301 tgggatagag agctggcttc aaagaaaaat cctaaactca ttaatgccct tcggcgatgt
361 tttttctgga gatttatgtt ctatggaatc tttttatatt taggggaagt caccaaagca
421 gtacagcctc tcttactggg aagaatcata gcttcctatg acccggataa caaggaggaa
481 cgctctatcg cgatttatct aggcataggc ttatgccttc tctttattgt gaggacactg

Issue 7: sequence formats – how many?!
• Many different sequence formats
• Different pieces of software use different ones, so check
yours is in the correct format
• They are ASCII Text and use the IUPAC standard one-letter
code.
• Most sequence formats include at least one ID name, placed
usually somewhere towards the top of the sequence format
• You can convert between different formats using the program
seqret
(http://embossgui.sourceforge.net/demo/seqret.html)

Common sequence format
• FASTA is the most common
• >sp|P02700|OPSD_SHEEP Rhodopsin - Ovis aries (Sheep).
MNGTEGPNFYVPFSNKTGVVRSPFEAPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLY
VTVQHKKLRTPLNYILLNLAVADLFMVFGGFTTTLYTSLHGYFVFGPTGCNLEGFFATLG
GEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLVGWSRYIP
QGMQCSCGALYFTLKPEINNESFVIYMFVVHFSIPLIVIFFCYGQLVFTVKEAAAQQQES
ATTQKAEKEVTRMVIIMVIAFLICWLPYAGVAFYIFTHQGSDFGPIFMTIPAFFAKSSSV
YNPVIYIMMNKQFRNCMLTTLCCGKNPLGDDEASTTVSKTETSQVAPA

Multiple FASTA sequences in one file
>SEQUENCE_1
MTEITAAMVKELRESTGAGMMDCKNALSETNGDFDKAVQLLREKGLGKAAKKADRLAAEG
LVSVKVSDDFTIAAMRPSYLSYEDLDMTFVENEYKALVAELEKENEERRRLKDPNKPEHK
IPQFASRKQLSDAILKEAEEKIKEELKAQGKPEKIWDNIIPGKMNSFIADNSQLDSKLTL
MGQFYVMDDKKTVEQVIAEKEKEFGGKIKIVEFICFEVGEGLEKKTEDFAAEVAAQL
>SEQUENCE_2
SATVSEINSETDFVAKNDQFIALTKDTTAHIQSNSLQSVEELHSSTINGVKFEEYLKSQI
ATIGENLVVRRFATLKAGANGVVNGYIHTNGRVGVVIAAACDSAEVASKSRDLLRQICMH

You can often download sequences in FASTA
(and other) formats

Sequence Retrieval System (SRS)
• A network browser for databanks in molecular biology
• SRS arose from the lack of standard formats amongst diverse
databases gathered together at any one site
• SRS permits rapid searching, allowing users to retrieve, link &
access all interconnected resources (e.g., nucleic acid, EST, protein
sequence, protein pattern, protein structure, bibliographic, etc.)
• SRS allows queries to be formulated across a range of different db
types via a single interface, without having to worry about
underlying data-structures, query languages, etc.

Allows you to search across
ALL NCBI databases
Entrez

Issue 8: Keeping up-to
to-date with tools
Bioinformatics Resources:
• http://anil.cchmc.org/BioInfoRes.html
• http://www.expasy.org/links.html
• http://www.hsls.pitt.edu/guides/genetics/obrc
• Introductory and Advanced Training Programmes
– NWeHealth/NIBHI at The University of Manchester
– EBI/NCBI and other institutions
– Online Training – EMBER (http://www.ember.man.ac.uk)

Issue 9: Which software/database is
most reliable? Which is the best?
• Database
– look for it in the NAR database or issue
– Has it got long-term funding? Anything at NCBI or EBI is safe.
• Software
– Who hosts it?
– Has it been long-term funded?
– Is it cited many times?
– Does it have a comparison to other software of its type?
TRY A HANDFUL OF TOOLS AND MAKE A
COMPARISON OF RESULTS!

Issue 10: Data Security and Clinical
Data
• Tools are available to the public and are often not secure
• They are not within the NHS firewall
• An example: Alamut is a client/server application
FIREWALL
Purchase request
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Ground Rules in Bioinformatics
Ground Rules in Bioinformatics
• Don't always believe what programs tell you
– they're often misleading & sometimes wrong!
• Don't always believe what databases tell you
– they're often misleading & sometimes wrong!
• Don't always believe what trainers tell you
– they're often misleading & sometimes wrong!
• In short, don't be a naive user
– when computers are applied to biology, it is vital to
understand the difference between mathematical &
biological significance
• If you really need support – ask a bioinformatics
researcher!

We all are bioinformaticians!
“Bioinformatics has become too central to biology to be
left to specialist bioinformaticians. Biologists are all
bioinformaticians now.”
[Stein, L.D.]