Sequence Analysis

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Sequence Analysis
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Prequisites to Sequence Analysis

• Basic Biology so you can understand the language
  of the databases Central Dogma (transcription
  Translation, Eukaryotes, CoDing Sequence (CDS),
  3UTR, 5UTR, introns, exons, promoters, codons,
  start codons, stop codons, secondary structure,
  tertiary structure).
• Before you can analyze sequences.. You have to
  understand their structure.. And know about Basic
  Biological Database Searching
• Collecting data is the most important and
  difficult part in bioinformatics.

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Central Dogma
DNA -gt RNA -gt protein - for all living things

• Why would the cell want to have an intermediate
  between DNA and the proteins it encodes?
• Gene information can be amplified by having many
  copies of an RNA made from one copy of DNA.
• Regulation of gene expression can be effected by
  having specific controls at each element of the
  pathway between DNA and proteins. The more
  elements there are in the pathway, the more
  opportunities there are to control it in
  different circumstances.
• In Eukaryotes, the DNA can then stay pristine and
  protected, away from the caustic chemistry of the
  cytoplasm.

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Eukaryotic Central Dogma
Donor
Acceptor
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Transcription
Information strand ( )
Template strand ( - )
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Translation

• http//wsrv.clas.virginia.edu/rjh9u/gif/protein.m
  ov

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Convention for nucleotides in database

• Because the mRNA is actually read off the minus
  strand of the DNA, the nucleotide sequence are
  always quoted on the minus strand.
• In bioinformatics the sequence format does NOT
  make a difference between Uracil and Thymine.
  There is no symbol for Uracil.. It is always
  represented by a T.
• Even genomic sequence follows that convention. A
  gene on the plus strand is quoted so that it is
  in the same strand as its product mRNA.

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Biological Databases

• Nucleotide databases
• Genbank International Collaboration
• NCBI(USA), EMBL(Europe), DDBJ (Japan and Asia)
• A bank means No curation. Submission to these
  database is required for publication in a
  journal.
• Organism specific databases (Exercise Find URLs
  using search engines)
• FlyBase
• ChickGBASE
• pigbase
• wormpep
• YPD (Yeast Protein Database)
• SGD(Saccharomyces Genome Database)

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• Protein Databases
• NCBI
• Swiss Prot(Free for academic use, otherwise
  commercial. Licensing restrictions on discoveries
  made using the DB. 1998 version free of any
  licensing)
• http//us.expasy.org/sprot/
• NCBI has the latest free version.
• Translated Proteins from Genbank Submissions
• EMBL
• TrEMBL is a computer-annotated supplement of
  SWISS-PROT that contains all the translations of
  EMBL nucleotide sequence entries not yet
  integrated in SWISS-PROT
• PIR

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• Structure databases
• PDB Protein structure database.
• http//www.rcsb.org/pdb/
• MMDB NCBIs version of PDB with entrez links.
• http//www.ncbi.nlm.nih.gov
• Genome Mapping Information
• http//www.ornl.gov/sci/techresources/Human_Genome
  /home.shtml
• NCBI(Human)
• Genome Centers
• Stanford, Washington University, Stanford
• Research Centers and Universities

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• Litterature databases
• NCBI Pubmed All biomedical litterature.
• www.ncbi.nlm.nih.gov
• Abstracts and links to publisher sites for
• full text retrieval/ordering
• journal browsing.
• Publisher web sites.
• Biomednet Commercial site for literature search.
  
• Pathways Database
• KEGG Kyoto Encyclopedia of Genes and Genomes
  http//www.genome.jp/kegg/

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Primary Databases

• A primary Database is a repository of data
  derived from experiments or from research
  knowledge.
• Genbank (Nucleotide repository)
• Protein DB, Swissprot
• PDB (MMDB) are primary databases.
• Pubmed (litterature)
• Genome Mapping databases.
• Kegg Database.(pathways)

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Secondary Databases

• A secondary database contains information derived
  from other sources.
• Refseq (Curetted collection of Genbank at NCBI)
• Unigene (Clustering of ESTs at NCBI)
• Organism-specific databases are often a mix
  between primary and secondary.
• Expressed Sequence Tag (EST) is a short
  sub-sequence of a protein-coding DNA sequence. It
  was originally intended as a way to identify gene
  transcripts.

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FASTA Format
MOST important data format!!!

• gtidentifier descriptive text
• nucleotide of amino-acid
• sequence on multiple lines if needed.
• Example
• gtgi41embX63129.1BTA1AT B.taurus mRNA for
  alpha-1-anti-trypsin
• GACCAGCCCTGACCTAGGACAGTGAATCGATAATGGCACTCTC
• CATCACGCGGGGCCTTCTGCTGCTGGC .

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• gi41embX63129.1BTA1AT is a header of fasta
  format.
• -gt Problem is there are many different headers
• GenBank gigi-numbergbaccessionlocus
• EMBL Data Library gigi-numberembaccessionlocus
  
• DDBJ, DNA Database of Japan gigi-numberdbjacces
  sionlocus
• NBRF PIR pirentry
• Protein Research Foundation prfname
• SWISS-PROT spaccessionentry name
• Brookhaven Protein Data Bank pdbentrychain
• Patents patcountrynumber
• GenInfo Backbone Id bbsnumber
• General database identifier gnldatabaseidentifie
  r
• NCBI Reference Sequence refaccessionlocus
• Local Sequence identifier lclidentifier

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Feature table(NCBIEMBL/DDBJ)

• http//www.ncbi.nlm.nih.gov/collab/FT/index.html

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Entrez

• Index Based search system. Each field in the
  database is searchable
• All primary databases are interlinked as one big
  relational database.
• (e.g. Pubmed links in Genbank records)
• http//www.ncbi.nlm.nih.gov/Entrez/
• Tutorials
• http//www.ncbi.nlm.nih.gov/Education/index.html

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SWISSPROT
http//us.expasy.org/sprot/

• Core data protein sequence data the citation
  information and the taxonomic data
• Annotation
• Function(s) of the protein
• Domains and sites. For example calcium binding
  regions, ATP-binding sites, zinc fingers,
  homeobox, kringle, etc.
• Post-translational modification(s). For example
  carbohydrates, phosphorylation, acetylation,
  GPI-anchor, etc.
• Secondary structure
• Quaternary structure. For example homodimer,
  heterotrimer, etc.
• Similarities to other proteins
• Disease(s) associated with deficiencies in the
  protein
• Sequence conflicts, variants, etc.

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Break
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Weka

• http//www.cs.waikato.ac.nz/ml/weka/
• GUI
• With the provided source code, a user can easily
  modify the program.

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• Input file format
• Comments start with this
• Header
• _at_relation name of the learning concept
• _at_attribute name real for numeric attributes
• _at_attribute name list of attribute names
  separated by comma for nominal attributes
• Ex. _at_attribute fuel_type diesel, gas
• _at_data This tells the data starts from the next
  line.
• Ex. _at_data
• 6, 148, 72, 35, 0, 33, 6, diesel, 34, no

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• Examples for using the source code in java
• Read in a input file
• BufferedReader reader new BufferedReader(new
  FileReader(filename))
• Instances data new Instances(reader)
• Preprocesses for instanced read in
• data.setClassIndex(data.numAttributes() -1)
• int seed 1234342
• data.randomize(new Random(seed))
• Training a classifier
• MultilayerPerceptron nn new MultilayerPerceptron
  ()
• String args new String -L, 0.3,
  -M, 0.2, -N, 500
• nn.setOptions(args)
• nn.buildClassifier(data)
• Test instances
• N-fold cross validation
• Using separated test file

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• Test instances
• N-fold cross validation Need to save some of the
  instances from the input file for the test
• This can be done easily with Wekas function.
• Ex. Instances data new Instances(reader)
• // This is important step for n-fold cross
  validation
• data.stratify(numFolds)
• Instances train data.trainCV(numFolds,
  fold)
• Instances test data.testCV(numFolds,
  fold)
• // Randomize each data set
• Using a separated test file just use same method
  to read in an input file and test each individual
  instance on the trained classifier

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