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Reading for the Next Week. Sequence Analysis and Alignment Chapter 5, Chapter 8, Chapter 11 Only about the 1st third of each chapter. Sequence Files. Fasta format, has simplest structure >Sequence Title [new line] Sequence [new line] very useful for handling sequence alone

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reading for the next week
Reading for the Next Week
  • Sequence Analysis and Alignment
  • Chapter 5, Chapter 8, Chapter 11
  • Only about the 1st third of each chapter
sequence files
Sequence Files
  • Fasta format, has simplest structure

>Sequence Title [new line]

Sequence [new line]

  • very useful for handling sequence alone
  • usually included as one of the formats supported by programs that use sequence
example of fasta format
Example of Fasta Format



genbank flat file
  • holdover from earlier versions of GENBANK, the US government-supported public database
  • DNA-centric, sequence based view of data
  • contains a number of fields with non-sequence information

LOCUS CHKLCAMR 3545 bp mRNA linear VRT 30-NOV-1995

DEFINITION Chicken liver cell adhesion molecule L-CAM mRNA, complete cds.

ACCESSION M16260 J04074 M22179

VERSION M16260.1 GI:212244

KEYWORDS cadherin; glycoprotein; liver cell adhesion molecule.

SOURCE Gallus gallus cDNA to mRNA.

ORGANISM Gallus gallus

Eukaryota; Metazoa; Chordata; Craniata; Vertebrata; Euteleostomi;

Archosauria; Aves; Neognathae; Galliformes; Phasianidae;

Phasianinae; Gallus.

REFERENCE 1 (bases 201 to 3545)

AUTHORS Gallin,W.J., Sorkin,B.C., Edelman,G.M. and Cunningham,B.A.

TITLE Sequence analysis of a cDNA clone encoding the liver cell adhesion

molecule, L-CAM

JOURNAL Proc. Natl. Acad. Sci. U.S.A. 84 (9), 2808-2812 (1987)

MEDLINE 87204217


FEATURES Location/Qualifiers

source 1..3545

/organism="Gallus gallus"




/dev_stage="10-11 day old embryo"

mRNA <1..3545

/product="L-CAM mRNA"

CDS 51..2714


/product="liver cell adhesion protein precursor"




sig_peptide 51..128

mat_peptide 531..2711

/product="liver cell adhesion protein"

BASE COUNT 757 a 1125 c 1051 g 612 t

ORIGIN 20 bp upstream of KpnI site.

1 agctccgtgc gcagcggtac ccgtaccggt accggcccgg tccctgagcc atgggccggc

61 ggtggggttc…

other formats
Other Formats
  • XML - extensible markup language
    • similar to HTML only can implement user-defined tags
  • Graphic
    • extracts positions from features and creates a graphical output
database types

Database Types

Characteristics, Strengths and Weaknesses

what is a database
What is a Database?
  • well-defined storage method for digital data
  • allows for relatively rapid retrieval of data
  • allows for complex conditional retrieval
three main types used in bioinformatics
Three Main Types Used in Bioinformatics
  • Flat File
    • text stored in a file in stereotyped format
    • Hierarchical adds “tree” organization
  • Relational
    • a set of tables, with unique identifiers, and overlapping content
  • Object Oriented
    • data stored as part of a data structure (the object) that includes methods for manipulating the data
flat file database
Flat File Database
  • data is stored as an “unstructured” record
  • relationships between the data are inherent in the database schema, the description of the syntax of the storage file
flat file database1
Flat File Database
  • Advantages
    • low overhead, do not need to have a complex computational superstructure to organize the data and keep track of it in memory
    • retrieval is not computationally complex
    • can take advantage of generalized standards for information organization
    • no random access, therefore the simplicity of storage imposes a cost on access and manipulation
      • partially resolved by indexing
    • change in the schema requires parsing and rewriting the whole database
    • all linkages between data entries must be explicitly defined either in the schema or by software that accesses the database
relational databases
Relational Databases
  • functionally consist of a set of tables, where each row in the table contains a set of properties of some entity
  • extensive formal analysis of relational approach has yielded a set of “normalizations” that maximize the interconnections between information, minimize redundancies
relational databases1
Relational Databases
  • Advantages
    • readily available database management systems (DBMSs) that handle the computational overhead invisibly
    • high interconnectivity of data enhances data mining process
    • Structured Query Language (SQL) exists to make searching automated and relatively rapid, even complex searches
changing schema does not necessarily involve rewriting whole database; can add new tables or new columns to existing tables
  • most common commercial database type therefore lots of support available (if you have the money)
  • wide usage means user skills are generalizable
    • overhead (computational and expertise) makes cost high for small databases
    • content-based query only rudimentary, can not do complex “fuzzy” queries within SQL
    • all implementations do not fully conform to theoretical criteria, therefore problems arise in large databases and/or complex queries
object oriented databases
Object-Oriented Databases
  • based on the object concept, a computational entity that consists of data and a set of methods that will perform operations on that data
  • ACeDB, the core DBMS for the C. elegans sequencing project is object oriented
object oriented databases1
Object-Oriented Databases
  • Advantages
    • pre-existing schema is already worked out if you use ACeDB (
    • a lot of procedural programming is not needed because methods for data manipulation are intrinsic to the object
    • natural database for object-oriented languages like C++ and Java
    • not easy to tweak; the DBMS is fairly complex, really only the developer community can alter it
    • if their data model is not adequate for your project, there is no easy way to expand it
    • therefore, tends to be good for specific genomes, high throughput operations, not databases set up and maintained by small users for idiosyncratic projects
combined relational object
Combined Relational/Object
  • Relational database (tables) that can hold objects
  • As implemented, the DBMS simulates the object by creating a set of hidden tables
  • Larger computational overhead, less user control of database structure
  • each database type has strengths and weaknesses
  • choice of database to use depends on many cost factors (money, computational overhead, learning curve for use, pre-existing support)
  • there is no single right choice
  • the core GENBANK archival database is a flat file format
  • historically that is the way it started
  • when a major revamping was undertaken in the mid 90s, stayed with flat file format, but introduced a defined hierarchical data model using ASN.1
asn 1
  • the underlying structure of the GENBANK database uses Abstract Syntax Notation as the syntax definition
  • this is a standard, general syntax definition for holding information in a machine-parseable form
  • hierarchical structure helps organize data

Seq-entry ::= set {

level 1 ,

class nuc-prot ,

descr {

title "Chicken liver cell adhesion molecule L-CAM mRNA, and translated

products" ,


std {

year 1995 ,

month 11 ,

day 30 } ,

source {

org {

taxname "Gallus gallus" ,

common "chicken" ,

db {


db "taxon" ,


genbank data model
GENBANK Data Model
  • to implement a database, you must have a data model
  • for flat files, consists of a set of rules about
    • the format of data storage
    • the syntax of the storage
  • implementation of any data analysis or manipulation is the responsibility of the user
genbank data model1
GENBANK Data Model
  • explicitly defined in on-line document
  • note: although not object oriented, the specification uses much of the terminology of object-oriented programming
  • has a at least one Seq-id
  • contains information about a biological sequence
    • virtual - contains a molecular type, a size, and topology (e.g. a band on a gel, an intron whose sequence has not been determined)
raw - simple single sequence, which has all the properties of virtual plus actual sequence data
  • segmented - contains identifiers for other bioseqs and relative positional information, thus yielding a size
  • map - contains a rough size and co-ordinates that represent some kind of map data
bioseq sets
Bioseq Sets
  • sets of bioseq entities that are related somehow
    • nuc-prot set - nucleotide type bioseq and one or more associated protein type bioseqs
    • population set - set of related bioseqs that are aligned with each other. This is a basic type for population and phylogenetic studies
seq annot
  • A self-contained annotation that refers to a specific bio-seq entity
  • Can have multiple seq-annots
  • These elements hold the annotation data, e.g. positions of start sites, stop site, introns, regulatory sequences
our lab project
Our Lab Project
  • one of the main elements of the labs in this course is designing a database, populating it, analyzing the sequences in various ways, and annotating the database
  • we will use a flat file format to store data on a family of proteins
  • therefore, we need to define a schema