BIOL 266 Final Exam Review

structural bioinformatics

field that predicts and models function from 3D structure

genomics

field that predicts phenotype from genotype

molecular novelty

field that novel genes and gene functions from newly sequenced genomes and metagenomes

evolutionary adaptation

field that analyzes how changes in genotype lead to changes in phenotype and evolutionary adaptation

computational biology

the application of computational tools to solve biological problems (many disciples, ie/ genomics, biophysics, ecology, molecular bio, etc.)

bioinformatics

more emphases on analysis of high-throughput data, notably genome-sequence data. A subset of computational biology.

pattern discovery

subtask of comp bio -learn patterns from biological data

prediction

subtask of comp bio -use patterns to predict biological function

integration

subtask of comp bio -develop models that connect levels of info

simulation

subtask of comp bio - model behavior of biological systems on a computer

engineering

subtask of comp bio - design novel biological systems for specific purposes

therapy

subtask of comp bio - design molecular therapeutics to combat disease

wet lab

data generation testing

dry lab

interpretation, prediction, model-building, hypothesis-generation

algorithm

a set of rules or instructions specifying how to solve a problem

tool

implementation of an algorithm (software)

sequence analysis

determining the optimal alignment between sequences, searching databases, for homologs, organization and interpretation of data

phylogenetic analysis

organization of sequences according to their evolutionary relationships

genome analysis

finding and analyzing genes within the context of an entire genome

transcriptomics and proteomics

examines levels of gene or protein expression

network and systems biology

analyzing a biological system as a network of interacting components

synthetic biology

the use of computers to design new biological systems

dna polymers

specific sequences of nucleotides

nitrogenous base

nucleotides differ by which _________ they contain

genome

organism's DNA-based genetic instructions; composed of genes

genes

dna instructions for making proteins

central dogma

DNA --> RNA --> protein

transcription

assisted by RNA polymerase

translation

assisted by ribosomes

mRNA, tRNA, rRNA

three main types of RBA; messenger, transfer, ribosomal

RNA

-primary structure similar to DNA

-can be single or double stranded

-can exhibit different conformations (unlike DNA)

-contains U instead of T

gene expression

process of using DNA info to make mRNA and proteins

promoter sequence

what RNA polymerases look for to recognize beginning of genes

enhancers

. Modulate gene expression and can be far from the gene. Needed on top of promoters for eukaryotes.

prokaryotes

use +ve and -ve regulation for transcription

genetic code

RNA to amino acids

ORF

long stretches of DNA that are uninterrupted by stop codons and therefore encode protein

genes

ORFs + additional regulatory info

start codon

met, AUG, translation of DNA to RNA

stop codons

UAA, UAG, UGA, expected once every 20th codon

hydrophobic amino acids

amino acids with long alkyl side chains. More likely to be found in the interior of proteins. A I L M P V F W

hydrophilic (polar) amino acids

C N Q S T Y G

Charged amino acids

(-) D E, (+) K R H

sequencing

determining the exact nucelotide sequence of DNA

sequencing methods

Maxam- Gilbert: chemical degradation

Dideoxy (Sanger): chain termination

next gen (high throughput): many types

next gen methods

illumina (solexa)

-up to 1 Tb/rn (>5 human genomes)

-125 base pairs (paired end)

454 pyrosequencing

-up to 600 Mb/run

400-500 base pairs (single direction)

evolution

changes in inherited characteristics of biological populations over successive generations

genotype

organism's genetic information

phenotype

observable features of an organism, encoded by genotype

mutations

-point, duplication, insertion, deletion

-drive differences between species (variation)

homology

similarity due to common ancestry

homologous

evolutionarily related

NCBI GenBank

part of the national centre for biotechnology information, part of US national institute of health

protein sequencing timeline

1955: first complete sequence (insulin, Ryle et al.)

1965: ~20

1980:~1500

today: ~200 million

nucleotide sequencing timeline

1953: structure, watson and crick

60s-70s: small RNAs, cloning, then PCRs

1982: creation of genbank, simpler, democratization of data

sequence revolution

1980s/90s

-development of more efficient computer hardware and software

-birth of bioinformatics (term was coined in the 70s as the study of info processes in biotic systems)

DNA databases

NCBI GenBank: WGS, CoreNucleotide, dbGSS

RNA databases

NCBI: GEO, dbEST, UniGene

protein databases

NCBI and others: NCBI protein, UniProt, Protein Data Bank

flow of info

curation --> annotation --> release

core data

-key info in the db entry and minilan info req'd to identify it

-included data derived from experimental results (ex. sequence, structural data)

annotations

-all additional info, 2ndary info, may change over time

-ex. known or predicted functional info

purines

guanine and adenine

pyrimidines

thymine and cytosine

flatfile db

-data (ex. sequences) are stored as a text file or a collection of text files

-flat, as in sheet of paper

-easy to input, distribute, search, and retrieve data

relational databases

-data stored within a number of tables linked together by a shared field, the key (which must be unique to each record)

-handles huge mounts of data, reducing data in memory, faster search and retrieval

fasta file type

-.fa, .faa, .fna, .fasta

-header followed by raw data

ncbi genbank file type

-header, features, dequence

-each sequence filed with an accession number

-any revisions made, version number changes

accession number

~4-10 numbers/letters to identify specific DNA and protein sequence records

feature key

keyword indicating functional group

location

instructions for finding the feature

qualifiers

auxiliary info about a feature

protein dbs

NCBI protein, UniProtKB, Protein Information Resource (PIR), SWISS-PROT, TrEMBL

entries

databases are composed of

common queries

gene/protein name/function, db identifiers, species names, raw sequences

logic

these are operators that indicate relationships among searches. Ex. AND, OR, NOT, NOR, NAND, XOR, XNOR

homology searches

best way to find genes/proteins related to yours of interest

data quality and info content

redundancy, efficiency, automatic and manual quality control

computer error

incorrect annotations, missed relationships (insufficient info extraction)

human error

multiple contributions, vector sequence left in, PCR chimeras, taxonomic misidentification, trivial data entries

quality control

manual approach to deal with errors, ex. 20% of fungi sequences were misidentified

sequence alignment

identification of character matches preserving character order

true alignment

reflects evolutionary relationship between 2+ sequences that share a common ancestor (homology)

global alignment

attempt to align entire sequence, ex. NW

local alignment

stretches of sequences with highest density of matches are aligned, ex. SW

function, structure, evolutionary information

aligning sequences useful to discover

similarity, patterns, relationships

alignments reveal

score and compute

to understand alignments we need to:

a good alignment has

many matches, few mismatches, few gaps

dynamic programming

Used by both NW and SW, solves the problem by breaking it down into subproblems

db search

needed to find closest homolog of a given sequence

-important for predicting sequence function, genome annotation, phylogenetics, determining taxonomic identity of a sequence

SSEARCH

-extension of pairwise alignment

-instead 1v1, 1vmany

-problem: speed

-use for for comparing local dbs

BLAST

-basic local alignment search tool

-faster than SW

-word-based (k-tuples), ungapped, locally optimal

0larger word length permits inexact matches between words

-heuristic procedure

-minimum word length: 3 for proteins, 16 for nt

E value

expectation; the number of matches with scores equivalent to or better than S that are expected to occur in a db search by chance.

-borderline significant <0.01

-highly significant <1e-10

BLAST programs

blastp: protein query v protein db

blastn: nt query v nt db

blastx: translated nt v protein db

tblastn: protein v translated nt db

tblastx: translated nt v translated nt db

PSI-BLAST: detection of emote protein homology using profiles

BLAST process

1. break query into words

2.search for matches

3. extend matches in both directions until score beloe threshold

4.merge HSPs into a longer alignment (further extend and allow gaps)

5. report statistical significance

BLAST artifacts

-longer the sequence higher the score (this is natural)

-query sequence w/ repeats artificially inflates score

-low complexity regions

-conservative with short query sequences

multiple sequence alignment

>2 sequences, basis of phylogenetic reconstruction, indicates patterns of conservation and variation (for finding functional residues, motifs), greater accuracy of overall alignment

order

can affect end result of MSA

MSA challenges

-finding best alignment that takes mutations/gaps for ALL sequences into account

-scoring entire alignment

placement and scoring of gaps

-cannot easily extend dynamic programming algorithms like SW or NW