# Bioinformatics

## Looking on a database

We have one sequence, called query

We compare our sequences with each sequence in a Database

(sequences in the database are called subjects)

We get the score of each alignment

We report all subjects with score over a threshold

## Columns in tabular output

• qseqid: query sequence id
• sseqid: subject sequence id
• pident: percentage of identical positions
• length: alignment length (sequence overlap)
• mismatch: number of mismatches
• gapopen: number of gap openings
• qstart: start of alignment in query
• qend: end of alignment in query
• sstart: start of alignment in subject
• send: end of alignment in subject
• evalue: expect value
• bitscore: bit score

## qseqid and sseqid

query sequence id

subject sequence id

## pident and length

percentage of identical positions

• How many letters are the same in the aligned region

alignment length

• May be longer or shorter than the query or the subject

## mismatch and gapopen

number of mismatches

• How many letters are different in the aligned region

number of gap openings

• How many initiation of gaps, independent of their length

## qstart and qend

start of alignment in query

end of alignment in query

## sstart and send

start of alignment in subject

end of alignment in subject

expect value

bit score

# Alignment score depends on Substitution matrix

## Score can change

If mismatches and gaps have different cost, the score will change

Sometimes the optimal alignment changes

Therefore alignments are meaningless without knowing the scoring matrices

Later we will discuss how to choose the “best” scoring matrix for each case

# E-value

## What is “a good score”?

We want big scores

How big is big enough?

We need to make several hypothesis

The most common hypothesis is statistical

## Larger scores, less hits

A hit is a subject with score over a threshold

Larger score thresholds give less hits

We can estimate the number of hits in a given database, assuming randomness

That is called Expected value

## Expected value as a threshold

In practice, we choose a small Expected value

(usually called E-value)

Something like 10-5 or 10-20

What we find is not random
and maybe it is biologically meaningful

## E-value depends on the database

The formula for E-value depends on

• The score $$S$$
• The query size $$m$$
• The database size $$n$$
• The substitution scoring matrix, via $$k$$ and $$λ$$

$E=kmn\exp(-λ S)$

Same alignments in different databases have different E-value
but the same score

# Many flavors of BLAST

## Types of BLAST

Depending on the alphabet of the query and subject

BlastN
Search nucleotides in nucleotide databases
BlastP
Search proteins in protein databases
BlastX
Search nucleotide in protein databases.
Each query is translated into 6 putative proteins

## Types of BLAST

TBlastN
Search proteins in nucleotide databases.
Each subject is translated into 6 putative proteins
TblastX
Search nucleotides in nucleotide databases
Translate each query and each subject into 6 proteins
Compares all the resulting proteins

## NCBI protein databases

nr
Non-redundant protein sequences
refseq_protein
Reference proteins
refseq_select
Reference Select proteins

## What is “Non-Redundant”?

These databases get data from several sources

Sometimes two people upload the same sequence but with different ID

For example, EMBL ID, GenBank ID, RefSeq ID, etc.

This database combines all identical entries into one, and keeps all the alternative IDs

## NCBI protein databases

landmark
Model Organisms
swissprot
UniProtKB/Swiss-Prot
pat_aa
Patented protein sequences

## NCBI protein databases

pdb
Protein Data Bank proteins
env_nr
Metagenomic proteins
tsa_nr
Transcriptome Shotgun Assembly proteins

## NCBI nucleotide databases

Human G+T
Human genomic plus transcript
Mouse G+T
Mouse genomic plus transcript
nr/nt
Nucleotide collection

## NCBI nucleotide databases

Bacteria and Archaea
16S ribosomal RNA sequences
refseq_select
Reference Select sequences
refseq_rna
Reference RNA sequences

## NCBI nucleotide databases

refseq_representative_genomes
RefSeq Representative genomes
refseq_genomes
RefSeq Genome Database

SRA
TSA
Transcriptome Shotgun Assembly
HTGS
High throughput genomic sequences

## NCBI nucleotide databases

pat
Patent sequences
pdb
nucleotides in Protein Data Bank
RefSeq_Gene
Human RefSeqGene sequences

## BlastN variants

megablast
Highly similar sequences
discontiguous megablast
More dissimilar sequences
blastn
Somewhat similar sequences

## BlastP variants

blastp
protein-protein BLAST.
PSI-BLAST
Position-Specific Iterated BLAST.
builds a position-specific scoring matrix.
PHI-BLAST
Pattern Hit Initiated BLAST.
limits alignments to those that match a pattern in the query.

## BlastP variants

Quick BLASTP
Accelerated protein-protein BLAST.
very fast and works best if the target percent identity is 50% or more.
DELTA-BLAST
Domain Enhanced Lookup Time Accelerated BLAST.
builds a PSSM using a Conserved Domain Database search.
searches a sequence database.