1. Problem with N-W and S-W
They are exhaustive, with stringent statistical thresholds
As the databases got larger, these began to take too long
– computational constraints
Need a program that runs an algorithm that is based on dynamic programming (substrings) but uses a heuristic approach (takes assumptions, quicker, not as refined).
Therefore can deal with comparing a sequence against 100 million others in a relatively short time… the bigger the database, the more optimal the results.

2. Two heuristic approaches
Each is based on different assumptions and calculations of probability thresholds (ie the statistical significance of results)
FASTA
All proteins in the db are equally likely to be related to the query
 probability multiplied by the number of sequences in database
2. BLAST
Query more likely to be related to a sequence its own length or shorter
 probability multiplied by N/n
N: total number of residues in database
n: length of subject sequence

3. Basic Local Alignment Search Tool
The BLAST algorithm
Basic Local Alignment Search Tool
Breaks down your sequence into smaller segments (words)
Does the same for all sequences in the database
Looks for exact matches, word by word, and expands those up- and down- stream one base at a time, allowing for a certain number of mismatches
Stops when sequence ends or statistical significance becomes too low (too many mismatches)
Can find more than one area of similarity between two sequences

4. The BLAST algorithm STEP 1: k-mers in query sequence query sequence
… PQGEFGFAT…
query sequence
PQG
QGE
GEF
EFG …
words
STEP 2: Score the words: Match all 3-mers obtained in step 1 with all 203 matches.
With a threshold T, keep the best scoring words.

5. The BLAST algorithm
STEP 3: organize the search words in a tree for efficient retrieval
STEP 4: repeat step 2-3 for each 3-mer
STEP 5: scan the database sequences to find exact matches of words,
extend the exact matches to high-scoring segment pair (HSP).
… PQGEFGFAT…
query sequence
… AQGEFEAQT…
database sequence …
HSP, score: 22

6. The BLAST algorithm STEP 6: evaluate the significance of the HSP score
Karlin-Altschul statistics: Using the HSPs, we calculate the expected number
of HSPs with score at least S:
K, l … constant
m, n length of query and database sequence

7. The BLAST algorithm
Bit scores: Raw scores have little meaning without detailed knowledge of the
scoring system used, or more simply its statistical parameters K and l.
Unless the scoring system is understood, citing a raw score alone is like citing
a distance without specifying feet, meters, or light years. Therefore we normalize
to a Bitscore
so that
which is independent of K and l.

8. The BLAST algorithm
P-values:  The number of random HSPs with score ≥S is described by a Poisson
distribution (i.e. the probability of finding exactly a HSPs with score ≥S is given by
The chance of finding zero HSP with score ≥S is e-E. So the chance to find at least
one HSP with score ≥S is

9. The BLAST algorithm
So far our considerations were for pairwise alignments. What are we doing if
we search a database?
Correction for multiple testing, because a priori all proteins in the database are
a priori equally likely to be related to the query.
Therefore in the simplest case, E-value is corrected by the number of sequences
In the database:
E’= E*N
An alternative view is that a query is a priori more likely to be related to a long
than to a short sequence, because long sequences are often composed of
multiple distinct domains. If we assume the a priori chance of relatedness is
proportional to sequence length, then the pairwise E-value involving a database
sequence of length n should be multiplied by N/n, where N is the total length of
the database in residues.

10. BLAST Using online BLAST from Bio.Blast import NCBIWWW
database type
from Bio.Blast import NCBIWWW
result_handle = NCBIWWW.qblast("blastn", "nt", " ")
Blast program
sequence or
from Bio.Blast import NCBIWWW
from Bio import SeqIO
record = SeqIO.read("m_cold.fasta", format="fasta")
result_handle = NCBIWWW.qblast("blastn", "nt", record.seq)
result_handle = NCBIWWW.qblast("blastn", "nt", record.format(“fasta”)
Just sequence
whole seq. information

11. BLAST Using local BLAST
- First we create a command line prompt (for example):
blastn -query opuntia.fasta -db nr -out opuntia.xml -evalue 0.001
- then we us the BLAST Python wrappers from BioPython:
Blast
program
query
sequence
output
file
evalue
threshold
database
from Bio.Blast.Applications import NcbiblastnCommandline
blastn_exe = ”path to blastn"
blastn_cline = NcbiblastnCommandline(blastn_exe, query=“query.fasta", db=”opuntia.fasta", evalue=0.001, out="opuntia.xml”)
blastn_cline()

12. BLAST Parsing BLAST output list of alignments holds the information
from Bio.Blast import NCBIXML
result_handle = open("opuntia.xml")
blast_record = NCBIXML.read(result_handle)
for alignment in blast_record.alignments:
for hsp in alignment.hsps:
if hsp.expect < 0.001:
print (alignment.title)
print (alignment.length)
print (hsp.expect)
print (hsp.query)
print (hsp.match)
print (hsp.sbjct)
list of alignments
holds the information
about Blast output
E-value
query sequence
alignment
matched db seq.

13. Algorithm evolution
Smith Waterman Local alignment algorithm – finds small, locally similar regions (substrings), matrix-based, each cell in the matrix defined the end of a potential alignment.
BLAST – start with highest scoring short pairs and extend and down the sequence.
Great, but when you’re talking about millions of reads…

15. Sanger Sequencing DNA is fragmented Cloned to a plasmid vector
Cyclic sequencing reaction
Separation by electrophoresis
First generation: S35 isotope
Second generation: fluorescent tags
Third generation: automation

16. “Next” generation sequencing
Not Sanger based biochemistry
DNA is fragmented and sequenced directly
Much more chemistry, physics, and computer science
Characterized by
Parallel Sequencing
High Throughput
Cost
Generates billions of sequences per experiment, highly computational

17. Human Genome Project
ENCODE project
HapMap project
SNP consortium
Individual human genomes
James Watson, Craig Venter,
3 asian gentlemen

18. General Workflow (Illumina)

19. Workflow Outcomes

20. Data output Different technologies, though essentially similar
Differences in number, length and quality of the sequences
and in format of output
Making data handling across platforms and analysis a challenge
The SOLiD 3 Plus currently provides up to 60 gigabases of data in a 12- to 14-day run, and up to a billion sequence tags per run; and the Helicos platform generates 21 to 28 gigabases of data in eight days, and routinely analyzes 600 to 800 million usable strands per run info.

21. Sequencing Workflow

22. Step 4a. Data Analysis

23. Step 4b. Read mapping
Align your sequences onto a reference genome or other region
(unless you are de novo sequencing a new species)
Determine the coordinates and annotations if known
.. TAGTACCCCATCTTGTAGGTCTGAAACACAAAGTGTGGGGTGTCTAGGGAAGAAGGTGTGTGACCAGGGAGGTCCC ..
Genome
ATCTTGTAGG
GAAACACAAAGTG
GTCTAGGGAAGAAGG

24. Sequence Coverage Deep coverage = more accurate results
Deep coverage => detecting variants
Deep coverage = more expensive!

25. From geneious .com

26. NGS pipeline for finding variants
from Dolled-Filhart et al, 2013

27. It’s all about the alignment
Meyerson et al. Nature Reviews Genetics 11, (October 2010)

28. NGS alignment algorithms
Smith Waterman
BLAST
Enter BWT
BLAT is precomputed
BLAST