Download Slide 1

Biostatistics-Lecture 15
High-throughput sequencing and
sequence alignment
Ruibin Xi
Peking University
School of Mathematical Sciences
High-throughput sequencing
 HTS platforms




Roche 454 platforms
Illumina/Solexa platforms (most widely used)
Applied Biosystem (ABI) SOLiD
Helicos HeliSopeTM sequencer(single molecular
sequencing)
 Life Technologies platforms
 The throughput is increasing and the price is
dropping
 Short read but high throughput
High-throughput sequencing
 HTS platforms




Roche 454 platforms
Illumina/Solexa platforms (most widely used)
Applied Biosystem (ABI) SOLiD
Helicos HeliSopeTM sequencer(single molecular
sequencing)
 Life Technologies platforms
 The throughput is increasing and the price is
dropping
 Short read but high throughput
What sequencing data can do
 Detection of genomic variations (Whole genome
sequencing or targeted sequencing)
Single nucleotide polymorphisms (SNP)
Copy number variations (CNV)
Structural variations (SV)




Analyze protein interactions with DNA (ChIP-seq)
Whole Transcriptome study (RNA-seq)
DNA methylation study
and many more …..
Sequencing (Illunima)
Mardis Nature Reivew Genetics (2010)
What the data look like?
Fastq Format detail:
1st line: the name of a short read
2nd line: the read itself (a short sequence of A,C,G,T)
3rd line: the name of the short read or plus (+) sign
4th line: the quality score
General strategy for analyzing HTS
data
• Alignment-based
• Assembly-based
Comparing two DNA sequences
How can we evaluate an alignment
How can we evaluate an alignment
• Scores:
– Mutation (mismatch): 0
– Match: 1
– Gap: -1
Find a best alignment
• Exhaustively search all possible alignments
– Computationally too expensive!!!
• Observation: for a pair (i,j)
Find a best alignment
Find a best alignment—Solution I
Find a best alignment—Solution II
Dynamical programming
Dynamical programming
Dynamical programming
Dynamical programming
Dynamical programming
Dynamical programming
Dynamical programming
Dynamical programming
Semiglobal alignment
Semiglobal alignment
Semiglobal alignment
Semiglobal alignment
Local Alignment
Local Alignment
Local Alignment
Local Alignment
BLAST and BLAT
• BLAST: Basic Local Alignment Search Tool
• BLAT: BLAST Like Alignment Tool
BLAST
• BLAST:
– A search algorithm for finding local alignments of
two sequences S and T
– An associated theory for evaluating the statistical
significant
• Terminology and notation
– S(a,b): scoring system
– High-scoring Segment Pair (HSP):
• Cannot be extended or shortened without dropping the
score
BLAST
• The number of HSPs with a score ≥ S
approximately follows a Poisson Distribution
(under the null hypothesis) with parameter
– Assumptions
Some probability to take positive score
– Based on extreme value theory
– E-value
– Bit score
BLAST
• Algorithm: seed and extend
– Build an index for k-mers of the query sequence
– Find the hits of the k-mers in the database
sequence in the query sequence
– Extend the seeds with a score ≥ a threshold and
find the HSPs with a score ≥ S
– Evaluate the statistical significance
BLAT
• Strategy: seed and extend
– In the seed stage, detects regions of two
sequences that are likely to be homologous
– In the extend stage, those regions are examined in
detail and alignments are produced.
• Index the non-overlapping K-mers of the
database sequences instead of the query
sequence
BLAT
• Strategy: seed and extend
– In the seed stage, detects regions of two
sequences that are likely to be homologous
– In the extend stage, those regions are examined in
detail and alignments are produced.
• Index the non-overlapping K-mers of the
database sequences instead of the query
sequence
BLAT
• Seeding strategy
– Single perfect K-mer matches
– Single near perfect K-mer matches
– Multiple perfect K-mer matches
BLAT
• Some definitions
BLAT
• Single perfect match
– the probability that a specific K-mer in a homologous
region of the database matches perfectly the
corresponding K-mer in the query
– Sensitivity: the probability of a hit (at least one nonoverlapping K-mers in the database matches perfectly
with the corresponding K-mer in the query)
– Specificity: the expected number of non-overlapping
K-mers that matches, assuming all letters are equally
likely
BLAT
• Single perfect match
– the probability that a specific K-mer in a homologous
region of the database matches perfectly the
corresponding K-mer in the query
– Sensitivity: the probability of a hit (at least one nonoverlapping K-mers in the database matches perfectly
with the corresponding K-mer in the query)
– Specificity: the expected number of non-overlapping
K-mers that matches, assuming all letters are equally
likely
BLAT
• Single imperfect match
– The probability
– The sensitivity
– The specificity
BLAT
• Single imperfect match
– The probability
– The sensitivity
– The specificity
BLAT
• Multiple perfect Matches
– Probability
– Sensitivity
– Specificity
BLAT
• Multiple perfect Matches
– Probability
– Sensitivity
– Specificity
BLAT
• Clumping the hits
– The hit list L is sorted by database coordinate.
– The list L is split into buckets of size 64 kb each, based on
the database coordinate.
– Each bucket is sorted along the diagonal, i.e. hits are
sorted by the value of database position minus query
position.
– Hits that are within the gap limit are grouped together into
proto-clumps.
– Hits within proto-clumps are then sorted by their database
coordinate and put into real clumps
– Clumps within 300 bp or 100 amino acids of each other in
the database are merged
BLAT
• Nucleotide alignment
– A hit list is generated between the query sequence q
and the homologous region h in the database, looking
for smaller, perfect K-mers.
– If a K-mer w in q matches multiple K-mers in h, then w
is repeatedly extended by one until the match is
unique or exceeds a certain size.
– The hits are extended as far as possible, without
mismatches
– Overlapping hits are merged.
– Then extensions using indels followed by matches are
considered.