This document discusses DNA sequencing and sequence assembly. It defines sequencing as determining the order of nucleotides in DNA, and sequence assembly as aligning and merging DNA fragments to reconstruct the original sequence. It describes the shotgun sequencing method using Sanger sequencing that randomly fragments DNA, sequences the fragments, and assembles the sequence by finding overlaps between fragments. It provides an example of fragmenting and assembling a DNA sequence. It discusses using long reads for sequencing, which have higher error rates but allow assembly into longer contigs compared to short read sequencing.

1. SAJAN SINGH RATHORE
Roll No. :- MCA/25019/18

2.  SEQUENCING  Sequencing is the operation of
determining the precise order of nucleotides of a
given DNA molecule. It is used to determine the
order of the four bases adenine(A),guanine(G),
cytosine(C),and thymine(T), in a strand of DNA.
 SEQUENCE ASSEMBLY  The process of aligning
and merging fragments from a longer DNA
sequence in order to reconstruct the original
sequence is known as sequence Assembly.

3.  The shotgun sequencing method using the
Sanger sequencing operates as follows: The
target DNA molecule is broken into small
fragments , each of which is sequenced .
 Sequence is assembled by searching for overlaps
between the sequences of individual fragments.
 Whole- genome “shotgun” sequencing starts by
copying and fragmenting the DNA.
 Shotgun refers to the random fragmentation of
the whole genome

4.  EXAMPLE:-
 INPUT : GGCGTCTATATCTCGGCTCTAGGCCCTCATTTTTT
 Copy: GGCGTCTATATCTCGGCTCTAGGCCCTCATTTTTT
 GGCGTCTATATCTCGGCTCTAGGCCCTCATTTTTT
 GGCGTCTATATCTCGGCTCTAGGCCCTCATTTTTT
 GGCGTCTATATCTCGGCTCTAGGCCCTCATTTTTT
Fragment: GGCGTCTA TATCTCGG CTCTAGGCCCTC ATTTTTT
GGC GTCTATAT CTCGGCTCTAGGCCCTCA TTTTTT
GGCGTC TATATCT CGGCTCTAGGCCCT CATTTTTT
GGCGTCTAT ATCTCGGCTCTAG GCCCTCA TTTTTT


5.  Assume sequencing produces such a large fragments that almost all
genome positions are covered by many fragments….
 CTAGGCCCTCAATTTTT
 CTCTAGGCCCTCAATTTTT
 GGCTCTAGGCCCTCATTTTTT
 CTCGGCTCTAGCCCCTCATTTT
 TATCTCGACTCTAGGCCCTCA
 TATCTCGACTCTAGGCC
 TCTATATCTCGGCTCTAGG
GGCGTCTATATCTCG
GGCGTCGATATCT
GGCGTCTATATCT
GGCGTCTATATCTCGGCTCTAGGCCCTCATTTTTT (Reconstruct this).

6.  CTAGGCCCTCAATTTTT
 CTCTAGGCCCTCAATTTTT
 GGCTCTAGGCCCTCATTTTTT
 CTCGGCTCTAGCCCCTCATTTT
 TATCTCGACTCTAGGCCCTCA
 TATCTCGACTCTAGGCC
 TCTATATCTCGGCTCTAGG
 GGCGTCTATATCTCG Overlapping
GGCGTCGATATCT
GGCGTCTATATCT Coverage at this point = 6

7.  Key term: Usually it’s short term for average coverage: the average number
of reads covering a position in the genome.
 CTAGGCCCTCAATTTTT
 CTCTAGGCCCTCAATTTTT
 GGCTCTAGGCCCTCATTTTTT
 CTCGGCTCTAGCCCCTCATTTT
 TATCTCGACTCTAGGCCCTCA 177 nucleotides
 TATCTCGACTCTAGGCC
 TCTATATCTCGGCTCTAGG
GGCGTCTATATCTCG
GGCGTCGATATCT
GGCGTCTATATCT
GGCGTCTATATCTCGGCTCTAGGCCCTCATTTTTT 35 nucleotides
Average coverage = 177 / 35 = 5.05

8.  Using Long reads :-
 Greater than 10,000bp reads are common.
 Higher error rate (5-15%).
 Key computational challenge: overcome high
error rate.
 Long read assembly pipeline :-
 First Reads  than build overlap graph  layout
(Bundle stretches of the overlap graph into
contigs )  consensus(Pick most likely
nucleotides sequence for each contig )  contigs


9.  HIGH accuracy, very high throughput.
 Short read length limits ability to resolve
repeats .
 Key computational challenge: efficiently
assemble large numbers of short reads.
 Large Genomes :-
 Short reads : ~10,000bp contigs.
 Long reads: ~1,000000bp contigs
 Long reads data is much more expensive.