Genome sequencing and assembling

1
Genome sequencing and assembling

• Chitta Baral

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Basic ideas and limitations

• Current lab techniques can sequence small (say
  700 base pairs) DNA pieces.
• Use restriction enzymes to cut DNA pieces
• Sort pieces of different sizes using gel
  electrophoresis and use the sorting to read them
• Mapping and Walking
• Sequence one piece, get 700 letters, make a
  primer that allowed you to read the next 700, and
  work sequentially down the clone
• Estimate for human genome sequencing using this
  method 100 years
• Shotgun sequencing (introduced by Sanger et al.
  1977) for sequencing genomes
• Obtain random sequence reads from a genome
• Assemble them into contigs on the basis of
  sequence overlaps
• Straightforward for simple genomes (with no or
  few repeat sequences)
• Merge reads containing overlapping sequence
• Shotgun sequencing is more challenging for
  complex (repeat-rich) genomes two approaches

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Shotgun sequencing 2 approaches

• Hierarchical shotgun approach
• Generating an overlapping set of
  intermediate-sized (e.g. bacterial artificial
  chromosomes with 200 KB inserts) clones, and
  keeping a map of that (it took 2 yrs for mapping
  e-coli)
• Subjecting each of these clones to shotgun
  sequencing, and using the map to get the whole
  sequence.
• Used in S. cerevisiae (yeast), C. elegans
  (nematode), A. thaliana (mustard weed) and by the
  International Human Genome Sequencing Consortium
  (started in 1990, draft made available in 2000)
• Whole-genome shotgun (WGS) approach
• Generating sequence reads directly from a
  whole-genome library
• Using computational techniques to reassemble in
  one step.
• Used for Drosophila melanogaster (fruit fly) and
  by Celera Genomics (formed 1998) for human
  genome.

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Sequencing small DNA pieces

• Use DNA cloning or PCR to make multiple copies.
• Put in 4 testtubes marked G, A, T and C
• In testtube G use restriction enzymes that cuts
  at G.
• Do the above step for the other testubes.
• Use gel electrophoresis separately for the
  content in each testtube.
• The data results in the table on the left.
• Reading the table we get G has lengths 1, 7, 12,
  13, 19 A has lengths 2, 6, 8, 11, 14,15,16 T
  has length 4, 5, 9, 18 and C has length 3, 10,
  17.
• This gives us the sequence.

G A T C
G --------------
A --------------
C --------------
T --------------
T --------------
A --------------
G --------------
A --------------
T --------------
C --------------
A --------------
G --------------
G --------------
A --------------
A --------------
A --------------
C --------------
T --------------
G --------------
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The ARACHNE WGS assembler outline of assembly
algorithm

• Input data
• Paired end reads obtained by sequencing both ends
  of a plasmid of known insert size.
• Assumes each base in each read has an associated
  quality score (say one obtained by PHRED program)
• Quality score q corresponds to the probability
  10-q/10 that the base is incorrect (40
  corresponds to 99.99 accuracy)
• Initial step eliminates terminal regions whose
  quality is low.
• Eliminates reads containing very little
  high-quality sequence
• Eliminates known vector sequences and known
  contaminants (eg. Sequence from the bacterial
  host or cloning vector)

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Cont.

• Overlap detection and alignment
• Create a sorted table of each k-letter subword
  (k-mer) together with its source (which read) and
  its position within the read.
• Exclude k-mers that occur with extremely high
  frequency
• corresponds to highly repeated sequences
• used to increase the efficiency of the overlap
  detection process
• Identify all instances of read pairs that share
  one or more overlapping k-mer, and a 3 step
  process (similar to FASTA) to align the reads
  effciently
• (i) Merge overlapping shared k-mers, (ii) Extend
  the shared k-mers to alignments, (iii) Refine the
  alignment by dynamic programming.
• Some valid alignments may be missed and some
  invalid ones may result.

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ARACHNE Error correction

• Error detection and correction
• Generate multiple alignments among overlapping
  reads
• Identify instances where a base is overwhelmingly
  outvoted by bases aligned to it (taking into
  account the score quality)
• Similarly correct occasional inserts and deletes
  (mostly due to sequencing errors)

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ARACHNE Evaluation of alignments

• Evaluation of alignments
• Assign a penalty score to each aligned pair of
  overlapping reads
• Penalty scores are assigned to each discrepant
  base, based on the sequence quality score at the
  base and flanking bases on either side.
• Discrepancies in high quality sequences are
  assigned high penalty, and discrepancies in low
  quality sequences are penalized less heavily.
• The penalty scores for individual discrepancies
  are combined to yield an overall penalty score
  for the alignment.
• Overlaps incurring too high a penalty are
  discarded
• Likely chimeric reads are also detected and
  discarded
• Reads that contain genomic sequence from two
  disparate locations are termed chimeric.

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ARACHNE paired pairs

• Identification of paired pairs
• Paired reads reads which are known to be related
  with respect to orientation and distance.
• Searches for instances of two plasmids of similar
  insert size with sequence overlap occurring at
  both ends. (together called paired pairs)
• These instances are extended by building
  complexes of such pairs
• 
  
• 
  
• 
  
• Collection of paired pairs are merged together
  into contigs.

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ARACHNE Contig assembly

• When repeats are absent correct assembly can be
  easily obtained by merging all the overlapping
  reads.
• In presence of repeats, false overlaps may arise
  between reads derived from different copies of a
  repeat
• ARACHNE identifies potential repeat boundaries
  and avoids assembling contigs across such
  boundaries
• Potential repeat boundary a read r can be
  extended by x and y, but x and y dont overlap
• Merge overlapping read pairs that do not cross a
  marked repeat boundary.

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ARACHNE repeat contigs and supercontigs

• Detection of repeat contigs identified 2 ways
• Unusually high depth of coverage
• Conflicting links to multiple, distinct,
  non-overlapping contigs, reflecting the multiple
  regions that flank the repeat in the genome.
• aRb, cRd, eRf will result in aR-, -cR-,
• Creation of supercontigs
• After marking repeat contigs the unmarked contigs
  (called unitigs) are assembled.
• Use forward-reverse links from reads to order and
  orient unique contigs into supercontigs

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ARACHNE Filling gaps in supercontigs

• Layout is a set of contigs each of which is an
  ordered list of contigs with interleaved gaps
  corresponding to 2 kind of regions
• Regions marked as repeat contigs (which were
  omitted in supercontig construction)
• Regions for which there are insufficient number
  of shotgun reads to allow assembly
• Fill gap using repeat contigs
• For every pair of consecutive contigs with an
  interleaving gap in a supercontig S, the program
  tries to find a path of pairwise overlapping
  contigs that fill the gap.
• Forward-reverse links from S guide the
  construction of the path by identifying contigs
  likely to fall in the gap.

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Consensus derivation and postconsensus merger

• The layout of overlapping reads is converted into
  consensus sequence with quality scores.
• Done by converting pair-wise alignments of reads
  into multiple alignments, and deriving the
  consensus base by weighed voting.