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Genome Assembly and Finishing Alla Lapidus, Ph.D. Associate Professor Fox Chase Cancer Center. A typical Microbial (and not only) project. Sequencing. Draft assembly . Goals: Completely restore genome Produce high quality consensus. FINISHING . Annotation. Public release.

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Genome Assembly

and Finishing

Alla Lapidus, Ph.D.

Associate Professor

Fox Chase Cancer Center


A typical Microbial (and not only) project





Completely restore genome

Produce high quality consensus



Public release

evolution of microbial drafts
Sanger only

4x of 3kb plasmids + 4x of 8kb plasmids + 1x of fosmids

~ $50k for 5MB genome draft

Evolution of Microbial Drafts
  • Hybrid Sanger/pyrosequence/Illumina
    • 4x 8kb Sanger + 15 x coverage 454 shotgun + 20x Illumina (quality improvement)
    • ~ $35k for 5MB genome draft

454 + Solexa

-20x coverage 454 standard + 4x coverage 454 paired end (PE) + 50x coverage Illumina shotgun (quality improvement; gaps)

- ~ $10k per 5MB genome

Solexa only - low cost; too fragmented; good assembler is needed!

Solexa +PacBio - low cost; better sachffolding

library preparation sanger
Library Preparation - Sanger

DNA fragmentation

Random fragment DNA

assembly assembler
Sanger reads only (phrap, PGA, Arachne)


  • Hybrid Sanger/pyrosequence/Solexa (no special assemblers; use Newbler, PGA, Arachne)





454 contig



454 shreds





Shotgun reads

PE reads

Assembly (assembler)
  • 454/Solexa (Newbler, PCAP, Velvet, ALLPATH etc) –
draft assembly what we get

PCR product




PCR - sequence

Draft assembly - what we get

Assembly: set of contigs




Ordered sets of contigs (scaffolds)




Clone walk

(Sanger lib)

New technologies: no clones to walk off even if you can scaffold contigs

(bPCR – new approach of gap closing)

primer walking

PCR – sequence

(un captured gaps)

PCR product

Template: gDNA

Primer walking

Clone walk

(captured gaps)

Clone A

why do we have gaps
Why do we have gaps

What are gaps ?

- Genome areas not covered by random shotgun

  • Sequencing coverage may not span all regions of the genome, thus producing gaps in the assembly – colony picking
  • Assembly results of the shotgun reads may produce misassembled regions due to repetitive sequences (new and old tech)
  • A biased base content (this can result in failure to be cloned, poor stability in the chosen host-vector system, or inability of the polymerase to reliably copy the sequence):
  • ~ AT-rich DNA clones poorly in bacteria (cloning bias;
  • promoters like structures {Sanger} )=> uncaptured gaps
  • ~GC rich DNA is difficult to PCR and to sequence and often
  • requires the use of special chemistry => captured gaps
  • ~ high AT and GC content caused by problematic PCR (new tech)
high gc sequencing problems
High GC sequencing problems:
  • The presence of small hairpins (inverted repeat sequences) in the
  • DNA that re anneal ether during sequencing or electrophoresis
  • resulting in failed sequencing reactions or unreadable electrophoresis
  • results. (This can be aided by adding modifiers to the reaction,
  • sequencing smaller clones and running gels at higher temperatures in
  • the presence of stronger denaturants).
why more than one platform
Why more than one platform?

454 - high quality reliable skeletons of genomes (454 std + 454 PE): correctly assembled contigs; problems with repeats (unassembled or assembled in contigs outside of main scaffolds); homopolymer related frame shifts

Illumina data is used to help improve the overall consensus quality, correct frameshifts and to close secondary structure related gaps; not ready for de-novo assembly of complex genomes (too many gaps!)

Sanger – finishing reads; fosmids – larger repeats and templates for primer walk – less cost effective but very useful in many cases


454 (pyrosequence) and low GC genomes

Thermotoga lettingae TMO

Draft assembly +454

- 2 total contigs; 1 contigs >2kb

- 454 – no cloning

Sanger based draft assembly:

- 55 total contigs; 41 contigs >2kb

- 38GC% - biased Sanger libraries

<166bp> - average length of gaps

454 and high gc projects
454 and High GC projects

Xylanimonas cellulosilytica DSM 15894 (3.8 MB; 72.1% GC)

PGA assembly - 9x of 8kb +454

PGA assembly - 9x of 8kb

nextgen high quality drafts at jgi multiple sequencing platforms
NextGen high Quality Drafts at JGI (multiple sequencing platforms)


Unassembled 454 reads

Solexa contig

454/Sanger contig

Fosmid ends* and 454 PE

1.Pyrosequence and Sanger to obtain main ordered and oriented part of the assembly –

Newbler assembler

2. GapResolution (in house tool) to close some (up to 40%) gaps using unassembled 454 data –

PGA or Newbler assemblers

3. Solexa reads to detect and correct errors in consensus –

in house created tool (the Polisher) and close gaps (Velvet)

* Fosmids ends not used for microbes

solving gaps gapresopution tool
Solving gaps: gapResopution tool

Step 2Assemble reads in contigs adjacent

to the gap and reads obtained from contigs

outside the scaffold. Sometimes use assembler

other than Newbler for sub-assemblies (PGA)

Step 1For each gap, identify read

pairs from contigs found on different




Consensus from



Gap (due to repeat)

Read pairs that are found in contigs outside of this scaffold

solving gaps gapresopution tool ii
Solving gaps: gapResopution tool (II)



Design sequencing reactions to close gap

Step 3If gap is not closed, tool designs

designs primers for sequencing reactions

Step 4Iterate as necessary (in sub-assemblies)

solexa for gaps
Velvet assembly

Blast Velvet contigs against Newbler ends

Use proper Velvet contigs to close gaps

Solexa for gaps

Velvet contig

454 Contig


Illumina reads

Velvet contigs close gaps caused by

hairpins and secondary structures

low quality areas areas of potential frameshifts
Low quality areas – areas of potential frameshifts

Assemblies contain low quality regions (red tags)


Homopoymer related frameshifts

Frameshift 1 (AAAAA, should be AAAA)

homopolymers (n>=3)

Frameshift 2 (CCCC, should be CCC)

Modified from N. Ivanova (JGI)

polisher software for consensus quality improvement
Polisher: software for consensus quality improvement

Step 2:Analyze and correct consensus errors

Step 1:Align Illumina data to 454-only

or Sanger/454 hybrid assembly







Illumina reads







a. Illumina coverage < 10X

b. Illumina coverage >= 10X and

<70% of Illumina bases agree

with the reference base

Illumina coverage >= 10X and at least 70% llumina bases disagrees with the reference base

Step 3:Design sequencing reactions for low

quality and unsupported Illumina areas

Sanger/454 low quality

Unsupported Illumina region

errors corrected by solexa

Finished consensus

454 contig

Sanger reads

Errors corrected by Solexa

Frame shift detected (454 contig)

























so what is finishing
The process of taking a rough draft assembly composed of

shotgun sequencing reads, identifying and resolving miss

assemblies, sequence gaps and regions of low quality to

produce a highly accurate finished DNA sequence.

So, what is Finishing?

Final quality:

Final error rate should be less than 1 per 50 Kb.

No gaps, no misassembled areas, no characters other than ACGT

genome projects archaea bacteria only
Genome projectsArchaea + Bacteria only







metagenomic assembly and finishing
Typically size of metagenomic sequencing project is very large

Different organisms have different coverage. Non-uniform sequence coverage results in significant under- and over-representation of certain community members

Low coverage for the majority of organisms in highly complex communities leads to poor (if any) assemblies

Chimerical contigs produced by co-assembly of sequencing reads originating from different species.

Genome rearrangements and the presence of mobile genetic elements (phages, transposons) in closely related organisms further complicate assembly.

No assemblers developed for metagenomic data sets

Metagenomic assembly and Finishing

The whole-genome shotgun sequencing approach was used for a number of microbial community projects, however useful quality control and assembly of these data require reassessing methods developed to handle relatively uniform sequences derived from isolate microbes.

qc annotation of poor quality sequence
QC: Annotation of poor quality sequence

To avoid this:

-make sure you use high quality sequence

-choose proper assembler

A Bioinformatician's Guide to Metagenomics . Microbiol Mol Biol Rev. 2008 December; 72(4): 557–578.

assembly mistakes
Assembly mistakes

A Bioinformatician's Guide to Metagenomics. Microbiol Mol Biol Rev. 2008 December; 72(4): 557–578.

recommendations for metagenomic assembly
Use Trimmer (Lucy etc) to treat reads PRIOR to assembly

None of the existing assemblers designed for metagenomic data but assemblers like PGA work better with paired reads information and produce better assemblies.

We currently test Newbler assembler for second generation sequencing: 454 only and 454/Solexa co-assembly

Recommendations for metagenomic assembly
metagenomic finishing approach

CAP reads


Non-CAP reads

Metagenomic finishing: approach

Candidatus Accumulibacter phosphatis(CAP)

Binning:Which DNA fragment

derived from which phylotype?

(BLAST; GC%; read depth)


Complete genome of Candidatus Accumulibacter phosphatis

~ 45%

merged assemblies k 31 and k 51 with minimus cloneview used for visualization
Merged assemblies ( k=31andk=51) with minimus(Cloneview used for visualization)
  • Green k=31
  • Purple k=51

Illumina only data