User:Jarle Pahr/Sequencing

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Nature focus issue - sequencing technology:


For a comparison of next-generation sequencing methods, see

See also: Tech summaries:

Sanger sequencing (chain termination method)

Pyrosequencing ("454 sequencing")

Pyrosequencing is a "sequence by synthesis" method developed by Mostafa Ronaghi and Pål Nyrén at the Royal Institute of Technology, Stockholm. Sequences are determined by observation of light emission upon addition of a nucleotide complementary to the first unpaired nucleotide of the template.

Quote from Wikipedia:Pyrosequencing:

"ssDNA template is hybridized to a sequencing primer and incubated with the enzymes DNA polymerase, ATP sulfurylase, luciferase and apyrase, and with the substrates adenosine 5´ phosphosulfate (APS) and luciferin."

Sequencing proceeds as follows:

  • Addition of one of the four dNTPs (dATPαS is substituted for ATP, as the former is not a substrate for luciferase). If the dNTP is complementary, DNA polyerase incorporates the nucleotide, releasing pyrophosphate (PPi).
  • ATP sulfurylase catalyzes reaction of PPi and adenosine 5' phosphosulfate to create ATP
  • ATP fuels luciferase-catalyzed conversion of luciferin to oxyluceferin, generating visible light.
  • Unincorporated nucleotides and ATP are degraded by apyrase.

454 sequencing performs massively parallel pyrosequencing. Library DNA containing adapter sequences are adsorbed to DNA-capturing beads. The DNA bound to each bead is then amplified by emulsion-PCR, in which the beads with bound DNA are mixed with PCR reagents and emulsion oil to create a water-in-oil emulsion containing many "microreactors" consisting of beads sorrounded by water. Following PCR amplification, the DNA-binding beads are isolated and deposited into the wells of a microtiter plate. Beads with pyrosequencing enzymes are then added to the plate. Finally, the pyrosequencing is performed, processing the plate in a sequencing machine. 400 000+ DNA fragments/beads can be processed per plate.

Using "multiplex identifiers", different genomic libraries can be bar-coded, facilitating sequencing of several libraries in the same sequencing run.


Platform Throughput (bases/run) Time per run Average (a)/mode (m) read length (nt) Accuracy Introduced (year)
GS FLX+ 700 Mbp 23h Up to 1000 700 bp (m)
GS Junior 35Mbp 12 h 400 400 bp (a) at Phred20/read



Introductory paper, 454 sequencing:

The development and impact of 454 sequencing

Direct Comparisons of Illumina vs. Roche 454 Sequencing Technologies on the Same Microbial Community DNA Sample

Overview of 454 sequencing:

Illumina (Solexa) sequencing

Platform Throughput (bases/run) (maximum) Time per run Read length (nt) Accuracy Features Introduced (year)
MiSeq Personal Sequencer Up to 8.5 gbp 4 - 48 h 250 >70% bases higher than Q30 at read length 2 x 300 bp
HiSeq 2500/1500 600 Gb 2 x 100 >80 % higher than Q30
HiSeq 2000/1000 300 Gb 2 x 100 >80 % higher than Q30
Genome Analyzer IIx 95 Gb 2 x 150 >80 % higher than Q30

MiSeq datasheet:

Side by side comparison of Illumina sequencers:

Illumina - an introduction to NGS:

Ion semiconductor sequencing

Ion Torrent: Platforms:

Platform Throughput (bases/run) Time per run Typical read length Accuracy Introduced (year)
Ion PGM sequencer 10 Mb to 1Gb 90 min+ 35-400 bp
Ion Proton sequencer 1 human genome 2h+ 100 bp

Nanopore sequencing

Oxford Nanopore:

Single molecule real time sequencing (Pacific Biosciences)

Microscopical wells on a chip (zero-mode waveguides) each contain a single DNA polymerase enzyme bound to the bottom of the well, which accept a single DNA molecule as template. Fluorescent labelled dNTPs are used for DNA synthesis. Upon incorporation of a dNTP, the fluorescence tag is cleaved from the nucleotide and diffuses from the observation area within the ZMW. The sequence is determined optically by observing incorporation events.


PacBio RS:

SOLiD sequencing (Applied Biosystems)

DNA nanoball sequencing


Qiagen GeneReader



High-throughput sequence assemblers often use shorter sub-sequences (k-mers, of length k) of produced reads in the assembly process. For example, reads of 100-mers may not be expected to capture all possible 100-mers in the genome.

By breaking reads into shorter k-mers, the resulting k-mers often represent nearly all k-mers from the genome for sufficiently small k, a prerequisite for assembly using de Bruijn graphs. (

De Bruijn graph

See also Compeaou et al. 2001, Nature Biotechnology - How to apply de Bruijn graphs to genome assembly:

  • Finding a hamiltonian cycle that visits all nodes of a graph is computationally expensive (NP-complete).
  • Easier to find a cycle that visits all edges of a graph (Eulerian cycle).
  • Ergo: Instead of assigning a k-mer to a node, we can assign a k-mer to an edge, allowing construction of a De Bruijn graph (

Bridge amplification


Genotyping by Sequencing (GBS)



Edit distance


Color Space/2-base encoding


See also

Targeted sequencing

Targeted "capturing kits" may be used to sequence a subset of genomic DNA. The human exome (as defined by the Consensus CDS (CCDS) project) totals about 38 Mb, covering about 1.22 % of the human genome

(The SureSelect Human All Exon Kit )

See also:


Paired-end reads

N50 Statistic

N50 length: In a collection of contigs, the longest length for which the subset of contigs consisting of all contigs with that length or longer contains at least half of the total of the length of the contig collection.

NG50: As N50, except that the goal is half of the total of the genome size.


See also:


Loss of Heterozygosity

Copy number variants (CNVs)

Short Tandem Repeats (STRs)

Genotyping of STRs is used to produce forensic DNA profiles. See


Sequence Read Archive:

European Nucleotide Archive:

Sequence alignment/Assembly

Compendium of HTS mappers:

Comparison of assemblers:


Bowtie - An ultrafast memory-efficient short read aligner:'

Primers and reviews:

NCBI primer on genome assembly methods:

Nature Biotechnology Primer - How to map billions of short reads onto genomes:

Bioinformatics, 2012: Tools for mapping high-throughput sequencing data:

A survey of sequence alignment algorithms for next-generation sequencing:

De novo assembly:

Optimal Assembly for High Throughput Shotgun Sequencing:

Counter-intuitevely, too high coverage can be problematic:

Sequencing services

Service Sample specification Primer specification Ship to Link
GATC LightRun Add 5 uL DNA (80-100 ng/uL plasmid or 20-80 ng/uL purified PCR product) + 5 uL 5uM (5 pmol/uL) primer to the same tube Tm 52-58 C, 17-19 bp, (8-9 G+C for 18-mer) G or C at 3' end (max 3 Gs or Cs), maximum 4bp run. GATC Biotech AG. European Custom Sequencing Centre. Gotrfied-Hagen-Strasse 20. 51105 Köln.
Macrogen Single-pass Add 20 uL DNA (100 ng/uL plasmid or 50 ng/uL purified PCR product) to one tube. Add 20µl primer (10 pmol/uL) to a separate tube. 18-25 bp, 40-60 % GC, Tm 55-60 Macrogen Europe,

IWO, Kamer IA3-195, Meibergdreef 39,1105 AZ Amsterdam Zuid-oost. Netherlands. Attention: J.S .Park.

Variant calling

Sequencing-based techniques



Sequencing/genomics centres


New York Genome Center:


Norwegian Cancer Genomics Consortium:

See also:

Sequencing facilities in Norway: (Incomplete)


Akershus University Hospital (Ahus): 1 x Ion Torrent

Norwegian High-Throughput Sequencing Centre (NSC) Oslo, Norway: 2 x Roche/454, 1 x Illumina HiSeq, 1 x PacBio, 1 x Ion Torrent, 1 x Illumina MiSeq

Helse Sør-Øst/University of Oslo Genomics Core Facility Oslo, Norway: 1 x Illumina GA2, 1 x MiSeq, 1 x HiSeq

NTNU Genomics Core Facility Sør-Trøndelag, Norway: 1 x HiSeq

Telemark Hospital Telemark, Norway: 1 x Illumina HiSeq




Contact persons:

Lex Nederbragt:

Dr. Leonardo A. Meza-Zepeda Head Helse Sør-Øst/ Univ. of Oslo Genomics Core Facility

Kjetill S. Jakobsen

Professor, Group Leader (CEES node)

Dag Erik Undlien

Professor, Group Leader (IMG node)


Custom primers

Name Length (bp) Sequence Tm (C) [calculated] Tm (C) [Analytical] GC (% / bp) Comment
pJP-1_seq5 18 CAGCGTGCGAGTGATTAT 53.9/60.6 (2)/52.6 (3) 50 Binds upstream of XylS region in pSB-M1g
pJP-1_seq6 18 AGACCACATGGTCCTTCT 57.5° (2)/52.8 ºC(3) 53.9 50 Binds near end of GFPmut3 in pSB-M1g
SeqMG1 AGCAGATCCACATCCTTGAA 62.7 (2)/53.7 (3) Binds at nt 5672 of pSB-M1g, upstream of AgeI site. Designed to Macrogen sequencing primer criteria.
pSB-SeqA 18 TGCAAGAAGCGGATACAG 56 / 60.7°C (2)/52.3 ºC (3) 50 Binds at nt 7729 of pSB-M1g, upstream of Pm promoter and PciI site.

Universal primers

Tm calculations: 1: CloneManager 2: Thermo Scientific 3: IDT Oligoanalyzer

A tale of three next generation sequencing platforms: comparison of Ion Torrent, Pacific Biosciences and Illumina MiSeq sequencers


Chromatogram viewers:

CodonCode aligner:



About SCF (sequence chromatogram format) files:

High-throughput sequencing tools:

SAM tools:

Burrows-Wheeler Aligner (BWA):

Maq: Mapping and Assembly with Qualities

See also

Genome Analysis Toolkit (GATK):

Sequencing quality and standards:

Sequencing projects

File formats

Sequence Alignment/Map (SAM) format: "A generic format for storing large nucleotide sequence alignments". Tab-delimited text format consisting of a header section (optional) and an alignment section.

See also:

Binary Compressed Sam format/Binary Alignment Format (BAM): Binary, compressed file format containing the same information as SAM files.

From : "Centers align sequence reads to a reference genome to produce a Sequence Alignment Map (SAM) format file. The SAM file is then converted into a binary form, or Binary-sequence Alignment Format (BAM) file"

See also

Variant Call Format (VCF):

Standard created by the 1000 Genomes Project.

From :

"The VCF format is a tab delimited format for storing variant calls and and individual genotypes. It is able to store all variant calls from single nucleotide variants to large scale insertions and deletions."

ABI (Applied Biosystems) format:


FASTQ files encode identified nucleotides together with their corresponding quality scores. The interpretation of the quality scores may vary depending on the source of the sequence, but the most used is the "Sanger format" (Phred quality scores).



Assembly of large genomes using second-generation sequencing.:

Comparison of variant-calling software

Nonhybrid, finished microbial genome assemblies from long-read SMRT sequencing data:

GAGE: A critical evaluation of genome assemblies and assembly algorithms:

Miller 2011 - Assembly Algorithms for Next-Generation Sequencing Data:


High-throughput sequencing for biology and medicine:


The state of NGS variant calling - Don't panic:

Assemblies: The good, the bad and the ugly:

A tale of three next generation sequencers:




Suggested work procedure when receiving sanger sequencing results (plasmids, etc.):

  • If applicable, compare the automatically trimmed sequenc (.fas) file and the expected sequence using BLAST or another sequence alignment tool. OR: consider using raw sequence copied from a chromatogram viewer.
  • If no hit is found, make sure that the most permissive algorithm (blastn or similar) is used. If still no hit is found, manually inspect the chromatogram (.abi) file using a chromatogram viewer. If the trimmed file is small compared to the raw sequence (low chromatogram quality) and the remainder appears sensible, re-do the search using "raw" called bases (copied directly from the chromatogram viewer). When making notes on sequence results, always write which sequence (PHRED-generated, "raw" sequence from chromatogram viewer?) which was used for a given analysis (f. ex. BLAST search). Otherwise, confusion may ensue: Not says 100 % match, BLAST search gives no/bad match, etc....
  • As a quick check, the sequence file can be searched for a short portion of the expected sequence, while allowing for some mistmatches (which may be present because of sequencing errors).
  • If disrepancies occur, inspect the chromatogram at the relevant positions.
  • If a hit is found for the desired sequence, check that the sequence is in the right position, and that the flanking sequences are correct.
  • Be aware that alignment may produce suboptimal results (indicating a worse fit than is actually the case), especially when aligning to circular sequences.

Three main "concerns" may appears:

  • Base differs from expected.
  • Base is uncalled ("n")
  • Indel/Gap

In all cases inspecting the chromatogram may resolve the issue. Automatically generated sequences should be considered a best guess by the computer.

Chromatogram interpretation:

Common causes of bad data from sanger sequencing:

  • Salt/alcohol/other contamination
  • GC rich of palindromic regions.
  • Double priming
  • Supression of signal after a strong signal: Happens most commonly for G's after A's, and often for G's after C's. Most often, weak G signals follow after multiple A's.

Common causes of mis-called bases:

Template preparation:



SEQanswers wiki:

SEQansers - how to:

Genome Reference Consortium:

List of NGS blogs:

NGS Necropolis:

Rob Carlson's blog:

Raw data