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BIT 815: Analysis of Deep Sequencing Data
• This course will cover methods for analysis of data from Illumina and Roche/454 high-throughput
sequencing, with or without a reference genome sequence, using free and open-source software
tools with an emphasis on the command-line Linux computing environment*
Lecture Topics:
• Types of samples and analyses
• Experimental design and analysis
• Data formats and conversion tools
• Alignment, de-novo assembly, and other analyses
• Computing needs and available resources
• Annotation
• Summarizing and visualizing results
Lab sessions meet in a computing lab, and will provide students with hands-on experience in managing
and analyzing datasets from Illumina and Roche/454 instruments, covering the same set of topics as
the lectures. Example datasets will be available from both platforms, for both DNA and RNA samples;
students who have their own datasets may contact the instructor prior to the course to discuss
opportunities for analysis of their data during the lab sessions.
* see for an overview
Introduction to the course and to each other
- background in biology, computing, and sequencing
- experiments of interest to participants
Course structure
- 3 two-hour blocks per week
* ~ 45 min lecture/discussion
* ~ 70 min lab exercises
- some assigned reading
- participation in classroom discussion is expected
- no exams
Course Objective
- to teach you how to teach yourself
The sequencing rate is growing faster than Moore’s Law
Stein (2010) Genome Biology 11:207
An alternative perspective from an independent source
Doubling time 19.8 months
Doubling time 2 months
Doubling time 7.3 months
Sequence data analysis is changing rapidly
- relatively few methods are completely static
- much of the software is still under active development
- new methods and tools are reported every month
- staying on the learning curve is essential
Why use Linux for sequencing data analysis?
- it is well-suited to the task
* preferred development platform for most tools
* modular design
* however … it’s built for speed, not for comfort
Modular design in Linux – a ‘toolbox’ approach
• Individual components of
the Linux operating system
are written as separate
• Different programs can have
similar functions
• A Linux “distribution” is a
collection of programs that
work together as an
operating system
• Users have the power to add
new programs, or take away
existing programs that are
not being used, to optimize
system performance
A map of the actual software components of the kernel
Why is modularity an advantage?
- adding new software is relatively straightforward
- the operating system can be continually upgraded
- adding tools to the toolbox is easy
- staying on the learning curve is essential
There is always more than one way to do it
- some sequence analysis tasks have matured to stability
- most have not, and are still changing
- ‘best practices’ are also changing, and subject to dispute
Linux distributions
- collections of ‘tools’ targeted to different user groups
- some are commercial, most are not
- five or six account for most of the users
- many dozens of variants available, mostly of minor
Which to use for sequencing data analysis?
- Ubuntu
* widely-used distribution with good hardware support
* base for Bio-Linux, with pre-installed bioinformatics
* Bio-Linux is also available as an Amazon EC2 machine
image for cloud computing
Amazon Web Services
- A commercial resource for computing infrastructure
- provides access to ‘virtual machines’, or VMs, for users
- a VM is an ‘instance’ of a ‘machine image’
- the underlying image is the same for every instance
- user-generated files are lost when the instance terminates
Using AWS for this course
- Cloudbiolinux is a machine image built on Ubuntu
- We will use laptops (your own or BIT-provided) as terminals
- Connection will be through Secure SHell (SSH), using PuTTY
- A graphical interface is available through NX Client
Sequencing technology overview
- Two different systems on campus: Illumina GAIIx, 454
- A similar overall strategy for highly-parallel sequencing
- Different approaches taken at virtually every step
- These different platforms produce data with different
- Other platforms are available off-campus, but are not a
focus of the course
- DNA molecules are fragmented and ligated to adaptors
- individual DNA molecules are immobilized on a surface
- a series of nucleotide addition reactions are carried out
- the nucleotide added is detected after each addition
- a data file is produced containing the DNA sequences of
many fragments
Sequencing technology overview - 454
DNA fragmentation – usually sonication
Adaptor oligonucleotide addition
Images from
Sequencing technology overview - 454
A single molecule immobilized on a bead
PCR amplification in oil-water emulsion
creates ~10 million copies per bead
Images from
Sequencing technology overview - 454
DNA-containing beads deposited in wells
of PicoTiterPlate , along with smaller beads
with immobilized enzymes for light
“Pyrosequencing” produces light when any
nucleotide is incorporated, so only a single
nucleotide is provided during a cycle, and
light output is recorded during each cycle
Sequencing technology overview - 454
A ‘flowgram’ showing light output from each cycle of base addition
one flowgram is produced for each of the ~1 million wells in a PicoTiterPlate
TACG ‘key’
Sequencing technology overview – Illumina GAIIx
Illumina uses a glass ‘flowcell’, about the size of a microscope slide, with 8 separate ‘lanes’.
The GAIIx instrument focuses the laser and light detection system only on one of the two
surfaces inside the flowcell; the new HiSeq instrument scans both surfaces and therefore
doubles the yield of sequence data per lane. Additional improvements in scanning and
increases in cluster density make the difference closer to 4x or 5x more data from a HiSeq.
Sequencing technology overview – Illumina GAIIx
Fragment DNA, ligate adaptor oligos
Single-stranded DNA binds to flowcell surface
Sequencing technology overview – Illumina GAIIx
Surface-bound primers are extended by DNA polymerase across annealed ssDNA molecules,
the DNA is denatured back to single strands, and the free ends of immobilized strands anneal
again to oligos bound on surface of flowcell. This ‘bridge PCR’ continues until a cluster of
~ 1000 molecules is produced on the surface of the flowcell, all descended from the single
molecule that bound at that site. After PCR, the free ends of all DNA strands are blocked.
Sequencing technology overview – Illumina GAIIx
Another perspective of the amplification process, showing the clusters of products
Sequencing technology overview – Illumina GAIIx
Sequencing technology overview – Illumina GAIIx
Sequencing technology overview – Illumina GAIIx
Although four different colors
are used for the fluorescent
nucleotides, only two lasers are
used to excite the
fluorescence. The fluorescent
labels are grouped in pairs labels on A and G are excited
by one laser, and labels on C
and T are excited by the other
This means that distinguishing
between the A signal and the G
signal is more difficult for the
instrument than A versus C or
A versus T. Base substitution
errors are the most common
type of sequencing error for
Illumina instruments.
Understanding FASTQ format
or “what do all these symbols mean?”
See for more details
Instrument ID lane tile X Y barcode read#
Header lines sequence quality scores
• Quality scores are numbers that represent the probability that
the given base call is an error.
• These probabilities are always less than 1, so the value is given
as 10 times minus log(10) of the probability
• For example, an error probability of 0.001 (1x10-3) is represented
as a quality score of 30.
• The numbers are converted into text characters so they occupy
less space – a single character is as meaningful as 2 numbers
plus a space between adjacent values
Understanding FASTQ format
Illumina v1.8 header version:
@HWI-EAS209:06:FC706VJ:5:58:5894:21141 1:N:ATCACG
Instrument /flowcell ID lane tile X Y barcode read#
Header lines sequence quality scores
Unfortunately, at least four different ways of converting numbers
to characters have been used, and header line formats have also
changed, so one aspect of data analysis is knowing what you have.
Illumina flowcell geometry (GAIIx)
A flowcell has 8 lanes, which are physically separated.
Each lane is imaged during each cycle of sequencing
in 120 separate images, called ‘tiles’, which are not
physically separated.
Tiles within a lane are numbered from 1 to 60 down
the length of the lane, then from 61 to 120 back up
the other side.
SolexaQA quality checking
A program written in Perl (a programming language designed for manipulating text files)
that samples a specified number of reads from each tile and calculates a range of
summary statistics . Mean quality scores for each cycle are calculated by default;
minimum/maximum values and variances can also be calculated if desired.
The program returns data in both matrix form and in more visually-informative graphical
formats, including a heatmap showing the quality per cycle for every tile and a plot
showing the mean quality per cycle for each tile and the global mean quality. A
histogram is also returned, showing the distribution of lengths of the longest segment of
each read that surpasses a user-specified quality score or error probability threshold (p =
0.05 by default).
Another Perl program,, is provided that will trim the reads to leave only
the longest contiguous segment that surpasses the quality threshold, and write the
trimmed reads to a new FASTQ-format file for further use.
SolexaQA quality checking
An example heatmap output from
SolexaQA, from a sequencing run of
particularly poor quality. This
particular run consists of 75 cycles
(shown left to right), and 100 tiles
(shown top to bottom.
Note that tile 75 failed to yield any
sequences that passed the Solexa
quality filtering software. The
coloring of the heat map is shown
as error probability, with the darkest
shade indicating p=0.75, the same
as random guessing. The quality
values clearly differ widely across
tiles at cycles after about 25, as well
as decreasing within a tile as the
cycle number increases.
Occasional isolated cycle failures for
individual tiles may be the result of
a bubble or other problem with
reagent flow in the flowcell.
SolexaQA quality checking
An example plot from the same dataset, showing the dramatic differences in average
quality across tiles (the dotted lines), and the increase in error probability with cycle
SolexaQA quality checking
The distribution of lengths of longest segments with error probability < 0.05 from the
same dataset. This is an unusually poor quality dataset, but illustrates the capabilities of
SolexaQA well.

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