Transcript bchm6280_lect1_16x

Course Expectations
Sequencing technology and
(very) large datasets
5/17/2016
Goals for the course
• Understand how next-generation sequencing
technologies are used in biomedical research
• Learn how to use publicly available
databases/websites to find specific information
about genes
• Learn how to analyze gene lists to form hypotheses
that can be tested experimentally
• Learn to write a results section for a manuscript
Logistics
• Course website:
– http://biochem.slu.edu/bchm628/
• Contact:
– Phone: 977-8858
– Email: [email protected]
• Office – DRC 611
– Call or email.
– Usually at WashU on Thursday afternoons
• Lab – DRC 654
Exercise format
• There will be 6 exercises, each consisting of 2-4 sections
which represent a biological question to be answered
with bioinformatics tools/resources from that week or
earlier weeks.
• You’ll provide the answer in the same format as you
would write for the results section of a paper
1.
2.
3.
4.
Why did you do this experiment or analysis?
What did you actually do?
What did you observe?
What does it mean?
• Include supporting data
– Figures with figure legends
– Correctly formatted tables of data.
Exercises, cont
• You will hand in your exercise via email
– Exercise in Word or PDF format
– Supplemental data in Excel, Word or PDF format.
• The exercise should print in portrait orientation.
• The exercise should include a header with your name
at the top and the file should be named:
– Your Name-Ex #.
• There is a penalty for turning in your exercises after
the deadline. The timestamp on your email is the
final determination of whether an exercise is on-time
or not.
Final project
• This will be a project summary of the analyses that
you will do over the course of the 4 weeks.
• You will be asked to choose 3 genes from your gene
lists that you would follow-up on at the bench.
– You will be asked to give a rationale for making the choices
that you did.
• You will analyze the three genes virtually using some
of the tools from weeks 1- 4.
• You will be asked to propose hypothetical bench
experiments for the genes
• Final project will be due June 21st at 3:00 pm.
Data tables
In general, columns describe attributes and rows contain
the individual data. The first row contains a header. If you
have lots of data, it is generally formatted to have more
rows than columns.
Table 1: Gene expression for WT cells under conditions X,Y, Z.
Gene name
Log 2 (Cond.
X/untreated)
Log 2 (Cond.
Y/untreated)
Log 2 (Cond.
Z/untreated)
NM_00522
2.56
3.12
2.75
NM_06588
-1.25
-1.02
-0.98
Table 2: Comparison of clinical parameters for groups 1 and 2.
Clinical parameter
ALT/AST ratio
Leukocyte count
1 Statistical
2
Group 1
(avg ± mean)
Group 2
(avg ± mean)
P-value
25 ± 1
35 ± 2
0.0021
1200 ± 32
950 ± 65
0.0512
significance was determined by a Mann-Whitney test
Statistical significance was determined by 2-tailed t-test
Data tables, cont
• For the purposes of this class, the tables should be
formatted to fit onto a letter size page in portrait
orientation.
• If your table is so wide that it forces the page into
landscape orientation, then it should be included as a
supplemental attachment to the exercise. If the table
extends past 1 page, then include it as a supplemental
attachment.
• Refer to supplemental tables in your write-up and number
then and the file as Name_SuppTable1, ect.
• Supplemental tables can be in Excel format.
Figures
• If you can export the figure from whatever program
in jpeg or png format, those can be inserted into a
Word document easily.
• PDFs can be converted to other formats using
Illustrator
• There are some online converters
– http://www.wikihow.com/Convert-PDF-to-JPEG
• Screen capture and placement may also work.
• Talk to me if you have issues.
• I won’t be very picky about high resolution.
Figures, cont.
• Figures should have figure legends. The figure
legends should describe the experiment that lead to
the data in the figure and include an explanation for
any symbols used.
• Figures should be numbered consecutively and
should not take up more than ¼ of the page. If larger
than that, include as supplemental data.
• Create a text box in Word, write the figure legend
and then insert the figure above the figure legend.
This will allow you to resize as necessary.
• Again, talk to me if you have issues.
Grading
• Grading:
– Exercises
– Final exam
– Class attendance
65 %
25 %
10 %
• Grading policy handout
– Details about late assignment and tests
Lecture outline
• Overview of sequencing a genome
• Next generation sequencing
• High-throughput experiments by sequencing
• Genome browsers
Genome sequencing
Approach depends on the source, size, complexity and
goal for the data for a given organism
Goal?
– De novo sequencing
– Re-sequencing for annotation
– Sequencing to identify variations
• Size and complexity
– Virus, bacterial, single-celled eukaryote, mammal, plant
– Quasi-species or repetitive sequences
• Sample prep
– Can it be cultured?
– Tissue source: unlimited or limited quantities?
– Virus levels, RNA or DNA
Genome sizes
Genome size
(base pairs)
Number of
genes
Hepatitis C virus
0.01 x 106
10
Epstein-Barr virus
0.172 x 106
37
Bacterium (E. coli)
4.6 x 106
4406
Yeast (S. cerevisiae)
12.5 x 106
6172
Nematode worm (C. elegans)
100.3 x 106
19,099
Thale cress (A. thaliana)
115.4 x 106
25,498
Fruit fly (D. melanogaster)
128.3 x 106
13,601
Corn (Z. mays)
2500 x 106
39,469
Human (H. sapiens)
3223 x 106
20,500
Wheat (T. aestivium)
5500 x 106 (x 3)
~95,000
Organism
Types of questions
• How many genes?
– How many functional genetic elements
– miRNAs, ncRNAs
• What’s different about this genome compared to another
one?
– Virulence differences in pathogenic organisms
– What is the cause of this particular phenotype?
• What taxonomic groups are represented in this
population of bacteria, viruses or fungi?
• How do the gene expression patterns change between
samples (and across time)?
• Where does this transcription factor bind in the genome?
DNA sequencing – Overview
• Gel electrophoresis
– Predominant in 1980s
• Whole genome strategies
Cost/base for DNA sequence
1.0E+02
1.0E+01
Physical mapping (BAC clones)
Walking
Shotgun sequencing
Capillary sequencing machines
1.0E+00
• Computational fragment assembly
• Next generation technologies
1.0E-05
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– Polony based sequencing
– Novel assembly techniques
1.0E-01
1.0E-02
1.0E-03
1.0E-04
1.0E-06
1.0E-07
Traditional approach
1. Shear the very large genome into smaller chunks
2. Clone in vectors that can support large inserts
3. Digest and separate on high resolution gel to
determine the clone overlap
4. Pick minimum number of clones
5. Shotgun sequence each clone
6. Read the traces and assemble
7. Make the gene calls
8. Load it into a genome viewer
BAC library in DNA sequencing
Shotgun sequencing
D
Sequence each clone
Individual
sequence
reads
Contig assembly
E
Contig A
Gap
Contig B
Paired reads vs single reads
Single reads
• M13 clones
• robotic template prep
Contig A
Gap
Contig B
Paired reads
• Plasmids, cosmids, BACs
Contig A
Gap
Contig B
Gap closure!!
Prefer 3-10 mate pairs per gap
Inserts of different, but known sizes
Steps to Assemble a Genome
Some Terminology
read a 500-900 long word that comes
1. Find
reads
outoverlapping
of sequencer
mate pair a pair of reads from two ends
of the same insert fragment
2. Merge some “good” pairs of reads into
contigssequence formed
contig longer
a contiguous
by several overlapping reads
with no gaps
3. Link contigs
to formand
supercontigs
supercontig
an ordered
oriented set
(scaffold)
of contigs, usually by mate
pairs
consensus sequence derived from the
4. Derive multiple
consensus
sequence
sequence
alignment
of reads in contig
..ACGATTACAATAGGTT..
Target: 30X coverage or >30 high quality reads per base
Assembled into chromosomes
• Refseq nomenclature:
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NT: genomic sequence of complete gene
NC: chromosome
NM: mRNA sequence
NP: protein sequence
Assembly: completed genome, multiple assemblies
Calling the genes
• De novo computer algorithms
– Identify coding sequences by GC content
– Start and stop sites
– Intron/exon boundaries
• Comparison with other known genes
• EST libraries
Sanger method
Sanger sequencing reached its technical limits
• Only modestly parallel (394 lanes/machine)
• Long read lengths (500-900 bp) & >99.9% correct
• Need to clone the DNA to obtain enough for
sequencing reaction
• At SLU: cost for typical Sanger sequencing is $56/sample with reliable 500 bp of sequence
DNA sequencing timeline
How many sequenced genomes?
NCBI: >16,000 genomes deposited
JGI (Joint Genome Institute):
>8000 complete
>28,000 draft genomes
NGS sequencing
• Polony: discrete clonal amplifications of a single DNA
molecule, grown in a gel matrix. The clusters can then
be individually sequenced, producing short reads
• Polony-based or cluster-based sequencing is the basis of
most second generation sequencers
Typical NGS workflow:
1. Library construction to add adapters to sequence
2. Template CLONAL amplification (on a bead or chip)
3. Massively PARALLEL sequencing
Library Prep:
~ 6 hours
Illumina NGS
A) Fragment DNA
B) Repair ends/Add A overhang DNA
C) Ligate adapters
D) Select ligated DNA
Cluster generation
~ 6 hours
E) Attach DNA to flow cell
F) Bridge amplification
G) Generate clusters
H) Anneal sequencing primer
Sequencing
2-6 days
I) Extend 1st base, read & deblock
K) Generate base calls
J) Repeat to extend strand
Illumina HiSeq and miSeq
• 100 – 200 bp read lengths
• Available locally with MoGene and Cofactor
Genomics
• GTAC (Wash U) has HiSeq 2500, HiSeq 3000 and
MiSeq. They offer read lengths from 50bp to 250 bp
(single- and paired-end)
• Why not use this for all sequencing?
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Cost is ~300-400/library and ~$1100/lane of sequencing
Generate Tb of data per run
Gb per lane
Sample prep limitations
Ion Torrent – measures pH changes
Done on a semiconductor chip
Ion Torrent workflow
Illumina vs Ion Torrent
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Illumina has greater capacity but longer run times
Latest versions of both have read lengths ~200 bp
SLU has an Ion Torrent machine
Cost is ~$270/sample, including the sequencing
• Can do single- or pair-end reads
• Paired end are 2X cost for library construction, but
necessary for de novo genome assembly
Bioinformatics challenges
• Each flow cell in the Illumina Hiseq 2500 can
generate a billion bases of sequence
– Raw read files are Tb in size
– Processed read files are several 700-800 Mb
– Alignment files 150-300 Mb
• Assembly of millions of short (75-100 bp) reads into
vertebrate genome
– Need high-performance compute (HPC) cluster for
vertebrate sized genomes*
• What biomolecular species to interrogate
– 25,000 genes
– 160,000 transcripts
– miRNA, non-coding RNA
Sequencing has become a standard technique
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•
•
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RNA sequencing for expression
ChIP sequencing for TF site identification
DNA sequencing for variants
Identification of populations/genetic changes in
highly variable viruses and bacteria
• Metagenomics
– Identification of unknown/non-culturable communities of
bacteria/viruses/fungi
Where is all this data deposited?
• NCBI: National Center for Biotechnology Information
• Databases are well integrated
• Well integrated with literature (PubMed)
• EBI: European Bioinformatics Institute
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Same base data as NCBI, but offers different front-end
Much better list-based searching
More protein-based information (domains, complexes &
interactions)
Not as well integrated with literature
Transcript variants differ from NCBI because of different
annotation pipelines
NCBI
Ensembl main page
Genome viewers
• Provides chromosomal context to the gene(s) of
interest
• See transcript variants in graphical view
• Have “tracks” of additional information:
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Variants (SNPs)
Expression data
Repetitive sequences
Comparative data (with other species)
Download genomic sequence
• Ensembl genome viewer (useast.ensembl.org)
• UCSC genome viewer (genome.ucsc.edu)
Genetic maps
• Chromosomal banding patterns
– Stain with Giemsa (G-banding pattern)
Chromosomes are
numbered based on size
Giemsa binds to phosphate
groups & attaches to regions
that are AT rich
Dark regions heterchromatic, late replicating and AT rich
Lighter regions euchromatic, early replicating and GC rich
Chromosome nomenclature
p (petite) =
short arm
q (queue) =
long arm
Bands are numbered going away from centromere
4q21.1 represents chromosome 4, long arm 2nd band, 1st sub-band
and 1st sub-sub-band
Today in computer lab
• Finding genes and transcripts using NCBI and EBI
• Visualization of genes and transcripts with genome
browsers