Curso de Genómica - UAT (VHIR) 2012 - Análisis de datos de NGS

Introduction to NGS
(Now Generation Sequencing)
Data Analysis
Alex Sánchez

Statistics and Bioinformatics Research Group
Statistics department, Universitat de Barelona

Statistics and Bioinformatics Unit
Vall d’Hebron Institut de Recerca

NGS Data analysis http://ueb.vhir.org/NGS2012

Outline
• Introduction
• Bioinformatics Challenges
• NGS data analysis: Some examples and workflows
• Metagenomics, De novo sequencing, Variant detection, RNA-
seq
• Software
• Galaxy, Genome viewers
• Data formats and quality control


Introduction


Why is NGS revolutionary?
• NGS has brought high speed not only to genome
sequencing and personal medicine,
• it has also changed the way we do genome research

Got a question on genome organization?

SEQUENCE IT !!!

Ana Conesa, bioinformatics researcher at
Principe Felipe Research Center


NGS means high sequencing capacity

GS FLX 454 HiSeq 2000 5500xl SOLiD
(ROCHE) (ILLUMINA) (ABI)

GS Junior

Ion TORRENT


NGS Platforms Performance

454 GS Junior
35MB


454 Sequencing


ABI SOLID Sequencing


Solexa sequencing


Applications of Next-Generation
Sequencing


Comparison of 2nd NGS


Some numbers

Platform 454/FLX Solex (Illum
a ina)AB S ID
OL
Read length ~350-400bp 36, 75, or 106 bp 50bp
Single read Yes Yes Yes
Paired-end Reads Yes Yes Yes
Long-insert (several Kbp) mate-paired reads Yes Yes No
Number of reads por instrument run 5.00K >100 M 400M
Max Data output 0.5Gbp 20.5 Gbp 20Gbp
Run time to 1Gb 6 Days > 1 Day >1 Day
Ease of use (workflow) Difficult Least difficult Difficult
Base Calling Flow Space Nucleotide space Color sapce
D Applica
NA tions
Whole genome sequencing and resequencing Yes Yes Yes
de novo sequencing Yes Yes Yes
Targeted resequencing Yes Yes Yes
Discovery of genetic variants ( SNPs, InDels, CNV, ...) Yes Yes Yes
Chromatin Immunopecipitation (ChIP) Yes Yes Yes
Methylation Analysis Yes Yes Yes
Metagenomics Yes No No
R Applica
NA tions Yes Yes Yes
Whole Transcriptome Yes Yes Yes
Small RNA Yes Yes Yes
Expression Tags Yes Yes Yes


Bioinformatics challenges of NGS


I have my sequences/images. Now what?


NGS pushes (bio)informatics needs up
• Need for computer power
• VERY large text files (~10 million lines long)
– Can’t do ‘business as usual’ with familiar tools such as Perl/Python.
– Impossible memory usage and execution time
• Impossible to browse for problems
• Need sequence Quality filtering
• Need for large amount of CPU power
• Informatics groups must manage compute clusters
• Challenges in parallelizing existing software or redesign of algorithms to work in a
parallel environment
• Need for Bioinformatics power!!!
• The challenges turns from data generation into data analysis!
• How should bioinformatics be structured
• Bigger centralized bioinformatics services? (or research groups providing service?)
• Distributed model: bioinformaticians must be part of the temas. Interoperability?


Data management issues
• Raw data are large. How long should be kept?
• Processed data are manageable for most people
– 20 million reads (50bp) ~1Gb
• More of an issue for a facility: HiSeq recommends
32 CPU cores, each with 4GB RAM

• Certain studies much more data intensive than other
– Whole genome sequencing
• A 30X coverage genome pair (tumor/normal) ~500 GB
• 50 genome pairs ~ 25 TB


So what?

• In NGS we have to process really big amounts of data,
which is not trivial in computing terms.

• Big NGS projects require supercomputing infrastructures

• Or put another way: it's not the case that anyone can do
everything.
– Small facilities must carefully choose their projects to be scaled
with their computing capabilities.


Computational infrastructure for NGS
• There is great variety but a good point to start with:

– Computing cluster
• Multiple nodes (servers) with multiple cores
• High performance storage (TB, PB level)
• Fast networks (10Gb ethernet, infiniband)
– Enough space and conditions for the equipment
("servers room")
– Skilled people (sysadmin, developers)
• CNAG, in Barcelona: 36 people, more than 50% of them
informaticians


Alternatives (1): Cloud Computing
• Pros
– Flexibility.
– You pay what you use.
– Don´t need to maintain a data center.
• Cons
– Transfer big datasets over internet is
slow.
– You pay for consumed bandwidth.
That is a problem with big datasets.
– Lower performance, specially in disk
read/write.
– Privacy/security concerns.
– More expensive for big and long
term projects.


Alternatives (2): Grid Computing
• Pros
– Cheaper.
– More resources available.
• Cons
– Heterogeneous
environment.
– Slow connectivity (specially
in Spain).
– Much time required to find
good resources in the grid.


In summary?
•“NGS” arrived 2007/8
•No-one predicted NGS in 2001 (ten years ago)
•Therefore we cannot predict what we will come
up against
•TGS represents specific challenges
–Large Data Storage
–Technology-aware software
–Enables new assays and new science
•We would have said the same about NGS….
•These are not new problems, but will require
new solutions
•There is a lag between technology and
software….


Bioinformatics and bioinformaticians
• The term bioinformatician means many things
• Some may require a wide range of skills
• Others require a depth of specific skills
• The best thing we can teach is the ability to learn and
adapt
• The spirit of adventure
• There is a definite skills shortage
• There always has been


Increasing importance of data analysis
needs


NGS data analysis


NGS data analysis stages


Quality control and preprocessing of
NGS data


Data types


Why QC and preprocessing
• Sequencer output:
– Reads + quality
• Natural questions
– Is the quality of my sequenced
data OK?
– If something is wrong can I fix it?
• Problem: HUGE files... How
do they look?
• Files are flat files and big...
tens of Gbs (even hard to
browse them)


Preprocessing sequences improves results


How is quality measured?

• Sequencing systems use to assign quality scores to each peak
• Phred scores provide log(10)-transformed error probability values:
If p is probability that the base call is wrong the Phred score is
Q = .10·log10p
– score = 20 corresponds to a 1% error rate
– score = 30 corresponds to a 0.1% error rate
– score = 40 corresponds to a 0.01% error rate
• The base calling (A, T, G or C) is performed based on Phred
scores.
• Ambiguous positions with Phred scores <= 20 are labeled with N.


Data formats
• FastA format (everybody knows about it)
– Header line starts with “>” followed by a sequence ID
– Sequence (string of nt).

• FastQ format (http://maq.sourceforge.net/fastq.shtml)
– First is the sequence (like Fasta but starting with “@”)
– Then “+” and sequence ID (optional) and in the following line are
QVs encoded as single byte ASCII codes
• Different quality encode variants

• Nearly all downstream analysis take FastQ as input
sequence


The fastq format
• A FASTQ file normally uses four lines per sequence.
– Line 1 begins with a '@' character and is followed by a sequence
identifier and an optional description (like a FASTA title line).
– Line 2 is the raw sequence letters.
– Line 3 begins with a '+' character and isoptionally followed by the same
sequence identifier (and any description) again.
– Line 4 encodes the quality values for the sequence in Line 2, and must
contain the same number of symbols as letters in the sequence.
• Different encodings are in use
• Sanger format can encode a Phred quality score from 0 to 93 using ASCII 33 to 126

@Seq description
GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTCAACTCACAGTTT
+
!''*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>>CCCCCCC65


Some tools to deal with QC
• Use FastQC to see your starting state.

• Use Fastx-toolkit to optimize different datasets and then
visualize the result with FastQC to prove your success!

• Hints:
– Trimming, clipping and filtering may improve quality
– But beware of removing too many sequences…

Go to the tutorial and try the exercises...


Applications
• [1] Metagenomics
• [2] De novo sequencing
• [3] Amplicon analysis
• [4] Variant discovery
• [5] Transcriptome analysis
• …and more …


[1] Metagenomics &other community-based “omics”

Zoetendal E G et al.
Gut 2008;57:1605-1615


[1] A metagenomics workflow
AAGACGTGGACA GTCCGTCACAACTGA
AAGACGTGGACAGATCTGCTCAGGCTAGCATGAAC
CATGCGTGCATG
GATAGGTGGACCGATATGCATTAGACTTGCAGGGC
AGTCGTCAGTCATGGG
Short reads (40-150 bps) Assembly Contigs

Gene prediction
1 3000 6000
1 3000 6000
1 2000

Homology searching ORFs
Proteins, families, functions
Functional classification
Ontologies

Binning

Sequences into species
Functional profiles

[1] Metagenomic Approaches
SMALL-SCALE: 16S rRNA gene profiling
The basic approach is to identify microbes in a complex
community by exploiting universal and conserved targets,
such as rRNA genesPetrosini.

Challenges and limitations: Chimeric sequences caused by
PCR amplification and sequencing errors.

LARGE-SCALE: Whole Genome Shotgun (WGS)
Whole-genome approaches enable to identify and
annotate microbial genes and its functions in the
community.
Challenges and limitations:
relatively large amounts of starting material required
potential contamination of metagenomic samples with host
genetic material
high numbers of genes of unknown function. Environmental Shotgun Sequencing (ESS).
A primer on metagenomics.
PLoS Comput Biol. 2010 Feb 26;6(2):e1000667.


[1] Comparative Metagenomics
Comparing two or more metagenomes is necessary to understand how genomic differences
affect, and are affected by the abiotic environment.

MEGAN can also be used to
compare the OTU composition
of two or more frequency-
normalized samples.
MG-RAST provides a
comparative functional and
sequence-based analysis for
uploaded samples

.
Other software based on
phylogenetic
data are UniFrac.

[1] Some Metagenomics projects

"whole-genome shotgun sequencing" was applied to microbial populations
A total of 1.045 billion base pairs of nonredundant sequence were analyzed

"whole-genome shotgun sequencing"
78 million base pairs of unique DNA sequence were analyzed

To date, 242 metagenomic projects are on going and 103 are completed
(www.genomesonline.org).


[2] De novo sequencing


[3] Amplicon analysis
Each amplicon (PCR product) is sequenced individually, allowing
for the identification of rare variants and the assignment of
haplotype information over the full sequence length

Some applications:
●
Detection of low-frequency (<1%) variants in complex mixtures
→ rare somatic mutations, viral quasispecies... Ultra-deep
amplicon sequencing
●
Identification of rare alleles associated with hereditary diseases,
heterozygote SNP calling... Ultra-broad amplicon sequencing

●
Metabolic profiling of environmental habitats, bacterial taxonomy
and phlylogeny 16S rRNA amplicon sequencing


[3] Example of raw data generation with GS-FLX

...


[3] Data Workflow
...

Data Processing

[3] Final output examples
...
NT substitution (error) matrices

Bar plots output example (with circular legend for the AA) AA frequency tables


[4] Variant discovery
Your aligner decides the type/amount of variants you can
identify
Naive SNP calling
Reads counting
Statistic support SNP calling
Maximum likelihood, Bayesian
Quality score recalibration
Recalibrate quality score from whole alignment
Local realignment around indels
Realign reads
Known variants (limited species)
dbSNP


[4] Example: Exome Variant Analysis


[4] Genotype calling tools


[4] GATK pipeline


[4]


[4] Many ongoing sequencing projects


[5] Transcriptome Analysis using NGS

RNA-Seq, or "Whole
Transcriptome Shotgun
Sequencing" ("WTSS")
refers to use of HTS
technologies to sequence
cDNA in order to get
information about a
sample's RNA content.

Reads produced by
sequencing

Aligned to a reference
genome to build
transcriptome mappings.


[5] Applications (1)  Whole transcriptome
analysis
mRNA AAAA

Fragmentation
 Detects expression of known and
novel mRNAs

RT  Identification of alternative
splicing events
cDNA library  Detects expressed SNPs or
mutations
 Identifies allele specific
sequencing expression patterns


[5] Applications (2) Differential expression
1.Reads are mapped to the reference
genome or transcriptome
2.Mapped reads are assembled into
expression summaries (tables of
counts, showing how may reads are in
coding region, exon, gene or junction);
3.The data are normalized;
4.Statistical testing of differential
expression (DE) is performed,
producing a list of genes with P-values
and fold changes.


[5] RNA Seq data analysis - Mapping

•Main Issues:
–Number of allowed mismatches End up with a list of
# of reads per transcript
–Number of multihits
–Mates expected distance These will be our (discrete)
response variable
–Considering exon junctions


[5] RNA Seq data analysis -Normalization
• Two main sources of bias
– Influence of length: Counts are proportional to the transcript
length times the mRNA expression level.
– Influence of sequencing depth: The higher sequencing depth, the
higher counts.

• How to deal with this
– Normalize (correct) gene counts to minimize biases.
– Use statistical models that take into account
length and sequencing depth


[5] RNA Seq - Differential expression methods

• Fisher's exact test or similar approaches.

• Use Generalized Linear Models and model counts using
– Poisson distribution.
– Negative binomial distribution.

• Transform count data to use existing approaches for
microarray data.

• …


[5] Advantages of RNA-seq

Unlike hybridization approaches does not require existing genomic
sequence

Expected to replace microarrays for transcriptomic studies

Very low background noise

Reads can be unabmiguously mapped

Resolution up to 1 bp

High-throughput quantitative measurement of transcript abundance

Better than Sanger sequencing of cDNA or EST libraries

Cost decreasing all the time

Lower than traditional sequencing

Can reveal sequence variations (SNPs)

Automated pipelines available


Software for NGS preprocessing and analysis


Which software for NGS (data) analysis?
• Answer is not straightforward.
http://seqanswers.com/wiki/Software/list
• Many possible classifications
– Biological domains
• SNP discovery, Genomics, ChIP-Seq, De-novo assembly, …
– Bioinformatics methods
• Mapping, Assembly, Alignment, Seq-QC,…
– Technology
• Illumina, 454, ABI SOLID, Helicos, …
– Operating system
• Linux, Mac OS X, Windows, …
– License type
• GPLv3, GPL, Commercial, Free for academic use,…
– Language
• C++, Perl, Java, C, Phyton
– Interface
• Web Based, Integrated solutions, command line tools, pipelines,…


Which software for NGS (data) analysis?
• Answer is not straightforward.
http://seqanswers.com/wiki/Software/list
• Many possible classifications
– Biological domains
• SNP discovery, Genomics, ChIP-Seq, De-novo assembly, …
– Bioinformatics methods
• Mapping, Assembly, Alignment, Seq-QC,…
– Technology
• Illumina, 454, ABI SOLID, Helicos, …
– Operating system
• Linux, Mac OS X, Windows, …
– License type
• GPLv3, GPL, Commercial, Free for academic use,…
– Language
• C++, Perl, Java, C, Phyton
– Interface
• Web Based, Integrated solutions, command line tools, pipelines,…

NGS Data analysis http://ueb.ir.vhebron.net/NGS

Some popular tools and places


http://galaxy.psu.edu/

Galaxy Site

62

Obtain data from many data
sources including the
UCSC Table Browser, Prepare data for further
BioMart, WormBase, analysis by rearranging
or your own data. or cutting data columns, Analyze data by finding
filtering data and many overlapping regions,
other actions. determining statistics,
phylogenetic analysis
and much more

63

User Register

contains links to
Shows the history
the downloading, of analysis steps,
pre-procession and displays data and result
viewing
analysis tools menus and
data inputs

NGS Data analysis http://ueb.vhir.org/NGS2012 64

Get Data
from Database


Upload File File Format

Upload or paste file

67

FASTQ file manipulation:
format conversation,
summary statistics,
trimming reads,
filtering reads
by quality score…

Input: sanger FASTQ
Output: SAM format

Downstream analysis:
SAM -> BAM


Co
py
rig
ht
Op
en
He
lix.
No
us
e
or
re
pr
List saved histories and od
uct
shared histories. ion
Work on a current history, wit
ho
create new, share workflow ut
ex
pr
es
s
wri
tte
n
co
ns
en
NGS Data analysis http://ueb.vhir.org/NGS2012 t2
7

Creates a workflow, allows
user to repeat analysis
using different datasets.


DATA VISUALIZATION


Why is visualization important?
make large amounts of data more interpretable
glean patterns from the data
sanity check / visual debugging
more…


History of Genome Visualization

1800s 1900s 2000s
time


What is a “Genome Browser”
linear representation of a genome
position-based annotations, each called a track
continuous annotations: e.g. conservation
interval annotations: e.g. gene, read alignment
point annotations: e.g. SNPs
user specifies a subsection of genome to look at


Server-side model
(e.g. UCSC, Ensembl, Gbrowse)

serve
• central data
r
store
• renders
images
• sends to client

client
• requests
images
• displays
images


Client-side model
(e.g. Savant, IGV)

serve
• stores data
r

client HTS
• local HTS machine
store
• renders
images
• displays
images

Rough comparison of Genome
Browsers
UCSC Ensembl GBrowse Savant IGV
Model Server Server Server Client Client
Interactive
HTS support
Database of
tracks
Plugins

No support Some support Good support


Limitations of most genome
browsers
do not support multiple genomes simultaneously
do not capture 3-dimensional conformation
do not capture spatial or temporal information
do not integrate well with analytics
cannot be customized

The SAVANT
GENOME BROWSER
has been created
to overcome these
limitations


Integrative Genomics Viewer (IGV)

he Integrative Genomics Viewer (IGV) is a high-performance visualization tool
for interactive exploration of large, integrated datasets. It supports a wide variety
of data types including sequence alignments, microarrays, and genomic
annotations.

Acknowledgements
 Grupo de investigación en Estadística y Bioinformática del
departamento de Estadística de la Universidad de
Barcelona.

 All the members at the Unitat d’Estadística i Bioinformàtica
del VHIR (Vall d’Hebron Institut de Recerca)

 Unitat de Serveis Científico Tècnics (UCTS) del VHIR (Vall
d’Hebron Institut de Recerca)

 People whose materials have been borrowed or who have
contributed with their work
 Manel Comabella, Rosa Prieto, Paqui Gallego, Javier
Santoyo, Ana Conesa, Thomas Girke and Silvia
Cardona.…


Gracias por la atención y la paciencia


Curso de Genómica - UAT (VHIR) 2012 - Análisis de datos de NGS

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