2015 Bioc4010 lecture1and2

Next-Generation Sequence Analysis
for Biomedical Applications
BIOC 4010/5010
Lecture 1
Dr. Dan Gaston
Postdoctoral Fellow Department of Pathology
Dr. Karen Bedard Lab

LECTURE 1
Introduction to Next-Gen Sequencing

Overview: Lecture 1
• Why Next-Gen Sequencing Matters
• What is Next-Gen Sequencing
• Bioinformatics Workflows
• Types of Next-Gen Experiments
• Working with the Human Genome
• Slides available on slideshare:
– http://www.slideshare.net/DanGaston

Major Areas in Human Disease
Genomics
• Complex Disease
– Genome Wide Association Studies (GWAS)
• Mendelian Disease
– Whole Genome/Exome Sequencing
– Transcriptomics
– Genetic Linkage – Sanger Sequencing
• Cancer
– Tumour Genomics
– Transcriptomics

Traditional Diagnosis of Genetic
Disease
• Genetic Counselors/Physicians order
individual testing of genes based on patient
phenotype
• For rare diseases or unusual phenotypes may
run tens to hundreds of tests
• …..EXPENSIVE (Easily thousands of dollars)

Next Generation Diagnosis of Genetic
Disease
• NGS-Based Targeted Sequencing Panels
• Clinical Exome
• Clinical Genome

Genetic Disease Research: The Slow and
Traditional Way in the Dark Ages (circa 2009)

Genetic Disease Research: Cutis Laxa
Chromosome 9:
120,962,282 -133,033,431

Cutis Laxa
• Linked Genomic Region ~13Mb in size
• Contains 143 Genes
• Prioritize and select genes for individual
sanger sequencing
• …Slow
• …Laborious
• …Can be expensive

Human Genomics: More Power!
• $5,000 - $10,000 to sequence whole genome
– Dropping towards $1000 for sequencing only
• ~$1000 to sequence only protein-coding
portion (exome, later)

Clinical Genomics
• Rapid diagnosis of genetic disease in NICU cases
• Quicker and cheaper than sequential genetic
testing (traditional method)

Personalized Medicine: Oncology
Tumour Sample
DNA
Non-Tumour
Sample
DNA
Databases and
Annotations
Sequence
Tumour
Specific
Mutations
Tumour
Classification
Drugs

Personalized Medicine: Oncology
Welch JS, et al. JAMA, 2011;305, 1577

Personalized Medicine: Monitoring For
Cancer Chemotherapy Resistance

Composition of Human Genome
Size: 3.2 Gb

Genomic Content
Chromosome Base pairs Variations Confirmed proteins Putative proteins Pseudogenes miRNA rRNA Misc ncRNA
1 249,250,621 4,401,091 2,012 31 1,130 134 66 106
2 243,199,373 4,607,702 1,203 50 948 115 40 93
3 198,022,430 3,894,345 1,040 25 719 99 29 77
4 191,154,276 3,673,892 718 39 698 92 24 71
5 180,915,260 3,436,667 849 24 676 83 25 68
6 171,115,067 3,360,890 1,002 39 731 81 26 67
7 159,138,663 3,045,992 866 34 803 90 24 70
8 146,364,022 2,890,692 659 39 568 80 28 42
9 141,213,431 2,581,827 785 15 714 69 19 55
10 135,534,747 2,609,802 745 18 500 64 32 56
11 135,006,516 2,607,254 1,258 48 775 63 24 53
12 133,851,895 2,482,194 1,003 47 582 72 27 69
13 115,169,878 1,814,242 318 8 323 42 16 36
14 107,349,540 1,712,799 601 50 472 92 10 46
15 102,531,392 1,577,346 562 43 473 78 13 39
16 90,354,753 1,747,136 805 65 429 52 32 34
17 81,195,210 1,491,841 1,158 44 300 61 15 46
18 78,077,248 1,448,602 268 20 59 32 13 25
19 59,128,983 1,171,356 1,399 26 181 110 13 15
20 63,025,520 1,206,753 533 13 213 57 15 34
21 48,129,895 787,784 225 8 150 16 5 8
22 51,304,566 745,778 431 21 308 31 5 23
X 155,270,560 2,174,952 815 23 780 128 22 52
Y 59,373,566 286,812 45 8 327 15 7 2
mtDNA 16,569 929 13 0 0 0 2 22

Short Reads
Millions of “short reads” 75-
150bp each
Usually “paired”

FastQ Format
Read ID
Sequence
Quality line

FastQ Quality Scores
Quality Score (Q) Probability of incorrect base call Base call accuracy
10 1 in 10 90%
20 1 in 100 99%
30 1 in 1000 99.90%
40 1 in 10000 99.99%
50 1 in 100000 100.00%
Q = -10 log10 P

Quality Scores of Sequencing Reads

General Genomics Workflow
Quality Control of Raw
Data
Raw Data
Analysis
Alignment to reference
genome
Whole Genome
Mapping
Detection of genetic variation
(SNPs, Indels, SV)
Variant Calling
Linking variants to biological
information
Annotation

Find the Location of Each Read in the
Genome
• Problems:
– Short sequence

Genome
• Problems:
– Short sequence
– Millions of short sequences

Genome
• Problems:
– Short sequence
– Big genome

Genome
• Problems:
– Short sequence
– Big genome
– Mismatches
• Polymorphisms
• Sequencing errors

Genome
• Problems:
– Short sequence
– Big genome
– Mismatches
• Polymorphisms
– Insertions and deletions

Genome
• Problems:
– Short sequence
– Big genome
– Mismatches
• Polymorphisms
– Insertions and deletions
– May be processing many (100’s) of individuals

Short Read Mapping
…CCATAGGCTATATGCGCCCTATCGGCAATTTGCGGTATAC…
GCGCCCTA
GCCCTATCG
GCCCTATCG
CCTATCGGA
CTATCGGAAA
AAATTTGC
AAATTTGC
TTTGCGGT
TTGCGGTA
GCGGTATA
GTATAC…
TCGGAAATT CGGTATAC
TAGGCTATA
AGGCTATAT
AGGCTATAT
AGGCTATAT
GGCTATATG
CTATATGCG
…CC
…CC
…CCA
…CCA
…CCAT
ATAC…
C…
C…
…CCAT
1) Report location of genome where read matches best
2) Minimize mismatches
3) Mismatches with lower quality bases better than
mismatches with higher quality bases

Short Read Mapping: Brute Force
Method (Stupid)
Simple conceptually: Compare each query k-mer to all k-
mers of genome
Scales with size of the genome and the reads (Not
particularly well)
Genome = AGCATGCTGCAGTCATGCTTAGGCTA
Read = GCT

Solution
Index the Reference Genome
Indexing the reference is like constructing a phone
book, quickly move towards the relevant portion of the
genome and ignore the rest.

Suffix Array
Split genome into all suffixes (substrings) and sort
alphabetically
Allows query to be searched against an alphabetical
reference, skipping 96% of the genome
Ex: banana$
banana$ $
anana$ a$
nana$ ana$
ana$ anana$
na$ banana$
a$ nana$
$ na$

Short Read Alignment: Binary Search
• Searching the index efficiently is still a
problem…
Index # Sequence Pos Pos
1 ACAGATTACC… 6
2 ACC… 13
3 AGATTACC… 8
4 ATTACAGATTACC… 3
5 ATTACC… 10
6 C… 15
7 CAGATTACC… 7
8 CC… 14
9 GATTACAGATTACC… 2
10 GATTACC… 9
11 TACAGATTACC… 5
12 TACC… 12
13 TGATTACAGATTACC… 1
14 TTACAGATTACC… 4
15 TTACC… 11
Search for GATTACA…

Binary Search
• Initialize search range to entire list
– mid = (hi+lo)/2; middle = suffix[mid]
– if query matches middle: done
– else if query < middle: pick low range
– else if query > middle: pick hi range
• Repeat until done or empty range

Applied to Human Genome
• In practice simple methods of indexing the
genome can create very large data structures
– Suffix Array: > 12 GB
• Solution: Apply complex procedures that allow
you to index and compress the data:
– Burrows-Wheeler Transform
– FM-Index

Burrows-Wheeler Transform
• Similar in many ways to creation of Suffix
Array
BANANA$

Array
BANANA$
BANANA$
ANANA$B
NANA$BA
ANA$BAN
NA$BANA
A$BANAN
$BANANA
Circular
Permutation

Array
BANANA$
BANANA$
ANANA$B
NANA$BA
ANA$BAN
NA$BANA
A$BANAN
$BANANA
$BANANA
A$BANAN
ANA$BAN
ANANA$B
BANANA$
NA$BANA
NANA$BA
Lexicographical
Sort

Array
BANANA$Burrows-Wheeler
Matrix
$BANANA
A$BANAN
ANA$BAN
ANANA$B
BANANA$
NA$BANA
NANA$BA

Array
BANANA$
$BANANA
A$BANAN
ANA$BAN
ANANA$B
BANANA$
NA$BANA
NANA$BA

Array
BANANA$
T(string) = ANNB$AA
Transformed String:
Compressible and Reversible
$BANANA
A$BANAN
ANA$BAN
ANANA$B
BANANA$
NA$BANA
NANA$BA

Array
BANANA$
T(string) = ANNB$AA
Suffix Array
$BANANA
A$BANAN
ANA$BAN
ANANA$B
BANANA$
NA$BANA
NANA$BA
6
5
3
1
0
4
2

Array
BANANA$
TT(string) = ANNB$AA
FM-Index
$BANANA
A$BANAN
ANA$BAN
ANANA$B
BANANA$
NA$BANA
NANA$BA
6
5
3
1
0
4
2
6, 5, 3, 1, 0, 4, 2
+
+
Character Count Tables

Short Read Aligners
• BLAT: BLAST-Like Alignment Tool
• MAQ: First to take in to account quality scores
• Bowtie: One of the first to use BWT, ungapped
alignment only
• BWA: One of the first to use BWT. First gapped
BWT, incredibly fast and memory efficient
• Bowtie2: Allows indels
• SOAP, SOAP2: Also use BWT
• … and many more

Next-Gen Sequencing Experiments
• Whole Genome Sequencing
• Targeted Exome Sequencing
• RNA-Seq
• ChIP-Seq
• CLIP-Seq

Transcriptomics: RNA-Seq
• Sequence the actively transcribed genes in a
cell line or tissue
– Only about 20% of genes are transcribed in
particular cell types
• Two types:
– Poly-A selection
– Total RNA + ribodepletion
• Many experimental questions can be
addressed

RNA-Seq: Gene Expression
Condition 1
Condition 2

RNA-Seq: Differential Splicing
Exon1 Exon 2 Exon 3

RNA-Seq: Novel/Non-Canonical Exon
Discovery
Exon1 Exon 2 Exon 3Exon X

RNA-Seq: Gene Fusion Events
Exon1 Exon 2 Exon 3
Gene 2 Exon 4

RNA-Seq
• Important to take in to account biological
variability. A sample of cells is a mixed population
– Replicates!
• Not suited for discovering polymorphisms due to
higher error rates introduced by reverse
transcription step (RNA -> cDNA)
• High false positive rates for fusion gene discovery,
novel exons, when low expression levels

LECTURE 2
Identifying and Annotating Genomic Variation for Disease Gene Discovery

Overview/Objectives
• Genetic Variation
– Types
• Identifying Genetic Variation
– Methods
• Annotation of Genes and Variants
– Methods
– Sources
• Gene/Variant Prioritization
– Methods

Genetic Variation
• dbSNP (NCBI) build 142
– Catalogs Single Nucleotide Variants (SNV)
– 365 Million Submitted
– 113 Million Validated
– 54 Million in Genes
– 36 Million With Frequency in Populations
• 50-80% of mutations involved in inherited
disease caused by SNVs
– May be an overestimate due to lack of knowledge

SNP vs SNV
• Technically a polymorphism is a variation that
doesn’t cause disease and is common in a
population
• What is common?
– Greater than 5% in a population a typical
definition
– Definition for rare ranges from < 0.1% to < 1.0%

FREQUENCY OF GENETIC VARIANTS
Studies and Populations

Frequency of Polymorphisms:
Common vs Rare
• Mendelian disorders are caused by rare
variation, < 1% frequency in the relevant
population
• Leverage large projects aimed at assessing
genetic diversity in populations around the
world

Exome Sequencing Project
• Multi-Institutional
• Total possible patient pool of > 250,000
individuals, well phenotyped
– Includes healthy individuals and diseased
• Currently 6700 exomes sequenced
– 4420 European descent
– 2312 African American
• 1.2 million coding variations
– Most extremely rare/unique
– Many population specific

Other Resources and Projects
• Exome Aggregation Consortium: 60,000
Exomes

Other Resources and Projects
• Exome Aggregation Consortium: 60,000
Exomes
• Personal Genome Project (Ongoing)
• 100,000 Genomes Project (UK, Ongoing)
• BGI (Announced, China): 1 Million Genomes
• Precision Medicine Initiative (US, Announced):
1 Million Genomes

Population Matters
• Most variations in protein-coding genes
occurred fairly recently (last 20,000 years)
– Adaptation to agriculture and diet changes,
pathogen exposure and urban living

Population Matters
• Most variations in protein-coding genes occurred
fairly recently (last 20,000 years)
– Adaptation to agriculture and diet changes, pathogen
exposure and urban living
• Monogenic diseases have different prevalence in
different populations
– Cystic fibrosis in European population
– Hereditary Hemochromatosis in Northern Europeans
– Tay-Sachs in Ashkenazi Jews
– Sickle-Cell Anemia in Sub-Saharan African populations

DISCOVERING GENETIC VARIATION
Finding the Needles

Finding All Needles
SNPs
ATCCTGATTCGGTGAACGTTATCGACGATCCGATCGA
CGGTGAACGTTATCGACGATCCGATCGAACTGTCAGC
GGTGAACGTTATCGACGTTCCGATCGAACTGTCAGCG
TGAACGTTATCGACGTTCCGATCGAACTGTCAGCGGC
GTTATCGACGATCCGATCGAACTGTCAGCGGCAAGCT
TTATCGACGATCCGATCGAACTGTCAGCGGCAAGCT
ATCCTGATTCGGTGAACGTTATCGACGATCCGATCGAACTGTCAGCGGCAAGCTGATCGATCGATCGATGCTAGTG
TCGACGATCCGATCGAACTGTCAGCGGCAAGCTGAT
ATCCGATCGAACTGTCAGCGGCAAGCTGATCG CGAT
TCCGATCGAACTGTCAGCGGCAAGCTGATCG CGATC
TCCGATCGAACTGTCAGCGGCAAGCTGATCGATCGA
GATCGAACTGTCAGCGGCAAGCTGATCG CGATCGA
AACTGTCAGCGGCAAGCTGATCG CGATCGATGCTA
TGTCAGCGGCAAGCTGATCGATCGATCGATGCTAG
INDELs
TCAGCGGCAAGCTGATCGATCGATCGATGCTAGTG
reference genome

Finding All Needles
SNPs
ATCCGATCGAACTGTCAGCGGCAAGCTGATCG CGAT
TCCGATCGAACTGTCAGCGGCAAGCTGATCG CGATC
TCCGATCGAACTGTCAGCGGCAAGCTGATCGATCGA
GATCGAACTGTCAGCGGCAAGCTGATCG CGATCGA
AACTGTCAGCGGCAAGCTGATCG CGATCGATGCTA
TGTCAGCGGCAAGCTGATCGATCGATCGATGCTAG
INDELs
TCAGCGGCAAGCTGATCGATCGATCGATGCTAGTG
reference genome
All regions with mismatches are potential variants

Genotype Calling: Determining the
Type of Needle, The Absurdly Simple
Way (Stupid)
TTCCGATCGAACTGTCAGCGGCAAGCTGATCGATCGA
reference genome
Read depth at base: 10 T: 4 A: 6
Genotype: Heterozygous A/T

Genotype Calling: The Absurdly Simple
Way (Stupid)
• Doesn’t account for sequencing error
• Doesn’t account for sequencing bias
• Doesn’t count for bias in short-read mapping
process
• Doesn’t account for mapping error
• Doesn’t consider any external source of
information regarding populations or known
genetic variations

Genotype Calling: The Absurdly Simple
Way (Slightly less Stupid)
• Algorithm:
– Count all aligned bases that pass quality threshold
(e.g. >Q20)
– If #reads with alternative base > lower bound (20%)
and < upper bound (80%) call heterozygous alt
– Else if > upper bound call homozygous alternative
– Else call homozygous reference
• …But what about base qualities for more than
keeping reads?

Improving Genotype Calling: Local
Realignment

What’s Missing
• No estimate of the confidence (stats) of
variant and genotype calls

What’s Missing
• Doesn’t account robustly for known sources of
error

What’s Missing
error
• Doesn’t make use of any sources of external
information

What’s Missing
error
• Doesn’t make use of any sources of external
information
• Doesn’t include base qualities

Improving Genotype Calling: Bayes
Theorem

Improving Genotype Calling: Bayes
Theorem
Prior Probability
Error Model
All Possible Genotypes

Improved Genotype Calling: Prior
Probability
• Known Polymorphic Site?
– Allele Frequencies
• Global rate of polymorphisms
• Other samples
• Substitution Type

Substitution Type
• Transition:
– Purine to Purine (A to G)
– Pyrimidine to Pyrimidine (C to T)
• Transversion
– Purine to Pyrimidine
• Transition/Transversion ratio
– Transitions 2x as common (Genome Wide)
– 4x when looking only at exons
– Random Error: 0.5

Prior Probability Example
Assume:
Heterozygous SNP Rate of 0.001
Homozygous SNP Rate of 0.0005
Reference: G
Transition/Transversion Ratio: 2

Prior Probability Example
A C G T
A 3.33x10-4 1.11x10-7 6.67x10-4 1.11x10-7
C 8.33x10-5 1.67x10-4 2.78x10-8
G 0.9985 1.67x10-4
T 8.33x10-5
Assume:
Heterozygous SNP Rate of 0.001
Homozygous SNP Rate of 0.0005
Reference: G
Transition/Transversion Ratio: 2

Improved Genotype Calling: Error
Rates
Predicted Base
A C G T
Actual
Base
A - 57.7 17.1 25.2
C 34.9 - 11.3 53.9
G 31.9 5.1 - 63.0
T 45.9 22.1 32.0 -
If a base was miscalled, what is it most likely to be called
as instead?

Variant Calling
• SNP Calls infested with False Positives
– Machine artifacts
– Mis-mapped reads
– Mis-aligned indels
• 5 – 20% false positive rate

Decisions and Trade-Offs
• Option 1: Use stringent program options for
calling variants and hard filtering early to
produce only highly-confident call set.

– Pro: Few false positives
– Con: Will miss real variants

• Option 2: Use less stringent (but reasonable)
options and filtering. Produce high-confidence
call set. Progressive filtering at later stage

calling variants and hard filtering early to produce
only highly-confident call set.
– Pro: Won’t miss real variants
– Con: Many more false positives

calling variants and hard filtering early to produce
only highly-confident call set.
– Con: False positives
– Pro: Won’t miss real variants

How Good Are My Calls?
• How many called SNPs?
– Human average of 1 heterozygous SNP / 1000
bases
• Fraction of variants already in dbSNP
– ~90%
• Transition/Transversion ratio
– Transitions 2x as common
• 3x when looking only at exons

ANNOTATING VARIANTS
Methods and Practices

Identifying Genetic Variation Causing
Genetic Disease

Discovering Genetic Variants Causing
Mendelian Disease
4 million genetic variants
2 million associated with
protein-coding genes
10,000 possibly
of disease
causing type
1500 <1%
frequency in
population
Single Causal
Genetic Variant

If a problem cannot be
solved, enlarge it.
--Dwight D. Eisenhower
Supreme Commander Allied Forces:
Second World War
34th President USA

Variant Annotation Pipeline Example

Transcript Effects: Impact
Exon 1 Intron 1 Exon 2Reference
Start
TAA
Stop

Start
TAA
StopmRNA coding for protein
Splice Sites

Patient
Start
TAA
Exon 1 Intron 1 Exon 2
Splice Sites

Patient
Start
TAA
Splice Sites
TAC
Tyr

Patient
Start
TAA
Splice Sites
TAC
TyrSplice Site Loss

Patient
Start
TAA
Splice Sites
TAC
TyrSplice Site Loss
Missense

Patient
Start
TAA
Splice Sites
TAC
TyrSplice Site Loss
Missense/Frameshift Stop Gain

Example: SIFT Algorithm
Input Query
Sequence
Psi-BLAST
Homologs
Alignment
Multiple
Sequence
Alignment
Multiple
Sequence
Alignment
PSSM
Normalize
By most
frequent AA
Score

Prediction Take-Away
The more conserved a site is the more likely
any substitution is to be deleterious
However: Current methods have pretty poor
performance, not suitable for clinical-level
diagnosis

Classifying Genetic Variants
4 million
variants
Intronic
Unknown Splice Site
Potential
Disease Causing
Exonic
Amino Acid
Changing
Known Genetic
Disease Variant
Stop Loss / Stop
Gain
Missense
Mutation
Known
Polymorphism in
Population
Silent Mutation Splice Site
Potential
Disease Causing
Intergenic

Annotating Genes and Variants
• Is variant in a known protein-coding gene?
– What does the gene do?
– What molecular pathways?
– What protein-protein interactions?
– What tissues is it expressed in?
– When in development?
4 million genetic variants
2 million associated with
protein-coding genes
10,000 possibly
of disease
causing type
1500 <1%
frequency in
population

Identifying Genetic Regions of Interest

Number of Genes in Genomic Regions
of Interest

IGNITE Project: Local Controls
• IGNITE: Tasked with studying rare monogenic
diseases identified in Atlantic Canada
• Atlantic Canada harbours several non-
represented population groups and sub-
groups…

IGNITE Project: Local Controls
• IGNITE: Tasked with studying rare monogenic
diseases identified in Atlantic Canada
• Atlantic Canada harbours several non-
represented population groups and sub-
groups…
– Acadians
– Native American
– Non-Acadian/European Descent

Population Frequency
• Mendelian disorders are rare
• If variation is in database, is it associated with
disease?
• Causal variation also needs to be rare
– Cutoff somewhere in the < 0.1 - < 1% range
– Should appear rarely or not at all in local controls
– Track with disease in family members under study

CASE STUDIES
IGNITE: Brain Calcification, Charcot-Marie-Tooth and Cutis Laxa

IGNITE Data Pipeline and Integration
Mapped
Region(s)
Known Genes
Gene
Definitions
Pathway and
Interactions
Annotated
Genomic Variants
Filter
Sort
Prioritize
Gene
Annotations

Brain Calcification
• 84 genes in chromosome 5 region
• No likely homozygous or compound heterozygous
variants within region shared between two
patients
• 29 genes with at least one targeted region with
little or no sequencing coverage
• Many only lacked coverage in 5’ and 3’ UTRs
• Collaborators performed statistical tests for
possibly copy-number variations of targeted
regions using exome sequencing data

Charcot-Marie-Tooth: Genetic Mapping
Chromosome 9:
120,962,282 -133,033,431

Cutis Laxa: Genetic Mapping
Chromosome 17:
79,596,811-81,041,077

Charcot-Marie-Tooth Cutis Laxa
• 143 genes in region
• 13 known causative genes
– MPZ
– PMP22
– GDAP1
– KIF1B
– MFN2
– SOX
– EGR2
– DNM2
– RAB7
– LITAF (SIMPLE)
– GARS
– YARS
– LMNA
• 52 genes in region
• 5 known causative genes
– ATP6V0A2
– ELN
– FBLN5
– EFEMP2
– SCYL1BP1
– ALDH18A1

Pathway and Interaction Data
• 37 pathways
– Clathrin-derived vesicle
budding
– Lysosome vesicle
biogenesis
– Endocytosis
– Golgi-associated vesicle
biogenesis
– Membrane trafficking
– Trans-Golgi network vesicle
budding
• Primarily LMNA or DNM2
• 10 pathways
– Phagosome
– Collecting duct acid
secretion
– Lysosome
– Protein digestion and
absorption
– Metabolic pathways
– Oxidative phosphorylation
– Arginine and proline
metabolism
• Primarily ATP6V0A2

Simple Prioritization
Pathways and Protein-Protein
Interactions of Known Genes
Pathways and Protein-Protein
Interactions of Variant Genes

Results: Charcot-Marie-Tooth
• 8 Genes Prioritized
Gene Interactions Pathway
LRSAM1 Multiple Endocytosis
DNM1 DNM2 -
FNBP1 DNM2 -
TOR1A MNA -
STXBP1 Multiple Five
SH3GLB2 - Endocytosis
PIP5KL1 - Endocytosis
FAM125B - Endocytosis
• For more information
– Guernsey et al (2010) PLoS Genetics. 6(8): e1001081

Results: Cutis Laxa
• 10 genes prioritized
Gene Interactions Pathway
HEXDC Multiple Phagosome
HG5 - Phagosome
HG5 Multiple Lysosome, Protein digestion
SIRT7 Multiple Metabolic Pathways
FASN - Metabolic Pathways
DCXR - Metabolic Pathways
PYCR1 - Metabolic Pathways,
Arginine/Proline
PCYT2 - Metabolic Pathways
ARHGDIA - Oxidative Phosphorylation
• For more information
– Guernsey et al (2009) Am J Hum Genet. 85(1): 120-9

2015 Bioc4010 lecture1and2

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