2 md2016 annotation

contigs
scaffolds
longest scaffold

Genome Sequencing
Step by step…
• Library construction and sequencing
• Base-calling: Quality Control
• Assembly (repeat as necessary)
• Annotation (repeat as necessary)
• Publish!

Genomes vary greatly in size
Organism Genome size (Mb)
Nanoarchaeum equitans 0.49
Mycoplasma genitalium 0.58
Escherichia coli 4.68
Methanosarcina acetivorans 5.75
Saccharomyces cerevisiae 12
Caenorhabditis elegans 90
Drosophila melanogaster 180
Zea mays 2500
Homo sapiens 3500
Hordeum (barley) 5100

Bacterial/archaeal genomes range from 0.5Mb-> 13Mb
and from ~500 genes to > 10,000 genes

Genome Size Variability
• Extent of gene duplication
• Repetitive DNA
• Gene size
– number and length of introns
• Space between genes (% coding)
– regulatory regions
– heterochromatin

How do genes get names?
How to ﬁnd genes in genomes
Problems and strategies in genome annotation
Databases that are useful for annotation
How are genes related to other genes?
RAST

11
How does a gene get a name?

Genome fragment of Nitrosocaldus yellowstonii
How does a gene get a name?
ORF = CDS = gene ?http://www.genenames.org

Automated Annotation Pipelines/Servers
• Provide fast analysis of genomic sequences
o
gene identiﬁcation & function prediction
• Used to rely on information in public databases 
(beware!)
• Now often based on re-analysis of published
genomes
• Rely on “curated” reference genomes
like Prokka

Box 2 | Gene prediction versus gene annotation
Although the terms ‘gene prediction’ and ‘gene annotation’ are often used as if they are synonyms, they are not. With a
few exceptions, gene predictors find the single most likely coding sequence (CDS) of a gene and do not report untranslated
regions (UTRs) or alternatively spliced variants. Gene prediction is therefore a somewhat misleading term. A more
accurate description might be ‘canonical CDS prediction’.
Nature Reviews | Genetics
229,500 229,000 228,500 228,000 227,500 226,500227,000
bp
5′UTR 3′UTR
Gene annotation resulting
from synthesizing all
available evidence
(two alternative splice forms)
Protein evidence
(BLASTX)
mRNA or EST evidence
(Exonerate)
Gene prediction
(SNAP)
Start codon Stop codon
More types of data help annotation

Nature Reviews | Genetics
Post process gene predictions to
add UTRs and alternatively spliced
transcripts based on evidence
Consensus-
based chooser
Consensus-
based chooser
Run battery of ab initio
gene predictors
Align ESTs, proteins and RNA-seq data to genome
Run battery gene predictors in evidence-driven mode
Run single ab initio
gene predictor
Best consensus CDS
model for each gene
Best consensus mRNA
model(s) for each gene mRNA model(s) for each
gene most consistent
with evidence
Most likely CDS
model for each gene
Optional manual curation using genome browser
Manually curated
gene models
Increasing accuracy
Consensus-
based chooser
Evidence-
based chooser
Best consensus CDS
model for each gene
Option 2:
predict and choose
Option 3:
full-scale annotation pipelines
Option 1:
predict
nreasintimeaneort
Increasinguseofevidence
Figure 2 | Three basic approaches to genome annotation and some common variations. Approaches are
compared on the basis of relative time, effort and the degree to which they rely on external evidence, as opposed to

Bacteria/
Archaea
Eucaryotes
Genome Organization in 
Bacteria/Archaea vs. Eucaryotes 
Gene identiﬁcation in Bacteria and Archaea is
(mostly) easier than in Eucaryotes
gene 1 gene 2 gene 3

17
ugh to ensure acceptance of
9, 10]. There has also been a
neration techniques such as
g experimental methods
n of a protein’s role and
. These annotations would
se they are based on actual
than homology. Currently
idence tags stating how the
, however, they are often
s. Including evidence quali-
dea of the reliability of the
concept of assigning a level
is not novel, but is seldom
ome of the current steps for
otation and offers a guide to
oblems that are encountered
tion. It goes on to identify
ce genomes and why choos-
not always the best option.
of the public sequence data-
st possible next steps toward
rehensive annotation with
errors.
erial genomes Figure 1: A generic process for bacterial genome
Richardson and Watson
atMhttp://bib.oxfordjournals.org/Downloadedfrom
Steps in Genome Annotation

Identiﬁcation of protein-coding regions
Intrinsic evidence
• Absence of stop codons (TAA,TGA,TAG)
• Sufﬁcient open-reading frame (ORF) length (~100 a.a.)
• Presence of start codon (ATG, GTG, TTG)
• Minimize gene overlap
• presence of other sequence motifs (TATA, RBS, splice sites, polyA)
Extrinsic evidence
• Similarity to “known” genes from other organisms (HOMOLOGY)
• Expression data (mRNA sequencing, proteomics)
• Predicted sequence analysis (e.g., protein structure modeling)

20
What are several ways that could explain sequence similarity
between molecular sequences?
What are potential pitfalls with assigning homology?
How do we generally assign homology?

the Mycoplasma genitalium genome1
(Fig. 1). Where two
groups’ descriptions are completely incompatible, at least
one must be in error. In my analysis, there is no penalty
sions – a likely occurrence because all relied on simil
methods and data. This evaluation also ignores minor d
agreements in annotation, and disparities in degree
specificity (possibly indicating problematic overpredicti
of function4
). Therefore, the true error rate must
greater than these figures indicate.
There are several possible reasons why the function
analyses have mistakes, as described at greater length els
where5–8
. For example, it may be that the similar
between the genomic query and database sequence
insufficient to reliably detect homology, an issue solvab
by appropriate use of modern and accurate sequence com
parison procedures9,10
. A more difficult problem is accura
inference of function from homology. Typical databa
searching methods are valuable for finding evolutionar
related proteins, but if there are only about 1000 maj
superfamilies in nature11,12
, then most homologs mu
have different molecular and cellular functions.
The annotation problem escalates dramatically beyo
the single genome, for genes with incorrect functions a
entered into public databases8
. Subsequent search
against these databases then cause errors to propagate
future functional assignments. The procedure need cyc
only a few times without corrections before the resourc
that made computational function determination possib
– the annotation databases – are so polluted as to
almost useless. To prevent errors from spreading out
control, database curation by the scientific commun
will be essential4,13
.
To ensure that databases are kept usable, the intent o
gene annotation should be clear: does it indicate homolo
ortholog, and/or functional equivalence? Fortunately, som
databases already incorporate this information explici
(e.g. Ref. 14). Errors will, of course, still creep in. To he
FIGURE 1. Comparison of annotations
Three dots represent (left to right) Frasier et al.1
, Koonin et al.2
and Ouzounis et
al.3
annotations for each of the 468 M. genitalium genes. (Tentative cases
001 051 101 151 201 251 301 351 401 451
M. genitalium
Black circle = no annotation
468 genes
Colored circle = different
Blue circle = same annotation
TIG April 1999, volume 15, No. 4 13
atory of Molecular Biology, Hills Road,
UK. M. Levitt, C. Chothia, B. Al-Lazikani
provided stimulating discussion.
No. groups No. Annotations per group Total No.
annotating gene genes annotations conflicts
Frasier Koonin Ouzounis
et al.1
et al.2
et al.3
0 33 – – – – N/A
1b
95 14 15 66 95 N/A
2 318 279 317 40 636 45
3 22 22 22 22 66 10
Sum (2+3) 340 301 339 62 702 55
Summary of annotations made by each group (Fig. 1), minimal number of conflicting annotations (s
the resulting minimal fraction of annotations that are erroneous.
a
Frasier et al.1
data from http://www.tigr.org/tdb/mdb/mgdb/mgdb.html. Koonin et al.2
data from ht
nlm.nih.gov/Complete_Genomes/Mgen. Ouzounis et al.3
data from http://www.embl-heidelberg
mycogen.new.html. Instances where Ouzounis et al.3
reported SWISS-PROT annotation of the same gene w
avoid duplication with Frasier et al.1
entries. However, even if all of these 300 annotations are included
annotation error rate drops only to 6%. All annotations were collected in 1996, shortly after the genom
b
No comparative analysis is possible when only one group made an annotation.
al. (1995) The minimal gene complement of Mycoplasma
nce 270, 397–403
l. (1996) Sequencing and analysis of bacterial genomes.
4–416
al. (1996) Novelties from the complete genome of Mycoplasma
Microbiol. 20, 898–900
(1998) Protein annotation: detective work for function prediction.
, 248–250
nd Koonin, E.V. (1998) Sources of systematic error in functional
nomes: domain rearrangement, non-orthologous gene
nd operon disruption. In Silico Biol. 1, 7
Zhang, X. (1997) The challenges of genome sequence annotation or
he details’. Nat. Biotechnol. 15, 1222–1223
998) Predicting function: from genes to genomes and back.
, 707–725
roch, A. (1996) Go hunting in sequence databases but watch out for
s Genet. 12, 425–427
al. (1998) Assessing sequence
hods with reliable structurally identified distant evolutionary
oc. Natl. Acad. Sci. U. S. A.
al. (1994) Issues in searching molecular sequence databases.
19–129
11 Chothia, C. (1992) Proteins. One thousand families for the molecular biologist.
Nature 357, 543–544
12 Brenner, S.E. et al. (1997) Population statistics of protein structures: lessons from
structural classifications. Curr. Opin. Struct. Biol. 7, 369–376
13 Smith, T.F. (1998) Functional genomics – bioinformatics is ready for the challenge.
Trends Genet. 14, 291–329
14 Tatusov, R.L. et al. (1997) A genomic perspective on protein families. Science 278,
631–637

COMMENTErrors in genome annotation
FIGURE 2. Example annotations and analysis
(a) Consistent annotations. Annotations were generally considered consistent for this analysis if either the function or the gene name match (e.g. mg463; mg010).
An exception is when one group uses a gene name and another specifically notes that the current gene is a paralog and not identical (consider mg010). Where the
descriptions from different groups were compatible, but of different levels of specificity, this was considered a correct assignment (e.g. mg225). The difficulty of
reconciling pairs of descriptions to determine whether they reflect compatible functions makes this analysis imprecise. Generally, the approach here is generous
and should err on the side of detecting too few errors; it is usually more permissive than Ref. 5. mg463: Frasier et al.1
and Koonin et al.2
describe different aspects
of function, but give the same gene name. The Ouzounis et al.3
description is compatible with that from Koonin et al.2
, but less specific. All three annotations are
considered correct for this analysis. mg010: Frasier et al.1
and Ouzounis et al.3
agree that this is a DNA primase. Koonin et al.2
use a different gene name and
explicitly state that this is a truncated protein. Because of the common functional descriptions, all three are considered correct. However, if Koonin et al.2
had been
more explicit in indicating a functional difference, then their annotation would have been marked as conflicting. (Note that mg250 is also annotated as a DNA primase
by all three groups.) mg225: the Ouzounis et al.3
annotation of histidine permease is more specific than the Koonin et al.2
description of amino acid permease. It may
be that histidine permease is an (incorrect) overprediction of function, or it could be correct. The two annotations are considered consistent, and the decision of
Frasier et al.1
not to provide a function is not penalized. (b) Inconsistent annotations. mg302: lack of a functional assignment from Frasier et al.1
is not penalized.
The Koonin et al.2
and Ouzounis et al.3
annotations are wholly inconsistent. This leads to a conflict and a minimum error rate of 50%. Note that the assessment
(a)
mg463
Frasier et al. High level kasgamycin resistance (ksgA)
Koonin et al. rRNA (adenosine-N6, N6-)-dimethyltransferase (ksgA)
Ouzounis et al. Dimethyladenosine transfe [sic]
mg010
Frasier et al. DNA primase (dnaE)
Koonin et al. DNA primase (truncated version) (DnaGp)
Ouzounis et al. DNA primase (EC 2.7.7.-)
mg225
Frasier et al. Hypothetical protein
Koonin et al. Amino acid permease
Ouzounis et al. Histidine permease
(b)
mg302
Frasier et al. No database match
Koonin et al. (Glycerol-3-phosphate?) permease
Ouzounis et al. Mitochondrial 60S ribosomal protein L2
mg448
Frasier et al. Pilin repressor (pilB)
Koonin et al. Putative chaperone-like protein
Ouzounis et al. PilB protein
mg085
Frasier et al. Hydroxymethylglutaryl-CoA reductase (NADPH)
Koonin et al. ATP(GTP?)-utilizing enzyme
Ouzounis et al. NADH-ubiquinone oxidoredu [sic]
Two kinds of problems
insufﬁcient similarity to assume homology
inference of function from homology

24
Table 1 Statistics for different annotations for H. utahensis genome along with the extended annotations. For orphan and functional genes
genes and the percentage relative to the total number of annotated genes
Annotation features NCBI AAMG RAST Extend
Original Complemented by
annotation of function
from AAMG and RAST
from NCBI and RAST
from NCBI and AAMG
EA
CDS 2998 2998 3040 3040 3041 3041 2980
rRNA 4 4 3 3 3 3 4
tRNA 45 45 45 45 45 45 45
ncRNA 1 1 0 0 0 0 0
frameshift/Pseudo 0 0 0 0 0 0 0
Total 3048 3048 3088 3088 3089 3089 3029
Orphan genes 1014 (33.27 %) 777 (25.49 %) 885 (28.66 %) 837 (27.10 %) 1203 (38.94 %) 819 (26.51 %) 672 (22
Functional genes 2034 (66.73 %) 2271 (74.51 %) 2203 (71.34 %) 2251 (72.90 %) 1886 (61.06 %) 2270 (73.49 %) 2357 (7
Another issue with annotation
Not all proteins have homologs in
another genome — check out
Giardia

Reﬂecting annotation uncertainty in
gene names
• “Domain”-containing protein 
Predicted protein contains a region similar to a recognized
protein domain or fold
– ankyrin-repeat domain containing protein
• Conserved hypothetical protein 
Predicted protein is homologous to predicted proteins in at least
one other (distinct!) organism
• Hypothetical protein 
Nothing is known about the predicted protein (no known
homologs)
Avoid “-like” as homology is a yes/no

Dangers of Serial Annotation
• Function is generally “inferred” from homology
• Poor annotations are propagated in the public sequence databases
(GenBank) - think the Telephone Game
• Failure to examine functional assignation leads to deterioration of data
and errors
• Manual curation is needed to validate annotation and add valuable
information
• Particularly important for representatives of new lineages
– often homologous genes in new lineages are very different from those
in other organisms
– need good annotation of “anchor” genomes for subsequent
sequencing

27
Leave databases better than you found ‘em
Keep your messy thoughts outta databases.

30
0.4
LCGC14AMP_05736710
Crenactin
LCGC14AMP and Lokiarchaeum (4/1)
Actin and related sequences
Arp2
LCGC14AMP (5)
Arp1
LCGC14AMP and
Lokiarchaeum
(11/1)
LCGC14AMP/Lokiarchaeum (11/2)
LCGC14AMP (2)
Arp3
LCGC14AMP (2)
LCGC14AMP_06532160
100
100
51
83
100
100
96
100
100
100
100
a c
b
Nitrosopumilus maritimus SCM1 0
LCGC14AMP and
Lokiarchaeum (5/1)
RESEARCH ARTICLE
What if you found eukaryotic genes in an archaeon?
30

32
How do genes evolve?
Speciation —> Diversiﬁcation
Gene duplication —> Diversiﬁcation

Almost half of the genes in any
genome are in gene families
33
and are deleted from the genome. The rate of duplication
that gives rise to stably maintained genes is the birth rate
multiplied by the retention rate, which is expected to
ﬂuctuate with gene function, among other things.
Duplicated genes are often referred to as paralogous
genes, which form gene families. Several authors have
tabulated the distribution of gene family size for a few
completely sequenced genomes [11,12] and this varies
substantially among species and gene families [13]; for
instance, the biggest gene family in D. melanogaster is the
Table 1. Prevalence of gene duplication in all three domains of
lifea
Total
number
of genes
Number of duplicate
genes (% of
duplicate genes) Refs
Bacteria
Mycoplasma pneumoniae 677 298 (44) [65]
Helicobacter pylori 1590 266 (17) [66]
Haemophilus inﬂuenzae 1709 284 (17) [67]
Archaea
Archaeoglobus fulgidus 2436 719 (30) [68]
Eukarya
Saccharomyces cerevisiae 6241 1858 (30) [67]
Caenorhabditis elegans 18 424 8971 (49) [67]
Drosophila melanogaster 13 601 5536 (41) [67]
Arabidopsis thaliana 25 498 16 574 (65) [69]
Homo sapiens 40 580b
15 343 (38) [11]
a
Use of different computational methods or criteria results in slightly different
estimates of the number of duplicated genes [12].
b
The most recent estimate is ,30 000 [61].

Paralogous Gene Families
- many genes in the genome are present in “families” and
each gene in a gene family shares a common ancestry
(homologs)
- gene families arise from duplication and subsequent
diversiﬁcation by various mechanisms
how are these copies different from alleles?
full
duplicated
dead new
ancestral
Possible fates of duplicated genes:
sub

Evolutionary fates of duplicated genes
pseudogenization - non-functional when accumlates a stop
codon.
this gene is eventually lost from genome. but young
pseudogenes would still recognizable as a homolog. why?
35
conservation of function - extra copy could provide
greater amounts of protein. why?

36
subfunctionalization - extra copy could have a new
function (or a sub-function). why?
– most proteins have > 1 function (could be expressed
differently in different parts of cell/tissue or at different
times)
– if greater amounts of a protein not advantageous, extra
copy would be selected against unless…
– subfunctionalization - both copies adopt some functions
of parent gene (moonlighting functions)
– sometimes this can be differential gene expression in
different tissues

37
Neofunctionalization - extra copy could have a
novel function. why?
–often a related function (not entirely new)
–opsin gene family is a good example
–this could require a lot of mutations in new gene copy

Orthologs and paralogs
a A*b* c BC*
Ancestral gene
Duplication to give 2
copies = paralogs on the
same genome
orthologousorthologous
paralogous
A*C*b*
A mixture of orthologs
and paralogs sampled
potential problem

Orthologs: Homologs inherited after speciation.
Gene phylogeny may match organismal phylogeny.
Paralogs: Homologs produced by gene duplication.
Multiple homologs in a given species or evidence that
gene duplication involved through phylogenetic
analysis and lack of match to organismal phylogeny
Gene phylogeny does not match organismal phylogeny
in a tree where most genes do match organismal
phylogeny well.

Using phylogeny to check for
paralogs (or orthologs)
–multiple copies of a gene in a genome
–look at which clades contain the paralogs
–duplication events can occur > once
–can be paralog loss/gain
40

41
actin
ARPs
crenactin
41
0.4
LCGC14AMP_05736710
Crenactin
Arp2
LCGC14AMP (5)
Arp1
LCGC14AMP and
Lokiarchaeum
(11/1)
LCGC14AMP (2)
Arp3
LCGC14AMP (2)
LCGC14AMP_06532160
100
100
51
83
100
100
96
100
100
100
100
a c
b
Lokiar
Eur
Lokiarch_12
Arf-family
Lok
Lokiarc
170290521 C
Lokiarch_31930
Lokiarchaeum
Lok
Lokiarch
Sar1-fam
Lokiarch
3154254
Lokiarchaeum (3)
Rab-family (7
5
Lokiarchaeum (4)
Lokiarchae
Lokiarch_45420
Lokiarc
51
79
100
99
100
71
87
99
84
96
97
89
100
95
82
93
69
100
68
61
82
99
97
Arabidopsis thaliana
Thalassiosira pseudonana
Methanopyrus kandleri AV19
Pyrobaculum aerophilum IM2
Aciduliprofundum boonei T469
Korarchaeum cryptoﬁlum OPF8
Caldiarchaeum subterraneum
Myxococcus xanthus DK1622
3
1
2
2
4
31
113
4
LCGC14AMP and
Lokiarchaeum (5/1)
RESEARCH ARTICLE
Actins are part of a gene family
ARP = actin related protein

42
from sequence-based homology searches
(Fig. 1). Despite this variance, two features
are preserved between prokaryotic and
eukaryotic actins. The first common feature
in multistrand filament architectures. This
maintenance of contacts within a strand
suggests that the primordial actin filament
was single-stranded. In PNAS, Braun et al.
packing (6, 7). L
crographs (EMs)
indicated a struc
either single- or
(6). Now, Braun
in an 18-Å cry
crenactin can for
in vitro.
In determinin
sents a record o
actin filament,
functions must
appears to inte
proteins, the arca
as a cell shape-d
ment has some
actin homologs M
bulin homolog F
a dedicated cell s
idence from bact
filaments have a
sequences and f
evolved to becom
tion (1). Conseq
why crenactin fo
ment may be tha
mal for its role
Fig. 1. Relatedness of actins. The structures of actin protofilaments (2, 9–15) are shown below a maximum-
likelihood phylogenetic tree of the actin protein sequences. The structures are aligned via the central protomer, Author contributions: U.G
Actins are part of a larger gene family

43
actin
crenactin
MreB
43
0.4
LCGC14AMP_05736710
Crenactin
Arp2
LCGC14AMP (5)
Arp1
LCGC14AMP and
Lokiarchaeum
(11/1)
LCGC14AMP (2)
Arp3
LCGC14AMP (2)
LCGC14AMP_06532160
100
100
51
83
100
100
96
100
100
100
100
a c
b
68
Pyrobaculum aerophilum IM2
Aciduliprofundum boonei T469
Korarchaeum cryptoﬁlum OPF8
3
1
2
2
4
LCGC14AMP and
Lokiarchaeum (5/1)
RESEARCH ARTICLE
Actins are part of a gene family
Actin and Arp 2/3 required for motility
Arps = actin related proteins (and are not actin)

44
centerofthetree)but,exceptincarefullycalibratedcases,thisrelationshipis
not defined and probably varies between different parts of the tree.
defining subgroups by the deepest strongly supported node. Modified,
with permission, from Ref. [3].
Sc ARP4
Sp P23A10.08
Sp C23D3.09
Ce ZK616.4
Dm CG6546
Hs BAF53b
Mm BAF53a
Hs BAF53a
Sc ARP7
Sc ARP9
Sp C1071.06
Ce F42C5.9
At 8843903
Sc ARP8Sp C664.02Dm CG7846
Mm
12857259
Hs 104344709
Dm
CG12235
Hs 'ARP11'Ce
C49H3.8
M
m
'ARP11'
Sp
C56F2
Sc
ARP10
HsARP5
Dm
CG7940
ScARP5
ScARP6
CeARP6
Dmactin13E
GgARPX
Mm
'Actlike7b'
Hs'Actlike7b'
AtARP3
ScARP3
Dm
actin66b
Hs ARP3Mm 12835802
At ARP2
Sc ARP2
Dd ARP2
Dm ARP14D
ScARP1NcARP1
DmARP87C
Nc
Ro7
At12321978
Os13486900
SpBC365.10
At6091748
SpCC550.12
Mm12842577
HsARPX
At11276982Hs11137605
Mm12838437
Hs10178893
Mm
'Actlike7a'
Hs'Actlike7a'
Hs ARP3b
Sp
ARP3
D
d
AR
P3
Ac
AR
P3
CeY71F9AL.16
NcARP3
Sp ARP2
Ac ARP2
Gg ARP2
Ce K07C5.1
Hs ARP2
M
m
12840619
Hs 13383265
M
m
12840134
CeY53F4B.22
AnARP1
Sp
ARP1
HsARP1bMmARP1b
MmARP1a
HsARP1a
TgActin
GlActin
DmARP53d
PfActin
SpActinScActin
AtActin
Dd actin
Metazoan
actins
Conventional
actins
ARP1
Dynein motility
(dynactin complex)
ARP3
Actin polymerization
(ARP 2/3 complex)
ARP6
Nuclear?
ARP5
Chromatin
remodeling
ARP4
Chromatin
remodeling
ARP8
Chromatin
remodeling
ARP10?
Dynein motility
(dynactin complex)
Confidence estimates:
ARP2
Actin polymerization
(ARP2/3 complex) At 18394608

45
actin
arps
crenactin
45
0.4
LCGC14AMP_05736710
Crenactin
Arp2
LCGC14AMP (5)
Arp1
LCGC14AMP and
Lokiarchaeum
(11/1)
LCGC14AMP (2)
Arp3
LCGC14AMP (2)
LCGC14AMP_06532160
100
100
51
83
100
100
96
100
100
100
100
a c
b
68
61
3
1
LCGC14AMP and
Lokiarchaeum (5/1)
RESEARCH ARTICLE
How conﬁdent are you in the functions
of the Loki actin homologs based on
this tree?

Some types of Protein Databases
Database Advantages Problems
nr (Translated GenBank
sequences)
• Everybody can submit data • Many errors, because there is no
manual inspection
• no additional information links
• redundant
UniProt (Trembl) • non-redundant dataset derived from
GenBank, DDBJ and Embl
• Links to additional information
• GO term annotations
Many errors, because there is no
manual inspection
RefSeq • mostly fully sequenced organisms
• data submitted by genome projects
• some entries are reviewed
• less links to other databases
• Not so many sequences as in nr
and Trembl
UniProt (SwissProt) • All entries reviewed
• Links to additional information
• Not so many sequences as in nr
and Trembl and RefSeq
Annika Joecker
Max-Planck Institute for Plant Breeding Research

Annika Joecker
Sources of Information
Many types of databases are used for genome annotation

09/29/10 Plant Computational Biology
UniProt – Cross-references
GO
Interpro
Pfam

CDD – Conserved Domain Database
• Contains protein domain models imported from Pfam,
SMART, COG (clusters of orthologous genes), KOG (euk
COGs)
• Curated and provided at NCBI
• Search tool: RPSBlast
• 27036 PSSMs (Position speciﬁc scoring matrices) (Dec
2008)
–Count amino acids at each position in multiple alignment
–Compute percentage
–Compute log ratio

Annika Joecker

Protein Domain Search: InterPro
• Database of protein families, domains and functional
sites
• Hosted at the European Bioinformatics Institute (EBI)
• Consortium of member databases (PROSITE, Pfam,
Prints, ProDom, SMART and TIGRFAMs,
Superfamily, Panther)
• Tool for searching: InterProScan
• http://www.ebi.ac.uk/Tools/InterProScan/
Annika Joecker

KEGG – Kyoto Encyclopedia of Genes
and Genomes
• Comprehensive database of biological
information:
–KEGG GENES: genes and proteins
–KEGG LIGAND: endogenous & exogenous
chemical building blocks
–KEGG PATHWAY: biochemical pathways
–KEGG BRITE: KEGG-based ontology
• Web and stand-alone based tools

• A way to capture
biological knowledge
in a written and
computable form
The Gene Ontology
• A set of concepts
and their relationships
to each other arranged
as a hierarchy
www.ebi.ac.uk/QuickGO
Less specific concepts
More specific concepts

Ontologies: The Scope of GO
1. Molecular Function
e.g. protein kinase activity
2. Biological Process
e.g. cell cycle
3. Cellular Component
e.g. mitochondrion
GO terms aim to describe the ‘normal’ functions/ processes/locations that gene
products are involved in
NO: pathological processes, experimental conditions or temporal information

Anatomy of a GO term
7/31/14
a. b.
c.

Microbes Online 
www.microbesonline.org
• Excellent resource for
microbial genome data
• Precomputed ortholog/
paralog searches
• Aligned protein
sequences for
phylogenetic analysis
• Pathway-based
organization of data

61
darker colors = higher % similarity

How to annotate metagenomic data?
all the ways we’ve discussed before
(think homologs....)

phylogenetic “binning”
40.2 Methods for the Phylogenetic Binning of Metagenome Sequence Samples
root
Gammaproteobacteria
Proteobacteria
Deltaproteobacteria
Epsilonproteobacteria
Betaproteobacteria
Alphaproteobacteria
Bacteroidia
Bacteroidetes
Firmicutes Bacilli
Clostridia
Archaea
Euryarchaeota
Thermoprotein
Methanomicrobia
Bacteria
Actinobacteria (class)
Actinobacteria
Cyanobacteria
Spirochaetes
Actinobacteria (class)
Actinobacteria
root
Archaea
Bacilli
Euryarchaeota
Methanobacteria
Bacteria
Firmicutes
Clostridia
(B)
(A)
Figure 40.1 Comparison of
composition of public database
microbial community analyzed
sequencing. (A) Taxonomic co
ﬁnished genomes present in Ge
May 2009: The large bias towa
Gammaproteobacteria is caused
by 164 genome sequences of E
strains. (B) Taxonomic compos
populations in the human gut e
genbank
metagenomic data
from human gut
1. homology-based assignment of
reads
(e.g., BLAST)
2. compositionally-based assignment
(e.g, %G+C, or nucleotide
frequencies - stretches of 2-9 nts)
assigning genomic data to different groups of organisms

2 md2016 annotation

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