Interpreting Results |
Repeat databases |
RepeatMasker is a program that screens DNA sequences for interspersed
repeats and low complexity DNA sequences. The output of the program is
a detailed annotation of the repeats that are present in the query
sequence as well as a modified version of the query sequence in which
all the annotated repeats have been masked (default: replaced by
Ns). On average, almost 50% of a human genomic DNA sequence currently
will be masked by the program. Sequence comparisons in RepeatMasker
are performed by the program cross_match, an efficient implementation
of the Smith-Waterman-Gotoh algorithm developed by Phil Green.
Sequences can be pasted in or uploaded as files, both in fasta format.
Multiple fasta format sequences may be pasted in at once or may be contained
within a file. Fasta format looks like this:
The submission form contains a text field for the full pathname of the
file containing the sequence data on the local system (i.e. where the
Netscape browser is running). By pressing the "Browse..."
button, you can use a file selection box to select the file without
having to type the path. When running the browser on a MacIntosh the
browse button works but the file name can not be typed in. On both the
PC and Mac the sequence file needs to be saved as 'text only'.
In principal, there is no limit to the length of the query sequence or
size of the batch file. However, the most common error message
obtained by users is due to timing out of the connection during the
submission of long sequences. Furthermore, longer sequences (> 10kb)
are queued (when necessary), whereas shorter sequences are handled
instantly. We strongly recommend submitting sequences longer than
cosmid size (40-50 kb) by e-mail (see also "sensitivity and
speed" below. When you routinely submit large sequences it may be
better to run RepeatMasker locally.
For faster service outside North America, mirror sites exist in England,
Israel and Australia (links to be added soon).
Output / return format
The program returns three or four output files for each query. One
contains the submitted sequence(s) in which all recognized
interspersed or simple repeats have been masked. In the masked areas,
each base is replaced with an N, so that the returned sequence is of
the same length as the original. A table annotating the masked
sequences as well as a table summarizing the repeat content of the
query sequence will be returned to your screen. Optionally a file with
alignments of the query with the matching repeats will be returned as
In the "html" return format (default when the browser runs
on a Mac or PC) all output is returned to your screen in one file. In
the "tar file" return format the masked sequence(s) and
alignments can be saved as compressed files. The "links"
return format returns links to these output files in a text format
(they look bad on the browser, but are fine when saved to your
When checked, alignments are returned in a file (ending in .aln) or to the screen.
Alignments are shown in order of appearance in
the query sequence.
Do not mask simple.../Only mask simple...
Regions of low complexity, like simple tandem
repeats, polypurine and AT-rich regions can lead to spurious matches
in database searches. By default they are masked along with the
With the option "Do not mask simple..." only interspersed
repeats are masked. This may, for example, be preferred when the
masked sequence will be fed to a gene prediction program.
Alternatively, with the option "Only mask simple...", one
can mask only these low complexity regions, e.g. when you are only
interested to quickly locate polymorphic simple repeats in a
Only mask Alus
By checking this option, you limit the masking and annotation to
(primate) Alu repeats. 7SL RNA (the ancestral sequence of Alus), SVA
(which contains several Alu sequences and a fragment of LTR5) and LTR5
are masked as well. This option only works for primate DNA.
Mask with Xs...
When checked, the repeat sequences are replaced by Xs instead of
Ns. This allows one to distinguish the masked areas from possibly
existing ambiguous bases or other stretches of Ns in the original
sequence. However, when running BLAST searches (and maybe other
programs) Xs are deleted out of the query and the returned BLAST
matches will have position numbers not necessarily corresponding to
that of the original sequence.
Since April 1999 the column widths in the annotation table are
adjusted to the maximum length of any string occurring in a column;
this allows long sequence names to be spelled out completely.
Previously a fixed column width table was returned, which can still be
obtained by checking this option button.
You can type in less frequently used options in UNIX command line style, like:
-div 20 -inv -GC 45
which will cause the program to only annotate and mask repeats less
than 20% diverged, return the alignments in the orientation of the
repeat consensus sequences, and use matrices optimal for a 45% GC
background nucleotide distribution.
With the option -div you can limit the masking and
annotation to a subset of less diverged (younger) repeats by choosing
a maximum divergence level of the repeat copy to its consensus
sequence. This option may be used to limit the masking to those
repeats that are either specific to primates or another mammalian
order for use in subsequent comparison of orthologous mammalian
loci. On average, interspersed repeats have diverged 18% in human
(~35% in mouse) from their consensus since the mammalian orders
separated, so typing '-div 18' in the advanced options box limits
masking to most primate specific repeats. Note that this method is
rather crude, mostly since the range of deterioration of repeats of
the same age is wide; many shared repeats may go unmasked and vice
Neutral mutation patterns differ significantly
depending on the GC richness of a locus and we have calculated optimal scoring matrices
for the alignment to consensus
sequences in a range of background GC levels. Usually, RepeatMasker
calculates the percentage of the sequence consisting of Gs and Cs and
uses the appropriate matrices. However, the program defaults to using
'average' 43% GC matrices when the query is shorter than 2000 bp or a
batch file is analyzed. Short sequences are less likely to share the
GC level of the locus. For example, CpG islands and exons are more GC
rich than the surrounding DNA, whereas a LINE1 element usually is more
AT rich than the background. In a batch file, RepeatMasker analyses
all sequences together with the same matrices. The percentage GC in
all the sequences combined may be inappropriate for some sequence
entries; using high GC level matrices in AT rich sequences (and vice
versa) may result in false masking.
One can override this behavior in two ways:
With the option -gc you can set the GC level to a certain percentage;
e.g. '-gc 37' lets the program use matrices appropriate for 37% GC
background. This could be useful, for example, when you have a batch
file of ESTs from a single locus with a known GC level.
Alternatively, the -gccalc option forces RepeatMasker to use
the actual GC level of a short sequence or the average GC level of a
batch of sequences. The latter sequences, for example, may be contigs
or reads in a sequencing project.
RepeatMasker transparently fragments large sequences in fragments of
101 kb with 1 kb overlaps. The -frag option allows one to
change the size of these fragments. Fragmentation was implemented to
allow the size of sequences and sequence batches to be unlimited. It
also can improve repeat detection when a genomic sequence contains
regions of DNA with significantly different GC levels (isochores);
sets of scoring matrices are chosen based on the
GC level of a fragment. The only visible effect of the fragmentation
is in the alignment files, where alignments at the edges of the
fragments can be duplicated and/or truncated.
Alignments are shown in the orientation of the query sequence. The
option -inv will return alignments in the orientation of the repeats.
In the process of finding all repeats, RepeatMasker temporarily cuts
out most full-length elements, young LINE1 3' ends, and close to
perfect simple repeats are deleted (both in human and rodent settings)
to unearth any possible underlying older repeat in which these
elements have inserted or expanded. The option -nocut skips
the above deletion step in the default procedure. RepeatMasker is
generally more sensitive including the deletion step.
When the option -xsmall is used a sequence is returned in the .masked
file in which repeat regions are in lower case and non-repetitive
regions are in capitals.
The option -small causes the whole masked sequence to be
returned in lower case, with repeats replaced by 'x's (or 'x's if
combined with -x).
Interspersed repeats are specific to a (group of) species, dependent
on the time of activity of the source transposable element. About half
of the repeats identified in human DNA are specific to primates,
i.e. they amplified after the eukaryotic radiation some 100 million
years ago. Most repeats that can be identified in mouse DNA are
specific to rodents, due to higher activity and faster mutation rates
in the rodent lineage. RepeatMasker has separate protocols optimized
for analysis of rodent and primate genomes. Interspersed repeats in
other mammals have not been so well catalogued as yet. Among these,
artiodactyl queries are treated best by RepeatMasker, but repeats
specific to other orders are also present.
The numbers of different repeat consensus sequences against which
queries of different species are compared gives an impression of how
far the different libraries are developed:
# of repeats total bp
primates 563 664160
rodents 466 487006
other mammals 347 243730
other vertebrates 52 53994
Drosophila 65 167423
Arabidopsis 98 275516
grasses 27 67789
Note that the majority of sequences against which rodent and especially
other mammalian queries are compared are repeats identified in the
human genome and thought to predate the mammalian radiation.
Whereas the mammalian libraries represent heavily manipulated and
expanded versions of Repbase libraries, the
non-mammalian libraries were extracted with very limited curation. The
vertebrate (chicken, Xenopus, etc.) and grasses (maize, rice)
libraries are especially fetal. No summary tables are returned for
Speed and sensitivity
On average, with default settings, a 10 kb human cosmid will be
analyzed in about 30-40 seconds if no one else is using the server at
For longer sequences the required time increases pretty much linearly
with the sequence length. Sequences shorter than 10 kb are analyzed
disproportionally faster. This is partially due to the program, e.g. a
batch file of 200 human sequences of 400 bp (total 80 kb) is analyzed
within 2 minutes, but we also have implemented a queuing system for
sequences longer than 10 kb, making the request of lower priority the
longer the query sequence. The speed is further somewhat dependent on
the repeat content of the sequence; repeat dense regions, especially
Alu-rich regions, are analyzed faster.
The program can be run at three levels of speed or sensitivity. The
only difference between these settings is the minimum match or word
length in the initial (not quite) hashing step of the cross_match
program (see the
cross_match/phrap documentation). The "slow" setting
will take about 3 times longer and will find and mask 0-5% more
repetitive DNA sequences than the default setting. The
"quick" settings miss 5-10% of the sequences masked by
default, but will be 3 to 6 times faster. The alignments may extend
more or be somewhat more accurate in the more sensitive settings as
At the sensitive settings RepeatMasker currently finds, on average,
47% of human genomic DNA to be derived from interspersed repeats.
RepeatMasker is very sensitive in comparison with other programs,
although comparison to some is skewed because of the use of much
Selectivity and matches to coding sequences
The cutoff Smith-Waterman scores for masking interspersed repeats are
conservative, since masking of one short potentially interesting
region generally is more harmful than not masking a number of hard to
find matches. If there are any false matches, they tend to have scores
close to the cutoff, which is 225 for most repeats, 300 for the
low-complexity LINE1 search, and 180 for the very old MIR, LINE2 and
We tested for the occurrence of false matches in randomized and in
inverted (but not complemented) DNA. To check a variety of conditions,
four 150 to 400 kb DNA fragments were analyzed ranging in GC level
from 36% to 54%. To retain seeds for Smith Waterman alignments,
randomization was done at the 10 bp word level. Note that the inverted
sequences retain the low complexity and simple repeat patterns of the
original sequences. Even at sensitive settings, for which false
matches are most likely, this version of RepeatMasker reported no
(false) matches at all to interspersed repeats in the randomized or
inverted sequences. No simple repeats were reported in the randomized
RepeatMasker returned only a single probably false match (71 bp) when
analyzing a batch of 4440 coding regions in human mRNAs (7,200,000 bp)
at sensitive settings. The coding regions were collected from GenBank,
based on annotations, filtered for the presence of complete ORFs and
initiator methionines, and made more or less non-redundant. When each
coding region was analyzed individually using the -gccalc option, 5
matches (414 bp, 0.006%) were falsely masked (156 bp at default speed,
76 bp at quick settings). In this analysis each sequence was analyzed
with matrices chosen based on the actual GC level, even for very short
sequences, while in the batch analysis of the coding regions the
'average' 43% GC matrices were used.
RepeatMasker is most commonly used to avoid spurious matches in
database searches. Generally this step is strongly recommended before
doing BLASTN or BLASTX equivalent searches with mammalian DNA
The most common concern is of course if RepeatMasker ever masks coding
We found that false matches in coding regions
are extremely rare, but did identify 38 genuine fragments of
interspersed repeats (4214 bp) in the (annotated) coding regions of
the 4440 human mRNAs (7.2 Mb) analyzed (excluding annotated coding
sequences of LINE1 elements and endogenous retroviruses). We verified
matches with lower scores by comparing the translation products to
close homologous or redundant entries in the database (the repeat
matching regions always were exactly missing). In the majority of
these cases, the sequences appear to be improperly annotated or to
represent either artificially or naturally defective mRNAs
(e.g. alternatively spliced exons comprised of a small fragment of a
repeat). Genuine overlaps of interspersed repeats with coding
sequences usually involve terminal regions of the ORFs. Since the
transposable element derived region is unique to the protein in that
(group of) species, the masking does not interfere with database
However, some cautionary comments are necessary. First, a few active
cellular genes are derived from transposable elements. For example, I
have identified 7 examples of human genes derived from (DNA
transposon) transposases. These genes will be partially masked by a
(related) DNA transposon in the repeat database. EST and cDNA matches
beyond the masked region should alert you.
Also be aware that RepeatMasker screens for small RNA pseudogenes and
will therefore mask the active small RNA genes as well (I think the
tRNA list is complete, I stopped adding snRNAs unless I found an
indication that they have created many pseudogenes). The number of
matches to small RNAs are listed in the overview table; (close to)
exact matches are possibly active genes, although related active genes
not in the database may show diverged matches.
A final caution relates to the fact that 3' UTRs of transcripts are
about as dense in interspersed repeats as intergenic regions
are. Thus, many ESTs are completely masked as repetitive DNA. I
recommend that, when you compare a genomic sequence against the EST
database or use ESTs as a query in nucleotide searches, you search
with the unmasked sequence as well; use a long minimum match (word
length/ word size) like 40 bp to identify exact matches and avoid most
background. Unfortunately the maximum word length that can be used in
the NCBI BLASTN program is 18 (apparently due to memory limitations).
Use in association with gene prediction programs
Predicting genes from a masked sequence faces several problems. First,
one should not mask low complexity regions, e.g. to avoid masking
trinucleotide repeats in coding regions. But even with only
interspersed repeats masked, gene prediction programs may fail to
identify exons correctly. As mentioned above, sometimes tail ends of
coding regions may have originated from transposable elements. Even if
no coding regions have been masked, splice sites may be compromised;
e.g. the polypyrimidine region that is part of the acceptor splice
site may be contained within a repeat.
Thus, I generally recommend to run a gene prediction program on
unmasked DNA (as well) and compare the predicted genes and exons with
the RepeatMasker output. Some gene prediction program allow you to
force certain exons out of the predictions (e.g. often the old ORFs of
LINE1 elements and endogenous retroviruses are included in
genes). Work is also in progress at several sites to incorporate
RepeatMasker into gene prediction programs, in which cases matches to
repeats are weighted in along with the other parameters used.
Many people mask repeats before designing primers or oligo probes from
sequence data. I've been told often that primers/probes designed from
regions unmasked by RepeatMasker have a much better success rate. A
cautionary note here is that unmasked regions not necessarily are
unique in the genome (e.g. many lower copy repeats are not in the
database yet) and experiments should be performed as if no filtering
against repeats has been done.
The alignments can help in designing primers from sequences that are
completely masked. Regions that diverge much from the consensus are
less likely to misbehave than others.
RepeatMasker is sometimes used during assembly of large genomic
sequences. This procedure probably is most useful in very Alu rich
regions; in that situation I recommend to only mask the Alus, and
maybe limit the masking to those Alus less than 15% diverged (-div
How to read the results
The annotation file contains the cross_match output lines. It lists
all best matches (above a set minimum score) between the query
sequence and any of the sequences in the repeat database or with low
complexity DNA. The term "best matches" reflects that a
match is not shown if its domain is over 80% contained within the
domain of a higher scoring match, where the "domain" of a
match is the region in the query sequence that is defined by the
alignment start and stop. These domains have been masked in the
returned masked sequence file. In the output, matches are ordered by
query name, and for each query by position of the start of the
1306 15.6 6.2 0.0 HSU08988 6563 6781 (22462) C MER7A DNA/MER2_type (0) 336 103
12204 10.0 2.4 1.8 HSU08988 6782 7714 (21529) C TIGGER1 DNA/MER2_type (0) 2418 1493
279 3.0 0.0 0.0 HSU08988 7719 7751 (21492) + (TTTTA)n Simple_repeat 1 33 (0)
1765 13.4 6.5 1.8 HSU08988 7752 8022 (21221) C AluSx SINE/Alu (23) 289 1
12204 10.0 2.4 1.8 HSU08988 8023 8694 (20549) C TIGGER1 DNA/MER2_type (925) 1493 827
1984 11.1 0.3 0.7 HSU08988 8695 9000 (20243) C AluSg SINE/Alu (5) 305 1
12204 10.0 2.4 1.8 HSU08988 9001 9695 (19548) C TIGGER1 DNA/MER2_type (1591) 827 2
711 21.2 1.4 0.0 HSU08988 9696 9816 (19427) C MER7A DNA/MER2_type (224) 122 2
This is a sequence in which a Tigger1 DNA transposon has integrated
into a MER7 DNA transposon copy. Subsequently two Alus integrated in
the Tigger1 sequence. The simple repeat is derived from the poly A of
the Alu element. The first line is interpreted like this:
1306 = Smith-Waterman score of the match, usually complexity adjusted
The SW scores are not always directly comparable. Sometimes
the complexity adjustment has been turned off, and a variety of
scoring-matrices are used.
15.6 = % substitutions in matching region compared to the consensus
6.2 = % of bases opposite a gap in the query sequence (deleted bp)
0.0 = % of bases opposite a gap in the repeat consensus (inserted bp)
HSU08988 = name of query sequence
6563 = starting position of match in query sequence
7714 = ending position of match in query sequence
(22462) = no. of bases in query sequence past the ending position of match
C = match is with the Complement of the consensus sequence in the database
MER7A = name of the matching interspersed repeat
DNA/MER2_type = the class of the repeat, in this case a DNA transposon
fossil of the MER2 group (see below for list and references)
(0) = no. of bases in (complement of) the repeat consensus sequence
prior to beginning of the match (so 0 means that the match extended
all the way to the end of the repeat consensus sequence)
2418 = starting position of match in database sequence (using top-strand numbering)
1465 = ending position of match in database sequence
An asterisk (*) in the final column (no example shown) indicates that
there is a higher-scoring match whose domain partly (<80%) includes
the domain of this match.
Note that the SW score and divergence numbers for the three Tigger1
lines are identical. This is because the information is derived from a
single alignment (the Alus were deleted from the query before the
alignment with the Tigger element was performed). The program makes
educated guesses about many fragments if they are derived from the
same element (e.g. it knows that the MER7A fragments represent one
insert). In a next version I can identify each element with a unique
ID, if interest exists (this could help to represent repeats cleaner
in graphic displays).
Alignments are shown in order of appearance in the query sequence.
These alignments may be most generally useful for designing PCR
primers in a region full of repeats. It is possible to get primers
that work in a whole genome, when the 3' end of it lies in a region of
(even a common) repeat that is very different from the consensus.
Alignments are shown in the orientation of the query sequence unless
the option -inv is typed in in the option box.
Here is an example of an alignment of a MIR spanning an Alu element
deleted in an earlier step:
665 28.45 2.93 5.02 g5129s420 7350 7882 (1924) C MIR#SINE/MIR (1) 261 28 3
g5129s420 7350 ATCATAACAAACATTTAT--GGTGCCTCCTATGGAGCAGGGATTTTGCTT 7397
v v i i i v viv v i v v v
C MIR#SINE/MIR 261 ATAATAACCAACATTTATTGAGCGCTTACTATGTGCCAGGCACTGTTCTA 212
g5129s420 7398 AGGACTCTGAACTATAT---CTTACTT-GTCTTCATTAAAAACCTTATGA 7443
vi i iv i i i i i i v i
C MIR#SINE/MIR 211 AGCGCTTTACA-TGTATTAACTCATTTAATCCTCA-CAACAACCCTATGA 164
g5129s420 7444 AAAAGGTACTATTATTAACTGGGGXTGGGTTGTTTAACAGATAAGAAAGC 7787
iiv v i iii v i i i
C MIR#SINE/MIR 163 GGTAGGTACTATTATTATCC---------CCATTTTACAGATGAGGAAAC 123
g5129s420 7788 TTAAGAATTAGAGAGATAAATTATCTTGCTTAAGGTAACACAGTTAACAA 7837
v i v i i v v v ii v i ii
C MIR#SINE/MIR 122 TGAGGCA-CAGAGAGGTTAAGTAACTTGCCCAAGGTCACACAGCTAGTAA 74
g5129s420 7838 GCATTAG-GTCAAAGTTTGAACTCGGGCAGTCTGACTACAGAGCCC 7882
iivi i iiii i i i i v i
C MIR#SINE/MIR 73 GTGGCAGAGCCGGGATTCGAACCCAGGCAGTCTGGCTCCAGAGTCC 28
Transitions / transversions = 1.96 (45 / 23)
Gap_init rate = 0.03 (8 / 234), avg. gap size = 2.38 (19 / 8)
In cross_match alignments the mismatches are indicated, where
"-" indicates an insertion/deletion, "i" a
transition (G<->A, C<->T) and "v" a transversion
(all other substitutions). The position of the deleted Alu in the
query is indicated with an "X".
The lines in the annotation table describing this match appear as:
665 28.4 2.9 5.0 g5129s420 7350 7467 (533) C MIR SINE/MIR (1) 261 149
2222 10.2 2.7 0.0 g5129s420 7468 7762 (238) C AluSg SINE/Alu (7) 303 1
665 28.4 2.9 5.0 g5129s420 7763 7882 (118) C MIR SINE/MIR (113) 149 28
Discrepancies between alignments and annotation
Most discrepancies between alignments and annotation result from
adjustments made to produce more legible annotation. This annotation
also tends to be closer to the biological reality than the raw
cross_match output. For example, adjustments often are necessary
when a repeat is fragmented through deletions, insertions, or an
inversion. Many subfamilies of repeats closely resemble each other,
and when a repeat is fragmented these fragments can be assigned
different subfamily names in the raw output. The program often can
decide if fragments are derived from the same integrated transposable
element and which subfamily name is appropriate (subsequently given to
all fragments). This can result in discrepancies in the repeat name
and matching positions in the consensus sequence (subfamily consensus
sequences differ in length).
Some other discrepancies are specific to LINE elements. These repeats
do not appear as complete elements in the consensus database. This is
mostly a result of the contrast in conservation over the length of its
sequence during its evolution in the mammalian genome; the ~3 kb ORF2
region of LINE1 has been very conserved, whereas the untranslated
regions and ORF1 to a lesser degree have evolved very fast. Thus the
3' end or 5' end of an ancient LINE1 does not even remotely resemble
that of the currently active LINE1, whereas the coding region for
reverse transcriptase is closely related. Thus, many subfamilies have
been defined for both the 5' and 3' UTRs (25 and 50, resp.) of LINE1
elements in human DNA, whereas only three ORF2 entries are present in
the database. It is not only hard to extend all subfamilies from the
beginning to the end, but it also appears that different 3' ends may
have been associated with the same 3' ends, and vice versa. On top of
that, including 50 full length (6.2-8 kb) LINE1 elements in the
database would make the program very slow. LINE1 elements therefore
are presented in the database in 3 (or more) pieces, and the program
tries to put these pieces together as well as possible. As a result
both the names of the repeats and position numbering in the consensus
sequence are generally different in the alignments than in the output
file. The LINE2 elements are likewise broken up in the databases, in
3' UTRs for different subfamilies and one ORF2 region.
The 3' UTR of LINE1 subfamilies ranges from 500 bp to over 2000 bp (in
L1MC/D3), and the length of the 5' UTR is even more variable, even
between subfamilies that show strong similarity in the 3' UTR. To
allow the LINE1 fragments to be put together, all position numbers in
older LINE1 subfamilies are adjusted to the position of ORF2 (the
conserved part of LINE1) in a complete L1PA2 element. Since some older
elements have much longer 5' UTRs or ORF1-ORF2 linker regions than
L1PA2, this sometimes results in the assignment of negative position
numbers for the 5' end of LINEs.
Finally, you may find large discrepancies in position numbering if an
element includes tandem repeat units. For example, MER109 contains
multiple ~300 bp repeat units; this can lead to overlapping
matches. In the output such matches are fused.
The summary (.tbl) file
The summary file is pretty much self explanatory. Below is an example.
file name: A-355G7.fasta
total length: 139958 bp
GC level: 41.03 %
bases masked 91491 bp ( 65.37 %)
number of length percentage
elements* occupied of sequence
SINEs: 46 12182 bp 8.70 %
ALUs 41 11603 bp 8.29 %
MIRs 5 579 bp 0.41 %
LINEs: 42 52641 bp 37.61 %
LINE1 38 52296 bp 37.37 %
LINE2 4 345 bp 0.25 %
LTR elements: 20 13441 bp 9.60 %
MaLRs 10 5618 bp 4.01 %
Retrov. 4 5131 bp 3.67 %
MER4_group 3 1439 bp 1.03 %
DNA elements: 8 1741 bp 1.24 %
MER1_type 7 1114 bp 0.80 %
MER2_type 1 627 bp 0.45 %
Mariners 0 0 bp 0.00 %
Unclassified: 5 9215 bp 6.58 %
Total interspersed repeats: 89220 bp 63.75 %
Small RNA: 0 0 bp 0.00 %
Satellites: 0 0 bp 0.00 %
Simple repeats: 20 1647 bp 1.18 %
Low complexity: 9 437 bp 0.31 %
* most repeats fragmented by insertions or deletions
have been counted as one element
The sequence(s) were assumed to be of primate origin.
RepeatMasker version 11/06/98 default
ProcessRepeats version 06/16/98
The four main classes mentioned in this table are well defined (see my 1996 review in COGD) and form a good basis for a
summary or visual presentation of the repeats in a locus. Among the
subclasses, some uncertainty of classification remains; it is
especially hard to predict if an LTR is derived from an endogenous
retrovirus or a non-autonomous LTR element. Also, not all subclasses
are listed and the total for the classes is often higher than the sum
of the sub classes. Note that the "MER" subclasses and the different
MER interspersed repeats are not necessarily related to each
other. The term MER (MEdium Reiterated repeats) was introduced for
purely administrative purposes to give the beast a name. I named the
MER1, MER2, and MER4 groups after the first member of each group that
was identified as an interspersed repeat.
The program tries very hard to find out which repeat fragments were
derived from the same insertion event of a transposable element. The
estimated number of events still tend to be an overestimate.
The 'bases masked' number is calculated from the total number of Xs in
the masked sequences (before these are changed to Ns or lower case
letters). The other numbers are derived from the annotation (.out)
file. Discrepancies between the 'bases masked' number and the sum of
'total interspersed repeats', small RNA, satellites and low complexity
are generally very small. They are mostly accounted for by unmasked
regions between flanking identical simple repeats, annotated as one
stretch if fewer than 10 bases separate them, and fragments of repeats
shorter than 10 bp which are not annotated but are masked.
Low-complexity DNA and simple repeats
By default, along with the interspersed repeats, RepeatMasker masks
low-complexity DNA. Simple repeats (micro-satellites) can originate at
any site in the genome, and therefore have an interspersed
character. Other low-complexity DNA, primarily poly-purine/
poly-pyrimidine stretches, or regions of extremely high AT or GC
content will result in spurious matches in some database searches as
well (especially in the ungapped BLASTN searches). For example,
extremely AT-rich regions consistently will give very low probability
matches to mitochondrial DNA in BLASTN searches. The settings are very
stringent, and we think that few if any sequences informative in
database searches are masked as low-complexity DNA. However, one may
opt to skip the low-complexity masking, for example when using
RepeatMasker in conjunction with a gene prediction program.
Under the current settings a 100 bp stretch of DNA is masked when it
is >87% AT or >89% GC, a 30 bp stretch has to contain 29 A/T (or GC)
nucleotides. The settings are slightly more stringent than the
original settings, partly because the new, gapped BLAST programs are
less sensitive to short regions of low complexity. In coding regions I
did not find extensive regions (>10 bp) masked as low complexity
DNA that would not be masked by the combined XNU and SEG filters
routinely used in BLASTX.
Finding polymorphic simple repeats
Although RepeatMasker does a good job in masking simple repeats to
avoid spurious matches in database searches, it is not written to find
and indicate all possibly polymorphic simple repeat sequences. Only
di- to pentameric and some hexameric repeats are scanned for and
simple repeats shorter than 20 bp are ignored. Combining the "Only
mask simple.." button option with a "div" option (e.g. -div 10)
will produce a list of simple repeats that are 90% or more perfect.
However, this list may not be not complete; e.g. two perfect 40 bp
long (CA)n repeats interrupted by 10 Ts are aligned in one piece and
may be reported as having > 10% divergence from the consensus. Of
course most hexameric and longer unit repeats won't be reported
either. A site dedicated to identifying polymorphic tandem repeats can
be found at UTSW
Reference repeat databases
The interspersed repeat databases screened by RepeatMasker are based
on the repeat databases (Repbase Update)
copyrighted by the Genetic Information Research Institute (G.I.R.I.).
The Repbase Update database contains annotation of most repeats with
respect to divergence level, affiliation, etc. The nomenclature of
the interspersed repeats in the output of RepeatMasker is nearly
identical to that of the reference database which in most cases
corresponds to that in the literature.
We have calculated statistically optimal scoring matrices for the
alignment of neutrally diverging (non-selected) sequences in human DNA
to their original sequence. These matrices have been in use since the
May 1998 release. The matrices were derived from alignments of DNA
transposon fossils to their consensus sequences (Arian Smit, Arnie Kas
& Phil Green, in preparation...). A series of different matrices are
used dependent on the divergence level (14-25%) of the repeats and the
background GC level (35-53%, neutral mutation patterns differ
significantly in different isochores).
These matrices are (close to) optimal for human genomic sequences
longer than 10 kb, for which length the GC level usually is
representative of the isochore in which the sequence lives. However,
the GC level of small fragments can diverge a lot from the surrounding
(e.g. a fragment spanning a CpG island, a GC rich exon or an AT-rich
LINE1 element) and RepeatMasker defaults to using matrices derived for
a 43% GC background when a sequence is shorter than 2000 bp or when a
batch file is submitted. When the appropriate background GC level is
known, this can be entered with the -gc
We haven't published a paper on RepeatMasker yet, unless you call this
expanding help file a publication. We'd appreciate it if you could
refer to the web site in your publications (Smit, AFA & Green, P
Smit, A.F.A. (1996) Origin of interspersed repeats
in the human genome. Curr. Opin. Genet. Devel. 6 (6), 743-749.
Smit, A.F.A. (1996) Structure and evolution of mammalian interspersed
repeats. PhD dissertation, USC. (lots of otherwise unpublished
information here, available under order number 9636751 at the UMI web site)
Schmid, C. W. (1996). Alu: structure, origin, evolution, significance,
and function of one-tenth of human DNA. Prog Nucleic Acids Res Mol Biol
Jurka, J. (1996) Origin and evolution of Alu repetitive elements. In "
The impact of short interspersed elements (SINEs) on the host genome. Maraia,
R.J., editor. Springer Verlag.
Batzer, M. A., Deininger, P. L., Hellmann Blumberg, U., Jurka, J., Labuda,
D., Rubin, C. M., Schmid, C. W., Zietkiewicz, E., and Zuckerkandl, E. (1996).
Standardized nomenclature for Alu repeats. J Mol Evol 42, 3-6.
SINE/MIR & LINE/L2
Smit, A. F. A., and Riggs, A. D. (1995). MIRs are classic, tRNA-derived
SINEs that amplified before the mammalian radiation. Nucleic Acids Res
Smit, A. F. A., Toth, G., Riggs, A. D., Jurka, J., Ancestral mammalian-wide
subfamilies of LINE-1 repetitive sequences. J Mol Biol 246, 401-417.
Smit, A. F. A. (1993). Identification of a new, abundant superfamily of
mammalian LTR-transposons. Nucleic Acids Res 21, 1863-72.
Wilkinson, D. A., Mager, D. L., and Leong, J. C. (1994). Endogenous Human
Retroviruses. In The Retroviridae, J. A. Levy, ed. (New York: Plenum Press),
Smit, A. F. A., and Riggs, A. D. (1996). Tiggers and other DNA transposon
fossils in the human genome. Proc Natl Acad Sci USA 93, 1443-8.
Improvements and new features
The database of human/mammalian-wide repeats was expanded 2.5
fold. Among the new additions are the (long) internal sequences of
Databases of repeats from other species than primates, rodents
or artiodactyls can now be screened, although the program is not optimized
to do so and the quality of the databases is not at the same level.
Through optimization of the cross_match searches, the program more
sensitive and selective, especially with regard to detection of low
complexity sequences and old LINE1 elements.
The RepeatMasker output is now processed by a second script to create annotation
ready for database submission. Some of the more obvious improvements in
the output are (i) overlapping matches are generally resolved, (ii) LINE1
fragments are annotated with position numbers as in a full L1 element,
and (iii) when an Alu or LINE1 is fragmented information from both or all
fragments is used to assign a subfamily name.
Alignments are shown without interruption by other cross_match output
and in the order of appearance in the query sequence.
A summary table has been added which shows, among other things, the repeat
composition of the query sequence.
- major expansion of the rodent libraries and significant update
of the human libraries as well, especially in LINE1 elements.
- scripts modified to accommodate new entries in databases
- simple repeats masking optimized by including pentamers and
using a more stringent matrix
- several bugs fixed (e.g. sequences without repeats are now counted)
- table now displays the parameters used
- the program is more robust and accepts most 'almost but not quite
fasta' format files
- large sequences are analyzed in fragments of 100 kb to reduce the
memory requirements of the program. Similarly files with very many
sequence entries are divided up. You shouldn't notice any of this in
the output files.
- matrices are used that are optimal for the divergence level of the
repeats to which the query is compared and the background nucleotide
- another big update of the human repeat databases.
- the small RNA sequences have been corrected and expanded (all tRNAs
should be there now)
- the summary table now lists the amount of small RNA (pseudo)genes,
simple repeats and low complexity DNA identified
- close to perfect simple repeats, full-length shorter interspersed
repeats and young LINE1 3' ends are temporarily excised from the
sequence (in both human and rodent analysis) to allow better detection
of any underlying repeats.
- the "Skip simple, low complexity region masking" really skips all
simple repeats now
- alignments are shown in the orientation of the query sequence
- among many bugs fixed is one involving sequence names including a
number between parentheses
This version uses the 1998 cross_match release. The difference for
RepeatMasker is mainly in the complexity adjusted length of the
matches that function as kernels for Smith Waterman alignments and the
matrix dependent adjustment of the score for complexity of the
The full description ('>') lines are now retained in the masked file.
The .out file table is returned with flexible length columns allowing
the full length of long query sequence names to be displayed.
Optionally, the old fixed width table can still be obtained.
Simple repeat and satellite masking has been improved again; their
annotation has changed a bit, most notably they are now all listed in
the orientation of the query sequence
Several new options are available:
- A mRNA/EST option prevents false masking due to inappropriate matrix
choice and low complexity matches to LINE1 elements in short GC rich
regions like coding regions.
- You can limit the masking to Alus when masking primate DNA
- You can limit the masking to younger repeats by setting a maximum
allowed divergence to the consensus sequence
- The sequences identified as repeats can be returned in lower case
(rest in capitals) rather than masked out by Ns or Xs.
- You can set the background GC level (determining which matrices are
used) overriding the program's calculations.
Among bugs fixed since May 1998 are those responsible for distorted
output for sequences with names ending in .seq and for sequences
without a header line. Also, sequence files from PCs and Mac with
hidden carriage returns are handled appropriately.
All the command line options are now available on the web site.
The default return format of the annotation file is changed, hopefully
in a way that does not interfere with any type of parsing; the width
of the columns is now adjusted to the longest entry in that column,
allowing query names to be spelled out in full, and usually leading to
Arabidopsis, Drosophila, and grass repeat libraries were added; other
repeat libraries were updated.
Three measures were taken to eliminate the (few) false positives:
- Use of the actual average GC level of sequences in a batch file may
sometimes lead to false masking (or failure to mask) in sequences that
diverge largely from the average. Thus, by default, all batch files
are now analyzed with the innocuous 43% matrices.
- one entry, responsible for 90% of false masking in GC rich regions,
is deleted from the 'tough L1' library.
- the matrix used for identification of the most diverged sequences in
very GC rich regions, based on too little data and too much
extrapolation, was 'too easy' on the mismatches and has been
Thanks to these measures the 'mrna' option is not necessary and has
A bug is fixed that led to (wildly) improper annotation for some
sequences fully consisting of repeats (all bases masked). A series of
lesser bugs were taken care of. New bugs were introduced, probably.
Data submission form.
For further information and to obtain a local copy contact:
For information on a commercial license please contact::
The Genome Center home page
NCKU Bioinformatics Center