Trimmomatic Manual: V0.32
Introduction
Trimmomatic is a fast, multithreaded command line tool that can be used to trim and crop
Illumina (FASTQ) data as well as to remove adapters. These adapters can pose a real problem
depending on the library preparation and downstream application.
There are two major modes of the program: Paired end mode and Single end mode. The
paired end mode will maintain correspondence of read pairs and also use the additional
information contained in paired reads to better find adapter or PCR primer fragments
introduced by the library preparation process.
Trimmomatic works with FASTQ files (using phred + 33 or phred + 64 quality scores,
depending on the Illumina pipeline used). Files compressed using either „gzip‟ or „bzip2‟ aresupported, and are identified by use of „.gz‟ or „.bz2‟ file extensions.

Implemented trimming steps (Quick reference)
Trimmomatic performs a variety of useful trimming tasks for illumina paired-end and single
ended data. The selection of trimming steps and their associated parameters are supplied on
the command line.
The current trimming steps are:ILLUMINACLIP: Cut adapter and other illumina-specific sequences from the read.
SLIDINGWINDOW: Performs a sliding window trimming approach. It starts
scanning at the 5‟ end and clips the read once the average quality within the window
falls below a threshold.
MAXINFO: An adaptive quality trimmer which balances read length and error rate tomaximise the value of each read
LEADING: Cut bases off the start of a read, if below a threshold quality
TRAILING: Cut bases off the end of a read, if below a threshold quality
CROP: Cut the read to a specified length by removing bases from the end
HEADCROP: Cut the specified number of bases from the start of the read
MINLEN: Drop the read if it is below a specified length
AVGQUAL: Drop the read if the average quality is below the specified level
TOPHRED33: Convert quality scores to Phred-33
TOPHRED64: Convert quality scores to Phred-64

Running Trimmomatic
Processing Order
The different processing steps occur in the order in which the steps are specified on thecommand line. It is recommended in most cases that adapter clipping, if required, is done as
early as possible, since correctly identifying adapters using partial matches is more difficult.

Single End Mode
For single-ended data, one input and one output file are specified. The required processing
steps (trimming, cropping, adapter clipping etc.) are specified as additional arguments after
the input/output files.
java -jar <path to trimmomatic jar> SE [-threads <threads>] [-phred33 | -phred64] [-trimlog<logFile>] <input> <output> <step 1> ...
orjava -classpath <path to trimmomatic jar> org.usadellab.trimmomatic.TrimmomaticSE [threads <threads>] [-phred33 | -phred64] [-trimlog <logFile>] <input> <output> <step 1> ...
-phred33 or -phred64 specifies the base quality encoding. If no quality encoding is specified,
it will be determined automatically (since version 0.32). The prior default was -phred64.
Specifying a trimlog file creates a log of all read trimmings, indicating the following details:
the read namethe surviving sequence length
the location of the first surviving base, aka. the amount trimmed from the start
the location of the last surviving base in the original readthe amount trimmed from the end
Multiple steps can be specified as required, by using additional arguments at the end as
described in the section processing steps.
For input and output files adding .gz/.bz2 to an extension tells Trimmomatic that the file isprovided in gzip/bzip2 format or that Trimmomatic should gzip/bzip2 the file, respectively.

Paired End Mode
For paired-end data, two input files, and 4 output files are specified, 2 for the 'paired' output
where both reads survived the processing, and 2 for corresponding 'unpaired' output where aread survived, but the partner read did not.

Figure 1: Flow of reads in Trimmomatic Paired End modejava -jar <path to trimmomatic.jar> PE [-threads <threads] [-phred33 | -phred64] [-trimlog<logFile>] >] [-basein <inputBase> | <input 1> <input 2>] [-baseout <outputBase> |<unpaired output 1> <paired output 2> <unpaired output 2> <step 1> ...
orjava -classpath <path to trimmomatic jar> org.usadellab.trimmomatic.TrimmomaticPE [threads <threads>] [-phred33 | -phred64] [-trimlog <logFile>] [-basein <inputBase> | <input
1> <input 2>] [-baseout <outputBase> | <paired output 1> <unpaired output 1> <paired
output 2> <unpaired output 2> <step 1> ...
-phred33 or -phred64 specifies the base quality encoding. If no quality encoding is specified,
it will be determined automatically (since version 0.32). The prior default was -phred64.
-threads indicates the number of threads to use, which improves performance on multi-corecomputers. If not specified, it will be chosen automatically.
Specifying a trimlog file creates a log of all read trimmings, indicating the following details:
the read namethe surviving sequence length
the location of the first surviving base, aka. the amount trimmed from the start
the location of the last surviving base in the original readthe amount trimmed from the end
Multiple steps can be specified as required, by using additional arguments at the end as
described in the section processing steps.

Input/Output Files
Paired-end mode requires 2 input files (for forward and reverse reads) and 4 output files (forforward paired, forward unpaired, reverse paired and reverse unpaired reads).
Since these files often have similar names, the user has the option to provide either theindividual file names, or just one name from which the file names can be derived.
For input files, either of the following can be used:
Explicitly naming the 2 input files
Naming the forward file using the -basein flag, where the reverse file can be
determined automatically. The second file is determined by looking for common
patterns of file naming, and changing the appropriate character to reference the reverse
file. Examples which should be correctly handled include:o Sample_Name_R1_001.fq.gh -> Sample_Name_R2_001.fq.gz
o Sample_Name.f.fastq -> Sample_Name.r.fastqo Sample_Name.1.sequence.txt -> Sample_Name.2.sequence.txt
For output files, either of the following can be used:
Explicity naming the 4 output files
Providing a base file name using the –baseout flag, from which the 4 output files canbe derived. If the name “mySampleFiltered.fq.gz” is provided, the following 4 filenames will be used:
o mySampleFiltered_1P.fq.gz - for paired forward reads
o mySampleFiltered_1U.fq.gz - for unpaired forward reads
o mySampleFiltered_2P.fq.gz - for paired reverse reads
o mySampleFiltered_2U.fq.gz - for unpaired reverse reads
For input and output files adding .gz to an extension tells Trimmomatic that the file isprovided in gzipped format or that Trimmomatic should gzip the file, respectively. Thisextension can be used with both explicitly named and template-based file naming.

Processing Steps in Detail
Most processing steps take one or more settings, delimited by ':' (a colon)
ILUMINACLIP
This step is used to find and remove Illumina adapters.
Identifying adapter or other contaminant sequences within a dataset is inherently a trade off
between sensitivity (ensuring all contaminant sequences are removed) and specificity (leaving
all non-contaminant sequence data intact). This problem is even more acute when only a smallpart of the contaminant sequence is included within the read. The possibility of sequencing
errors within the reads complicates the process still further.
Although adapter and other technical sequences can potentially occur in any location withinreads, by far the most common cause of adapter contamination is sequencing of a DNA
fragment which is shorter than the read length. In this scenario, the beginning of the readcontains valid data, but when the end of the fragment is reached, the sequencer continues to„read-through‟ into the adapter. This results in a partial or full adapter sequence towards the 3‟
end of the read. While a full adapter sequence can be identified relatively easily, reliably
identifying a short partial adapter sequence is inherently difficult.
Interestingly, in a paired-end dataset, „read-through‟ will occur on both the forward andreverse reads of a particular fragment in the same position, and also, since the fragment was
entirely sequenced from both ends, the non-adapter portion of the forward and reverse reads
will be reverse-complements. Since adapter read-through is a relatively common occurrence,and since Illumina datasets are often paired-end, Trimmomatic includes a second adapter
identification strategy, specifically for adapter read-though and which takes advantage of theadded evidence available in paired-end data. This strategy is known as „palindrome‟ mode.
The diagram below illustrates both strategies.
In A, the read contains the entire technical sequence within the read, and thus a standardalignment approach is sufficient to determine this fact. In B, only part of the technicalsequence is contained at the 3‟ end of the read, and thus only a short alignment can be used.
Below some length threshold, which depends on the relative costs of false positives and false
negatives, it is no longer possible to identify an adapter sequence, thus many short adapter
fragments will remain.
In D, the „palindrome‟ approach is used to check a similar situation with a short contaminantat the 3‟ end of the read. However, due to exploiting the additional data available in pairedend mode, the region tested as part of the alignment is much longer. Not only are both adapter
sequences tested at once, but the fragment sequence from each reads are also checked. Thisalignment is thus much more reliable than the short alignment in B, and allows adapter „readthough‟ to be detected even when only one base of the adapter has been sequenced.
„C‟ shows how palindrome mode can also detect long „read-though‟. In this example, there is
no useful fragment at all, and both reads begin with sequence from the adapters. None theless, there is still a sizeable alignment, and thus this scenario can be reliably identified.

Trimmomatic uses a two-step approach to find matches between the adapters and reads. First,
short sections of each adapter (maximum 16 bp) are tested in each possible position within thereads. If this short alignment, known as the „seed‟ is a perfect or sufficiently close match,
determined by the seedMismatch parameter (see below), the entire alignment between theread and adapter is scored. This two-step strategy results in considerable efficiency gains,since the seed alignment can be calculated very quickly, while the full alignment score is
calculated relatively rarely.
The full alignment score is calculated as follows. Each matching base increases the alignmentscore by 0.6, while each mismatch reduces the alignment score by Q/10. By considering thequality of the base calls, mismatches caused by read errors have less impact. A perfect matchof a 12 base sequence will score just over 7, while 25 bases are needed to score 15. As such
we recommend values of between 7 - 15 as the threshold value for simple alignment mode. .
For palindromic matches, a longer alignment is possible, as described above. Therefore thisthreshold can be higher, in the range of 30. Even though this threshold is very high (requiringa match of almost 50 bases) Trimmomatic is still able to identify very, very short adapter
fragments. (See Figure 2 panels C and D, where the alignment regions are shown).

ILLUMINACLIP:<fastaWithAdaptersEtc>:<seed mismatches>:<palindrome clip
threshold>:<simple clip threshold>fastaWithAdaptersEtc: specifies the path to a fasta file containing all the adapters,
PCR sequences etc. The naming of the various sequences within this file determines
how they are used. See the section below or use one of the provided adapter files
seedMismatches: specifies the maximum mismatch count which will still allow a fullmatch to be performed

palindromeClipThreshold: specifies how accurate the match between the two 'adapter
ligated' reads must be for PE palindrome read alignment.
simpleClipThreshold: specifies how accurate the match between any adapter etc.
sequence must be against a read.
ILLUMINACLIP also supports two additional optional parameters, which affect palindrome
mode only.
ILLUMINACLIP:<fastaWithAdaptersEtc>:<seed mismatches>:<palindrome clip
threshold>:<simple clip threshold>:<minAdapterLength>
ILLUMINACLIP:<fastaWithAdaptersEtc>:<seed mismatches>:<palindrome clip
threshold>:<simple clip threshold>:<minAdapterLength>:<keepBothReads>
minAdapterLength: In addition to the alignment score, palindrome mode can verify
that a minimum length of adapter has been detected. If unspecified, this defaults to 8 bases,for historical reasons. However, since palindrome mode has a very low false positive rate, thiscan be safely reduced, even down to 1, to allow shorter adapter fragments to be removed.
keepBothReads: After read-though has been detected by palindrome mode, and theadapter sequence removed, the reverse read contains the same sequence information as theforward read, albeit in reverse complement. For this reason, the default behaviour is toentirely drop the reverse read. By specifying „true‟ for this parameter, the reverse read will
also be retained, which may be useful e.g. if the downstream tools cannot handle a
combination of paired and unpaired reads.
SLIDINGWINDOW
Perform a sliding window trimming, cutting once the average quality within the window falls
below a threshold. By considering multiple bases, a single poor quality base will not cause theremoval of high quality data later in the read.
SLIDINGWINDOW:<windowSize>:<requiredQuality>
windowSize: specifies the number of bases to average acrossrequiredQuality: specifies the average quality required.

MAXINFO
Performs an adaptive quality trim, balancing the benefits of retaining longer reads against thecosts of retaining bases with errors.
For many applications, the “value” of a read is a balance between three factors:
Minimal read length: The read needs to be long enough that it can be uniquely locatedwithin the target sequence. Extremely short reads, which can be placed into manydifferent locations within the target sequence, provide little value. The length requiredbefore a read is likely to be unique depends on the size and complexity of the target
sequence, but a typical target length would be in the order of 40 bases.
Additional read length: There may be added value in retaining additional bases,
beyond those needed to uniquely place a read. This is dependent primarily on theapplication. For pure counting applications, such as RNA-Seq, unique placement issufficient. For assembly or variant finding tasks, additional bases provide extra
evidence for or against putative results, and thus can be valuable.
Error sensitivity: The downstream analysis can be more or less sensitive to errors
within the data. This is determined by the tools and settings used. One extreme would
be tools were a single base error would cause the entire read to be ignored, whichfavours aggressive quality trimming. The other extreme would be tools which cantolerate or even correct a large number of errors, which favour retaining as much dataas possible.
Two user provided values are used, the “target read length”, which affects the first scoring
factor, and the “strictness” which affects the balance between the second and third factor.
Trimming is applied to the 3‟ end of the read, by considering these 3 factors at every possible
position, as scoring as follows:
Minimal read length: The difference between the putative read length and the target
read length is scored using a logistic function. This means that reads shorter the target
length are heavily penalized, but most of the scoring benefit can be achieved by reads
only marginally longer than this target.
Additional read length: The putative read length is scored linearly, and weighted by
the “leniency” of the trimming, where leniency = (1 – strictness).
Error sensitivity: The quality scores of the putatively retained bases are combined to
calculate the probability that the read is error-free. This score is then weighted by the“strictness”.
The combined score is calculated for each possible read length, and the optimal score is used
to determine where the read should be trimmed. In practice, the different factors combine asfollows:
At very short read lengths, the minimal read length factor dominates. This will heavily
penalize reads which are too short to be useful.
Once the target read length has been achieved, the minimal read length factor penalty
becomes a modest bonus. However, once the read is significantly longer than thetarget length, further bonuses from the minimal read length factor are limited, due tothe logistic function.
The additional read length factor then provides a modest benefit as additional bases
are retained. This is countered by the increasing penalty as the „error-free‟ probability
drops with increasing read length. The balance between these two factors is controlled
by the “strictness” parameter.
For most reads, depending on the quality of the read and the “strictness” setting, theincreasing penalty from the likelihood of error exceeds the bonus of retaining
additional bases at some point, and the read is trimmed accordingly.

MAXINFO:<targetLength>:<strictness>targetLength: This specifies the read length which is likely to allow thelocation of the read within the target sequence to be determined.
strictness: This value, which should be set between 0 and 1, specifies thebalance between preserving as much read length as possible vs. removal of incorrectbases. A low value of this parameter (<0.2) favours longer reads, while a high value(>0.8) favours read correctness.

LEADING
Remove low quality bases from the beginning. As long as a base has a value below thisthreshold the base is removed and the next base will be investigated.
LEADING:<quality>
quality: Specifies the minimum quality required to keep a base.
TRAILING
Remove low quality bases from the end. As long as a base has a value below this thresholdthe base is removed and the next base (which as trimmomatic is starting from the 3‟ prime end
would be base preceding the just removed base) will be investigated. This approach can beused removing the special illumina „low quality segment‟ regions (which are marked withquality score of 2), but we recommend Sliding Window or MaxInfo instead
TRAILING:<quality>
quality: Specifies the minimum quality required to keep a base.
CROP
Removes bases regardless of quality from the end of the read, so that the read has maximallythe specified length after this step has been performed. Steps performed after CROP might ofcourse further shorten the read.
CROP:<length>
length: The number of bases to keep, from the start of the read.
HEADCROP
Removes the specified number of bases, regardless of quality, from the beginning of the read.
HEADCROP:<length>
length: The number of bases to remove from the start of the read.

MINLEN
This module removes reads that fall below the specified minimal length. If required, it should
normally be after all other processing steps. Reads removed by this step will be counted andincluded in the „dropped reads‟ count presented in the trimmomatic summary.
MINLEN:<length>
length: Specifies the minimum length of reads to be kept.
TOPHRED33
This (re)encodes the quality part of the FASTQ file to base 33.
TOPHRED33 (no further parameters)
TOPHRED64
This (re)encodes the quality part of the FASTQ file to base 64.
TOPHRED64 (no further parameters)

Examples
Paired End
java -jar trimmomatic-0.30.jar PE s_1_1_sequence.txt.gz s_1_2_sequence.txt.gz
lane1_forward_paired.fq.gz lane1_forward_unpaired.fq.gz lane1_reverse_paired.fq.gz
lane1_reverse_unpaired.fq.gz ILLUMINACLIP:TruSeq3-PE.fa:2:30:10 LEADING:3
TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36
This will perform the following in this order
Remove Illumina adapters provided in the TruSeq3-PE.fa file (provided). Initially
Trimmomatic will look for seed matches (16 bases) allowing maximally 2
mismatches. These seeds will be extended and clipped if in the case of paired end
reads a score of 30 is reached (about 50 bases), or in the case of single ended reads ascore of 10, (about 17 bases).
Remove leading low quality or N bases (below quality 3)
Remove trailing low quality or N bases (below quality 3)
Scan the read with a 4-base wide sliding window, cutting when the average quality perbase drops below 15
Drop reads which are less than 36 bases long after these steps

Single End
java -jar trimmomatic-0.30.jar SE s_1_1_sequence.txt.gz lane1_forward.fq.gz
ILLUMINACLIP:TruSeq3-SE:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15
MINLEN:36
This will perform the same steps, using the single-ended adapter file. (Of course the :30:
parameter has no effect, but a value has to be specified nevertheless)

The Adapter Fasta
Using one of the supplied Fasta Files
Illumina adapter and other technical sequences are copyrighted by Illumina, but we have been
granted permission to distribute them with Trimmomatic. Suggested adapter sequences areprovided for TruSeq2 (as used in GAII machines) and TruSeq3 (as used by HiSeq and
MiSeq machines), for both single-end and paired-end mode. These sequences have not been
extensively tested, and depending on specific issues which may occur in library preparation,other sequences may work better for a given dataset. As a rule of thumb newer libraries will
use TruSeq3, but this really depends on your service provider.
If you use FASTQC, the „Overrepresented Sequences‟ report can help indicate which adapter
file is best suited for your data.
“Illumina Single End” or “Illumina Paired End” sequences indicate single-end or paired-end
TruSeq2 libraries, and the appropriate adapter files are “TruSeq2-SE.fa” and “TruSeq2-PE.fa”
respectively.
“TruSeq Universal Adapter” or “TruSeq Adapter, Index …” indicates TruSeq-3 libraries, andthe appropriate adapter files are “TruSeq3-SE.fa” or “TruSeq3-PE.fa”, for single-end andpaired-end data respectively.
Adapter sequences for TruSeq2 multiplexed libraries, indicated by “Illumina Multiplexing…”, and the various RNA library preparations are not currently included.
We are planning to support additional library preparations, such as Nextera, in the future,
based on user feedback.
Making custom clipping files
Trimmomatic uses two strategies for adapter trimming: Palindrome and Simple
With 'simple' trimming, each adapter sequence is tested against the reads, and if a sufficiently
accurate match is detected, the read is clipped appropriately.
'Palindrome' trimming is specifically designed for the case of 'reading through' a shortfragment into the adapter sequence on the other end. In this approach, the appropriate adapter
sequences are 'in silico ligated' onto the start of the reads, and the combined adapter+read
sequences, forward and reverse are aligned. If they align in a manner which indicates 'readthrough', the forward read is clipped and the reverse read dropped (since it contains no newdata).
Providing sequences for palindrome trimming
Naming of the sequences determines how they are used.
For 'Palindrome' clipping, a matched pair (or multiple matched pairs) of adapter sequencesmust be provided. The sequence names should both start with 'Prefix', and end in '/1' for theforward adapter and '/2' for the reverse adapter. The part of the name between „Prefix‟ and„/1‟ or „/2‟ must match exactly within each pair.

Sequences which begin with „Prefix‟ but do not form a matched pair are currently used insimple mode, but in future this will be treated as an error.
Providing sequences for simple trimming
All sequences not used in palindrome mode are used in simple mode.
Those with names ending in '/1' or '/2' will be checked only against the forward or reverse
read respectively. All other sequences will be checked against both the forward and reverse
read.
If you want to check for the reverse-complement of a specific sequence, you need to
specifically include the reverse-complemented form of the sequence as well, with anothername. As an example have a look at the TruSeq2-PE.fa file
>PCR_Primer1
AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT>PCR_Primer1_rc
AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGTGGTCGCCGTATCATT

As one can see the Primer is included as forward as well as its reverse complement.
Trimmomatic by default does not check for the reverse complement, as certain sequences aremuch more likely occur in one orientation that the other.

Changelog

0.30
o Feature: Add „AVGQUAL‟ trimmero Bug Fix: Correct „half-window‟ clipping from end of reads in
SLIDINGWINDOW (bug introduced in 0.27)
o Performance: Various improvements to simple alignment in ILLUMINACLIPo Internal: Clean up & refactor ILLUMINACLIP implementation.
0.27
o Feature: Add support for „keep reverse‟ and „minimum adapter length‟ topalindrome mode in ILLUMINACLIPo Feature: Trimmomatic can be executed with –jar. The „old‟ method, using the
explicit class, continues to work.
o Feature: Add „MAXINFO‟ trimmer
o Feature: Improve support for short adapters in simple mode in
ILLUMINACLIP
o Performance: Improve FastQ parser and serializer
0.25
o Bug Fix: Handle concatenated Gzip files correctlyo Bug Fix: Ensure input files are read only once, to allow streaming via shello Bug Fix: Report failures correctly in multi-threaded mode
0.22
o Feature: Support ZIP and BZIP2 format
o Feature: Basic support for short adapters in simple mode in ILLUMINACLIP
0.20
o Feature: Multithreading support
o Feature: Summary trim statistics
o Bug Fix: Incorrect alignment score in ILLUMINACLIP