Filtlong is a tool for filtering long reads by quality. It can take a set of long reads and produce a smaller, better subset. It uses both read length (longer is better) and read identity (higher is better) when choosing which reads pass the filter.

Table of contents

• Requirements
• Installation
• Example commands (quick)
• Example commands (detailed)
• Full usage
• Method
• Read scoring
• Trimming and splitting
• FAQ
• Acknowledgements
• License

Requirements

• Linux or macOS
• C++ compiler (GCC 4.8 or later should work)
• zlib (usually included with Linux/macOS)

Installation

Filtlong builds into a stand-alone executable:

git clone https://github.com/rrwick/Filtlong.git
cd Filtlong
make -j
bin/filtlong -h

If you plan on using Filtlong a lot, I'd recommend copying it to a directory in your PATH:

cp bin/filtlong /usr/local/bin

Example commands (quick)

Without an external reference

filtlong --min_length 1000 --keep_percent 90 --target_bases 500000000 input.fastq.gz | gzip > output.fastq.gz

With an external reference

filtlong -1 illumina_1.fastq.gz -2 illumina_2.fastq.gz --min_length 1000 --keep_percent 90 --target_bases 500000000 --trim --split 500 input.fastq.gz | gzip > output.fastq.gz

Example commands (detailed)

These examples use a 1.3 Gbp read set that was part of a barcoded 1D MinION run. I assessed read identity by aligning the reads to a completed assembly using minimap2 and this script.

Here is what the reads look like before Filtlong. Each dot is a read and the marginal histograms show the length distribution (top) and identity distribution (right). The length N50 is 24,077 bp (i.e. half the bases are in a read 24,077 bp long or longer). The identity N50 is 85.60% (i.e. half the bases are in a read with 85.60% or higher identity).

Without an external reference

When you run Filtlong without an external reference, it judges read quality using the Phred quality scores in the FASTQ file.

filtlong --min_length 1000 --keep_percent 90 --target_bases 500000000 input.fastq.gz | gzip > output.fastq.gz

• --min_length 1000 ← Discard any read which is shorter than 1 kbp.
• --keep_percent 90 ← Throw out the worst 10% of reads. This is measured by bp, not by read count. So this option throws out the worst 10% of read bases.
• --target_bases 500000000 ← Remove the worst reads until only 500 Mbp remain, useful for very large read sets. If the input read set is less than 500 Mbp, this setting will have no effect.
• input.fastq.gz ← The input long reads to be filtered (must be FASTQ format).
• | gzip > output.fastq.gz ← Filtlong outputs the filtered reads to stdout. Pipe to gzip to keep the file size down.

Filtlong has cut the original 1.3 Gbp of reads down to a much better 500 Mbp subset. Short reads and low identity reads have been mostly removed. Length N50 = 36,827 bp Identity N50 = 88.53%

With Illumina read reference

When an external reference is provided, Filtlong ignores the Phred quality scores and instead judges read quality using k-mer matches to the reference (a more accurate gauge of quality). FASTA input reads are allowed when a reference is provided, and if used, Filtlong will produce FASTA output.

NOTE: I would only recommend using Illumina reads with Filtlong if they are good Illumina reads (high depth and complete coverage). See the FAQ section for more info.

filtlong -1 illumina_1.fastq.gz -2 illumina_2.fastq.gz --min_length 1000 --keep_percent 90 --target_bases 500000000 input.fastq.gz | gzip > output.fastq.gz

• -1 illumina_1.fastq.gz -2 illumina_2.fastq.gz ← Use Illumina reads as an external reference. You can instead use -a to provide an assembly as a reference, but Illumina reads are preferable if available.

With an external reference, Filtlong is better able to judge read quality, and now most remaining reads are 85% identity or better. The length distribution has suffered a bit, however, because when outputting a fixed amount of reads (500 Mbp in this case), there is a trade-off between length and quality. Length N50 = 28,713 bp Identity N50 = 88.94%

With trimming and splitting

When an external reference is provided, you can turn on read trimming and splitting to further increase read quality. See Trimming and splitting for more information.

filtlong -1 illumina_1.fastq.gz -2 illumina_2.fastq.gz --min_length 1000 --keep_percent 90 --target_bases 500000000 --trim --split 500 input.fastq.gz | gzip > output.fastq.gz

• --trim ← Trim bases from the start and end of reads which do not match a k-mer in the reference. This ensures the each read starts and ends with good sequence.
• --split 500 ← Split reads whenever 500 consecutive bases fail to match a k-mer in the reference. This serves to remove very poor parts of reads while keeping the good parts. A lower value will split more aggressively and a higher value will be more conservative.

Trimming and splitting has further improved the read identity. This is especially apparent at the short side of the length distribution where a lot more reads now exceed 92% identity. Some of these high-identity shorter reads will be parts of longer reads which were split. Length N50 = 28,407 bp Identity N50 = 89.37%

Length priority

You can adjust the relative importance of Filtlong's read metrics. In this example, more weight is given to read length.

filtlong -1 illumina_1.fastq.gz -2 illumina_2.fastq.gz --min_length 1000 --keep_percent 90 --target_bases 500000000 --trim --split 1000 --length_weight 10 input.fastq.gz | gzip > output.fastq.gz

• --length_weight 10 ← A length weight of 10 (instead of the default of 1) makes read length more important when choosing the best reads.
• --split 1000 ← This larger split value makes Filtlong less likely to split a read. I.e. a read has to have a lot of consecutive bad bases before it gets split. This helps to keep the output reads longer.

These settings greatly improve the length distribution, but the length-quality trade-off results in more low-identity reads. Length N50 = 43,877 bp Identity N50 = 87.89%

Quality priority

You can adjust the relative importance of Filtlong's read metrics. In this example, more weight is given to read quality.

filtlong -1 illumina_1.fastq.gz -2 illumina_2.fastq.gz --min_length 1000 --keep_percent 90 --target_bases 500000000 --trim --split 100 --mean_q_weight 10 input.fastq.gz | gzip > output.fastq.gz

• --mean_q_weight 10 ← A mean quality weight of 10 (instead of the default of 1) makes mean read quality more important when choosing the best reads.
• --split 100 ← This smaller split value makes Filtlong split reads more often. I.e. even a relatively small stretch of bad bases will result in a split, giving shorter reads but of higher quality.

These settings produce the best identity distribution, with most reads now 87% identity or better. Length now has a relatively lower weight in the score function, so many shorter reads are kept. Length N50 = 14,127 bp Identity N50 = 89.83%

Full usage

usage: filtlong {OPTIONS} [input_reads]

Filtlong: a quality filtering tool for Nanopore and PacBio reads

positional arguments:
   input_reads                          input long reads to be filtered

optional arguments:
   output thresholds:
      -t[int], --target_bases [int]        keep only the best reads up to this many total bases
      -p[float], --keep_percent [float]    keep only this percentage of the best reads (measured by
                                           bases)
      --min_length [int]                   minimum length threshold
      --max_length [int]                   maximum length threshold
      --min_mean_q [float]                 minimum mean quality threshold
      --min_window_q [float]               minimum window quality threshold

   external references (if provided, read quality will be determined using these instead of from the
   Phred scores):
      -a[file], --assembly [file]          reference assembly in FASTA format
      -1[file], --illumina_1 [file]        reference Illumina reads in FASTQ format
      -2[file], --illumina_2 [file]        reference Illumina reads in FASTQ format

   score weights (control the relative contribution of each score to the final read score):
      --length_weight [float]              weight given to the length score (default: 1)
      --mean_q_weight [float]              weight given to the mean quality score (default: 1)
      --window_q_weight [float]            weight given to the window quality score (default: 1)

   read manipulation:
      --trim                               trim non-k-mer-matching bases from start/end of reads
      --split [split]                      split reads at this many (or more) consecutive
                                           non-k-mer-matching bases

   other:
      --window_size [int]                  size of sliding window used when measuring window quality
                                           (default: 250)
      --verbose                            verbose output to stderr with info for each read
      --version                            display the program version and quit

   -h, --help                           display this help menu

For more information, go to: https://github.com/rrwick/Filtlong

Method

When run, Filtlong carries out the following steps:

1. If an external reference was provided, hash all of the reference's 16-mers.
   • If the reference is an assembly, then Filtlong simply hashes all 16-mers in the assembly.
   • If the reference is in Illumina reads, then the 16-mer has to be encountered a few times before it's hashed (to avoid hashing 16-mers that result from read errors).
2. Look at each of the input reads to get length and quality information.
   • If a read fails to meet any of the hard thresholds (--min_length, --max_length, --min_mean_q or --min_window_q) then it is marked as 'fail' now.
     • Note that --min_mean_q and --min_window_q are expressed as sequence percent identities from 0-100 (see how this is calculated in the Read scoring section), not as PHRED scores.
     • For advice on setting minimum quality thresholds, see Filtlong's scripts directory.
   • If --trim or --split was used, then 'child' reads are made here (see Trimming and splitting).
   • If --verbose was used, display detailed information about the read length, quality and trimming/splitting.
3. Gather up all reads eligible for output. If neither --trim nor --split was used, this is simply the original set of reads. If --trim or --split was used, then the child reads replace the original reads.
4. Give each read a final score (see Read scoring for more information).
5. If --target_bases and/or --keep_percent was used, sort the reads by their final score and set an appropriate threshold. Reads which fall below the threshold are marked as 'fail'.
   • If both --target_bases and --keep_percent are used, the threshold is set to the more stringent of the two.
6. Output all reads which didn't fail to stdout.
   • Reads are outputted in the same order as the input file (not in in quality-sorted order).

Read scoring

Reads are scored based on three separate metrics: length, mean quality and window quality:

• Length score
  The length score is pretty simple: longer is better. A read length of 5 kbp is considered mediocre and gives a score of 50. Shorter reads get a lower score and the score approaches 100 as the read length goes to infinity (here is a graph of the score function).

• Mean quality
  A read's mean quality is calculated in two different ways, depending on whether an external reference was used:

  • If there is no reference, the mean quality is the mean read identity as indicated by the Phred quality scores. For example, consider a read where all the fastq quality characters are +. The qscores for each base are 10 which equates to a 90% chance of being correct. This read would then have a mean quality score of 90.
  • If an external reference was used, then Filtlong tallies up the 16-mers in the reference. Read bases are then given a quality of either 100 (contained in a 16-mer from the reference) or 0 (not contained in a 16-mer from the reference). The read's mean quality is a mean of its base qualities.

• Mean quality score
  Read mean qualities are converted to a z-score and scaled to the range 0-100 to make the mean quality score. This means that the read with the worst mean quality in the input set will get a mean quality score of 0 and the read with the best mean quality will get a mean quality score of 100.

• Window quality
  The window quality is determined by looking at the mean quality for a sliding window over the read. The default window size is 250 but can be changed with --window_size. The final window quality is the lowest value for the read, i.e. the quality of the read's weakest point.

• Window quality score
  The window quality score is the mean quality score scaled down by the window quality to mean quality ratio. For example, consider a read with a mean quality of 90 which resulted in a mean quality score of 60. If the window quality was 45 (half the mean quality), then the window quality score will be 30 (half the mean quality score).

Final score
The read's final score is a combination of the three component scores:

• Filtlong first takes the weighted geometric mean of the length score and the mean quality score. The weights are equal (both 1) by default, but you can adjust this with --length_weight and --mean_q_weight to make one or the other more important.
• The score is then scaled down by a factor based on the window quality score to mean quality score ratio. This factor is adjusted by the relative strength of the window quality weight (--window_q_weight).
• Expressed mathematically:

The final score which is used to determine thresholds for the --keep_percent and --target_bases options and therefore determines whether a read is included in the output.

Trimming and splitting

If --trim or --split are used, then each read can result in one or more 'child' reads. These options can only be used with an external reference (i.e. Filtlong will not trim or split based on Phred quality scores).

Trim example

Consider the following example read. Bases which match a 16-mer to the reference are bold:

If Filtlong is run with --trim, any non-matching bases at the start and end are removed to produce this child read:

Only the child read (not the original read) is now eligible for outputting. It's a bit shorter than the original read, but its mean quality will be better.

Split example

If Filtlong is run with --trim --split 20, then in addition to trimming the ends, any run of non-matching bases 20 or longer will be removed. Using the same example, this results in two separate child reads:

These child reads are much shorter than the original read but are much higher quality. Note that a split setting of 20 was used for this toy example but is very low for a real run of Filtlong – it would be quite aggressive and result in reads being split into very many pieces. A value like 500 is more practical.

For an extreme example, this is what you'd get if you used --trim --split 1:

Now any run of non-matching bases is removed, regardless of length. The read is split into 3 child reads, each with perfect quality. Again, such a low setting would probably not be practical for a real read set.

Real read example

Here's a real example of Filtlong's trimming and splitting. The output shown is what you'd see if you ran Filtlong with the --verbose option.

The original read is quite long and has a decent mean quality. However, its window quality is zero, indicating that the read has windows where no 16-mers match the reference:

bf09f0e9-d27d-4a18-bced-f2536b62b3e5_Basecall_Alignment_template
            length = 117786     mean quality = 53.02      window quality =  0.00

The read's bad ranges are the coordinates non-matching start/end regions (because of --trim) or runs of non-matching bases (because of --split). The child ranges are the inverse, bases that aren't in the bad ranges:

        bad ranges = 0-25, 71401-71745, 72393-72683, 72742-73049, 77279-77627, 78710-79055, 85575-85947, 86620-86877, 89397-89682, 91451-91782, 94415-94764, 96010-96306, 96604-96886, 98691-99176, 102349-102655, 102913-103286, 103488-103828, 106124-106397, 113277-113581, 117784-117786
      child ranges = 25-71401, 71745-72393, 72683-72742, 73049-77279, 77627-78710, 79055-85575, 85947-86620, 86877-89397, 89682-91451, 91782-94415, 94764-96010, 96306-96604, 96886-98691, 99176-102349, 102655-102913, 103286-103488, 103828-106124, 106397-113277, 113581-117784

Each child range results in a child read. Here are the first few of them:

bf09f0e9-d27d-4a18-bced-f2536b62b3e5_Basecall_Alignment_template_26-71401
            length = 71376      mean quality = 74.09      window quality =  6.80

bf09f0e9-d27d-4a18-bced-f2536b62b3e5_Basecall_Alignment_template_71746-72393
            length = 648        mean quality = 15.12      window quality =  6.40

bf09f0e9-d27d-4a18-bced-f2536b62b3e5_Basecall_Alignment_template_72684-72742
            length = 59         mean quality = 98.31      window quality = 98.31

bf09f0e9-d27d-4a18-bced-f2536b62b3e5_Basecall_Alignment_template_73050-77279
            length = 4230       mean quality = 25.11      window quality =  6.40

And here is the read visualised with child ranges in blue and bad ranges in red (values are length in kbp):

Trimming/splitting has turned a very long read with some bad regions into some smaller reads (one of which is still quite long) without bad regions. Depending on the output thresholds (like --min_length and --keep_percent) many of the smaller child reads may fail and won't be outputted, leaving us with just the longest child reads.

FAQ

• Why is the logo a hot dog?
  • It's a footlong hot dog. Filtlong... footlong... get it?! Not all my Australian colleagues were familiar with footlong hot dogs, so maybe they are a US thing. Leave it to Americans to take an unhealthy food and make an extra large version 😄
• Why does Filtlong use a k-mer size of 16 when hashing reference k-mers?
  • Because I can fit a 16-mer sequence neatly into a 32-bit unsigned integer (like this). It also seemed like a good balance between small k-mers where there's more risk of chance matches and large k-mers where noisy long reads will struggle to match. I haven't empirically tested the effectiveness of different k-mer sizes though – might be good to check out for a future version of Filtlong.
• Is it ever a bad idea to use an external reference (like Illumina reads)?
  • If you provide Filtlong with an external reference, then long read qualities will be determined solely based on their k-mer matches to the reference. This is great if your Illumina reads have complete coverage. However, if they have poor coverage (i.e. parts of the genome are not represented in the Illumina reads), then long reads which span the poor-Illumina-coverage part of the genome may be erroneously considered low-quality.
  • Similarly, if there are genuine biological differences between your read sets, then the long reads may be erroneously considered low-quality in regions of difference. E.g. if your long read sample has a plasmid which isn't in your Illumina reads, then Filtlong could remove long reads from that plasmid.
  • If you think either of these cases applies to you, I'd recommend against using an external reference.
• Are FASTA inputs allowed?
  • Yes, but only if you use an external reference (with the -a or -1/-2 options). This is because Filtlong needs to assess read quality, and FASTA reads contain no quality information. If you use a FASTA input, Filtlong will produce a FASTA output.

Acknowledgements

I owe many thanks to Kat Holt and Louise Judd for keeping me well supplied with Nanopore reads.

Filtlong makes use of some nice open source libraries – thank you to the developers:

• Klib for easy fasta/fastq parsing
• args for command-line argument parsing
• C++ bloom filter library for some memory-saving in the k-mer counting

License

GNU General Public License, version 3