Skip to content
Switch branches/tags

Latest commit


Git stats


Failed to load latest commit information.
Latest commit message
Commit time

This is the README file for the preseq package. The preseq package is aimed at predicting the yield of distinct reads from a genomic library from an initial sequencing experiment. The estimates can then be used to examine the utility of further sequencing, optimize the sequencing depth, or to screen multiple libraries to avoid low complexity samples.


The preseq software will only run on 64-bit UNIX-like operating systems and was developed on both Linux and Mac. The preseq software requires a C++ compiler that supports C++11.


Installing from a release

  1. Download preseq-x.tar.gz from the releases tab of this repository.
  2. Unpack the archive:
$ tar -zxvf preseq-x.tar.gz
  1. Move into the preseq directory and create a build directory:
$ cd preseq-x
$ mkdir build && cd build
  1. Run the configuration script:
$ ../configure

If you do not want to install preseq system-wide, or if you do not have admin privileges, specify a prefix directory:

$ ../configure --prefix=/some/reasonable/place

Finally, if you want to build with HTSlib support (for the to-mr program) then you need to specify the following:

$ ../configure --enable-hts

And if you installed HTSlib yourself in some non-standard directory, you must specify the location like this:

$ ../configure --enable-hts CPPFLAGS='-I /path/to/htslib/headers' \
  1. Compile and install the tools:
$ make
$ make install

Installing from source

Developers looking to use the latest commits can compile the cloned repository using the Makefile within the src directory. The process is simple:

$ cd src/
$ make

If the desired input is in .bam format, htslib is required. Type

make HAVE_HTSLIB=1 all

The HTSLib library can be obtained here:


The input to preseq can be in 3 general formats:

  1. Mapped read locations in BED or BAM file format. The file should be sorted by chromosome, start position, end position, and finally strand if in BED format. If the file is in BAM format, then the file should be sorted using bamtools or samtools sort.
  2. The "counts histogram" which will have, for each count 1,2,..., the number of unique "species" (e.g. reads, or anything else) that appear with that count. Examples can be found in the data directory within the preseqR subdirectory. Note these should not have a count for "0", and they should not have any header above the counts. Just two columns of numbers, with the first column sorted and unique.
  3. The counts themselves, so just a file with one count on each line. These will be made into the "counts histogram" inside preseq right away.


Each program included in this software package will print a list of options if executed without any command line arguments. Many of the programs use similar options (for example, output files are specified with '-o').

We have provided a data directory to test each of our programs. Change to the data directory and try some of our commands. To predict the yield of a future experiment, use lc_extrap. For the most basic usage of lc_extrap to compute the expected yield, use the command on the following data:

preseq lc_extrap -o yield_estimates.txt

If the input file is in .bam format, use the -B flag:

preseq lc_extrap -B -o yield_estimates.txt SRR1106616_5M_subset.bam

For the counts histogram format, use the -H flag:

preseq lc_extrap -H -o yield_estimates.txt SRR1301329_1M_read.txt

The yield estimates will appear in yield_estimates.txt, and will be a column of future experiment sizes in TOTAL_READS, a column of the corresponding expected distinct reads in EXPECTED_DISTINCT, followed by two columns giving the corresponding confidence intervals.

To investigate the past yield of an experiment, use c_curve. c_curve can take in the same file formats as lc_extrap by using the same flags. The estimates will appear in estimates.txt with two columns. The first column gives the total number of reads in a theoretically smaller experiment and the second gives the corresponding number of distinct reads.

bound_pop provides an estimate for the species richness of the sampled population. The input file formats and corresponding flags are identical to c_curve and lc_extrap. The output provides the median species richness in the first column and the confidence intervals in the next two columns.

Finally, gc_extrap predicts the expected genomic coverage for a future experiment. It produces the coverage in an output format identical to lc_extrap. gc_extrap can only take in files in BED and mapped reads format (using the -B flag for BED):

preseq gc_extrap -B -o coverage_estimates.txt

More data is available in the additional_data.txt file in the data directory. For an extended write-up on our programs, please read the manual in the docs directory.


Two headers were added.


A mode pop_size has been added that uses the continued fraction approximation to the Good-Toulmin model and extrapolates as far as possible. Although bound_pop provides a good and reliable lower-bound, this new mode will give a more accurate estimate of the population size (e.g. total number of distinct molecules). It's not perfect yet, and in some cases if the population is more than a billion times larger than the sample, it will still only give a lower bound. But it works well on most data sets.


GSL has been completely removed, and a data directory has been added for users to test our programs.


We no longer require users to have GSL for all modules except for bound_pop. Users interested in using bound_pop can install GSL and follow the instructions above to configure with GSL.


The main change to this version is that if BAM/SAM format will be used as input, the HTSLib library must be installed on the system when preseq is built. Installation instructions above have been updated correspondingly. We also updated to use C++11, so a more recent compiler is required, but these days C++11 is usually supported.


A bug in defect mode was fixed and a rng seed was added to allow for reproducibility.


We have added a new module, bound_pop, to estimate a lower bound of the population sampled from. Interpolation is calculated by expectation rather than subsampling, dramatically improving the speed.


We have switched the dependency on the BamTools API to SAMTools, which we believe will be more convenient for most users of preseq. Minor bugs have been fixed, and algorithms have been refined to more accurately construct counts histograms and extrapolate the complexity curve. More options have been added to lc_extrap. c_curve and lc_extrap are now both under a single binary for easier use, and commands will now be written as preseq lc_extrap [OPTIONS] Furthermore, there are updates to the manual for any minor issues encountered when compiling the preseq binary.

We released an R package called preseqR along with preseq. This makes most of the preseq functionality available in the R statistical environment, and includes some new functionality. The preseqR directory contains all required source code to build this R package.


Andrew D. Smith

Timothy Daley


Preseq was originally developed by Timothy Daley and Andrew D. Smith at University of Southern California.


The preseq software for estimating complexity Copyright (C) 2014-2020 Timothy Daley and Andrew D Smith and Chao Deng and the University of Southern California

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see


Software for predicting library complexity and genome coverage in high-throughput sequencing.



No packages published