Quip compresses next-generation sequencing data with extreme prejudice. It supports input and output in the FASTQ and SAM/BAM formats, compressing large datasets to as little as 15% of their original size.
In the future will will provide binaries for a number of operating systems. In the mean time, you must install from source.
To install from github, you will need a C compiler, as well as a relatively recent version of automake and autoconf.
First clone the git repository with,
git clone git://github.com/dcjones/quip.git
Enter the quip directory
cd quip
Generate the configure script using
autoreconf -i
Then configure, compile, and install
./configure && make install
You will need only a C compiler to install from a source tarball.
Extract the source tarball
tar xzf quip-1.1.0.tar.gz
Enter the quip directory
cd quip-1.1.0
Then configure, compile, and install
./configure && make install
By default with install to /usr/local/, choose another prefix (e.g., your home
directory) with ./configure --prefix=$HOME.
Quip mostly works the same as gzip or bzip2. For example, if you do something like,
quip reads.fastq
You will get a file called reads.fastq.qp that is significantly smaller than
the original. Note that unlike gzip or bzip2, this will not delete the
original file.
For more details, see man quip after installing.
Quip implements an ensemble of compression techniques specially built to compress sequencing reads as much as possible. The basis of the algorithm is statistical compression of read identifiers, quality scores, and nucleotide sequences using arithmetic coding. In addition, we implement reference-based compression in which aligned reads are stored a positions within a genome. And, when no reference genome is available, an extremely efficient de novo assembly algorithm can transparently construct one.
In the following sections we give specific details.
The only requirement of read identifiers is that they uniquely identify the read. A single integer would do, but typically each read comes with a complex string containing the instrument name, run identifier, flow cell identifier, and tile coordinates. Much of this information is the same for every read and is simply repeated, inflating the file size.
To remove this redundancy, we use a form of delta encoding. A parser tokenizes the ID into separate fields which are then compared to the previous ID. Tokens that remain the same from read to read (e.g. instrument name) can be compressed to a negligible amount of space --- arithmetic coding produces codes of less than 1 bit in such cases. Numerical tokens are recognized and stored efficiently, either directly or as an offset from the token in the same position in previous read. Otherwise non-identical tokens are encoded by matching as much of the prefix as possible to the previous read's token before directly encoding the non-matching suffix.
The end result is that read IDs, which are often 50 bytes or longer, are typically stored in 2-4 bytes. Notably, in reads produced from Illumina instruments, most parts of the ID can be compressed to consume almost no space; the remaining few bytes are accounted for by tile coordinates. These coordinates are almost never needed in downstream analysis, so removing them as a preprocessing step would shrink file sizes even further. The parser used is suitably general so that no change to the compression algorithm would be needed.
To compress nucleotide sequences, we adopt a very simple model based on high-order Markov chains. The nucleotide at a given position in a read is predicted using the preceding twelve positions. This model uses more memory than traditional general-purpose compression algorithms (4^13 = 67,108,864 parameters are needed, each represented in 32 bits) but it is simple and extremely efficient (very little computation is required and run time is limited primarily by memory latency, as lookups in such a large table result in frequent cache misses).
An order-12 Markov chain also requires a very large amount of data to train, but there is no shortage with the datasets we wish to compress. Though less adept at compressing extremely short files, after compressing several million reads the parameters are tightly fit to the nucleotide composition of the dataset so that the remaining reads will be highly compressed. Compressing larger files only results in a tighter fit and higher compression.
It has been previously noted that the quality score at a given position is highly correlated with the score at the preceding position (Kozanitis 2011). This makes a Markov chain a natural model, but unlike nucleotides, quality scores are over a much larger alphabet (typically 41--46 distinct scores). This limits the order of the Markov chain: long chains will require a great deal of space and take a unrealistic amount of data to train.
To reduce the number of parameters, we use an order-3 Markov chain, but
coarsely bin the distal two positions. In addition to the preceding three
positions, we condition on the position within the read and a running count of
the number large jumps in quality scores between adjacent positions (where
a ``large jump'' is defined as |q_{i} - q_{i-1}| > 1), which allows reads
with highly variable quality scores to be encoded using separate models. Both
of these variables are binned to control the number of parameters.
We have also implemented lossless reference-based compression. Given aligned reads in SAM or BAM format, and the reference sequence to which they are aligned (in FASTA format), the reads are compressed preserving all information in the SAM/BAM file, including the header, read IDs, alignment information, and all optional fields allowed by the SAM format. Unaligned reads are retained and compressed using the Markov chain model.
To complement the reference-based approach, we developed an assembly-based approach which offers some of the advantages of reference-based compression, but requires no external sequence database and produces files which are entirely self-contained. We use the first (by default) 2.5 million reads to assemble contigs which are then used in place of a reference sequence database to encode aligned reads compactly as positions.
Once contigs are assembled, read sequences are aligned using a simple ``seed and extend'' method: 12-mer seeds are matched using a hash table, and candidate alignments are evaluated using Hamming distance. The best (lowest Hamming distance) alignment is chosen, assuming it falls below a given cutoff, and the read is encoded as a position within the contig set. Roughly, this can be thought of as a variation on the Lempel-Ziv algorithm: as sequences are read, they are matched to previously observed data, or in this case, contigs assembled from previously observed data. These contigs are not explicitly stored, but rather reassembled during decompression.
Traditionally, de novo assembly is extremely computationally intensive. The most commonly used technique involves constructing a de Bruijn graph, a directed graph in which each vertex represents a nucleotide k-mer present in the data for some fixed k (e.g., k = 25 is a common choice). A directed edge from a k-mer u to v occurs if and only if the (k - 1)-mer suffix of u is also the prefix of v. In principle, given such a graph, an assembly can be produced by finding an Eulerian path, i.e., a path that follows each edge in the graph exactly once (Pevzner 2001). In practice, since NGS data has a non-negligible error rate, assemblers augment each vertex with the number of observed occurrences of the k-mer and leverage these counts using a variety of heuristics to filter out spurious paths.
A significant bottleneck of the de Bruijn graph approach is building an implicit representation of the graph by counting and storing k-mer occurrences in a hash table. The assembler implemented in Quip overcomes this bottleneck to a large extent by using a data structure based on the Bloom filter to count k-mers. The Bloom filter (Bloom 1970) is a probabilistic data structure that represents a set of elements extremely compactly, at the cost of elements occasionally colliding and incorrectly being reported as present in the set. It is probabilistic in the sense that these collisions occur pseudo-randomly, determined by the size of the table and the hash functions chosen, but generally with low probability.
The Bloom filter is generalized in the counting Bloom filter, in which an arbitrary count can be associated with each element (Fan 2000), and further refined in the d-left counting Bloom filter (dlCBF) (Bonomi 2006), which requires significantly less space than the already quite space efficient counting Bloom filter. We base our assembler on a realization of the dlCBF. Because we use a probabilistic data structure, k-mers are occasionally reported to have incorrect (inflated) counts. The assembly can be made less accurate by these incorrect counts, however a poor assembly only results in slightly increasing the compression ratio. Compression remains lossless regardless of the assembly quality, and in practice collisions in the dlCBF occur at a very low rate (this is explored in the results section).
Given a probabilistic de Bruijn graph, we assemble contigs using a very simple greedy approach. A read sequence is used as a seed and extended on both ends one nucleotide at a time by repeatedly finding the most abundant k-mer that overlaps the end of the contig by k-1 bases. More sophisticated heuristics have been developed, but we choose to focus on efficiency, sacrificing a degree of accuracy.
Counting k-mers efficiently with the help of Bloom filters was previously explored by (Melsted 2011), who use it in addition, rather than in place, of a hash table. The Bloom filter is used as a ``staging area'' to store k-mers occurring only once, reducing the memory required by the hash table. Concurrently with our work, (Pell 2011) have also developed a probabilistic de Bruijn graph assembler, but using a non-counting Bloom filter. While they demonstrate a significant reduction in memory use, unlike other de Bruijn graph assemblers, only the presence or absence of a k-mer is stored, not its abundance, which is essential information when the goal is producing accurate contigs.
In designing the file format used by Quip, we included several useful features
to protect data integrity. First, output is divided into blocks of several
magabytes each. In each block a separate 64 bit checksum is computed for read
identifiers, nucleotide sequences, and quality scores. When the archive is
decompressed, these checksums are recomputed on the decompressed data and
compared to the stored checksums, verifying the correctness of the output. The
integrity of an archived dataset can also be checked with the quip --test
command.
Apart from data corruption, reference-based compression creates the possibility of data loss if the reference used for compression is lost, or an incorrect reference is used. To protect against this, Quip files store a 64 bit hash of the reference sequence, ensuring that the same sequence is used for decompression. To assist in locating the correct reference, the file name, and the lengths and names of the sequences used in compression are also stored and accessible without decompression.
Additionally, block headers store the number of reads and bases compressed in
the block, allowing summary statistics of a dataset to be listed without
decompression using the quip --list command.
Christos Kozanitis, Chris Saunders, Semyon Kruglyak, Vineet Bafna, and George Varghese. Compressing genomic sequence fragments using SlimGene. Journal of Computational Biology : a Journal of Computational Molecular Cell Biology, 18(3):401-13, March 2011. ISSN 1557-8666. doi: 10.1089/cmb.2010.0253.
P.A.Pevzner, H.Tang, and M.S.Waterman. An Eulerian path approach to DNA fragment assembly. Proceedings of the National Academy of Sciences of the United States of America, 98(17):9748-53, August 2001. ISSN 0027-8424. doi: 10.1073/pnas.171285098.
Li Fan, Pei Cao, J. Almeida, and A.Z. Broder. Summary cache: a scalable wide- area Web cache sharing protocol. IEEE/ACM Transactions on Networking, 8(3):281-293, June 2000. ISSN 10636692. doi: 10.1109/90.851975.
Flavio Bonomi, Michael Mitzenmacher, and Rina Panigrahy. An improved construction for counting Bloom filters. 14th Annual European Symposium on Algorithms, LNCS 4168, pages 684–695, 2006.
Pall Melsted and Jonathan K Pritchard. Efficient counting of k-mers in DNA sequences using a Bloom Filter. BMC Bioinformatics, 12(1):333, 2011. ISSN 1471-2105. doi: 10.1186/1471-2105-12-333.
Jason Pell, Arend Hintze, R Canino-Koning, and Adina Howe. Scaling metagenome sequence assembly with probabilistic de Bruijn graphs. Arxiv preprint arXiv:, I(1):1–11, 2011.