14,245 research outputs found
MSPKmerCounter: A Fast and Memory Efficient Approach for K-mer Counting
A major challenge in next-generation genome sequencing (NGS) is to assemble
massive overlapping short reads that are randomly sampled from DNA fragments.
To complete assembling, one needs to finish a fundamental task in many leading
assembly algorithms: counting the number of occurrences of k-mers (length-k
substrings in sequences). The counting results are critical for many components
in assembly (e.g. variants detection and read error correction). For large
genomes, the k-mer counting task can easily consume a huge amount of memory,
making it impossible for large-scale parallel assembly on commodity servers.
In this paper, we develop MSPKmerCounter, a disk-based approach, to
efficiently perform k-mer counting for large genomes using a small amount of
memory. Our approach is based on a novel technique called Minimum Substring
Partitioning (MSP). MSP breaks short reads into multiple disjoint partitions
such that each partition can be loaded into memory and processed individually.
By leveraging the overlaps among the k-mers derived from the same short read,
MSP can achieve astonishing compression ratio so that the I/O cost can be
significantly reduced. For the task of k-mer counting, MSPKmerCounter offers a
very fast and memory-efficient solution. Experiment results on large real-life
short reads data sets demonstrate that MSPKmerCounter can achieve better
overall performance than state-of-the-art k-mer counting approaches.
MSPKmerCounter is available at http://www.cs.ucsb.edu/~yangli/MSPKmerCounte
De Novo Assembly of Nucleotide Sequences in a Compressed Feature Space
Sequencing technologies allow for an in-depth analysis
of biological species but the size of the generated datasets
introduce a number of analytical challenges. Recently, we
demonstrated the application of numerical sequence representations
and data transformations for the alignment of short
reads to a reference genome. Here, we expand out approach
for de novo assembly of short reads. Our results demonstrate
that highly compressed data can encapsulate the signal suffi-
ciently to accurately assemble reads to big contigs or complete
genomes
These are not the k-mers you are looking for: efficient online k-mer counting using a probabilistic data structure
K-mer abundance analysis is widely used for many purposes in nucleotide
sequence analysis, including data preprocessing for de novo assembly, repeat
detection, and sequencing coverage estimation. We present the khmer software
package for fast and memory efficient online counting of k-mers in sequencing
data sets. Unlike previous methods based on data structures such as hash
tables, suffix arrays, and trie structures, khmer relies entirely on a simple
probabilistic data structure, a Count-Min Sketch. The Count-Min Sketch permits
online updating and retrieval of k-mer counts in memory which is necessary to
support online k-mer analysis algorithms. On sparse data sets this data
structure is considerably more memory efficient than any exact data structure.
In exchange, the use of a Count-Min Sketch introduces a systematic overcount
for k-mers; moreover, only the counts, and not the k-mers, are stored. Here we
analyze the speed, the memory usage, and the miscount rate of khmer for
generating k-mer frequency distributions and retrieving k-mer counts for
individual k-mers. We also compare the performance of khmer to several other
k-mer counting packages, including Tallymer, Jellyfish, BFCounter, DSK, KMC,
Turtle and KAnalyze. Finally, we examine the effectiveness of profiling
sequencing error, k-mer abundance trimming, and digital normalization of reads
in the context of high khmer false positive rates. khmer is implemented in C++
wrapped in a Python interface, offers a tested and robust API, and is freely
available under the BSD license at github.com/ged-lab/khmer
Large-scale compression of genomic sequence databases with the Burrows-Wheeler transform
Motivation
The Burrows-Wheeler transform (BWT) is the foundation of many algorithms for
compression and indexing of text data, but the cost of computing the BWT of
very large string collections has prevented these techniques from being widely
applied to the large sets of sequences often encountered as the outcome of DNA
sequencing experiments. In previous work, we presented a novel algorithm that
allows the BWT of human genome scale data to be computed on very moderate
hardware, thus enabling us to investigate the BWT as a tool for the compression
of such datasets.
Results
We first used simulated reads to explore the relationship between the level
of compression and the error rate, the length of the reads and the level of
sampling of the underlying genome and compare choices of second-stage
compression algorithm.
We demonstrate that compression may be greatly improved by a particular
reordering of the sequences in the collection and give a novel `implicit
sorting' strategy that enables these benefits to be realised without the
overhead of sorting the reads. With these techniques, a 45x coverage of real
human genome sequence data compresses losslessly to under 0.5 bits per base,
allowing the 135.3Gbp of sequence to fit into only 8.2Gbytes of space (trimming
a small proportion of low-quality bases from the reads improves the compression
still further).
This is more than 4 times smaller than the size achieved by a standard
BWT-based compressor (bzip2) on the untrimmed reads, but an important further
advantage of our approach is that it facilitates the building of compressed
full text indexes such as the FM-index on large-scale DNA sequence collections.Comment: Version here is as submitted to Bioinformatics and is same as the
previously archived version. This submission registers the fact that the
advanced access version is now available at
http://bioinformatics.oxfordjournals.org/content/early/2012/05/02/bioinformatics.bts173.abstract
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