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

Extreme Scale De Novo Metagenome Assembly

Metagenome assembly is the process of transforming a set of short, overlapping, and potentially erroneous DNA segments from environmental samples into the accurate representation of the underlying microbiomes's genomes. State-of-the-art tools require big shared memory machines and cannot handle contemporary metagenome datasets that exceed Terabytes in size. In this paper, we introduce the MetaHipMer pipeline, a high-quality and high-performance metagenome assembler that employs an iterative de Bruijn graph approach. MetaHipMer leverages a specialized scaffolding algorithm that produces long scaffolds and accommodates the idiosyncrasies of metagenomes. MetaHipMer is end-to-end parallelized using the Unified Parallel C language and therefore can run seamlessly on shared and distributed-memory systems. Experimental results show that MetaHipMer matches or outperforms the state-of-the-art tools in terms of accuracy. Moreover, MetaHipMer scales efficiently to large concurrencies and is able to assemble previously intractable grand challenge metagenomes. We demonstrate the unprecedented capability of MetaHipMer by computing the first full assembly of the Twitchell Wetlands dataset, consisting of 7.5 billion reads - size 2.6 TBytes.Comment: Accepted to SC1

Scaling metagenome sequence assembly with probabilistic de Bruijn graphs

Deep sequencing has enabled the investigation of a wide range of environmental microbial ecosystems, but the high memory requirements for {\em de novo} assembly of short-read shotgun sequencing data from these complex populations are an increasingly large practical barrier. Here we introduce a memory-efficient graph representation with which we can analyze the k-mer connectivity of metagenomic samples. The graph representation is based on a probabilistic data structure, a Bloom filter, that allows us to efficiently store assembly graphs in as little as 4 bits per k-mer, albeit inexactly. We show that this data structure accurately represents DNA assembly graphs in low memory. We apply this data structure to the problem of partitioning assembly graphs into components as a prelude to assembly, and show that this reduces the overall memory requirements for {\em de novo} assembly of metagenomes. On one soil metagenome assembly, this approach achieves a nearly 40-fold decrease in the maximum memory requirements for assembly. This probabilistic graph representation is a significant theoretical advance in storing assembly graphs and also yields immediate leverage on metagenomic assembly

MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph

MEGAHIT is a NGS de novo assembler for assembling large and complex metagenomics data in a time- and cost-efficient manner. It finished assembling a soil metagenomics dataset with 252Gbps in 44.1 hours and 99.6 hours on a single computing node with and without a GPU, respectively. MEGAHIT assembles the data as a whole, i.e., it avoids pre-processing like partitioning and normalization, which might compromise on result integrity. MEGAHIT generates 3 times larger assembly, with longer contig N50 and average contig length than the previous assembly. 55.8% of the reads were aligned to the assembly, which is 4 times higher than the previous. The source code of MEGAHIT is freely available at https://github.com/voutcn/megahit under GPLv3 license.Comment: 2 pages, 2 tables, 1 figure, submitted to Oxford Bioinformatics as an Application Not

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

Focus: A Graph Approach for Data-Mining and Domain-Specific Assembly of Next Generation Sequencing Data

Next Generation Sequencing (NGS) has emerged as a key technology leading to revolutionary breakthroughs in numerous biomedical research areas. These technologies produce millions to billions of short DNA reads that represent a small fraction of the original target DNA sequence. These short reads contain little information individually but are produced at a high coverage of the original sequence such that many reads overlap. Overlap relationships allow for the reads to be linearly ordered and merged by computational programs called assemblers into long stretches of contiguous sequence called contigs that can be used for research applications. Although the assembly of the reads produced by NGS remains a difficult task, it is the process of extracting useful knowledge from these relatively short sequences that has become one of the most exciting and challenging problems in Bioinformatics. The assembly of short reads is an aggregative process where critical information is lost as reads are merged into contigs. In addition, the assembly process is treated as a black box, with generic assembler tools that do not adapt to input data set characteristics. Finally, as NGS data throughput continues to increase, there is an increasing need for smart parallel assembler implementations. In this dissertation, a new assembly approach called Focus is proposed. Unlike previous assemblers, Focus relies on a novel hybrid graph constructed from multiple graphs at different levels of granularity to represent the assembly problem, facilitating information capture and dynamic adjustment to input data set characteristics. This work is composed of four specific aims: 1) The implementation of a robust assembly and analysis tool built on the hybrid graph platform 2) The development and application of graph mining to extract biologically relevant features in NGS data sets 3) The integration of domain specific knowledge to improve the assembly and analysis process. 4) The construction of smart parallel computing approaches, including the application of energy-aware computing for NGS assembly and knowledge integration to improve algorithm performance. In conclusion, this dissertation presents a complete parallel assembler called Focus that is capable of extracting biologically relevant features directly from its hybrid assembly graph

Assembling large, complex environmental metagenomes

The large volumes of sequencing data required to sample complex environments deeply pose new challenges to sequence analysis approaches. De novo metagenomic assembly effectively reduces the total amount of data to be analyzed but requires significant computational resources. We apply two pre-assembly filtering approaches, digital normalization and partitioning, to make large metagenome assemblies more comput\ ationaly tractable. Using a human gut mock community dataset, we demonstrate that these methods result in assemblies nearly identical to assemblies from unprocessed data. We then assemble two large soil metagenomes from matched Iowa corn and native prairie soils. The predicted functional content and phylogenetic origin of the assembled contigs indicate significant taxonomic differences despite similar function. The assembly strategies presented are generic and can be extended to any metagenome; full source code is freely available under a BSD license.Comment: Includes supporting informatio