Sasha Ames

Scalable metagenomic taxonomy classification using a reference genome database

Motivation: Deep metagenomic sequencing of biological samples has the potential to recover otherwise difficult-to-detect microorganisms and accurately characterize biological samples with limited prior knowledge of sample contents. Existing metagenomic taxonomic classification algorithms, however, do not scale well to analyze large metagenomic datasets, and balancing classification accuracy with computational efficiency presents a fundamental challenge. Results: A method is presented to shift computational costs to an off-line computation by creating a taxonomy/genome index that supports scalable metagenomic classification. Scalable performance is demonstrated on real and simulated data to show accurate classification in the presence of novel organisms on samples that include viruses, prokaryotes, fungi and protists. Taxonomic classification of the previously published 150 giga-base Tyrolean Iceman dataset was found to take <20 h on a single node 40 core large memory machine and provide new insights on the metagenomic contents of the sample. Availability: Software was implemented in C++ and is freely available at http://sourceforge.net/projects/lmat Contact: allen99@llnl.gov Supplementary information: Supplementary data are available at Bioinformatics online.

On the Role of NVRAM in Data-intensive Architectures: An Evaluation

Data-intensive applications are best suited to high-performance computing architectures that contain large quantities of main memory. Creating these systems with DRAM-based main memory remains costly and power-intensive. Due to improvements in density and cost, non-volatile random access memories (NVRAM) have emerged as compelling storage technologies to augment traditional DRAM. This work explores the potential of future NVRAM technologies to store program state at performance comparable to DRAM. We have developed the PerMA NVRAM simulator that allows us to explore applications with working sets ranging up to hundreds of gigabytes per node. The simulator is implemented as a Linux device driver that allows application execution at native speeds. Using the simulator we show the impact of future technology generations of I/O-bus-attached NVRAM on an unstructured-access, level-asynchronous, Breadth-First Search (BFS) graph traversal algorithm. Our simulations show that within a couple of technology generations, a system architecture with local high performance NVRAM will be able to effectively augment DRAM to support highly concurrent data-intensive applications with large memory footprints. However, improvements will be needed in the I/O stack to deliver this performance to applications. The simulator shows that future technology generations of NVRAM in conjunction with an improved I/O runtime will enable parallel data-intensive applications to offload in-memory data structures to NVRAM with minimal performance loss.

DI-MMAP--a scalable memory-map runtime for out-of-core data-intensive applications

We present DI-MMAP, a high-performance runtime that memory-maps large external data sets into an application’s address space and shows significantly better performance than the Linux mmap system call. Our implementation is particularly effective when used with high performance locally attached Flash arrays on highly concurrent, latency-tolerant data-intensive HPC applications. We describe the kernel module and show performance results on a benchmark test suite, a new bioinformatics metagenomic classification application, and on a level-asynchronous Breadth-First Search (BFS) graph traversal algorithm. Using DI-MMAP, the metagenomics classification application performs up to 4× better than standard Linux mmap. A fully external memory configuration of BFS executes up to 7.44× faster than traditional mmap. Finally, we demonstrate that DI-MMAP shows scalable out-of-core performance for BFS traversal in main memory constrained scenarios. Such scalable memory constrained performance would allow a system with a fixed amount of memory to solve a larger problem as well as provide memory QoS guarantees for systems running multiple data-intensive applications.

Searching and navigating petabyte-scale file systems based on facets

As users interact with file systems of ever increasing size, it is becoming more difficult for them to familiarize themselves with the entire contents of the file system. In petabyte-scale systems, users must navigate a pool of billions of shared files in order to find the information they are looking for. One way to help alleviate this problem is to integrate navigation and search into a common framework. One such method is faceted search. This method originated within the information retrieval community, and has proved popular for navigating large repositories, such as those in e-commerce sites and digital libraries. This paper introduces faceted search and outlines several current research directions in adapting faceted search techniques to petabyte-scale file systems.

DI-MMAP: A High Performance Memory-Map Runtime for Data-Intensive Applications

We present DI-MMAP, a high-performance runtime that memory-maps large external data sets into an applications address space and shows significantly better performance than the Linux mmap system call. Our implementation is particularly effective when used with high performance locally attached Flash arrays on highly concurrent, latency-tolerant data-intensive HPC applications. We describe the kernel module and show performance results on a benchmark test suite and on a new bioinformatics metagenomic classification application. For the complex metagenomics classification application, DI-MMAP performs up to 4.88× better than standard Linux mmap.

Using populations of human and microbial genomes for organism detection in metagenomes

Identifying causative disease agents in human patients from shotgun metagenomic sequencing (SMS) presents a powerful tool to apply when other targeted diagnostics fail. Numerous technical challenges remain, however, before SMS can move beyond the role of research tool. Accurately separating the known and unknown organism content remains difficult, particularly when SMS is applied as a last resort. The true amount of human DNA that remains in a sample after screening against the human reference genome and filtering nonbiological components left from library preparation has previously been underreported. In this study, we create the most comprehensive collection of microbial and reference-free human genetic variation available in a database optimized for efficient metagenomic search by extracting sequences from GenBank and the 1000 Genomes Project. The results reveal new human sequences found in individual Human Microbiome Project (HMP) samples. Individual samples contain up to 95% human sequence, and 4% of the individual HMP samples contain 10% or more human reads. Left unidentified, human reads can complicate and slow down further analysis and lead to inaccurately labeled microbial taxa and ultimately lead to privacy concerns as more human genome data is collected.

QMDS: a file system metadata management service supporting a graph data model-based query language

File system metadata management has become a bottleneck for many data-intensive applications that rely on high-performance file systems. Part of the bottleneck is due to the limitations of an almost 50-year-old interface standard with metadata abstractions that were designed at a time when high-end file systems managed less than 100 MB. Todays high-performance file systems store 7–9 orders of magnitude more data, resulting in a number of data items for which these metadata abstractions are inadequate, such as directory hierarchies unable to handle complex relationships among data. Users of file systems have attempted to work around these inadequacies by moving application-specific metadata management to relational databases to make metadata searchable. Splitting file system metadata management into two separate systems introduces inefficiencies and systems management problems. To address this problem, we propose QMDS: a file system metadata management service that integrates all file system metadata and uses a graph data model with attributes on nodes and edges. Our service uses a query language interface for file identification and attribute retrieval. We present our metadata management service design and architecture and study its performance using a text analysis benchmark application. Results from our QMDS prototype show the effectiveness of this approach. Compared to the use of a file system and relational database, the QMDS prototype shows superior performance for both ingest and query workloads.

Design and Implementation of a Metadata-rich File System

Despite continual improvements in the performance and reliability of large scale file systems, the management of user-defined file system metadata has changed little in the past decade. The mismatch between the size and complexity of large scale data stores and their ability to organize and query their metadata has led to a de facto standard in which raw data is stored in traditional file systems, while related, application-specific metadata is stored in relational databases. This separation of data and semantic metadata requires considerable effort to maintain consistency and can result in complex, slow, and inflexible system operation. To address these problems, we have developed the Quasar File System (QFS), a metadata-rich file system in which files, user-defined attributes, and file relationships are all first class objects. In contrast to hierarchical file systems and relational databases, QFS defines a graph data model composed of files and their relationships. QFS incorporates Quasar, an XPATH-extended query language for searching the file system. Results from our QFS prototype show the effectiveness of this approach. Compared to the de facto standard, the QFS prototype shows superior ingest performance and comparable query performance on user metadata-intensive operations and superior performance on normal file metadata operations.

QMDS: A File System Metadata Management Service Supporting a Graph Data Model-Based Query Language

File system metadata management has become a bottleneck for many data-intensive applications that rely on high-performance file systems. Part of the bottleneck is due to the limitations of an almost 50 year old interface standard with metadata abstractions that were designed at a time when high-end file systems managed less than 100 MB. Todays high-performance file systems store 7 to 9 orders of magnitude more data, resulting in numbers of data items for which these metadata abstractions are inadequate, such as directory hierarchies unable to handle complex relationships among data. Users of file systems have attempted to work around these inadequacies by moving application-specific metadata management to relational databases to make metadata searchable. Splitting file system metadata management into two separate systems introduces inefficiencies and systems management problems. To address this problem, we propose QMDS: a file system metadata management service that integrates all file system metadata and uses a graph data model with attributes on nodes and edges. Our service uses a query language interface for file identification and attribute retrieval. We present our metadata management service design and architecture and study its performance using a text analysis benchmark application. Results from our QMDS prototype show the effectiveness of this approach. Compared to the use of a file system and relational database, the QMDS prototype shows superior performance for both ingest and query workloads.

Searching more genomic sequence with less memory for fast and accurate metagenomic profiling

Software for rapid, accurate, and comprehensive microbial profiling of metagenomic sequence data on a desktop will play an important role in large scale clinical use of metagenomic data. Here we describe LMAT-ML (Livermore Metagenomics Analysis Toolkit-Marker Library) which can be run with 24 GB of DRAM memory, an amount available on many clusters, or with 16 GB DRAM plus a 24 GB low cost commodity flash drive (NVRAM), a cost effective alternative for desktop or laptop users. We compared results from LMAT with five other rapid, low-memory tools for metagenome analysis for 131 Human Microbiome Project samples, and assessed discordant calls with BLAST. All the tools except LMAT-ML reported overly specific or incorrect species and strain resolution of reads that were in fact much more widely conserved across species, genera, and even families. Several of the tools misclassified reads from synthetic or vector sequence as microbial or human reads as viral. We attribute the high numbers of false positive and false negative calls to a limited reference database with inadequate representation of known diversity. Our comparisons with real world samples show that LMAT-ML is the only tool tested that classifies the majority of reads, and does so with high accuracy.