No abstract

Sequence-level searches on large collections of RNA sequencing experiments, such as the NCBI Sequence Read Archive (SRA), would enable one to ask many questions about the expression or variation of a given transcript in a population. Existing approaches, such as the sequence Bloom tree, suffer from fundamental limitations of the Bloom filter, resulting in slow build and query times, less-than-optimal space usage, and potentially large numbers of false-positives. This paper introduces Mantis, a space-efficient system that uses new data structures to index thousands of raw-read experiments and facilitates large-scale sequence searches. In our evaluation, index construction with Mantis is 6× faster and yields a 20% smaller index than the state-of-the-art split sequence Bloom tree (SSBT). For queries, Mantis is 6-108× faster than SSBT and has no false-positives or -negatives. For example, Mantis was able to search for all 200,400 known human transcripts in an index of 2,652 RNA sequencing experiments in 82 min; SSBT took close to 4 days.

The colored de Bruijn graph-a variant of the de Bruijn graph which associates each edge (i.e., k-mer) with some set of colors -is an increasingly important combinatorial structure in computational biology. Iqbal et al. demonstrated the utility of this structure for representing and assembling a collection (population) of genomes, and showed how it can be used to accurately detect genetic variants. Muggli et al. introduced VARI, a representation of the colored de Bruijn graph that adopts the BOSS representation for the de Bruijn graph topology and achieves considerable savings in space over Cortex, albeit with some sacrifice in speed. The memory-efficient representation of VARI allows the colored de Bruijn graph to be constructed and analyzed for large datasets, beyond what is possible with Cortex.In this paper, we introduce Rainbowfish, a succinct representation of the color information of the colored de Bruijn graph that reduces the space usage even further. Our representation also uses BOSS to represent the de Bruijn graph, but decomposes the color sets based on an equivalence relation and exploits the inherent skewness in the distribution of these color sets. The Rainbowfish representation is compressed based on the 0th-order entropy of the color sets, which can lead to a significant reduction in the space required to store the relevant information for each edge. In practice, Rainbowfish achieves up to a 20× improvement in space over VARI. Rainbowfish is written in C++11 and is available at https://github.com/COMBINE-lab/rainbowfish.

Supplementary data are available at Bioinformatics online.

Current data centers require storage capacities of hundreds of terabytes to petabytes. Time-critical applications such as on-line transaction processing depend on getting adequate performance from the storage subsystem; otherwise, they fail. It is difficult to provide predictable quality of service at this level of complexity, because I/O workloads are extremely variable and device behavior is poorly understood. Ensuring that unrelated but competing workloads do not affect each other's performance is still more difficult, and equally necessary. We present SLEDS, a distributed controller that provides statistical performance guarantees on a storage system built from commodity components. SLEDS can adaptively handle unpredictable workload variations so that each client continues to get the performance it needs even in the presence of misbehaving, competing peers. After evaluating SLEDS on a heterogeneous mid-range storage system, we found that it is vastly superior to the raw system in its ability to provide performance guarantees, while only introducing a negligible overhead.

a Motivation. Sequence-level searches on large collections of RNA-seq experiments, such as the NIH Sequence Read Archive (SRA), would enable one to ask many questions about the expression or variation of a given transcript in a population. Bloom filter-based indexes and variants, such as the Sequence Bloom Tree, have been proposed in the past to solve this problem. However, these approaches suffer from fundamental limitations of the Bloom filter, resulting in slow build and query times, less-than-optimal space usage, and large numbers of false positives.Results. This paper introduces Mantis, a space-efficient data structure that can be used to index thousands of rawread experiments and facilitate large-scale sequence searches on those experiments. Mantis uses counting quotient filters instead of Bloom filters, enabling rapid index builds and queries, small indexes, and exact results, i.e., no false positives or negatives. Furthermore, Mantis is also a colored de Bruijn graph representation, so it supports fast graph traversal and other topological analyses in addition to large-scale sequence-level searches.In our performance evaluation, index construction with Mantis is 4.4× faster and yields a 20% smaller index than the state-of-the-art split sequence Bloom tree (SSBT). For queries, Mantis is 6×-108× faster than SSBT and has no false positives or false negatives. For example, Mantis was able to search for all 200,400 known human transcripts in an index of 2652 human blood, breast, and brain RNA-seq experiments in one hour and 22 minutes; SBT took close to 4 days and AllSomeSBT took about eight hours.Mantis is written in C++11 and is available at https://github.com/splatlab/mantis.

Recently, there has been a huge growth in the amount of data processed by enterprises and the scientific computing community. Two promising trends ensure that applications will be able to deal with ever increasing data volumes: First, the emergence of cloud computing, which provides transparent access to a large number of compute, storage and networking resources; and second, the development of the MapReduce programming model, which provides a highlevel abstraction for data-intensive computing. However, the design space of these systems has not been explored in detail. Specifically, the impact of various design choices and run-time parameters of a MapReduce system on application performance remains an open question.To this end, we embarked on systematically understanding the performance of MapReduce systems, but soon realized that understanding effects of parameter tweaking in a large-scale setup with many variables was impractical. Consequently, in this paper, we present the design of an accurate MapReduce simulator, MRPerf, for facilitating exploration of MapReduce design space. MRPerf captures various aspects of a MapReduce setup, and uses this information to predict expected application performance. In essence, MRPerf can serve as a design tool for MapReduce infrastructure, and as a planning tool for making MapReduce deployment far easier via reduction in the number of parameters that currently have to be hand-tuned using rules of thumb.Our validation of MRPerf using data from medium-scale production clusters shows that it is able to predict application performance accurately, and thus can be a useful tool in enabling cloud computing. Moreover, an initial application of MRPerf to our test clusters running Hadoop, revealed a performance bottleneck, fixing which resulted in up to 28.05% performance improvement.

MotivationAlmost all de novo short-read genome and transcriptome assemblers start by building a representation of the de Bruijn Graph of the reads they are given as input. Even when other approaches are used for subsequent assembly (e.g. when one is using ‘long read’ technologies like those offered by PacBio or Oxford Nanopore), efficient k-mer processing is still crucial for accurate assembly, and state-of-the-art long-read error-correction methods use de Bruijn Graphs. Because of the centrality of de Bruijn Graphs, researchers have proposed numerous methods for representing de Bruijn Graphs compactly. Some of these proposals sacrifice accuracy to save space. Further, none of these methods store abundance information, i.e. the number of times that each k-mer occurs, which is key in transcriptome assemblers.ResultsWe present a method for compactly representing the weighted de Bruijn Graph (i.e. with abundance information) with essentially no errors. Our representation yields zero errors while increasing the space requirements by less than 18–28% compared to the approximate de Bruijn graph representation in Squeakr. Our technique is based on a simple invariant that all weighted de Bruijn Graphs must satisfy, and hence is likely to be of general interest and applicable in most weighted de Bruijn Graph-based systems.Availability and implementation https://github.com/splatlab/debgr.Supplementary information Supplementary data are available at Bioinformatics online.