MOTIVATION: Raw nanopore signal analysis is a common approach in genomics to provide fast and resource-efficient analysis without translating the signals to bases (i.e. without basecalling). However, existing solutions cannot interpret raw signals directly if a reference genome is unknown due to a lack of accurate mechanisms to handle increased noise in pairwise raw signal comparison. Our goal is to enable the direct analysis of raw signals without a reference genome. To this end, we propose Rawsamble, the first mechanism that can identify regions of similarity between all raw signal pairs, known as all-vs-all overlapping, using a hash-based search mechanism. RESULTS: We use these overlaps to construct de novo assembly graphs with an existing assembler, miniasm, off-the-shelf. To our knowledge, these are the first de novo assemblies ever constructed directly from raw signals without basecalling. Our extensive evaluations across multiple genomes of varying sizes show that Rawsamble provides a significant speedup (on average by 5.01× and up to 23.10×) and reduces peak memory usage (on average by 5.74× and up to by 22.00×) compared to a conventional genome assembly pipeline using the state-of-the-art tools for basecalling (Dorado's fastest mode) and overlapping (minimap2) on a CPU. We find that around one-third of Rawsamble's overlapping pairs are also found by minimap2. We find that when we use overlapping reads from Rawsamble, we can construct unitigs that are (i) as accurate as those built from minimap2's overlaps and (ii) up to half a chromosome in length (e.g. 2.3 million bases for E. coli). AVAILABILITY AND IMPLEMENTATION: Rawsamble is available at https://github.com/CMU-SAFARI/RawHash. We also provide the scripts to fully reproduce our results on our GitHub page.

Third-generation DNA sequencing technologies such as single-molecule real-time sequencing (SMRT) and nanopore sequencing have the potential to fill the gaps in the existing genome databases since the raw sequences produced by these machines are much longer than those of previous generations and therefore result in more contiguous assemblies. However, these long reads have a high error rate, which makes the assembly process computationally challenging. Moreover, since existing long-read assemblers are designed to run on a single machine, they either take days to complete or run out of memory on even moderate-sized datasets. In this paper, we present a distributed long-read assembler that can assemble large-scale noisy sequence datasets on thousands of cores, resulting in orders of magnitude faster assembly times. By effectively using the map-reduce computation model with a distributed hash-map, both built using a high-performance active messaging middleware, we can assemble a PacBio human genome dataset with 139 billion base-pairs (about 130 GB) in about 33 minutes (using 2,560 cores) compared to more than 38 hours (using 28 cores) with the current state-of-the-art assembler.

Online social networks are being widely used during unexpected large-scale disasters not only for sharing latest news but also requesting emergency rescues. Particularly, social network posts with their location information are becoming more important because they can be utilized for emergency management, urban planning, and various studies to understand effects of the disasters. Despite their importance, the percentage of such posts is generally tiny. In this paper, to address the location sparsity problem on Twitter in the event of disasters, we propose a deep learning-based framework to spatially analyze the disaster-related tweets by focusing on classifying tweets from affected areas of disasters. We also study effects of different deep learning architectures and input embedding techniques for this classification task. Our experimental results demonstrate that our ConvNet model with the Word2vec word embedding has the highest classification accuracy.

Recent advances in big data and deep learning technologies have enabled researchers across many disciplines to gain new insight into large and complex data. For example, deep neural networks are being widely used to analyze various types of data including images, videos, texts, and time-series data. In another example, various disciplines such as sociology, social work, and criminology are analyzing crowd-sourced and online social network data using big data technologies to gain new insight from a plethora of data. Even though many different types of data are being generated and analyzed in various domains, the development of distributed city-level cyberinfrastructure for effectively integrating such data to generate more value and gain insights is still not well-addressed in the research literature. In this paper, we present our current efforts and ultimate vision to build distributed cyberinfrastructure which integrates big data and deep learning technologies with a variety of data for enhancing public safety and livability in cites. We also introduce several methodologies and applications that we are developing on top of the cyberinfrastructure to support diverse community stakeholders in cities.

Spurred by a widening gap between hardware accelerators and traditional processors, numerous bioinformatics applications have harnessed the computing power of GPUs and reported substantial performance improvements compared to their CPU-based counterparts. However, most of these GPU-based applications only focus on the read alignment problem, while the field of de novo assembly still relies mostly on CPU-based solutions. This is primarily due to the nature of the assembly workload which is not only compute-intensive but also extremely data-intensive. Such workloads require large memories, making it difficult to adapt them to use GPUs with their limited memory capacities. To the best of our knowledge, no GPU-based assembler reported in the recent literature has attempted to assemble datasets larger than a few tens of gigabytes, whereas real sequence datasets are often several hundreds of gigabytes in size. In this paper, we present a new GPU-accelerated genome assembler called LaSAGNA, which can assemble large-scale sequence datasets using a single GPU by building string graphs from approximate all-pair overlaps. LaSAGNA can also run on multiple GPUs across multiple compute nodes connected by a high-speed network to expedite the assembly process. To utilize the limited memory on GPUs efficiently, LaSAGNA uses a semi-streaming approach that makes at most a logarithmic number of passes over the input data based on the available memory. Moreover, we propose a two-level streaming model, from disk to host memory and from host memory to device memory, to minimize disk I/O. Using LaSAGNA, we can assemble a 400 GB human genome dataset on a single NVIDIA K40 GPU in 17 hours, and in a little over 5 hours on an 8-node cluster of NVIDIA K20s.

High-performance analysis of big data demands more computing resources, forcing similar growth in computation cost. So, the challenge to the HPC system designers is providing not only high performance but also high performance at lower cost. For high performance yet cost effective cyberinfrastructure, we propose a new system model augmenting Amdahl's second law for balanced system to optimize price-performance-ratio. We express the optimal balance among CPU-speed, I/O-bandwidth and DRAM-size (i.e., Amdahl's I/O-and memory-number) in terms of application characteristics and hardware cost. Considering Xeon processor and recent hardware prices, we showed that a system needs almost 0.17GBPS I/O-bandwidth and 3GB DRAM per GHz CPU-speed to minimize the price-performance-ratio for data-and compute-intensive applications. To substantiate our claim, we evaluate three different cluster architectures: 1) SupermikeII, a traditional HPC cluster, 2) SwatIII, a regular datacenter, and 3) CeresII, a MicroBrick-based novel hyperscale system. CeresII with 6-Xeon-D1541 cores (2GHz/core), 1-NVMe SSD (2GBPS I/O-bandwidth) and 64GB DRAM per node, closely resembles the optimum produced by our model. Consequently, in terms of price-performance-ratio CeresII outperformed both SupermikeII (by 65-85%) and SwatIII (by 40-50%) for data-and compute-intensive Hadoop benchmarks (TeraSort and WordCount) and our own benchmark genome assembler based on Hadoop and Giraph.

Genome sequencing technology has witnessed tremendous progress in terms of throughput as well as cost per base pair, resulting in an explosion in the size of data. Consequently, typical sequence assembly tools demand a lot of processing power and memory and are unable to assemble big datasets unless run on hundreds of nodes. In this paper, we present a distributed assembler that achieves both scalability and memory efficiency by using partitioned de Bruijn graphs. By enhancing the memory-to-disk swapping and reducing the network communication in the cluster, we can assemble large sequences such as human genomes (452 GB) on just two nodes in 14.5 hours, and also scale up to 128 nodes in 23 minutes. We also assemble a synthetic wheat genome with 1.1 TB of raw reads on 8 nodes in 18.5 hours and on 128 nodes in 1.25 hours.