Hardware acceleration of the pair HMM algorithm for DNA variant calling

With the advent of several accurate and sophisticated statistical algorithms and pipelines for DNA sequence analysis, it is becoming increasingly possible to translate raw sequencing data into biologically meaningful information for further clinical analysis and processing. However, given the large volume of the data involved, even modestly complex algorithms would require a prohibitively long time to complete. Hence it is urgent to explore non-conventional implementation platforms to accelerate genomics research. In this thesis, we present a Field-Programmable Gate Array (FPGA) accelerated implementation of the Pair Hidden Markov Model (Pair HMM) forward algorithm, the performance bottleneck in the HaplotypeCaller, a critical function in the popular Genome Analysis Toolkit (GATK) variant calling tool. We introduce the PE ring structure which, thanks to the fine-grained parallelism allowed by the FPGA, can be built into various configurations striking a trade-off between Instruction-Level Parallelism (ILP) and data parallelism. We investigate the resource utilization and performance of different configurations. Our solution can achieve a speed-up of up to 487x compared to the C++ baseline implementation on CPU and 1.56x compared to the previous best hardware implementation

Question Decomposition Tree for Answering Complex Questions over Knowledge Bases

Knowledge base question answering (KBQA) has attracted a lot of interest in recent years, especially for complex questions which require multiple facts to answer. Question decomposition is a promising way to answer complex questions. Existing decomposition methods split the question into sub-questions according to a single compositionality type, which is not sufficient for questions involving multiple compositionality types. In this paper, we propose Question Decomposition Tree (QDT) to represent the structure of complex questions. Inspired by recent advances in natural language generation (NLG), we present a two-staged method called Clue-Decipher to generate QDT. It can leverage the strong ability of NLG model and simultaneously preserve the original questions. To verify that QDT can enhance KBQA task, we design a decomposition-based KBQA system called QDTQA. Extensive experiments show that QDTQA outperforms previous state-of-the-art methods on ComplexWebQuestions dataset. Besides, our decomposition method improves an existing KBQA system by 12% and sets a new state-of-the-art on LC-QuAD 1.0.Comment: Accepted by AAAI202

Alternative Splicing Regulated by Butyrate in Bovine Epithelial Cells

As a signaling molecule and an inhibitor of histone deacetylases (HDACs), butyrate exerts its impact on a broad range of biological processes, such as apoptosis and cell proliferation, in addition to its critical role in energy metabolism in ruminants. This study examined the effect of butyrate on alternative splicing in bovine epithelial cells using RNA-seq technology. Junction reads account for 11.28 and 12.32% of total mapped reads between the butyrate-treated (BT) and control (CT) groups. 201,326 potential splicing junctions detected were supported by ≥3 junction reads. Approximately 94% of these junctions conformed to the consensus sequence (GT/AG) while ∼3% were GC/AG junctions. No AT/AC junctions were observed. A total of 2,834 exon skipping events, supported by a minimum of 3 junction reads, were detected. At least 7 genes, their mRNA expression significantly affected by butyrate, also had exon skipping events differentially regulated by butyrate. Furthermore, COL5A3, which was induced 310-fold by butyrate (FDR <0.001) at the gene level, had a significantly higher number of junction reads mapped to Exon#8 (Donor) and Exon#11 (Acceptor) in BT. This event had the potential to result in the formation of a COL5A3 mRNA isoform with 2 of the 69 exons missing. In addition, 216 differentially expressed transcript isoforms regulated by butyrate were detected. For example, Isoform 1 of ORC1 was strongly repressed by butyrate while Isoform 2 remained unchanged. Butyrate physically binds to and inhibits all zinc-dependent HDACs except HDAC6 and HDAC10. Our results provided evidence that butyrate also regulated deacetylase activities of classical HDACs via its transcriptional control. Moreover, thirteen gene fusion events differentially affected by butyrate were identified. Our results provided a snapshot into complex transcriptome dynamics regulated by butyrate, which will facilitate our understanding of the biological effects of butyrate and other HDAC inhibitors

Metagenome Plasticity of the Bovine Abomasal Microbiota in Immune Animals in Response to Ostertagia Ostertagi Infection

Infections in cattle by the abomasal nematode Ostertagia ostertagi result in impaired gastrointestinal function. Six partially immune animals were developed using multiple drug-attenuated infections, and these animals displayed reduced worm burdens and a slightly elevated abomasal pH upon reinfection. In this study, we characterized the abomasal microbiota in response to reinfection using metagenomic tools. Compared to uninfected controls, infection did not induce a significant change in the microbial community composition in immune animals. 16S rRNA gene-based phylogenetic analysis identified 15 phyla in the bovine abomasal microbiota with Bacteroidetes (60.5%), Firmicutes (27.1%), Proteobacteria (7.2%), Spirochates (2.9%), and Fibrobacteres (1.5%) being the most predominant. The number of prokaryotic genera and operational taxonomic units (OTU) identified in the abomasal microbial community was 70.8±19.8 (mean ± SD) and 90.3±2.9, respectively. However, the core microbiome comprised of 32 genera and 72 OTU. Infection seemingly had a minimal impact on the abomasal microbial diversity at a genus level in immune animals. Proteins predicted from whole genome shotgun (WGS) DNA sequences were assigned to 5,408 Pfam and 3,381 COG families, demonstrating dazzling arrays of functional diversity in bovine abomasal microbial communities. However, none of COG functional classes were significantly impacted by infection. Our results demonstrate that immune animals may develop abilities to maintain proper stability of their abomasal microbial ecosystem. A minimal disruption in the bovine abomasal microbiota by reinfection may contribute equally to the restoration of gastric function in immune animals

CARMA: Context-Aware Runtime Reconfiguration for Energy-Efficient Sensor Fusion

Autonomous systems (AS) are systems that can adapt and change their behavior in response to unanticipated events and include systems such as aerial drones, autonomous vehicles, and ground/aquatic robots. AS require a wide array of sensors, deep-learning models, and powerful hardware platforms to perceive and safely operate in real-time. However, in many contexts, some sensing modalities negatively impact perception while increasing the system's overall energy consumption. Since AS are often energy-constrained edge devices, energy-efficient sensor fusion methods have been proposed. However, existing methods either fail to adapt to changing scenario conditions or to optimize energy efficiency system-wide. We propose CARMA: a context-aware sensor fusion approach that uses context to dynamically reconfigure the computation flow on a Field-Programmable Gate Array (FPGA) at runtime. By clock-gating unused sensors and model sub-components, CARMA significantly reduces the energy used by a multi-sensory object detector without compromising performance. We use a Deep-learning Processor Unit (DPU) based reconfiguration approach to minimize the latency of model reconfiguration. We evaluate multiple context-identification strategies, propose a novel system-wide energy-performance joint optimization, and evaluate scenario-specific perception performance. Across challenging real-world sensing contexts, CARMA outperforms state-of-the-art methods with up to 1.3x speedup and 73% lower energy consumption.Comment: Accepted to be published in the 2023 ACM/IEEE International Symposium on Low Power Electronics and Design (ISLPED 2023