Alternative cleavage and polyadenylation within introns (intronic APA) generate shorter mRNA isoforms; however, their physiological significance remains elusive. In this study, we developed a comprehensive workflow to analyze intronic APA profiles using the mammalian target of rapamycin (mTOR)-regulated transcriptome as a model system. Our investigation revealed two contrasting effects within the transcriptome in response to fluctuations in cellular mTOR activity: an increase in intronic APA for a subset of genes and a decrease for another subset of genes. The application of this workflow to RNA-seq data from The Cancer Genome Atlas demonstrated that this dichotomous intronic APA pattern is a consistent feature in transcriptomes across both normal tissues and various cancer types. Notably, our analyses of protein length changes resulting from intronic APA events revealed two distinct phenomena in proteome programming: a loss of functional domains due to significant changes in protein length or minimal alterations in C-terminal protein sequences within unstructured regions. Focusing on conserved intronic APA events across 10 different cancer types highlighted the prevalence of the latter cases in cancer transcriptomes, whereas the former cases were relatively enriched in normal tissue transcriptomes. These observations suggest potential, yet distinct, roles for intronic APA events during pathogenic processes and emphasize the abundance of protein isoforms with similar lengths in the cancer proteome. Furthermore, our investigation into the isoform-specific functions of JMJD6 intronic APA events supported the hypothesis that alterations in unstructured C-terminal protein regions lead to functional differences. Collectively, our findings underscore intronic APA events as a discrete molecular signature present in both normal tissues and cancer transcriptomes, highlighting the contribution of APA to the multifaceted functionality of the cancer proteome. © 2024. The Author(s).

DNA short read alignment task has become a major sequential bottleneck to humongous amounts of data generated by next-generation sequencing platforms. In this paper, an energy-efficient and high-throughput Processing-in-Memory (PIM) accelerator based on DRAM (named Aligner-D) is presented to execute DNA short-read alignment with the state-of-the-art BWT alignment algorithm. We first present the PIM design that utilizes DRAM's internal high parallelism and throughput. It converts each DRAM array to a potent processing unit for alignment tasks. The proposed Aligner-D can efficiently execute the bulk bit-wise XNOR-based matching operation required by the alignment task with only 3-transistor/col overhead. We then introduce a highly parallel and customized read alignment algorithm based on BWT that supports both exact and inexact match tasks. Next, we present how to map the correlated data of the alignment task to utilize the parallelism from both new hardware and algorithm maximumly. The experimental results demonstrate that Aligner-D obtains $\sim 4\times $ , $\sim 2.45\times $ , $\sim 3.26\times $ , and $\sim 1.65\times $ improvement, respectively, compared with other in-memory computing platforms: Ambit (Seshadri et al., 2017), DRISA-1T1C (Li et al., 2017), DRISA-3T1C (Li et al., 2017), and ReDRAM (Angizi and Fan, 2019). As for DNA short read alignment, Aligner-D boosts the alignment throughput per Watt by $\sim 20104\times $ , $\sim 3522\times $ , $\sim 927\times $ , $\sim 88\times $ , $\sim 5.28\times $ , and $\sim 2.34\times $ , over ReCAM, CPU, GPU, FPGA, Ambit, and DRISA, respectively.

Magneto-Electric FET (MEFET) is a recently developed post-CMOS FET, which offers intriguing characteristics for high-speed and low-power design in both logic and memory applications. In this article, we present MeF-RAM, a non-volatile cache memory design based on 2-Transistor-1-MEFET (2T1M) memory bit-cell with separate read and write paths. We show that with proper co-design across MEFET device, memory cell circuit, and array architecture, MeF-RAM is a promising candidate for fast non-volatile memory (NVM). To evaluate its cache performance in the memory system, we, for the first time, build a device-to-architecture cross-layer evaluation framework to quantitatively analyze and benchmark the MeF-RAM design with other memory technologies, including both volatile memory (i.e., SRAM, eDRAM) and other popular non-volatile emerging memory (i.e., ReRAM, STT-MRAM, and SOT-MRAM). The experiment results for the PARSEC benchmark suite indicate that, as an L2 cache memory, MeF-RAM reduces Energy Area Latency (EAT) product on average by similar to 98% and similar to 70% compared with typical 6T-SRAM and 2T1R SOT-MRAM counterparts, respectively.

BACKGROUND: The eukaryotic genome is capable of producing multiple isoforms from a gene by alternative polyadenylation (APA) during pre-mRNA processing. APA in the 3'-untranslated region (3'-UTR) of mRNA produces transcripts with shorter or longer 3'-UTR. Often, 3'-UTR serves as a binding platform for microRNAs and RNA-binding proteins, which affect the fate of the mRNA transcript. Thus, 3'-UTR APA is known to modulate translation and provides a mean to regulate gene expression at the post-transcriptional level. Current bioinformatics pipelines have limited capability in profiling 3'-UTR APA events due to incomplete annotations and a low-resolution analyzing power: widely available bioinformatics pipelines do not reference actionable polyadenylation (cleavage) sites but simulate 3'-UTR APA only using RNA-seq read coverage, causing false positive identifications. To overcome these limitations, we developed APA-Scan, a robust program that identifies 3'-UTR APA events and visualizes the RNA-seq short-read coverage with gene annotations.; METHODS: APA-Scan utilizes either predicted or experimentally validated actionable polyadenylation signals as a reference for polyadenylation sites and calculates the quantity of long and short 3'-UTR transcripts in the RNA-seq data. APA-Scan works in three major steps: (i) calculate the read coverage of the 3'-UTR regions of genes; (ii) identify the potential APA sites and evaluate the significance of the events among two biological conditions; (iii) graphical representation of user specific event with 3'-UTR annotation and read coverage on the 3'-UTR regions. APA-Scan is implemented in Python3. Source code and a comprehensive user's manual are freely available at https://github.com/compbiolabucf/APA-Scan .; RESULT: APA-Scan was applied to both simulated and real RNA-seq datasets and compared with two widely used baselines DaPars and APAtrap. In simulation APA-Scan significantly improved the accuracy of 3'-UTR APA identification compared to the other baselines. The performance of APA-Scan was also validated by 3'-end-seq data and qPCR on mouse embryonic fibroblast cells. The experiments confirm that APA-Scan can detect unannotated 3'-UTR APA events and improve genome annotation.; CONCLUSION: APA-Scan is a comprehensive computational pipeline to detect transcriptome-wide 3'-UTR APA events. The pipeline integrates both RNA-seq and 3'-end-seq data information and can efficiently identify the significant events with a high-resolution short reads coverage plots. © 2022. The Author(s).

Microbes and viruses are known to alter host transcriptomes by means of infection. In light of recent challenges posed by the COVID-19 pandemic, a deeper understanding of the disease at the transcriptome level is needed. However, research about transcriptome reprogramming by post-transcriptional regulation is very limited. In this study, computational methods developed by our lab were applied to RNA-seq data to detect transcript variants (i.e., alternative splicing (AS) and alternative polyadenylation (APA) events). The RNA-seq data were obtained from a publicly available source, and they consist of mock-treated and SARS-CoV-2 infected (COVID-19) lung alveolar (A549) cells. Data analysis results show that more AS events are found in SARS-CoV-2 infected cells than in mock-treated cells, whereas fewer APA events are detected in SARS-CoV-2 infected cells. A combination of conventional differential gene expression analysis and transcript variants analysis revealed that most of the genes with transcript variants are not differentially expressed. This indicates that no strong correlation exists between differential gene expression and the AS/APA events in the mock-treated or SARS-CoV-2 infected samples. These genes with transcript variants can be applied as another layer of molecular signatures for COVID-19 studies. In addition, the transcript variants are enriched in important biological pathways that were not detected in the studies that only focused on differential gene expression analysis. Therefore, the pathways may lead to new molecular mechanisms of SARS-CoV-2 pathogenesis.

In this paper, for the first time, we propose a high-throughput and energy-efficient Processing-in-DRAM-accelerated genome assembler called PIM-Assembler based on an optimized and hardware-friendly genome assembly algorithm. PIM-Assembler can assemble large-scale DNA sequence dataset from all-pair overlaps. We first develop PIM-Assembler platform that harnesses DRAM as computational memory and transforms it to a fundamental processing unit for genome assembly. PIM-Assembler can perform efficient X(N)OR-based operations inside DRAM incurring low cost on top of commodity DRAM designs (similar to 5% of chip area). PIM-Assembler is then optimized through a correlated data partitioning and mapping methodology that allows local storage and processing of DNA short reads to fully exploit the genome assembly algorithm-level's parallelism. The simulation results show that PIM-Assembler achieves on average 8.4x and 2.3x higher throughput for performing bulk bitwise XNOR-based comparison operations compared with CPU and recent processing-in-DRAM platforms, respectively. As for comparison/addition-extensive genome assembly application, it reduces the execution time and power by similar to 5x and similar to 7.5x compared to GPU.