Accelerating spliced alignment of long RNA sequencing reads using parallel maximal exact match retrieval.

The genomics landscape has undergone a revolutionary transformation with the emergence of third-generation sequencing technologies. Fueled by the exponential surge in sequencing data, there is an urgent demand for accurate and rapid algorithms to effectively handle this burgeoning influx. Under such circumstances, we developed a parallelized, yet accuracy-lossless algorithm for maximal exact match (MEM) retrieval to strategically address the computational bottleneck of uLTRA, a leading spliced alignment algorithm known for its precision in handling long RNA sequencing (RNA-seq) reads. The design of the algorithm incorporates a multi-threaded strategy, enabling the concurrent processing of multiple reads simultaneously. Additionally, we implemented the serialization of index required for MEM retrieval to facilitate its reuse, resulting in accelerated startup for practical tasks. Extensive experiments demonstrate that our parallel algorithm achieves significant improvements in runtime, speedup, throughput, and memory usage. When applied to the largest human dataset, the algorithm achieves an impressive speedup of 10.78 × , significantly improving throughput on a large scale. Moreover, the integration of the parallel MEM retrieval algorithm into the uLTRA pipeline introduces a dual-layered parallel capability, consistently yielding a speedup of 4.99 × compared to the multi-process and single-threaded execution of uLTRA. The thorough analysis of experimental results underscores the adept utilization of parallel processing capabilities and its advantageous performance in handling large datasets. This study provides a showcase of parallelized strategies for MEM retrieval within the context of spliced alignment algorithm, effectively facilitating the process of RNA-seq data analysis. The code is available at https://github.com/RongxingWong/AcceleratingSplicedAlignment.

Nestedness theory suggests wetland fragments with large areas and macrophyte diversity benefit waterbirds.

Many artificial wetland constructions are currently underway worldwide to compensate for the degradation of natural wetland systems. Researchers face the responsibility of proposing wetland management and species protection strategies to ensure that constructed wetlands positively impact waterbird diversity. Nestedness is a commonly occurring pattern for biotas in fragmented habitats with important implications for conservation; however, only a few studies have focused on seasonal waterbird communities in current artificial wetlands. In this study, we used the nestedness theory for analyzing the annual and seasonal community structures of waterbirds in artificial wetlands at Lake Dianchi (China) to suggest artificial wetland management and waterbird conservation strategies. We carried out three waterbird surveys per month for one year to observe the annual, spring, summer, autumn, and winter waterbird assemblages in 27 lakeside artificial wetland fragments. We used the NeD program to quantify nestedness patterns of waterbirds at the annual and seasonal levels. We also determined Spearman partial correlations to examine the associations of nestedness rank and habitat variables to explore the factors underlying nestedness patterns. We found that annual and all four seasonal waterbird compositions were nested, and selective extinction and habitat nestedness were the main factors governing nestedness. Further, selective colonization was the key driver of nestedness in autumn and winter waterbirds. We suggest that the area of wetland fragments should be as large as possible and that habitat heterogeneity should be maximized to fulfill the conservation needs of different seasonal waterbirds. Furthermore, we suggest that future studies should focus on the least area criterion and that vegetation management of artificial wetland construction should be based on the notion of sustainable development for humans and wildlife.