RabbitFX: Efficient Framework for FASTA/Q File Parsing on Modern Multi-Core Platforms.

The continuous growth of generated sequencing data leads to the development of a variety of associated bioinformatics tools. However, many of them are not able to fully exploit the resources of modern multi-core systems since they are bottlenecked by parsing files leading to slow execution times. This motivates the design of an efficient method for parsing sequencing data that can exploit the power of modern hardware, especially for modern CPUs with fast storage devices. We have developed RabbitFX, a fast, efficient, and easy-to-use framework for processing biological sequencing data on modern multi-core platforms. It can efficiently read FASTA and FASTQ files by combining a lightweight parsing method by means of an optimized formatting implementation. Furthermore, we provide user-friendly and modularized C++ APIs that can be easily integrated into applications in order to increase their file parsing speed. As proof-of-concept, we have integrated RabbitFX into three I/O-intensive applications: fastp, Ktrim, and Mash. Our evaluation shows that the inclusion of RabbitFX leads to speedups of at least 11.6 (6.6), 2.4 (2.4), and 3.7 (3.2) compared to the original versions on plain (gzip-compressed) files, respectively. These case studies demonstrate that RabbitFX can be easily integrated into a variety of NGS analysis tools to significantly reduce associated runtimes. It is open source software available at https://github.com/RabbitBio/RabbitFX.

RabbitV: fast detection of viruses and microorganisms in sequencing data on multi-core architectures.

MOTIVATION: Detection and identification of viruses and microorganisms in sequencing data plays an important role in pathogen diagnosis and research. However, existing tools for this problem often suffer from high runtimes and memory consumption. RESULTS: We present RabbitV, a tool for rapid detection of viruses and microorganisms in Illumina sequencing datasets based on fast identification of unique k-mers. It can exploit the power of modern multi-core CPUs by using multi-threading, vectorization and fast data parsing. Experiments show that RabbitV outperforms fastv by a factor of at least 42.5 and 14.4 in unique k-mer generation (RabbitUniq) and pathogen identification (RabbitV), respectively. Furthermore, RabbitV is able to detect COVID-19 from 40 samples of sequencing data (255 GB in FASTQ format) in only 320 s. AVAILABILITY AND IMPLEMENTATION: RabbitUniq and RabbitV are available at https://github.com/RabbitBio/RabbitUniq and https://github.com/RabbitBio/RabbitV. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Heavy metal pollution, ecological risk, spatial distribution, and source identification in sediments of the Lijiang River, China.

The Lijiang River is of great ecological and environmental importance for Guilin City, which is located in the karst area of southeast China. Given its importance, a detailed evaluation of the heavy metals (HMs) in the river sediment is required. For the first time, 61 sediment samples were collected along the entire Lijiang River to determine pollution level and ecological risk posed by 10 HMs (Co, Cr, Cu, Mn, Ni, Pb, Zn, As, Hg, and Cd). These were assessed using the geo-accumulation index, potential ecological risk index, and modified degree of contamination. The results showed that the mean concentrations of the majority of HMs exceeded their corresponding background values and followed the trend: midstream > downstream > upstream. Based on the spatial distributions and pollution indices of the 10 HMs, the Lijiang River was found to have a high accumulation of Cd, Hg, Zn, and Pb in the sediments. The midstream area was the most polluted with respect to Cd and Hg, and also posed a relatively higher potential ecological risk than the downstream and upstream areas. The sources of the assessed HMs were inferred based on a correlation analysis and principal component analysis, which identified both natural and anthropogenic sources. A higher pollution potential was associated with Cd, Hg, Pb, and Zn in the midstream and downstream areas due to higher organic and carbonate content, urbanization, agricultural activities, and leisure activities (e.g., boating and cruises). In contrast, natural erosion and weathering processes were responsible for the HM concentrations in the upstream area. The findings of this study will help the local authorities to protect the important water resource of the Lijiang River.