FMAlign2: a novel fast multiple nucleotide sequence alignment method for ultralong datasets.

MOTIVATION: In bioinformatics, multiple sequence alignment (MSA) is a crucial task. However, conventional methods often struggle with aligning ultralong sequences. To address this issue, researchers have designed MSA methods rooted in a vertical division strategy, which segments sequence data for parallel alignment. A prime example of this approach is FMAlign, which utilizes the FM-index to extract common seeds and segment the sequences accordingly. RESULTS: FMAlign2 leverages the suffix array to identify maximal exact matches, redefining the approach of FMAlign from searching for global chains to partial chains. By using a vertical division strategy, large-scale problem is deconstructed into manageable tasks, enabling parallel execution of subMSA. Furthermore, sequence-profile alignment and refinement are incorporated to concatenate subsets, yielding the final result seamlessly. Compared to FMAlign, FMAlign2 markedly augments the segmentation of sequences and significantly reduces the time while maintaining accuracy, especially on ultralong datasets. Importantly, FMAlign2 enhances existing MSA methods by conferring the capability to handle sequences reaching billions in length within an acceptable time frame. AVAILABILITY AND IMPLEMENTATION: Source code and datasets are available at https://github.com/malabz/FMAlign2 and https://zenodo.org/records/10435770.

Application and Comparison of Machine Learning and Database-Based Methods in Taxonomic Classification of High-Throughput Sequencing Data.

The advent of high-throughput sequencing technologies has not only revolutionized the field of bioinformatics but has also heightened the demand for efficient taxonomic classification. Despite technological advancements, efficiently processing and analyzing the deluge of sequencing data for precise taxonomic classification remains a formidable challenge. Existing classification approaches primarily fall into two categories, database-based methods and machine learning methods, each presenting its own set of challenges and advantages. On this basis, the aim of our study was to conduct a comparative analysis between these two methods while also investigating the merits of integrating multiple database-based methods. Through an in-depth comparative study, we evaluated the performance of both methodological categories in taxonomic classification by utilizing simulated data sets. Our analysis revealed that database-based methods excel in classification accuracy when backed by a rich and comprehensive reference database. Conversely, while machine learning methods show superior performance in scenarios where reference sequences are sparse or lacking, they generally show inferior performance compared with database methods under most conditions. Moreover, our study confirms that integrating multiple database-based methods does, in fact, enhance classification accuracy. These findings shed new light on the taxonomic classification of high-throughput sequencing data and bear substantial implications for the future development of computational biology. For those interested in further exploring our methods, the source code of this study is publicly available on https://github.com/LoadStar822/Genome-Classifier-Performance-Evaluator. Additionally, a dedicated webpage showcasing our collected database, data sets, and various classification software can be found at http://lab.malab.cn/~tqz/project/taxonomic/.

Diagnosis of Small Bowel Obstruction using Targeted Enterography During Nasointestinal Decompression.

The aim of the study is to investigate the efficacy of using targeted enterography during intestinal decompression in the diagnosis of small bowel obstruction (SBO). Thirty-five patients with SBO and who had neither strangulation nor other contradictions received intestinal decompression, under the guidance of X-ray, using a 300-cm-long nasointestinal tube which reached the upper jejunum. Contrast radiography of intestines was performed when the tip of the decompression tube reached the obstruction by administering double-contrast medium, containing 20-100 ml 76 % gastrografin and 50-200 ml air, through the nasointestinal tube. Serial erect and supine plain abdominal radiographs were obtained. Intubation procedure was successful in all 35 patients. SBO was resolved in 20 patients, alleviated in 15 patients and 10 patients received surgery. Selective enterographies showed clear and high quality images. Imaging findings demonstrated no significant abnormality in six patients and adhesive SBO in 15. Furthermore, intestinal tumours were identified in four patients of which three were metastatic tumours and one was an original intestinal cancer; Crohn's disease was confirmed in three patients; radiation enteritis in three (one of them was misdiagnosed and was then confirmed as metastatic tumour during surgery); enteric intussusception was found in two patients; polyps in one patient and carcinoma of the ascending colon in one. Targeted enterography during nasointestinal decompression allows confirmation of pathology of SBO by direct identification of the location, the extent and aetiology of obstruction, thereby providing evidence for the choice of timing and strategy of surgery.