Accurate multiple sequence alignment (MSA) is imperative for the comprehensive analysis of biological sequences. However, a notable challenge arises as no single MSA tool consistently outperforms its counterparts across diverse datasets. Users often have to try multiple MSA tools to achieve optimal alignment results, which can be time-consuming and memory-intensive. While the overall accuracy of certain MSA results may be lower, there could be local regions with the highest alignment scores, prompting researchers to seek a tool capable of merging these locally optimal results from multiple initial alignments into a globally optimal alignment. In this study, we introduce Two Pointers Meta-Alignment (TPMA), a novel tool designed for the integration of nucleic acid sequence alignments. TPMA employs two pointers to partition the initial alignments into blocks containing identical sequence fragments. It selects blocks with the high sum of pairs (SP) scores to concatenate them into an alignment with an overall SP score superior to that of the initial alignments. Through tests on simulated and real datasets, the experimental results consistently demonstrate that TPMA outperforms M-Coffee in terms of aSP, Q, and total column (TC) scores across most datasets. Even in cases where TPMA's scores are comparable to M-Coffee, TPMA exhibits significantly lower running time and memory consumption. Furthermore, we comprehensively assessed all the MSA tools used in the experiments, considering accuracy, time, and memory consumption. We propose accurate and fast combination strategies for small and large datasets, which streamline the user tool selection process and facilitate large-scale dataset integration. The dataset and source code of TPMA are available on GitHub (https://github.com/malabz/TPMA).

Multiple sequence alignment is widely used for sequence analysis, such as identifying important sites and phylogenetic analysis. Traditional methods, such as progressive alignment, are time-consuming. To address this issue, we introduce StarTree, a novel method to fast construct a guide tree by combining sequence clustering and hierarchical clustering. Furthermore, we develop a new heuristic similar region detection algorithm using the FM-index and apply the k-banded dynamic program to the profile alignment. We also introduce a win-win alignment algorithm that applies the central star strategy within the clusters to fast the alignment process, then uses the progressive strategy to align the central-aligned profiles, guaranteeing the final alignment's accuracy. We present WMSA 2 based on these improvements and compare the speed and accuracy with other popular methods. The results show that the guide tree made by the StarTree clustering method can lead to better accuracy than that of PartTree while consuming less time and memory than that of UPGMA and mBed methods on datasets with thousands of sequences. During the alignment of simulated data sets, WMSA 2 can consume less time and memory while ranking at the top of Q and TC scores. The WMSA 2 is still better at the time, and memory efficiency on the real datasets and ranks at the top on the average sum of pairs score. For the alignment of 1 million SARS-CoV-2 genomes, the win-win mode of WMSA 2 significantly decreased the consumption time than the former version. The source code and data are available at https://github.com/malabz/WMSA2.

The microbiota-gut-brain axis denotes a two-way system of interactions between the gut and the brain, comprising three key components: (1) gut microbiota, (2) intermediates and (3) mental ailments. These constituents communicate with one another to induce changes in the host's mood, cognition and demeanor. Knowledge concerning the regulation of the host central nervous system by gut microbiota is fragmented and mostly confined to disorganized or semi-structured unrestricted texts. Such a format hinders the exploration and comprehension of unknown territories or the further advancement of artificial intelligence systems. Hence, we collated crucial information by scrutinizing an extensive body of literature, amalgamated the extant knowledge of the microbiota-gut-brain axis and depicted it in the form of a knowledge graph named MMiKG, which can be visualized on the GraphXR platform and the Neo4j database, correspondingly. By merging various associated resources and deducing prospective connections between gut microbiota and the central nervous system through MMiKG, users can acquire a more comprehensive perception of the pathogenesis of mental disorders and generate novel insights for advancing therapeutic measures. As a free and open-source platform, MMiKG can be accessed at http://yangbiolab.cn:8501/ with no login requirement.

MOTIVATION: Multiple sequence alignment (MSA) is a fundamental problem in bioinformatics. The quality of alignment will affect downstream analysis. MAFFT has adopted the Fast Fourier Transform method for searching the homologous segments and using them as anchors to divide the sequences, then making alignment only on segments, which can save time and memory without overly reducing the sequence alignment quality. MAFFT becomes slow when the dataset is large. RESULTS: We made a software, WMSA, which uses the divide-and-conquer method to split the sequences into clusters, aligns those clusters into profiles with the center star strategy and then makes a progressive profile-profile alignment. The alignment is conducted by the compiled algorithms of MAFFT, K-Band with multithread parallelism. Our method can balance time, space and quality and performs better than MAFFT in test experiments on highly conserved datasets. AVAILABILITY AND IMPLEMENTATION: Source code is freely available at https://github.com/malabz/WMSA/, which is implemented in C/C++ and supported on Linux, and datasets are available at https://github.com/malabz/WMSA-dataset. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cancerlectins, lectins linked to tumor progression, have become the focus of cancer therapy research for their carbohydrate-binding specificity. However, the specific characterization for cancerlectins involved in tumor progression is still unclear. By taking advantage of the g-gap tripeptide and tetrapeptide composition feature descriptors, we increased the accuracy of the classification model of cancerlectin and lectin to 98.54% and 95.38%, respectively. About 36 cancerlectin and 135 lectin features were selected for functional characterization by P/N feature ranking method, which particularly selects the features in positive samples. The specific protein domains of cancerlectins are found to be p-GalNAc-T, crystal and annexin by comparing with lectins through the exclusion method. Moreover, the combined GO analysis showed that the conserved cation binding sites of cancerlectin specific domains are covered by selected feature peptides, suggesting that the capability of cation binding, critical for enzyme activity and stability, could be the key characteristic of cancerlectins in tumor progression. These results will help to identify potential cancerlectin and provide clues for mechanism study of cancerlectin in tumor progression.