MOTIVATION: Genome sequencing technologies reveal a huge amount of genomic sequences. Neural network-based methods can be prime candidates for retrieving insights from these sequences because of their applicability to large and diverse datasets. However, the highly variable lengths of genome sequences severely impair the presentation of sequences as input to the neural network. Genetic variations further complicate tasks that involve sequence comparison or alignment. RESULTS: Inspired by the theory and applications of "spaced seeds," we propose a graph representation of genome sequences called "gapped pattern graph." These graphs can be transformed through a Graph Convolutional Network to form lower-dimensional embeddings for downstream tasks. On the basis of the gapped pattern graphs, we implemented a neural network model and demonstrated its performance on diverse tasks involving microbe and mammalian genome data. Our method consistently outperformed all the other state-of-the-art methods across various metrics on all tasks, especially for the sequences with limited homology to the training data. In addition, our model was able to identify distinct gapped pattern signatures from the sequences. AVAILABILITY AND IMPLEMENTATION: The framework is available at https://github.com/deepomicslab/GCNFrame.

BACKGROUND: Distance functions are fundamental for evaluating the differences between gene expression profiles. Such a function would output a low value if the profiles are strongly correlated-either negatively or positively-and vice versa. One popular distance function is the absolute correlation distance, [Formula: see text], where [Formula: see text] is similarity measure, such as Pearson or Spearman correlation. However, the absolute correlation distance fails to fulfill the triangle inequality, which would have guaranteed better performance at vector quantization, allowed fast data localization, as well as accelerated data clustering. RESULTS: In this work, we propose [Formula: see text] as an alternative. We prove that [Formula: see text] satisfies the triangle inequality when [Formula: see text] represents Pearson correlation, Spearman correlation, or Cosine similarity. We show [Formula: see text] to be better than [Formula: see text], another variant of [Formula: see text] that satisfies the triangle inequality, both analytically as well as experimentally. We empirically compared [Formula: see text] with [Formula: see text] in gene clustering and sample clustering experiment by real-world biological data. The two distances performed similarly in both gene clustering and sample clustering in hierarchical clustering and PAM (partitioning around medoids) clustering. However, [Formula: see text] demonstrated more robust clustering. According to the bootstrap experiment, [Formula: see text] generated more robust sample pair partition more frequently (P-value [Formula: see text]). The statistics on the time a class "dissolved" also support the advantage of [Formula: see text] in robustness. CONCLUSION: [Formula: see text], as a variant of absolute correlation distance, satisfies the triangle inequality and is capable for more robust clustering.

Next-generation sequencing expands the known phage genomes rapidly. Unlike culture-based methods, the hosts of phages discovered from next-generation sequencing data remain uncharacterized. The high diversity of the phage genomes makes the host assignment task challenging. To solve the issue, we proposed a phage host prediction tool-DeepHost. To encode the phage genomes into matrices, we design a genome encoding method that applied various spaced $k$-mer pairs to tolerate sequence variations, including insertion, deletions, and mutations. DeepHost applies a convolutional neural network to predict host taxonomies. DeepHost achieves the prediction accuracy of 96.05% at the genus level (72 taxonomies) and 90.78% at the species level (118 taxonomies), which outperforms the existing phage host prediction tools by 10.16-30.48% and achieves comparable results to BLAST. For the genomes without hits in BLAST, DeepHost obtains the accuracy of 38.00% at the genus level and 26.47% at the species level, making it suitable for genomes of less homologous sequences with the existing datasets. DeepHost is alignment-free, and it is faster than BLAST, especially for large datasets. DeepHost is available at https://github.com/deepomicslab/DeepHost.

Correction: BMC Bioinformatics (2023) 24:40 https://doi.org/10.1186/s12859-023-05161-y Following publication of the original article [1], it was reported that the article entitled “On triangle inequalities of correlation-based distances for gene expression profiles” was published in the regular issue of this journal instead of in the supplement issue. The details of the supplement in which this article ought to have been published are given below: This article has been published as part of BMC Bioinformatics Volume 23 Supplement 3, 2022: Selected articles from the International Conference on Intelligent Biology and Medicine (ICIBM 2021): bioinformatics. The full contents of the supplement are available online at https://bmcbioinformatics.biomedcentral.com/articles/supplements/volume-23-supplement-3. The publisher apologizes for any inconvenience caused. © 2023, The Author(s).

Motivation: When a temperate bacteriophage (phage) integrates into the chromosome of its host or to remain as latent episomal DNA, the prophage plays an important role in bacterial virulence acquisition and increase. With the recent release of thousands of bacterial high throughput sequencing data, there has been a growing interest in analyzing the impact of interdependency between a prophage and its host. One of the essential tasks is to acquire and identify the complete sequence of the prophage, i.e., the functional prophages. However, no existing tools can identify functional prophages from bacterial genomes. A few tools can predict putative prophage in bacterial genomes, but they are unable to distinguish functional prophages from defective prophages. To reduce the cost and to relieve the tedious biological experiments for functional prophage analysis, computational methods for predicting functional prophages based on bacterial high-throughput sequencing data would be an effective alternation. Results: This paper presents a method, LysoPhD, the first tool to predict functional prophage sequences using bacterial HTS data automatically and accurately. The main idea is to obtain whole genome of functional prophages from the bacterial genome based on phage gene annotation and reads-mediated contig connection. LysoPhD obtains whole genome sequences of functional prophages based on improved models. The obtained the sequences are verified by wet lab experiments. Specifically, LysoPhD is applied to a set of staphylococcus HTS data in the case studies. Ten functional prophages are predicted from 72 staphylococcus isolates. The 72 bacterial strains are then induced with Mitomycin C and subsequent deep sequencing verified of the 10 functional prophages. This shows that LysoPhD is a sensitive and accurate program for functional prophage prediction.

Language-independent embedded speech recognition is a necessary and important application. Considering personal privacy, collection difficulty of all the reference words, and limited storage space of mobile devices, language-independent (LI) embedded speech recognition should be classified into lightweight speaker-dependent (SD) cases. Dynamic time warping (DTW) is the state-of-the-art algorithm for small foot-print SD automatic speech recognition. To decrease the high computational complexity of DTW, and to avoid constraints-induced coarse approximation and inaccuracy problems, we introduce a novel confidence index dynamic time warping (CIDTW) approach. CIDTW defines a new cost function, called the confidence index cost function (CICF), to measure the similarity between merged speech training and testing data, while follows the same DTW process. With extensive experiments on three representative SD datasets, CIDTW achieves better accuracy and overall six times faster speeds compared with DTW.