WoLF PSORT is an extension of the PSORT II program for protein subcellular location prediction. WoLF PSORT converts protein amino acid sequences into numerical localization features; based on sorting signals, amino acid composition and functional motifs such as DNA-binding motifs. After conversion, a simple k-nearest neighbor classifier is used for prediction. Using html, the evidence for each prediction is shown in two ways: (i) a list of proteins of known localization with the most similar localization features to the query, and (ii) tables with detailed information about individual localization features. For convenience, sequence alignments of the query to similar proteins and links to UniProt and Gene Ontology are provided. Taken together, this information allows a user to understand the evidence (or lack thereof) behind the predictions made for particular proteins. WoLF PSORT is available at wolfpsort.org

In cancer research, the association between a gene and clinical outcome suggests the underlying etiology of the disease and consequently can motivate further studies. The recent availability of published cancer microarray datasets with clinical annotation provides the opportunity for linking gene expression to prognosis. However, the data are not easy to access and analyze without an effective analysis platform.

DBTBS, first released in 1999, is a reference database on transcriptional regulation in Bacillus subtilis , summarizing the experimentally characterized transcription factors, their recognition sequences and the genes they regulate. Since the previous release, the original content was extended by the addition of the data contained in 569 new publications, the total of which now reaches 947. The number of B. subtilis promoters annotated in the database was more than doubled to 1475. In addition, 463 experimentally validated B. subtilis operons and their terminators have been included. Given the increase in the number of fully sequenced bacterial genomes, we decided to extend the usability of DBTBS in comparative regulatory genomics. We therefore created a new section on the conservation of the upstream regulatory sequences between homologous genes in 40 Gram-positive bacterial species, as well as on the presence of overrepresented hexameric motifs that may have regulatory functions. DBTBS can be accessed at: http://dbtbs.hgc.jp .

In this study, we propose iDNA-ABF, a multi-scale deep biological language learning model that enables the interpretable prediction of DNA methylations based on genomic sequences only. Benchmarking comparisons show that our iDNA-ABF outperforms state-of-the-art methods for different methylation predictions. Importantly, we show the power of deep language learning in capturing both sequential and functional semantics information from background genomes. Moreover, by integrating the interpretable analysis mechanism, we well explain what the model learns, helping us build the mapping from the discovery of important sequential determinants to the in-depth analysis of their biological functions.

Here, we present DeepBIO, the first-of-its-kind automated and interpretable deep-learning platform for high-throughput biological sequence functional analysis. DeepBIO is a one-stop-shop web service that enables researchers to develop new deep-learning architectures to answer any biological question. Specifically, given any biological sequence data, DeepBIO supports a total of 42 state-of-the-art deep-learning algorithms for model training, comparison, optimization and evaluation in a fully automated pipeline. DeepBIO provides a comprehensive result visualization analysis for predictive models covering several aspects, such as model interpretability, feature analysis and functional sequential region discovery. Additionally, DeepBIO supports nine base-level functional annotation tasks using deep-learning architectures, with comprehensive interpretations and graphical visualizations to validate the reliability of annotated sites. Empowered by high-performance computers, DeepBIO allows ultra-fast prediction with up to million-scale sequence data in a few hours, demonstrating its usability in real application scenarios. Case study results show that DeepBIO provides an accurate, robust and interpretable prediction, demonstrating the power of deep learning in biological sequence functional analysis. Overall, we expect DeepBIO to ensure the reproducibility of deep-learning biological sequence analysis, lessen the programming and hardware burden for biologists and provide meaningful functional insights at both the sequence level and base level from biological sequences alone. DeepBIO is publicly available at https://inner.wei-group.net/DeepBIO.

DBTBS (http://dbtbs.hgc.jp) was originally released in 1999 as a reference database of published transcriptional regulation events in Bacillus subtilis, one of the best studied bacteria. It is essentially a compilation of transcription factors with their regulated genes as well as their recognition sequences, which were experimentally characterized and reported in the literature. Here we report its major update, which contains information on 114 transcription factors, including sigma factors, and 633 promoters of 525 genes. The number of references cited in the database has increased from 291 to 378. It also supports a function to find putative transcription factor binding sites within input sequences by using our collection of weight matrices and consensus patterns. Furthermore, though preliminarily, DBTBS now aims to contribute to comparative genomics by showing the presence or absence of potentially orthologous transcription factors and their corresponding cis-elements on the promoters of their potentially orthologously regulated genes in 50 eubacterial genomes.