Yihong Liu, Runsheng Chen, Lea Hirlimann, Ahmad Dawar Hakimi, Mingyang Wang, Amir Hossein Kargaran, Sascha Rothe, François Yvon, Hinrich Schuetze. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

MOTIVATION: There are many clustered transcriptionally active regions in the human genome, in which the transcription complex cannot immediately terminate transcription at the upstream gene termination site, but instead continues to transcribe intergenic regions and downstream genes, resulting in read-through transcripts. Several studies have demonstrated the regulatory roles of read-through transcripts in tumorigenesis and development. However, limited by the read length of next-generation sequencing, discovery of read-through transcripts has been slow. For long but also erroneous third-generation sequencing data, this study developed a novel minimizer sketch algorithm to accurately and quickly identify read-through transcripts. RESULTS: Readon initially splits the reference sequence into distinct active regions. It employs a sliding window approach within each region, calculates minimizers, and constructs the specialized structured arrays for query indexing. Following initial alignment anchor screening of candidate read-through transcripts, further confirmation steps are executed. Comparative assessments against existing software reveal Readon's superior performance on both simulated and validated real data. Additionally, two downstream tools are provided: one for predicting whether a read-through transcript is likely to undergo nonsense-mediated decay or encodes a protein, and another for visualizing splicing patterns. AVAILABILITY AND IMPLEMENTATION: Readon is freely available on GitHub (https://github.com/Bulabula45/Readon).

Small proteins is the general term for proteins with length shorter than 100 amino acids. Identification and functional studies of small proteins have advanced rapidly in recent years, and several studies have shown that small proteins play important roles in diverse functions including development, muscle contraction and DNA repair. Identification and characterization of previously unrecognized small proteins may contribute in important ways to cell biology and human health. Current databases are generally somewhat deficient in that they have either not collected small proteins systematically, or contain only predictions of small proteins in a limited number of tissues and species. Here, we present a specifically designed web-accessible database, small proteins database (SmProt, http://bioinfo.ibp.ac.cn/SmProt), which is a database documenting small proteins. The current release of SmProt incorporates 255 010 small proteins computationally or experimentally identified in 291 cell lines/tissues derived from eight popular species. The database provides a variety of data including basic information (sequence, location, gene name, organism, etc.) as well as specific information (experiment, function, disease type, etc.). To facilitate data extraction, SmProt supports multiple search options, including species, genome location, gene name and their aliases, cell lines/tissues, ORF type, gene type, PubMed ID and SmProt ID. SmProt also incorporates a service for the BLAST alignment search and provides a local UCSC Genome Browser. Additionally, SmProt defines a high-confidence set of small proteins and predicts the functions of the small proteins.

MOTIVATION: Long non-coding RNAs (lncRNAs) are essential in many molecular pathways, and are frequently associated with disease but the mechanisms of most lncRNAs have not yet been characterized. Genetic variations, including single nucleotide polymorphisms (SNPs) and structural variations, are widely distributed in the genome, including lncRNA gene regions. As the number of studies on lncRNAs grows rapidly, it is necessary to evaluate the effects of genetic variations on lncRNAs. RESULTS: A modification sites of lncRNAs and provided comprehensive effects of SNPs on transcription and modification of lncRNAs. We collected putatively translated open reading frames (ORFs) in lncRNAs, and identified both synonymous and non-synonymous SNPs in ORFs. We also collected expression quantitative trait loci of lncRNAs from the literature. Furthermore, we identified lncRNAs in CNV regions as prognostic biomarker candidates of cancers and predicted lncRNA gene fusion events from RNA-seq data from cell lines. The LncVar database can be used as a resource to evaluate the effects of the variations on the biological function of lncRNAs. AVAILABILITY AND IMPLEMENTATION: LncVar is available at http://bioinfo.ibp.ac.cn/LncVar CONTACT: [email protected] information: Supplementary materials are available at Bioinformatics online.

Single nucleotide variants (SNVs) have been discovered that they play crucial roles in disease pathogenesis as genetic factors. Featured by analyzing multiple SNVs in a biological module (e.g. exon, gene, etc.) collectively, the joint-SNVs studies are increasingly attractive in genome-wide association studies (GWASs), for which extensive efforts have been devoted to pursue effective multivariate methods. In this paper, we first reviewed several main streams of existing methods and their limitations in joint-SNVs studies. Then, we introduced a recently proposed novel method, namely statistic-space boundary based test (S-space BBT) to tackle these limitations. Via computational experiments on simulation datasets, not only we figured out the applicable scenarios for the six methods in considering the effect direction and whether the single significant is involved in, but also demonstrated the strong detecting sensitivity of S-space BBT under the different conditions of odds ratio, minor allele frequency, and the linkage disequilibrium. We anticipate that our study may provide clues for multivariate method selection, and that S-space BBT may play a promising role in the joint-SNVs analysis.

UNLABELLED: We here present BioCircos.js, an interactive and lightweight JavaScript library especially for biological data interactive visualization. BioCircos.js facilitates the development of web-based applications for circular visualization of various biological data, such as genomic features, genetic variations, gene expression and biomolecular interactions. AVAILABILITY AND IMPLEMENTATION: BioCircos.js and its manual are freely available online at http://bioinfo.ibp.ac.cn/biocircos/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: PIWI-interacting RNAs (piRNAs) are a class of small non-coding RNAs that are highly abundant in the germline. One important role of piRNAs is to defend genome integrity by guiding PIWI proteins to silence transposable elements (TEs), which have a high potential to cause deleterious effects on their host. The mechanism of piRNA-mediated post-transcriptional silencing was also observed to affect mRNAs, suggesting that piRNAs might play a broad role in gene expression regulation. However, there has been no systematic report with regard to how many protein-coding genes might be targeted and regulated by piRNAs. RESULTS: We trained a support vector machine classifier based on a combination of Miwi CLIP-Seq-derived features and position-derived features to predict the potential targets of piRNAs on mRNAs in the mouse. Reanalysis of a published microarray dataset suggested that the expression level of the 2587 protein-coding genes predicted as piRNA targets showed significant upregulation as a whole after abolishing the slicer activity of Miwi, supporting the conclusion that they are subject to piRNA-mediated regulation. AVAILABILITY AND IMPLEMENTATION: A web version of the method called pirnaPre as well as our results for browse is available at http://www.regulatoryrna.org/software/piRNA/piRNA_target_mRNA/index.php CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Transcription factor activities (TFAs), rather than expression levels, control gene expression and provide valuable information for investigating TF-gene regulations. Network Component Analysis (NCA) is a model based method to deduce TFAs and TF-gene control strengths from microarray data and a priori TF-gene connectivity data. We modify NCA to model gene expression regulation by non-Gaussian Factor Analysis (NFA), which assumes TFAs independently comes from Gaussian mixture densities. We properly incorporate a priori connectivity and/or sparsity on the mixing matrix of NFA, and derive, under Bayesian Ying-Yang (BYY) learning framework, a BYY-NFA algorithm that can not only uncover the latent TFA profile similar to NCA, but also is capable of automatically shutting off unnecessary connections. Simulation study demonstrates the effectiveness of BYY-NFA, and a preliminary application to two real world data sets shows that BYY-NFA improves NCA for the case when TF-gene connectivity is not available or not reliable, and may provide a preliminary set of candidate TF-gene interactions or double check unreliable connections for experimental verification.

BACKGROUND: Tumor cells are considered to have an aberrant cell state, and some evidence indicates different development states appearing in the tumorigenesis. Embryonic development and stem cell differentiation are ordered processes in which the sequence of events over time is highly conserved. The "cancer attractor" concept integrates normal developmental processes and tumorigenesis into a high-dimensional "cell state space", and provides a reasonable explanation of the relationship between these two biological processes from theoretical viewpoint. However, it is hard to describe such relationship by using existed experimental data; moreover, the measurement of different development states is also difficult. RESULTS: Here, by applying a novel time-ordered linear model based on a co-bisector which represents the joint direction of a series of vectors, we described the trajectories of development process by a line and showed different developmental states of tumor cells from developmental timescale perspective in a cell state space. This model was used to transform time-course developmental expression profiles of human ESCs, normal mouse liver, ovary and lung tissue into "cell developmental state lines". Then these cell state lines were applied to observe the developmental states of different tumors and their corresponding normal samples. Mouse liver and ovarian tumors showed different similarity to early development stage. Similarly, human glioma cells and ovarian tumors became developmentally "younger". CONCLUSIONS: The time-ordered linear model captured linear projected development trajectories in a cell state space. Meanwhile it also reflected the change tendency of gene expression over time from the developmental timescale perspective, and our finding indicated different development states during tumorigenesis processes in different tissues.

MOTIVATION: RNA secondary structures with pseudoknots are often predicted by minimizing free energy, which is proved to be NP-hard. Due to kinetic reasons the real RNA secondary structure often has local instead of global minimum free energy. This implies that we may improve the performance of RNA secondary structure prediction by taking kinetics into account and minimize free energy in a local area. RESULT: we propose a novel algorithm named FlexStem to predict RNA secondary structures with pseudoknots. Still based on MFE criterion, FlexStem adopts comprehensive energy models that allow complex pseudoknots. Unlike classical thermodynamic methods, our approach aims to simulate the RNA folding process by successive addition of maximal stems, reducing the search space while maintaining or even improving the prediction accuracy. This reduced space is constructed by our maximal stem strategy and stem-adding rule induced from elaborate statistical experiments on real RNA secondary structures. The strategy and the rule also reflect the folding characteristic of RNA from a new angle and help compensate for the deficiency of merely relying on MFE in RNA structure prediction. We validate FlexStem by applying it to tRNAs, 5SrRNAs and a large number of pseudoknotted structures and compare it with the well-known algorithms such as RNAfold, PKNOTS, PknotsRG, HotKnots and ILM according to their overall sensitivities and specificities, as well as positive and negative controls on pseudoknots. The results show that FlexStem significantly increases the prediction accuracy through its local search strategy. AVAILABILITY: Software is available at http://pfind.ict.ac.cn/FlexStem/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

We demonstrate a new algorithm named FlexStem to predict RNA secondary structures with pseudoknots. Our approach is based on the free energy minimization criterion, and utilizes a sophisticated energy model that is more accurate and supports more types of pseudoknots. By using a "maximal stem" and "stem merging" strategy, the search space for RNA structure prediction is significantly reduced. We have also developed a greedy search algorithm with perturbation on stems. The FlexStem algorithm is applied to a large number of sequences taken from Sprinzl, Pseudobase and other reliable resource. We find that FlexStem outperforms the well-known optimal and heuristic algorithms such as Mfold, PKNOTS, HotKnots and ILM in overall sensitivity and has comparable results to those algorithms in overall specificity. Performance evaluation demonstrate that FlexStem requires significantly less time than the optimal algorithm PKNOTS. In addition, our algorithm has better prediction results on pseudoknot-free sequences compared to competing algorithms.

BACKGROUND: Natural antisense transcripts (NATs) are endogenous RNA molecules that exhibit partial or complete complementarity to other RNAs, and that may contribute to the regulation of molecular functions at various levels. In recent years, large-scale NAT screens in several model organisms have produced much data, but there is no database to assemble all these data. AntiCODE intends to function as an integrated NAT database for this purpose. RESULTS: This release of antiCODE contains more than 30,000 non-redundant natural sense-antisense transcript pairs from 12 eukaryotic model organisms. In order to provide an integrated NAT research platform, efficient browser, search and Blast functions have been included to enable users to easily access information through parameters such as species, accession number, overlapping patterns, coding potential etc. In addition to the collected information, antiCODE also introduces a simple classification system to facilitate the study of natural antisense transcripts. CONCLUSION: Though a few similar databases also dealing with NATs have appeared lately, antiCODE is the most comprehensive among these, comprising almost all currently detected NAT pairs.

Abstract Summary: NMPP package is a bundle of user-customized tools based on established algorithms and methods to process self-designed NimbleGen microarray data. It features a command-line-based integrative processing procedure that comprises five major functional components, namely the raw microarray data parsing and integrating module, the array spatial effect smoothing and visualization module, the probe-level multi-array normalization module, the gene expression intensity summarization module and the gene expression status inference module. Availability: Contact: [email protected] Supplementary information: The supplementary materials, figures, testing dataset, software instructions and a typical workflow of using NMPP package are accessible through the above homepage.

BACKGROUND: As phenotypic features derived from heritable characters, the topologies of metabolic pathways contain both phylogenetic and phenetic components. In the post-genomic era, it is possible to measure the "phylophenetic" contents of different pathways topologies from a global perspective. RESULTS: We reconstructed phylophenetic trees for all available metabolic pathways based on topological similarities, and compared them to the corresponding 16S rRNA-based trees. Similarity values for each pair of trees ranged from 0.044 to 0.297. Using the quartet method, single pathways trees were merged into a comprehensive tree containing information from a large part of the entire metabolic networks. This tree showed considerably higher similarity (0.386) to the corresponding 16S rRNA-based tree than any tree based on a single pathway, but was, on the other hand, sufficiently distinct to preserve unique phylogenetic information not reflected by the 16S rRNA tree. CONCLUSION: We observed that the topology of different metabolic pathways provided different phylogenetic and phenetic information, depicting the compromise between phylogenetic information and varying evolutionary pressures forming metabolic pathway topologies in different organisms. The phylogenetic information content of the comprehensive tree is substantially higher than that of any tree based on a single pathway, which also gave clues to constraints working on the topology of the global metabolic networks, information that is only partly reflected by the topologies of individual metabolic pathways.

BACKGROUND: Tandem mass spectrometry (MS/MS) is a powerful tool for protein identification. Although great efforts have been made in scoring the correlation between tandem mass spectra and an amino acid sequence database, improvements could be made in three aspects, including characterization ofpeaks in spectra, adoption of effective scoring functions and access to thereliability of matching between peptides and spectra. RESULTS: A novel scoring function is presented, along with criteria to estimate the performance confidence of the function. Through learning the typesof product ions and the probability of generating them, a hypothetic spectrum was generated for each candidate peptide. Then relative entropy was introduced to measure the similarity between the hypothetic and the observed spectra. Based on the extreme value distribution (EVD) theory, a threshold was chosen to distinguish a true peptide assignment from a random one. Tests on a public MS/MS dataset demonstrated that this method performs better than the well-known SEQUEST. CONCLUSION: A reliable identification of proteins from the spectra promises a more efficient application of tandem mass spectrometry to proteomes with high complexity.

Transcription factors with a large number of target genes--transcription hub(s), or THub(s)--are usually crucial components of the regulatory system of a cell, and the different patterns through which they transfer the transcriptional signal to downstream cascades are of great interest. By profiling normalized abundances (A(N)) of basic regulatory patterns of individual THubs in the yeast Saccharomyces cerevisiae transcriptional regulation network under five different cellular states and environmental conditions, we have investigated their preferences for different basic regulatory patterns. Subgraph-normalized abundances downstream of individual THubs often differ significantly from that of the network as a whole, and conversely, certain over-represented subgraphs are not preferred by any THub. The THub preferences changed substantially when the cellular or environmental conditions changed. This switching of regulatory pattern preferences suggests that a change in conditions does not only elicit a change in response by the regulatory network, but also a change in the mechanisms by which the response is mediated. The THub subgraph preference profile thus provides a novel tool for description of the structure and organization between the large-scale exponents and local regulatory patterns.

A number of different approaches have been proposed to predict elemental component formulas (or molecular formulas) of molecular ions in low and medium resolution mass spectra. Most of them rely on isotope patterns, enumerate all possible formulas for an ion, and exclude certain formulas violating chemical constraints. However, these methods cannot be well generalized to the component prediction of fragment ions in tandem mass spectra. In this paper, a new method, FFP (Fragment ion Formula Prediction), is presented to predict elemental component formulas of fragment ions. In the FFP method, the prediction of the best formulas is converted into the minimization of the distance between theoretical and observed isotope patterns. And, then, a novel local search model is proposed to generate a set of candidate formulas efficiently. After the search, FFP applies a new multiconstraint filtering to exclude as many invalid and improbable formulas as possible. FFP is experimentally compared with the previous enumeration methods, and shown to outperform them significantly. The results of this paper can help to improve the reliability of de novo in the identification of peptide sequences.

MOTIVATION: Small RNA (sRNA) genes in Escherichia coli have been in focus recently, as 44 out of 55 experimentally confirmed sRNA genes have been precisely located in the genome. The object of this study is to analyze quantitatively the conservation of these sRNA genes and compare it with the conservation of protein-encoding genes, function-unknown regions and tRNA genes. RESULTS: The results show that within an evolutionary distance of 0.26, both sRNA genes and protein-encoding genes display a similar tendency in their degrees of conservation at the nucleotide level. In addition, the conservation of sRNA genes is much stronger than function-unknown regions, but much weaker than tRNA genes. Based on the conservation of studied sRNA genes, we also give clues to estimate the total number of sRNA genes in E.coli. SUPPLEMENTARY INFORMATION: Supplementary information is available at http://www.bioinfo.org.cn/SM/sRNAconservation.htm