Doublets in single-cell sequencing data, caused by the simultaneous capture of two or more cells within a single reaction volume, introduce biases that compromise downstream analysis. Existing doublet detection methods primarily focus on single-modality data and exhibit limited robustness across datasets. To overcome these limitations, we developed OmniDoublet, a multimodal doublet detection method that integrates transcriptomic and epigenomic data. OmniDoublet leverages the Jaccard similarity coefficient to calculate weights that assess the reliability of neighboring cells across modalities, combining doublet scores from different modalities into a final integrated score. It further employs a Gaussian mixture model (GMM) to establish thresholds, enabling accurate binary classification of cells as singlets or doublets based on the integrated score. OmniDoublet offers a robust framework for detecting doublets across diverse scenarios. Benchmarking against state-of-the-art methods across various datasets demonstrates that OmniDoublet achieves superior accuracy, robustness, and scalability. By harnessing the comprehensive information from multimodal single-cell data, OmniDoublet enhances doublet detection, enabling researchers to gain more accurate and reliable insights into cellular processes.

DNA methylation plays a crucial role in transcriptional regulation. Reduced representation bisulfite sequencing (RRBS) is a technique of increasing use for analyzing genome-wide methylation profiles. Many computational tools such as Metilene, MethylKit, BiSeq and DMRfinder have been developed to use RRBS data for the detection of the differentially methylated regions (DMRs) potentially involved in epigenetic regulations of gene expression. For DMR detection tools, as for countless other medical applications, P-values and their adjustments are among the most standard reporting statistics used to assess the statistical significance of biological findings. However, P-values are coming under increasing criticism relating to their questionable accuracy and relatively high levels of false positive or negative indications. Here, we propose a method to calculate E-values, as likelihood ratios falling into the null hypothesis over the entire parameter space, for DMR detection in RRBS data. We also provide the R package 'metevalue' as a user-friendly interface to implement E-value calculations into various DMR detection tools. To evaluate the performance of E-values, we generated various RRBS benchmarking datasets using our simulator 'RRBSsim' with eight samples in each experimental group. Our comprehensive benchmarking analyses showed that using E-values not only significantly improved accuracy, area under ROC curve and power, over that of P-values or adjusted P-values, but also reduced false discovery rates and type I errors. In applications using real RRBS data of CRL rats and a clinical trial on low-salt diet, the use of E-values detected biologically more relevant DMRs and also improved the negative association between DNA methylation and gene expression.

Precision livestock management requires animal traceability and disease trajectory, for which discriminating between or re-identifying individual animals is of significant importance. Existing re-identification (re-ID) methods are mostly proposed for persons and vehicles, compared with which animals are extraordinarily more challenging to be re-identified because of subtle visual differences between individuals. In this paper, we focus on image-based re-ID of yaks (Bos grunniens), which are indispensable livestock in local animal husbandry economy in Qinghai-Tibet Plateau. We establish the first yak re-ID dataset (called YakReID-103) which contains 2, 247 images of 103 different yaks with bounding box, direction-based pose, and identity annotations. Moreover, according to the characteristics of yaks, we modifiy several person re-ID and animal re-ID methods as baselines for yak re-ID. Experimental results of the baselines on YakReID-103 demonstrate the challenges in yak re-ID. We expect that the proposed benchmark will promote the research of animal biometrics and extend the application scope of re-ID techniques.

MOTIVATION: miRNA isoforms (isomiRs) are produced from the same arm as the archetype miRNA with a few nucleotides different at 5 and/or 3 termini. These well-conserved isomiRs are functionally important and have contributed to the evolution of miRNA genes. Accurate detection of differential expression of miRNAs can bring new insights into the cellular function of miRNA and a further improvement in miRNA-based diagnostic and prognostic applications. However, very few methods take isomiR variations into account in the analysis of miRNA differential expression. RESULTS: To overcome this challenge, we developed a novel approach to take advantage of the multidimensional structure of isomiR data from the same miRNAs, termed as a multivariate differential expression by Hotelling's T2 test (MDEHT). The utilization of the information hidden in isomiRs enables MDEHT to increase the power of identifying differentially expressed miRNAs that are not marginally detectable in univariate testing methods. We conducted rigorous and unbiased comparisons of MDEHT with seven commonly used tools in simulated and real datasets from The Cancer Genome Atlas. Our comprehensive evaluations demonstrated that the MDEHT method was robust among various datasets and outperformed other commonly used tools in terms of Type I error rate, true positive rate and reproducibility. AVAILABILITY AND IMPLEMENTATION: The source code for identifying and quantifying isomiRs and performing miRNA differential expression analysis is available at https://github.com/amanzju/MDEHT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: The rapid development of next-generation sequencing technology provides an opportunity to study genome-wide DNA methylation at single-base resolution. However, depletion of unmethylated cytosines brings challenges for aligning bisulfite-converted sequencing reads to a large reference. Software tools for aligning methylation reads have not yet been comprehensively evaluated, especially for the widely used reduced representation bisulfite sequencing (RRBS) that involves enrichment for CpG islands (CGIs). Results: We specially developed a simulator, RRBSsim, for benchmarking analysis of RRBS data. We performed extensive comparison of seven mapping algorithms for methylation analysis in both real and simulated RRBS data. Eighteen lung tumors and matched adjacent tissues were sequenced by the RRBS protocols. Our empirical evaluation found that methylation results were less consistent between software tools for CpG sites with low sequencing depth, medium methylation level, on CGI shores or gene body. These observations were further confirmed by simulations that indicated software tools generally had lower recall of detecting these vulnerable CpG sites and lower precision of estimating methylation levels in these CpG sites. Among the software tools tested, bwa-meth and BS-Seeker2 (bowtie2) are currently our preferred aligners for RRBS data in terms of recall, precision and speed. Existing aligners cannot efficiently handle moderately methylated CpG sites and those CpG sites on CGI shores or gene body. Interpretation of methylation results from these vulnerable CpG sites should be treated with caution. Our study reveals several important features inherent in methylation data, and RRBSsim provides guidance to advance sequence-based methylation data analysis and methodological development. Availability and implementation: RRBSsim is a simulator for benchmarking analysis of RRBS data and its source code is available at https://github.com/xwBio/RRBSsim or https://github.com/xwBio/Docker-RRBSsim. Supplementary information: Supplementary data are available at Bioinformatics online.

In named entity recognition, we often don't have a large in-domain training corpus or a knowledge base with adequate coverage to train a model directly.In this paper, we propose a method where, given training data in a related domain with similar (but not identical) named entity (NE) types and a small amount of in-domain training data, we use transfer learning to learn a domain-specific NE model.That is, the novelty in the task setup is that we assume not just domain mismatch, but also label mismatch.

Word embeddings -distributed word representations that can be learned from unlabelled data -have been shown to have high utility in many natural language processing applications.In this paper, we perform an extrinsic evaluation of four popular word embedding methods in the context of four sequence labelling tasks: part-of-speech tagging, syntactic chunking, named entity recognition, and multiword expression identification.A particular focus of the paper is analysing the effects of task-based updating of word representations.We show that when using word embeddings as features, as few as several hundred training instances are sufficient to achieve competitive results, and that word embeddings lead to improvements over out-of-vocabulary words and also out of domain.Perhaps more surprisingly, our results indicate there is little difference between the different word embedding methods, and that simple Brown clusters are often competitive with word embeddings across all tasks we consider.

OBJECTIVE: We study the use of speech recognition and information extraction to generate drafts of Australian nursing-handover documents. METHODS: Speech recognition correctness and clinicians' preferences were evaluated using 15 recorder-microphone combinations, six documents, three speakers, Dragon Medical 11, and five survey/interview participants. Information extraction correctness evaluation used 260 documents, six-class classification for each word, two annotators, and the CRF++ conditional random field toolkit. RESULTS: A noise-cancelling lapel-microphone with a digital voice recorder gave the best correctness (79%). This microphone was also the most preferred option by all but one participant. Although the participants liked the small size of this recorder, their preference was for tablets that can also be used for document proofing and sign-off, among other tasks. Accented speech was harder to recognize than native language and a male speaker was detected better than a female speaker. Information extraction was excellent in filtering out irrelevant text (85% F1) and identifying text relevant to two classes (87% and 70% F1). Similarly to the annotators' disagreements, there was confusion between the remaining three classes, which explains the modest 62% macro-averaged F1. DISCUSSION: We present evidence for the feasibility of speech recognition and information extraction to support clinicians' in entering text and unlock its content for computerized decision-making and surveillance in healthcare. CONCLUSIONS: The benefits of this automation include storing all information; making the drafts available and accessible almost instantly to everyone with authorized access; and avoiding information loss, delays, and misinterpretations inherent to using a ward clerk or transcription services.