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期刊文献 > Bioinformatics期刊选择月份

Bioinformatics

2023 Jan 27

Hecker D, Behjati Ardakani F, Karollus A

2. Unbiased pangenome graphs.

Bioinformatics

2023 Jan 1

Garrison E, Guarracino A.

3. Protein-to-genome alignment with miniprot.

Bioinformatics

2023 Jan 1

Li H.

4. CAPG: comprehensive allopolyploid genotyper.

Bioinformatics

2023 Jan 1

Kulkarni R, Zhang Y, Cannon SB

5. IMMerge: merging imputation data at scale.

Bioinformatics

2023 Jan 1

Zhu W, Chen HH, Petty AS

6. A Boolean algebra for genetic variants.

Bioinformatics

2023 Jan 1

Vis JK, Santcroos MA, Kosters WA

7. Tracing weak neuron fibers.

Bioinformatics

2023 Jan 1

Liu Y, Zhong Y, Zhao X

8. MIDAS2: Metagenomic Intra-species Diversity Analysis System.

Bioinformatics

2023 Jan 1

Zhao C, Dimitrov B, Goldman M

9. Hierarchical modelling of microbial communities.

Bioinformatics

2023 Jan 1

Glöckler M, Dräger A, Mostolizadeh R.

10. Cloud-native distributed genomic pileup operations.

Bioinformatics

2023 Jan 1

Wiewiórka M, Szmurło A, Stankiewicz P

11. EDIR: exome database of interspersed repeats.

Bioinformatics

2023 Jan 1

Vo Ngoc LDT, Osei R, Dohr K

12. Variable-selection ANOVA Simultaneous Component Analysis (VASCA).

Bioinformatics

2023 Jan 1

Camacho J, Vitale R, Morales-Jiménez D

13. Syllable-PBWT for space-efficient haplotype long-match query.

Bioinformatics

2023 Jan 1

Wang V, Naseri A, Zhang S

14. Clustering single-cell multi-omics data with MoClust.

Bioinformatics

2023 Jan 1

Yuan M, Chen L, Deng M.

15. Pharokka: a fast scalable bacteriophage annotation tool.

Bioinformatics

2023 Jan 1

Bouras G, Nepal R, Houtak G

16. htseq-clip: a toolset for the preprocessing of eCLIP/iCLIP datasets.

Bioinformatics

2023 Jan 1

Sahadevan S, Sekaran T, Ashaf N

17. PDBench: evaluating computational methods for protein-sequence design.

Bioinformatics

2023 Jan 1

Castorina LV, Petrenas R, Subr K

18. NGSNGS: next-generation simulator for next-generation sequencing data.

Bioinformatics

2023 Jan 1

Henriksen RA, Zhao L, Korneliussen TS.

19. Global FDR control across multiple RNAseq experiments.

Bioinformatics

2023 Jan 1

Liou L, Hornburg M, Robertson DS.

20. JBrowse Jupyter: a Python interface to JBrowse 2.

Bioinformatics

2023 Jan 1

De Jesus Martinez T, Hershberg EA, Guo E

Bioinformatics

2023 Jan 1

Adamer MF, Roellin E, Bourguignon L

22. Metadata retrieval from sequence databases with ffq.

Bioinformatics

2023 Jan 1

Gálvez-Merchán Á, Min KHJ, Pachter L

23. A heuristic algorithm solving the mutual-exclusivity-sorting problem.

Bioinformatics

2023 Jan 1

Vinceti A, Trastulla L, Perron U

24. Codetta: predicting the genetic code from nucleotide sequence.

Bioinformatics

2023 Jan 1

Shulgina Y, Eddy SR.

25. Attentive Variational Information Bottleneck for TCR-peptide interaction prediction.

Bioinformatics

2023 Jan 1

Grazioli F, Machart P, Mösch A

26. Easy NanoString nCounter data analysis with the NanoTube.

Bioinformatics

2023 Jan 1

Class CA, Lukan CJ, Bristow CA

27. Efficient querying of genomic reference databases with gget.

Bioinformatics

2023 Jan 1

Luebbert L, Pachter L.

28. CoMutDB: the landscape of somatic mutation co-occurrence in cancers.

Bioinformatics

2023 Jan 1

Jiang L, Yu H, Tang J

29. SAMStat 2: quality control for next generation sequencing data.

Bioinformatics

2023 Jan 1

Lassmann T.

30. PRAWNS: compact pan-genomic features for whole-genome population genomics.

Bioinformatics

2023 Jan 1

Javkar K, Rand H, Strain E

31. EWF: simulating exact paths of the Wright-Fisher diffusion.

Bioinformatics

2023 Jan 1

Sant J, Jenkins PA, Koskela J

32. benchdamic: benchmarking of differential abundance methods for microbiome data.

Bioinformatics

2023 Jan 1

Calgaro M, Romualdi C, Risso D

33. AlphaPulldown-a python package for protein-protein interaction screens using AlphaFold-Multimer.

Bioinformatics

2023 Jan 1

Yu D, Chojnowski G, Rosenthal M

34. Insane in the vembrane: filtering and transforming VCF/BCF files.

Bioinformatics

2023 Jan 1

Hartmann T, Schröder C, Kuthe E

35. Capturing variation in metagenomic assembly graphs with MetaCortex.

Bioinformatics

2023 Jan 1

Martin S, Ayling M, Patrono L

36. Treenome Browser: co-visualization of enormous phylogenies and millions of genomes.

Bioinformatics

2023 Jan 1

Kramer AM, Sanderson T, Corbett-Detig R.

37. Gos: a declarative library for interactive genomics visualization in Python.

Bioinformatics

2023 Jan 1

Manz T, L'Yi S, Gehlenborg N.

38. DiffCircaPipeline: a framework for multifaceted characterization of differential rhythmicity.

Bioinformatics

2023 Jan 1

Xue X, Zong W, Huo Z

39. SCExecute: custom cell barcode-stratified analyses of scRNA-seq data.

Bioinformatics

2023 Jan 1

Edwards N, Dillard C, Prashant NM

40. Discovering misannotated lncRNAs using deep learning training dynamics.

Bioinformatics

2023 Jan 1

Nabi A, Dilekoglu B, Adebali O

41. Poisson hurdle model-based method for clustering microbiome features.

Bioinformatics

2023 Jan 1

Qiao Z, Barnes E, Tringe S

42. DARTpaths, an in silico platform to investigate molecular mechanisms of compounds.

Bioinformatics

2023 Jan 1

Bhalla D, Steijaert MN, Poppelaars ES

43. mOWL: Python library for machine learning with biomedical ontologies.

Bioinformatics

2023 Jan 1

Zhapa-Camacho F, Kulmanov M, Hoehndorf R.

44. Vaa3D-x for cross-platform teravoxel-scale immersive exploration of multidimensional image data.

Bioinformatics

2023 Jan 1

Liang J, Deng L, Chen S

45. The COMER web server for protein analysis by homology.

Bioinformatics

2023 Jan 1

Dapkūnas J, Margelevičius M.

46. COVoc and COVTriage: novel resources to support literature triage.

Bioinformatics

2023 Jan 1

Caucheteur D, May Pendlington Z, Roncaglia P

47. dnadna: a deep learning framework for population genetics inference.

Bioinformatics

2023 Jan 1

Sanchez T, Bray EM, Jobic P

48. Dual Attention and Patient Similarity Network for drug recommendation.

Bioinformatics

2023 Jan 1

Wu J, Dong Y, Gao Z

MOTIVATION: Artificially making clinical decisions for patients with multi-morbidity has long been considered a thorny problem due to the complexity of the disease. Drug recommendations can assist doctors in automatically providing effective and safe drug combinations conducive to treatment and reducing adverse reactions. However, the existing drug recommendation works ignored two critical information. (i) Different types of medical information and their interrelationships in the patient's visit history can be used to construct a comprehensive patient representation. (ii) Patients with similar disease characteristics and their corresponding medication information can be used as a reference for predicting drug combinations. RESULTS: To address these limitations, we propose DAPSNet, which encodes multi-type medical codes into patient representations through code- and visit-level attention mechanisms, while integrating drug information corresponding to similar patient states to improve the performance of drug recommendation. Specifically, our DAPSNet is enlightened by the decision-making process of human doctors. Given a patient, DAPSNet first learns the importance of patient history records between diagnosis, procedure and drug in different visits, then retrieves the drug information corresponding to similar patient disease states for assisting drug combination prediction. Moreover, in the training stage, we introduce a novel information constraint loss function based on the information bottleneck principle to constrain the learned representation and enhance the robustness of DAPSNet. We evaluate the proposed DAPSNet on the public MIMIC-III dataset, our model achieves relative improvements of 1.33%, 1.20% and 2.03% in Jaccard, F1 and PR-AUC scores, respectively, compared to state-of-the-art methods. AVAILABILITY AND IMPLEMENTATION: The source code is available at the github repository: https://github.com/andylun96/DAPSNet.

49. SBML2HYB: a Python interface for SBML compatible hybrid modeling.

Bioinformatics

2023 Jan 1

Pinto J, Costa RS, Alexandre L

50. Insights from incorporating quantum computing into drug design workflows.

Bioinformatics

2023 Jan 1

Lau B, Emani PS, Chapman J

51. Global stabilizing control of large-scale biomolecular regulatory networks.

Bioinformatics

2023 Jan 1

An S, Jang SY, Park SM

52. Achieving improved accuracy for imputation of ancient DNA.

Bioinformatics

2023 Jan 1

Ausmees K, Nettelblad C.

53. Improving dynamic predictions with ensembles of observable models.

Bioinformatics

2023 Jan 1

Massonis G, Villaverde AF, Banga JR.

54. PEMT: a patent enrichment tool for drug discovery.

Bioinformatics

2023 Jan 1

Gadiya Y, Zaliani A, Gribbon P

55. STRIKE-GOLDD 4.0: user-friendly, efficient analysis of structural identifiability and observability.

Bioinformatics

2023 Jan 1

Díaz-Seoane S, Rey Barreiro X, Villaverde AF.

56. Robust genetic model-based SNP-set association test using CauchyGM.

Bioinformatics

2023 Jan 1

Kim Y, Chi YY, Shen J

MOTIVATION: Association testing on genome-wide association studies (GWAS) data is commonly performed under a single (mostly additive) genetic model framework. However, the underlying true genetic mechanisms are often unknown in practice for most complex traits. When the employed inheritance model deviates from the underlying model, statistical power may be reduced. To overcome this challenge, an integrative association test that directly infers the underlying genetic model from GWAS data has previously been proposed for single-SNP analysis. RESULTS: In this article, we propose a Cauchy combination Genetic Model-based association test (CauchyGM) under a generalized linear model framework for SNP-set level analysis. CauchyGM does not require prior knowledge on the underlying inheritance pattern of each SNP. It performs a score test that first estimates an individual P-value of each SNP in an SNP-set with both minor allele frequency (MAF) > 1% and three genotypes and further aggregates the rest SNPs using SKAT. CauchyGM then combines the correlated P-values across multiple SNPs and different genetic models within the set using Cauchy Combination Test. To further accommodate both sparse and dense signal patterns, we also propose an omnibus association test (CauchyGM-O) by combining CauchyGM with SKAT and the burden test. Our extensive simulations show that both CauchyGM and CauchyGM-O maintain the type I error well at the genome-wide significance level and provide substantial power improvement compared to existing methods. We apply our methods to a pharmacogenomic GWAS data from a large cardiovascular randomized clinical trial. Both CauchyGM and CauchyGM-O identify several novel genome-wide significant genes. AVAILABILITY AND IMPLEMENTATION: The R package CauchyGM is publicly available on github: https://github.com/ykim03517/CauchyGM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

57. The PRALINE database: protein and Rna humAn singLe nucleotIde variaNts in condEnsates.

Bioinformatics

2023 Jan 1

Vandelli A, Arnal Segura M, Monti M

58. Cytoscape.js 2023 update: a graph theory library for visualization and analysis.

Bioinformatics

2023 Jan 1

Franz M, Lopes CT, Fong D

59. GAVISUNK: genome assembly validation via inter-SUNK distances in Oxford Nanopore reads.

Bioinformatics

2023 Jan 1

Dishuck PC, Rozanski AN, Logsdon GA

60. syntenet: an R/Bioconductor package for the inference and analysis of synteny networks.

Bioinformatics

2023 Jan 1

Almeida-Silva F, Zhao T, Ullrich KK

61. VSTH: a user-friendly web server for structure-based virtual screening on Tianhe-2.

Bioinformatics

2023 Jan 1

Mo Q, Xu Z, Yan H

62. MacFrag: segmenting large-scale molecules to obtain diverse fragments with high qualities.

Bioinformatics

2023 Jan 1

Diao Y, Hu F, Shen Z

63. Efficient computation of contributional diversity metrics from microbiome data with FuncDiv.

Bioinformatics

2023 Jan 1

Douglas GM, Kim S, Langille MGI

64. igv.js: an embeddable JavaScript implementation of the Integrative Genomics Viewer (IGV).

Bioinformatics

2023 Jan 1

Robinson JT, Thorvaldsdottir H, Turner D

65. A benchmark for automatic medical consultation system: frameworks, tasks and datasets.

Bioinformatics

2023 Jan 1

Chen W, Li Z, Fang H

66. Systematic analysis of alternative splicing in time course data using Spycone.

Bioinformatics

2023 Jan 1

Lio CT, Grabert G, Louadi Z

67. Synggen: fast and data-driven generation of synthetic heterogeneous NGS cancer data.

Bioinformatics

2023 Jan 1

Scandino R, Calabrese F, Romanel A.

68. PrioriTree: a utility for improving phylodynamic analyses in BEAST.

Bioinformatics

2023 Jan 1

Gao J, May MR, Rannala B

69. Integrating heterogeneous knowledge graphs into drug-drug interaction extraction from the literature.

Bioinformatics

2023 Jan 1

Asada M, Miwa M, Sasaki Y.

70. Interactive network-based clustering and investigation of multimorbidity association matrices with associationSubgraphs.

Bioinformatics

2023 Jan 1

Strayer N, Zhang S, Yao L

71. Prediction of drug side effects with transductive matrix co-completion.

Bioinformatics

2023 Jan 1

Liang X, Fu Y, Qu L

72. Vaeda computationally annotates doublets in single-cell RNA sequencing data.

Bioinformatics

2023 Jan 1

Schriever H, Kostka D.

73. End-to-end learning of multiple sequence alignments with differentiable Smith-Waterman.

Bioinformatics

2023 Jan 1

Petti S, Bhattacharya N, Rao R

74. Discovering research articles containing evolutionary timetrees by machine learning.

Bioinformatics

2023 Jan 1

Stanojevic M, Andjelkovic J, Kasprowicz A

75. PANACEA: network-based methods for pharmacotherapy prioritization in personalized oncology.

Bioinformatics

2023 Jan 1

Ulgen E, Ozisik O, Sezerman OU.

76. libRoadRunner 2.0: a high performance SBML simulation and analysis library.

Bioinformatics

2023 Jan 1

Welsh C, Xu J, Smith L

77. Active learning for efficient analysis of high-throughput nanopore data.

Bioinformatics

2023 Jan 1

Guan X, Li Z, Zhou Y

78. CAMR: cross-aligned multimodal representation learning for cancer survival prediction.

Bioinformatics

2023 Jan 1

Wu X, Shi Y, Wang M

MOTIVATION: Accurately predicting cancer survival is crucial for helping clinicians to plan appropriate treatments, which largely improves the life quality of cancer patients and spares the related medical costs. Recent advances in survival prediction methods suggest that integrating complementary information from different modalities, e.g. histopathological images and genomic data, plays a key role in enhancing predictive performance. Despite promising results obtained by existing multimodal methods, the disparate and heterogeneous characteristics of multimodal data cause the so-called modality gap problem, which brings in dramatically diverse modality representations in feature space. Consequently, detrimental modality gaps make it difficult for comprehensive integration of multimodal information via representation learning and therefore pose a great challenge to further improvements of cancer survival prediction. RESULTS: To solve the above problems, we propose a novel method called cross-aligned multimodal representation learning (CAMR), which generates both modality-invariant and -specific representations for more accurate cancer survival prediction. Specifically, a cross-modality representation alignment learning network is introduced to reduce modality gaps by effectively learning modality-invariant representations in a common subspace, which is achieved by aligning the distributions of different modality representations through adversarial training. Besides, we adopt a cross-modality fusion module to fuse modality-invariant representations into a unified cross-modality representation for each patient. Meanwhile, CAMR learns modality-specific representations which complement modality-invariant representations and therefore provides a holistic view of the multimodal data for cancer survival prediction. Comprehensive experiment results demonstrate that CAMR can successfully narrow modality gaps and consistently yields better performance than other survival prediction methods using multimodal data. AVAILABILITY AND IMPLEMENTATION: CAMR is freely available at https://github.com/wxq-ustc/CAMR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

79. Imbalanced classification for protein subcellular localization with multilabel oversampling.

Bioinformatics

2023 Jan 1

Rana P, Sowmya A, Meijering E

MOTIVATION: Subcellular localization of human proteins is essential to comprehend their functions and roles in physiological processes, which in turn helps in diagnostic and prognostic studies of pathological conditions and impacts clinical decision-making. Since proteins reside at multiple locations at the same time and few subcellular locations host far more proteins than other locations, the computational task for their subcellular localization is to train a multilabel classifier while handling data imbalance. In imbalanced data, minority classes are underrepresented, thus leading to a heavy bias towards the majority classes and the degradation of predictive capability for the minority classes. Furthermore, data imbalance in multilabel settings is an even more complex problem due to the coexistence of majority and minority classes. RESULTS: Our studies reveal that based on the extent of concurrence of majority and minority classes, oversampling of minority samples through appropriate data augmentation techniques holds promising scope for boosting the classification performance for the minority classes. We measured the magnitude of data imbalance per class and the concurrence of majority and minority classes in the dataset. Based on the obtained values, we identified minority and medium classes, and a new oversampling method is proposed that includes non-linear mixup, geometric and colour transformations for data augmentation and a sampling approach to prepare minibatches. Performance evaluation on the Human Protein Atlas Kaggle challenge dataset shows that the proposed method is capable of achieving better predictions for minority classes than existing methods. AVAILABILITY AND IMPLEMENTATION: Data used in this study are available at https://www.kaggle.com/competitions/human-protein-atlas-image-classification/data. Source code is available at https://github.com/priyarana/Protein-subcellular-localisation-method. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

80. 3D-equivariant graph neural networks for protein model quality assessment.

Bioinformatics

2023 Jan 1

Chen C, Chen X, Morehead A

MOTIVATION: Quality assessment (QA) of predicted protein tertiary structure models plays an important role in ranking and using them. With the recent development of deep learning end-to-end protein structure prediction techniques for generating highly confident tertiary structures for most proteins, it is important to explore corresponding QA strategies to evaluate and select the structural models predicted by them since these models have better quality and different properties than the models predicted by traditional tertiary structure prediction methods. RESULTS: We develop EnQA, a novel graph-based 3D-equivariant neural network method that is equivariant to rotation and translation of 3D objects to estimate the accuracy of protein structural models by leveraging the structural features acquired from the state-of-the-art tertiary structure prediction method-AlphaFold2. We train and test the method on both traditional model datasets (e.g. the datasets of the Critical Assessment of Techniques for Protein Structure Prediction) and a new dataset of high-quality structural models predicted only by AlphaFold2 for the proteins whose experimental structures were released recently. Our approach achieves state-of-the-art performance on protein structural models predicted by both traditional protein structure prediction methods and the latest end-to-end deep learning method-AlphaFold2. It performs even better than the model QA scores provided by AlphaFold2 itself. The results illustrate that the 3D-equivariant graph neural network is a promising approach to the evaluation of protein structural models. Integrating AlphaFold2 features with other complementary sequence and structural features is important for improving protein model QA. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/BioinfoMachineLearning/EnQA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

81. AptaMat: a matrix-based algorithm to compare single-stranded oligonucleotides secondary structures.

Bioinformatics

2023 Jan 1

Binet T, Avalle B, Dávila Felipe M

82. Improving protein structure prediction using templates and sequence embedding.

Bioinformatics

2023 Jan 1

Wu F, Jing X, Luo X

MOTIVATION: Protein structure prediction has been greatly improved by deep learning, but the contribution of different information is yet to be fully understood. This article studies the impacts of two kinds of information for structure prediction: template and multiple sequence alignment (MSA) embedding. Templates have been used by some methods before, such as AlphaFold2, RoseTTAFold and RaptorX. AlphaFold2 and RosetTTAFold only used templates detected by HHsearch, which may not perform very well on some targets. In addition, sequence embedding generated by pre-trained protein language models has not been fully explored for structure prediction. In this article, we study the impact of templates (including the number of templates, the template quality and how the templates are generated) on protein structure prediction accuracy, especially when the templates are detected by methods other than HHsearch. We also study the impact of sequence embedding (generated by MSATransformer and ESM-1b) on structure prediction. RESULTS: We have implemented a deep learning method for protein structure prediction that may take templates and MSA embedding as extra inputs. We study the contribution of templates and MSA embedding to structure prediction accuracy. Our experimental results show that templates can improve structure prediction on 71 of 110 CASP13 (13th Critical Assessment of Structure Prediction) targets and 47 of 91 CASP14 targets, and templates are particularly useful for targets with similar templates. MSA embedding can improve structure prediction on 63 of 91 CASP14 (14th Critical Assessment of Structure Prediction) targets and 87 of 183 CAMEO targets and is particularly useful for proteins with shallow MSAs. When both templates and MSA embedding are used, our method can predict correct folds (TMscore > 0.5) for 16 of 23 CASP14 FM targets and 14 of 18 Continuous Automated Model Evaluation (CAMEO) targets, outperforming RoseTTAFold by 5% and 7%, respectively. AVAILABILITY AND IMPLEMENTATION: Available at https://github.com/xluo233/RaptorXFold. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

83. Updated site concordance factors minimize effects of homoplasy and taxon sampling.

Bioinformatics

2023 Jan 1

Mo YK, Lanfear R, Hahn MW

84. ManyFold: an efficient and flexible library for training and validating protein folding models.

Bioinformatics

2023 Jan 1

Villegas-Morcillo A, Robinson L, Flajolet A

85. PoDCall: positive droplet calling and normalization of droplet digital PCR DNA methylation data.

Bioinformatics

2023 Jan 1

Jeanmougin M, Brodal HP, Dietrichson Pharo H

86. LapTrack: linear assignment particle tracking with tunable metrics.

Bioinformatics

2023 Jan 1

Fukai YT, Kawaguchi K.

87. scFates: a scalable python package for advanced pseudotime and bifurcation analysis from single-cell data.

Bioinformatics

2023 Jan 1

Faure L, Soldatov R, Kharchenko PV

88. ASTER: accurately estimating the number of cell types in single-cell chromatin accessibility data.

Bioinformatics

2023 Jan 1

Chen S, Wang R, Long W

89. Scaling neighbor joining to one million taxa with dynamic and heuristic neighbor joining.

Bioinformatics

2023 Jan 1

Clausen PTLC.

90. bmVAE: a variational autoencoder method for clustering single-cell mutation data.

Bioinformatics

2023 Jan 1

Yan J, Ma M, Yu Z.

91. DrTransformer: heuristic cotranscriptional RNA folding using the nearest neighbor energy model.

Bioinformatics

2023 Jan 1

Badelt S, Lorenz R, Hofacker IL.

92. AccuVIR: an ACCUrate VIRal genome assembly tool for third-generation sequencing data.

Bioinformatics

2023 Jan 1

Yu R, Cai D, Sun Y.

93. Sc2Mol: a scaffold-based two-step molecule generator with variational autoencoder and transformer.

Bioinformatics

2023 Jan 1

Liao Z, Xie L, Mamitsuka H

94. TransFlow: a Snakemake workflow for transmission analysis of Mycobacterium tuberculosis whole-genome sequencing data.

Bioinformatics

2023 Jan 1

Pan J, Li X, Zhang M

95. Large-scale prediction of adverse drug reactions-related proteins with network embedding.

Bioinformatics

2023 Jan 1

Park J, Lee S, Kim K

96. CanMethdb: a database for genome-wide DNA methylation annotation in cancers.

Bioinformatics

2023 Jan 1

Zhao J, Qian F, Li X

97. Microbiome Toolbox: methodological approaches to derive and visualize microbiome trajectories.

Bioinformatics

2023 Jan 1

Banjac J, Sprenger N, Dogra SK.

98. Perceiver CPI: a nested cross-attention network for compound-protein interaction prediction.

Bioinformatics

2023 Jan 1

Nguyen NQ, Jang G, Kim H

99. PTMint database of experimentally verified PTM regulation on protein-protein interaction.

Bioinformatics

2023 Jan 1

Hong X, Li N, Lv J

100. Accurately modeling biased random walks on weighted networks using node2vec.

Bioinformatics

2023 Jan 1

Liu R, Hirn M, Krishnan A.

101. Integrative chromatin domain annotation through graph embedding of Hi-C data.

Bioinformatics

2023 Jan 1

Shokraneh N, Arab M, Libbrecht M.

102. Computational prediction and characterization of cell-type-specific and shared binding sites.

Bioinformatics

2023 Jan 1

Zhang Q, Teng P, Wang S

MOTIVATION: Cell-type-specific gene expression is maintained in large part by transcription factors (TFs) selectively binding to distinct sets of sites in different cell types. Recent research works have provided evidence that such cell-type-specific binding is determined by TF's intrinsic sequence preferences, cooperative interactions with co-factors, cell-type-specific chromatin landscapes and 3D chromatin interactions. However, computational prediction and characterization of cell-type-specific and shared binding sites is rarely studied. RESULTS: In this article, we propose two computational approaches for predicting and characterizing cell-type-specific and shared binding sites by integrating multiple types of features, in which one is based on XGBoost and another is based on convolutional neural network (CNN). To validate the performance of our proposed approaches, ChIP-seq datasets of 10 binding factors were collected from the GM12878 (lymphoblastoid) and K562 (erythroleukemic) human hematopoietic cell lines, each of which was further categorized into cell-type-specific (GM12878- and K562-specific) and shared binding sites. Then, multiple types of features for these binding sites were integrated to train the XGBoost- and CNN-based models. Experimental results show that our proposed approaches significantly outperform other competing methods on three classification tasks. Moreover, we identified independent feature contributions for cell-type-specific and shared sites through SHAP values and explored the ability of the CNN-based model to predict cell-type-specific and shared binding sites by excluding or including DNase signals. Furthermore, we investigated the generalization ability of our proposed approaches to different binding factors in the same cellular environment. AVAILABILITY AND IMPLEMENTATION: The source code is available at: https://github.com/turningpoint1988/CSSBS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

103. GWAS quality score for evaluating associated regions in GWAS analyses.

Bioinformatics

2023 Jan 1

Awasthi S, Chen CY, Lam M

104. Benchmarking and integration of methods for deconvoluting spatial transcriptomic data.

Bioinformatics

2023 Jan 1

Yan L, Sun X.

MOTIVATION: The rapid development of spatial transcriptomics (ST) approaches has provided new insights into understanding tissue architecture and function. However, the gene expressions measured at a spot may contain contributions from multiple cells due to the low-resolution of current ST technologies. Although many computational methods have been developed to disentangle discrete cell types from spatial mixtures, the community lacks a thorough evaluation of the performance of those deconvolution methods. RESULTS: Here, we present a comprehensive benchmarking of 14 deconvolution methods on four datasets. Furthermore, we investigate the robustness of different methods to sequencing depth, spot size and the choice of normalization. Moreover, we propose a new ensemble learning-based deconvolution method (EnDecon) by integrating multiple individual methods for more accurate deconvolution. The major new findings include: (i) cell2loction, RCTD and spatialDWLS are more accurate than other ST deconvolution methods, based on the evaluation of three metrics: RMSE, PCC and JSD; (ii) cell2location and spatialDWLS are more robust to the variation of sequencing depth than RCTD; (iii) the accuracy of the existing methods tends to decrease as the spot size becomes smaller; (iv) most deconvolution methods perform best when they normalize ST data using the method described in their original papers; and (v) the integrative method, EnDecon, could achieve more accurate ST deconvolution. Our study provides valuable information and guideline for practically applying ST deconvolution tools and developing new and more effective methods. AVAILABILITY AND IMPLEMENTATION: The benchmarking pipeline is available at https://github.com/SunXQlab/ST-deconvoulution. An R package for EnDecon is available at https://github.com/SunXQlab/EnDecon. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

105. Pseudoalignment tools as an efficient alternative to detect repeated transposable elements in scRNAseq data.

Bioinformatics

2023 Jan 1

de Villarreal JM, Kalisz M, Piedrafita G

106. ViMRT: a text-mining tool and search engine for automated virus mutation recognition.

Bioinformatics

2023 Jan 1

Tong Y, Tan F, Huang H

107. NetSHy: network summarization via a hybrid approach leveraging topological properties.

Bioinformatics

2023 Jan 1

Vu T, Litkowski EM, Liu W

MOTIVATION: Biological networks can provide a system-level understanding of underlying processes. In many contexts, networks have a high degree of modularity, i.e. they consist of subsets of nodes, often known as subnetworks or modules, which are highly interconnected and may perform separate functions. In order to perform subsequent analyses to investigate the association between the identified module and a variable of interest, a module summarization, that best explains the module's information and reduces dimensionality is often needed. Conventional approaches for obtaining network representation typically rely only on the profiles of the nodes within the network while disregarding the inherent network topological information. RESULTS: In this article, we propose NetSHy, a hybrid approach which is capable of reducing the dimension of a network while incorporating topological properties to aid the interpretation of the downstream analyses. In particular, NetSHy applies principal component analysis (PCA) on a combination of the node profiles and the well-known Laplacian matrix derived directly from the network similarity matrix to extract a summarization at a subject level. Simulation scenarios based on random and empirical networks at varying network sizes and sparsity levels show that NetSHy outperforms the conventional PCA approach applied directly on node profiles, in terms of recovering the true correlation with a phenotype of interest and maintaining a higher amount of explained variation in the data when networks are relatively sparse. The robustness of NetSHy is also demonstrated by a more consistent correlation with the observed phenotype as the sample size decreases. Lastly, a genome-wide association study is performed as an application of a downstream analysis, where NetSHy summarization scores on the biological networks identify more significant single nucleotide polymorphisms than the conventional network representation. AVAILABILITY AND IMPLEMENTATION: R code implementation of NetSHy is available at https://github.com/thaovu1/NetSHy. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

108. SBMLDiagrams: a python package to process and visualize SBML layout and render.

Bioinformatics

2023 Jan 1

Xu J, Jiang J, Sauro HM.

109. Comprehensive visualization of cell-cell interactions in single-cell and spatial transcriptomics with NICHES.

Bioinformatics

2023 Jan 1

Raredon MSB, Yang J, Kothapalli N

110. Biophysical informatics reveals distinctive phenotypic signatures and functional diversity of single-cell lineages.

Bioinformatics

2023 Jan 1

Chan TJ, Zhang X, Mak M.

111. TCRconv: predicting recognition between T cell receptors and epitopes using contextualized motifs.

Bioinformatics

2023 Jan 1

Jokinen E, Dumitrescu A, Huuhtanen J

112. KMCP: accurate metagenomic profiling of both prokaryotic and viral populations by pseudo-mapping.

Bioinformatics

2023 Jan 1

Shen W, Xiang H, Huang T

113. A unified mediation analysis framework for integrative cancer proteogenomics with clinical outcomes.

Bioinformatics

2023 Jan 1

Huang L, Long JP, Irajizad E

114. GeneticsMakie.jl: a versatile and scalable toolkit for visualizing locus-level genetic and genomic data.

Bioinformatics

2023 Jan 1

Kim M, Vo DD, Kumagai ME

115. Symbolic kinetic models in python (SKiMpy): intuitive modeling of large-scale biological kinetic models.

Bioinformatics

2023 Jan 1

Weilandt DR, Salvy P, Masid M

116. Accurate estimation of molecular counts from amplicon sequence data with unique molecular identifiers.

Bioinformatics

2023 Jan 1

Peng X, Dorman KS.

117. Revana: a comprehensive tool for regulatory variant analysis and visualization of cancer genomes.

Bioinformatics

2023 Jan 1

Ulrich E, Pfister SM, Jäger N.

118. Discovering comorbid diseases using an inter-disease interactivity network based on biobank-scale PheWAS data.

Bioinformatics

2023 Jan 1

Nam Y, Jung SH, Yun JS

119. DeepSom: a CNN-based approach to somatic variant calling in WGS samples without a matched normal.

Bioinformatics

2023 Jan 1

Vilov S, Heinig M.

120. GSEL: a fast, flexible python package for detecting signatures of diverse evolutionary forces on genomic regions.

Bioinformatics

2023 Jan 1

Abraham A, Labella AL, Benton ML

121. A framework for group-wise summarization and comparison of chromatin state annotations.

Bioinformatics

2023 Jan 1

Vu H, Koch Z, Fiziev P

122. screenwerk: a modular tool for the design and analysis of drug combination screens.

Bioinformatics

2023 Jan 1

Hanes R, Ayuda-Durán P, Rønneberg L

123. DFinder: a novel end-to-end graph embedding-based method to identify drug-food interactions.

Bioinformatics

2023 Jan 1

Wang T, Yang J, Xiao Y

124. DeepCellEss: cell line-specific essential protein prediction with attention-based interpretable deep learning.

Bioinformatics

2023 Jan 1

Li Y, Zeng M, Zhang F

125. Predicting protein stability changes upon mutation using a simple orientational potential.

Bioinformatics

2023 Jan 1

Hernández IM, Dehouck Y, Bastolla U

126. SEGCOND predicts putative transcriptional condensate-associated genomic regions by integrating multi-omics data.

Bioinformatics

2023 Jan 1

Klonizakis A, Nikolaou C, Graf T.

127. Inference of 3D genome architecture by modeling overdispersion of Hi-C data.

Bioinformatics

2023 Jan 1

Varoquaux N, Noble WS, Vert JP.

128. DeepPHiC: predicting promoter-centered chromatin interactions using a novel deep learning approach.

Bioinformatics

2023 Jan 1

Agarwal A, Chen L.

MOTIVATION: Promoter-centered chromatin interactions, which include promoter-enhancer (PE) and promoter-promoter (PP) interactions, are important to decipher gene regulation and disease mechanisms. The development of next-generation sequencing technologies such as promoter capture Hi-C (pcHi-C) leads to the discovery of promoter-centered chromatin interactions. However, pcHi-C experiments are expensive and thus may be unavailable for tissues/cell types of interest. In addition, these experiments may be underpowered due to insufficient sequencing depth or various artifacts, which results in a limited finding of interactions. Most existing computational methods for predicting chromatin interactions are based on in situ Hi-C and can detect chromatin interactions across the entire genome. However, they may not be optimal for predicting promoter-centered chromatin interactions. RESULTS: We develop a supervised multi-modal deep learning model, which utilizes a comprehensive set of features such as genomic sequence, epigenetic signal, anchor distance, evolutionary features and DNA structural features to predict tissue/cell type-specific PE and PP interactions. We further extend the deep learning model in a multi-task learning and a transfer learning framework and demonstrate that the proposed approach outperforms state-of-the-art deep learning methods. Moreover, the proposed approach can achieve comparable prediction performance using predefined biologically relevant tissues/cell types compared to using all tissues/cell types in the pretraining especially for predicting PE interactions. The prediction performance can be further improved by using computationally inferred biologically relevant tissues/cell types in the pretraining, which are defined based on the common genes in the proximity of two anchors in the chromatin interactions. AVAILABILITY AND IMPLEMENTATION: https://github.com/lichen-lab/DeepPHiC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

129. AutoDeconJ: a GPU-accelerated ImageJ plugin for 3D light-field deconvolution with optimal iteration numbers predicting.

Bioinformatics

2023 Jan 1

Su C, Gao Y, Zhou Y

130. RatesTools: a Nextflow pipeline for detecting de novo germline mutations in pedigree sequence data.

Bioinformatics

2023 Jan 1

Armstrong EE, Campana MG.

131. UPP2: fast and accurate alignment of datasets with fragmentary sequences.

Bioinformatics

2023 Jan 1

Park M, Ivanovic S, Chu G

132. GSEApy: a comprehensive package for performing gene set enrichment analysis in Python.

Bioinformatics

2023 Jan 1

Fang Z, Liu X, Peltz G.

133. GOThresher: a program to remove annotation biases from protein function annotation datasets.

Bioinformatics

2023 Jan 1

Joshi P, Banerjee S, Hu X

134. DeepRank-GNN: a graph neural network framework to learn patterns in protein-protein interfaces.

Bioinformatics

2023 Jan 1

Réau M, Renaud N, Xue LC

135. HyperHMM: efficient inference of evolutionary and progressive dynamics on hypercubic transition graphs.

Bioinformatics

2023 Jan 1

Moen MT, Johnston IG.

136. varAmpliCNV: analyzing variance of amplicons to detect CNVs in targeted NGS data.

Bioinformatics

2023 Jan 1

Kumar AA, Loeys B, Van De Beek G

137. EnDecon: cell type deconvolution of spatially resolved transcriptomics data via ensemble learning.

Bioinformatics

2023 Jan 1

Tu JJ, Li HS, Yan H

138. SCIBER: a simple method for removing batch effects from single-cell RNA-sequencing data.

Bioinformatics

2023 Jan 1

Gan D, Li J.

139. NanoSNP: a progressive and haplotype-aware SNP caller on low-coverage nanopore sequencing data.

Bioinformatics

2023 Jan 1

Huang N, Xu M, Nie F

MOTIVATION: Oxford Nanopore sequencing has great potential and advantages in population-scale studies. Due to the cost of sequencing, the depth of whole-genome sequencing for per individual sample must be small. However, the existing single nucleotide polymorphism (SNP) callers are aimed at high-coverage Nanopore sequencing reads. Detecting the SNP variants on low-coverage Nanopore sequencing data is still a challenging problem. RESULTS: We developed a novel deep learning-based SNP calling method, NanoSNP, to identify the SNP sites (excluding short indels) based on low-coverage Nanopore sequencing reads. In this method, we design a multi-step, multi-scale and haplotype-aware SNP detection pipeline. First, the pileup model in NanoSNP utilizes the naive pileup feature to predict a subset of SNP sites with a Bi-long short-term memory (LSTM) network. These SNP sites are phased and used to divide the low-coverage Nanopore reads into different haplotypes. Finally, the long-range haplotype feature and short-range pileup feature are extracted from each haplotype. The haplotype model combines two features and predicts the genotype for the candidate site using a Bi-LSTM network. To evaluate the performance of NanoSNP, we compared NanoSNP with Clair, Clair3, Pepper-DeepVariant and NanoCaller on the low-coverage (∼16×) Nanopore sequencing reads. We also performed cross-genome testing on six human genomes HG002-HG007, respectively. Comprehensive experiments demonstrate that NanoSNP outperforms Clair, Pepper-DeepVariant and NanoCaller in identifying SNPs on low-coverage Nanopore sequencing data, including the difficult-to-map regions and major histocompatibility complex regions in the human genome. NanoSNP is comparable to Clair3 when the coverage exceeds 16×. AVAILABILITY AND IMPLEMENTATION: https://github.com/huangnengCSU/NanoSNP.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

140. HelixGAN a deep-learning methodology for conditional de novo design of α-helix structures.

Bioinformatics

2023 Jan 1

Xie X, Valiente PA, Kim PM.

141. konnect2prot: a web application to explore the protein properties in a functional protein-protein interaction network.

Bioinformatics

2023 Jan 1

Kumar S, Sarmah DT, Asthana S

142. AWOT and CWOT for genotype and genotype-by-treatment interaction joint analysis in pharmacogenetics GWAS.

Bioinformatics

2023 Jan 1

Zhang H, Mehrotra DV, Shen J.

143. sAMPpred-GAT: prediction of antimicrobial peptide by graph attention network and predicted peptide structure.

Bioinformatics

2023 Jan 1

Yan K, Lv H, Guo Y

144. A data processing pipeline for the AACR project GENIE biopharma collaborative data with the {genieBPC} R package.

Bioinformatics

2023 Jan 1

Lavery JA, Brown S, Curry MA

145. FungiExp: a user-friendly database and analysis platform for exploring fungal gene expression and alternative splicing.

Bioinformatics

2023 Jan 1

Liu J, Zhang Y, Shi Y

146. GeoWaVe: geometric median clustering with weighted voting for ensemble clustering of cytometry data.

Bioinformatics

2023 Jan 1

Burton RJ, Cuff SM, Morgan MP

MOTIVATION: Clustering is an unsupervised method for identifying structure in unlabelled data. In the context of cytometry, it is typically used to categorize cells into subpopulations of similar phenotypes. However, clustering is greatly dependent on hyperparameters and the data to which it is applied as each algorithm makes different assumptions and generates a different 'view' of the dataset. As such, the choice of clustering algorithm can significantly influence results, and there is often not one preferred method but different insights to be obtained from different methods. To overcome these limitations, consensus approaches are needed that directly address the effect of competing algorithms. To the best of our knowledge, consensus clustering algorithms designed specifically for the analysis of cytometry data are lacking. RESULTS: We present a novel ensemble clustering methodology based on geometric median clustering with weighted voting (GeoWaVe). Compared to graph ensemble clustering methods that have gained popularity in single-cell RNA sequencing analysis, GeoWaVe performed favourably on different sets of high-dimensional mass and flow cytometry data. Our findings provide proof of concept for the power of consensus methods to make the analysis, visualization and interpretation of cytometry data more robust and reproducible. The wide availability of ensemble clustering methods is likely to have a profound impact on our understanding of cellular responses, clinical conditions and therapeutic and diagnostic options. AVAILABILITY AND IMPLEMENTATION: GeoWaVe is available as part of the CytoCluster package https://github.com/burtonrj/CytoCluster and published on the Python Package Index https://pypi.org/project/cytocluster. Benchmarking data described are available from https://doi.org/10.5281/zenodo.7134723. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

147. Systematic identification of biochemical networks in cancer cells by functional pathway inference analysis.

Bioinformatics

2023 Jan 1

Badshah II, Cutillas PR.

148. ABSP: an automated R tool to efficiently analyze region-specific CpG methylation from bisulfite sequencing PCR.

Bioinformatics

2023 Jan 1

Denoulet M, Brulé M, Anquez F

149. Asteroid: a new algorithm to infer species trees from gene trees under high proportions of missing data.

Bioinformatics

2023 Jan 1

Morel B, Williams TA, Stamatakis A.

150. Evaluating impacts of syntenic block detection strategies on rearrangement phylogeny using Mycobacterium tuberculosis isolates.

Bioinformatics

2023 Jan 1

Elghraoui A, Mirarab S, Swenson KM

151. DxFormer: a decoupled automatic diagnostic system based on decoder-encoder transformer with dense symptom representations.

Bioinformatics

2023 Jan 1

Chen W, Zhong C, Peng J

MOTIVATION: Symptom-based automatic diagnostic system queries the patient's potential symptoms through continuous interaction with the patient and makes predictions about possible diseases. A few studies use reinforcement learning (RL) to learn the optimal policy from the joint action space of symptoms and diseases. However, existing RL (or Non-RL) methods focus on disease diagnosis while ignoring the importance of symptom inquiry. Although these systems have achieved considerable diagnostic accuracy, they are still far below its performance upper bound due to few turns of interaction with patients and insufficient performance of symptom inquiry. To address this problem, we propose a new automatic diagnostic framework called DxFormer, which decouples symptom inquiry and disease diagnosis, so that these two modules can be independently optimized. The transition from symptom inquiry to disease diagnosis is parametrically determined by the stopping criteria. In DxFormer, we treat each symptom as a token, and formalize the symptom inquiry and disease diagnosis to a language generation model and a sequence classification model, respectively. We use the inverted version of Transformer, i.e. the decoder-encoder structure, to learn the representation of symptoms by jointly optimizing the reinforce reward and cross-entropy loss. RESULTS: We conduct experiments on three real-world medical dialogue datasets, and the experimental results verify the feasibility of increasing diagnostic accuracy by improving symptom recall. Our model overcomes the shortcomings of previous RL-based methods. By decoupling symptom query from the process of diagnosis, DxFormer greatly improves the symptom recall and achieves the state-of-the-art diagnostic accuracy. AVAILABILITY AND IMPLEMENTATION: Both code and data are available at https://github.com/lemuria-wchen/DxFormer. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

152. Evaluating homophily in networks via HONTO (HOmophily Network TOol): a case study of chromosomal interactions in human PPI networks.

Bioinformatics

2023 Jan 1

Apollonio N, Blankenberg D, Cumbo F

153. MiCellAnnGELo: annotate microscopy time series of complex cell surfaces with 3D virtual reality.

Bioinformatics

2023 Jan 1

Platt A, Lutton EJ, Offord E

154. GPGPS: a robust prognostic gene pair signature of glioma ensembling IDH mutation and 1p/19q co-deletion.

Bioinformatics

2023 Jan 1

Cheng L, Wu H, Zheng X

155. Visual Omics: a web-based platform for omics data analysis and visualization with rich graph-tuning capabilities.

Bioinformatics

2023 Jan 1

Li H, Shi M, Ren K

156. An approach of gene regulatory network construction using mixed entropy optimizing context-related likelihood mutual information.

Bioinformatics

2023 Jan 1

Lei J, Cai Z, He X

157. Digitally predicting protein localization and manipulating protein activity in fluorescence images using 4D reslicing GAN.

Bioinformatics

2023 Jan 1

Jiao Y, Gu L, Jiang Y

MOTIVATION: While multi-channel fluorescence microscopy is a vital imaging method in biological studies, the number of channels that can be imaged simultaneously is limited by technical and hardware limitations such as emission spectra cross-talk. One solution is using deep neural networks to model the localization relationship between two proteins so that the localization of one protein can be digitally predicted. Furthermore, the input and predicted localization implicitly reflect the modeled relationship. Accordingly, observing the response of the prediction via manipulating input localization could provide an informative way to analyze the modeled relationships between the input and the predicted proteins. RESULTS: We propose a protein localization prediction (PLP) method using a cGAN named 4D Reslicing Generative Adversarial Network (4DR-GAN) to digitally generate additional channels. 4DR-GAN models the joint probability distribution of input and output proteins by simultaneously incorporating the protein localization signals in four dimensions including space and time. Because protein localization often correlates with protein activation state, based on accurate PLP, we further propose two novel tools: digital activation (DA) and digital inactivation (DI) to digitally activate and inactivate a protein, in order to observing the response of the predicted protein localization. Compared with genetic approaches, these tools allow precise spatial and temporal control. A comprehensive experiment on six pairs of proteins shows that 4DR-GAN achieves higher-quality PLP than Pix2Pix, and the DA and DI responses are consistent with the known protein functions. The proposed PLP method helps simultaneously visualize additional proteins, and the developed DA and DI tools provide guidance to study localization-based protein functions. AVAILABILITY AND IMPLEMENTATION: The open-source code is available at https://github.com/YangJiaoUSA/4DR-GAN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

158. SysBiolPGWAS: simplifying post-GWAS analysis through the use of computational technologies and integration of diverse omics datasets.

Bioinformatics

2023 Jan 1

Falola O, Adam Y, Ajayi O

159. RaMP-DB 2.0: a renovated knowledgebase for deriving biological and chemical insight from metabolites, proteins, and genes.

Bioinformatics

2023 Jan 1

Braisted J, Patt A, Tindall C

160. A multi-view latent variable model reveals cellular heterogeneity in complex tissues for paired multimodal single-cell data.

Bioinformatics

2023 Jan 1

Wang Y, Lian B, Zhang H

161. Gene set proximity analysis: expanding gene set enrichment analysis through learned geometric embeddings, with drug-repurposing applications in COVID-19.

Bioinformatics

2023 Jan 1

Cousins H, Hall T, Guo Y

162. NcPath: a novel platform for visualization and enrichment analysis of human non-coding RNA and KEGG signaling pathways.

Bioinformatics

2023 Jan 1

Li Z, Zhang Y, Fang J

SUMMARY: Non-coding RNAs play important roles in transcriptional processes and participate in the regulation of various biological functions, in particular miRNAs and lncRNAs. Despite their importance for several biological functions, the existing signaling pathway databases do not include information on miRNA and lncRNA. Here, we redesigned a novel pathway database named NcPath by integrating and visualizing a total of 178 308 human experimentally validated miRNA-target interactions (MTIs), 32 282 experimentally verified lncRNA-target interactions (LTIs) and 4837 experimentally validated human ceRNA networks across 222 KEGG pathways (including 27 sub-categories). To expand the application potential of the redesigned NcPath database, we identified 556 798 reliable lncRNA-protein-coding genes (PCG) interaction pairs by integrating co-expression relations, ceRNA relations, co-TF-binding interactions, co-histone-modification interactions, cis-regulation relations and lncPro Tool predictions between lncRNAs and PCG. In addition, to determine the pathways in which miRNA/lncRNA targets are involved, we performed a KEGG enrichment analysis using a hypergeometric test. The NcPath database also provides information on MTIs/LTIs/ceRNA networks, PubMed IDs, gene annotations and the experimental verification method used. In summary, the NcPath database will serve as an important and continually updated platform that provides annotation and visualization of the pathways on which non-coding RNAs (miRNA and lncRNA) are involved, and provide support to multimodal non-coding RNAs enrichment analysis. The NcPath database is freely accessible at http://ncpath.pianlab.cn/. AVAILABILITY AND IMPLEMENTATION: NcPath database is freely available at http://ncpath.pianlab.cn/. The code and manual to use NcPath can be found at https://github.com/Marscolono/NcPath/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

163. scAWMV: an adaptively weighted multi-view learning framework for the integrative analysis of parallel scRNA-seq and scATAC-seq data.

Bioinformatics

2023 Jan 1

Zeng P, Ma Y, Lin Z.

164. Distinguishing imported cases from locally acquired cases within a geographically limited genomic sample of an infectious disease.

Bioinformatics

2023 Jan 1

Didelot X, Helekal D, Kendall M

165. A pairwise strategy for imputing predictive features when combining multiple datasets.

Bioinformatics

2023 Jan 1

Wu Y, Ren B, Patil P.