Searching journal content for articles similar to Issac and Raghava 14 (9): 1756.

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  1. Method: Combining DNA and protein alignments to improve genome annotation with LiftOn
    • Kuan-Hao Chao,
    • Jakob M. Heinz,
    • Celine Hoh,
    • Alan Mao,
    • Alaina Shumate,
    • Mihaela Pertea,
    • and Steven L. Salzberg
    Genome Res. February 2025 35: 311-325; Published in Advance December 27, 2024, doi:10.1101/gr.279620.124
    ...'Leary et al. 2016; Frankish et al. 2019; Salzberg 2019). The most widely used approaches to annotation are software pipelines such as MAKER, MAKER-P (Cantarel et al. 2008; Campbell et al. 2014), BRAKER, and BRAKER2 (Hoff et al. 2019; Brůna et al. 2021), which combine ab initio prediction, protein...
  2. Method: Automated interpretable artificial intelligence genomic prediction with AlGP
    • Lei Wei,
    • Ziqin Jiang,
    • Baoliang Fan,
    • Yidan Yan,
    • Zhenqiang Xu,
    • Xiaoxiang Hu,
    • and Yuzhe Wang
    Genome Res. March 5, 2026 ; Published in Advance March 5, 2026, doi:10.1101/gr.281006.125
    ....2541 and 0.2503, respectively, surpassing the optimal result for GBLUP (0.2363) (Fig. 3B). Similar results were observed for AGG traits. CatBoost maintained the best predictive performance after applying the two SNP selection strategies (Fig. 3C). We applied the aforementioned strategy to the whole using...
  3. Method: MCHelper automatically curates transposable element libraries across eukaryotic species
    • Simon Orozco-Arias,
    • Pío Sierra,
    • Richard Durbin,
    • and Josefa González
    Genome Res. December 2024 34: 2256-2268; Published in Advance December 9, 2024, doi:10.1101/gr.278821.123
    .... Genome-wide TE annotations are also improved, including larger unfragmented insertions. Moreover, MCHelper is an easy-to-install and easy-to-use tool.After two decades of sequencing projects, reference s for thousands of eukaryotic species are already available and many more are currently being sequenced...
    OPEN ACCESS ARTICLE
  4. Method: Protein domain embeddings for fast and accurate similarity search
    • Benjamin Giovanni Iovino,
    • Haixu Tang,
    • and Yuzhen Ye
    Genome Res. September 2024 34: 1434-1444; Published in Advance September 5, 2024, doi:10.1101/gr.279127.124
    ...-RecCut uses contact maps predicted from ESM-2 (together with contextual embeddings of individual residues), taking advantage of its capability of generating contextual embeddings without using MSA so there are no time-consuming iterative searches of similar sequences. In addition, we developed...
    OPEN ACCESS ARTICLE
  5. Method: Probing the eukaryotic microbes of ruminants with a deep-learning classifier and comprehensive protein databases
    • Ming Yan,
    • Thea O. Andersen,
    • Phillip B. Pope,
    • and Zhongtang Yu
    Genome Res. February 2025 35: 368-378; Published in Advance December 27, 2024, doi:10.1101/gr.279825.124
    ...-learning model combining a CNN (one-hot encoded nucleotide bases as inputs) and a feedforward neural network (FNN; k-mer frequencies as inputs) (Fig. 1A,B). GutEuk uses a two-stage classification with user-defined confidence levels at each stage: The first stage differentiates between prokaryotic and eukaryotic...
  6. Method: GeneMark-ETP significantly improves the accuracy of automatic annotation of large eukaryotic genomes
    • Tomáš Brůna,
    • Alexandre Lomsadze,
    • and Mark Borodovsky
    Genome Res. May 2024 34: 757-768; Published in Advance June 12, 2024, doi:10.1101/gr.278373.123
    ...MarkS-T was performed on these assembled transcripts using the rounds of model parameter estimation and ab initio gene predictions (Tang et al. 2015).View larger version: In this window In a new window Figure 1. High-level diagram of the GeneMark-ETP processing of genomic, RNA-seq, and protein sequences. For more...
    OPEN ACCESS ARTICLE
  7. Method: Incorporating valuable prior knowledge to improve deep learning prediction of genetic perturbation responses
    • Xiuhao Fu,
    • Chao Yang,
    • Chunyan Cui,
    • Aoyun Geng,
    • Yidi Sun,
    • Chao Zha,
    • Feifei Cui,
    • Leyi Wei,
    • Quan Zou,
    • Xin Gao,
    • and Zilong Zhang
    Genome Res. March 26, 2026 gr.281523.125; Published in Advance March 26, 2026, doi:10.1101/gr.281523.125
    ...interactions. 62 Deep learning can learn feature representations from transcriptomic data holds great 63 promise for predicting gene perturbations(Ahlmann-Eltze et al. 2025). Lotfollahi et al. 64 proposed scGen, a model that combines variational autoencoders with latent space 65 vector operations, which...
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  8. Method: Early feature extraction drives model performance in high-resolution chromatin accessibility prediction
    • Aayush Grover,
    • Till Muser,
    • Liine Kasak,
    • Lin Zhang,
    • Ekaterina Krymova,
    • and Valentina Boeva
    Genome Res. March 2026 36: 619-629; Published in Advance January 12, 2026, doi:10.1101/gr.281042.125
    ...for genomic data. Integrated into diverse architectures such as convoluted neural networks (CNNs), long short-term memory (LSTM), dilated CNNs, and transformers, ConvNeXt V2 blocks consistently improve performance, leading to similar prediction accuracy across these different model types. This reveals...
    OPEN ACCESS ARTICLE
  9. Resource: Comprehensive assessment of 11 de novo HiFi assemblers on complex eukaryotic genomes and metagenomes
    • Wenjuan Yu,
    • Haohui Luo,
    • Jinbao Yang,
    • Shengchen Zhang,
    • Heling Jiang,
    • Xianjia Zhao,
    • Xingqi Hui,
    • Da Sun,
    • Liang Li,
    • Xiu-qing Wei,
    • Stefano Lonardi,
    • and Weihua Pan
    Genome Res. February 2024 34: 326-340; Published in Advance March 1, 2024, doi:10.1101/gr.278232.123
    ...of both species abundance and the similarity among the constitutive s.Experiments on complex eukaryotic sExperimental results on real data with varying ploidyAll the assemblers were tested on real HiFi reads for rice (homozygous diploid), potato (heterozygous diploid), and wax apple (autotetraploid...
  10. Method: Modeling gene interactions in polygenic prediction via geometric deep learning
    • Han Li,
    • Jianyang Zeng,
    • Michael P. Snyder,
    • and Sai Zhang
    Genome Res. January 2025 35: 178-187; Published in Advance November 19, 2024, doi:10.1101/gr.279694.124
    ..., USA; 6Departments of Biostatistics & Biomedical Engineering, UF Genetics Institute, University of Florida, Gainesville, Florida 32603, USA Corresponding authors: zengjy@westlake.edu.cn, mpsnyder@stanford.edu, sai.zhang@ufl.eduAbstractPolygenic risk score (PRS) is a widely used approach for predicting...
    OPEN ACCESS ARTICLE
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