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1. eCAMI: simultaneous classification and motif identification for enzyme annotation

   https://doi.org/10.1093/bioinformatics/btz908  

   Xu, Jing ; Zhang, Han ; Zheng, Jinfang ; Dovoedo, Philippe ; Yin, Yanbin ; Xu, ed., Jinbo ( December 2019 , Bioinformatics)

   Carbohydrate-active enzymes (CAZymes) are extremely important to bioenergy, human gut microbiome, and plant pathogen researches and industries. Here we developed a new amino acid k-mer-based CAZyme classification, motif identification and genome annotation tool using a bipartite network algorithm. Using this tool, we classified 390 CAZyme families into thousands of subfamilies each with distinguishing k-mer peptides. These k-mers represented the characteristic motifs (in the form of a collection of conserved short peptides) of each subfamily, and thus were further used to annotate new genomes for CAZymes. This idea was also generalized to extract characteristic k-mer peptides for all the Swiss-Prot enzymes classified by the EC (enzyme commission) numbers and applied to enzyme EC prediction.

   This new tool was implemented as a Python package named eCAMI. Benchmark analysis of eCAMI against the state-of-the-art tools on CAZyme and enzyme EC datasets found that: (i) eCAMI has the best performance in terms of accuracy and memory use for CAZyme and enzyme EC classification and annotation; (ii) the k-mer-based tools (including PPR-Hotpep, CUPP and eCAMI) perform better than homology-based tools and deep-learning tools in enzyme EC prediction. Lastly, we confirmed that the k-mer-based tools have the unique ability to identify the characteristic k-mer peptides in the predicted enzymes.

   https://github.com/yinlabniu/eCAMI and https://github.com/zhanglabNKU/eCAMI.

   Supplementary data are available at Bioinformatics online.

    

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2. Network inference performance complexity: a consequence of topological, experimental and algorithmic determinants

   https://doi.org/10.1093/bioinformatics/btz105  

   Muldoon, Joseph J. ; Yu, Jessica S. ; Fassia, Mohammad-Kasim ; Bagheri, Neda ; Valencia, ed., Alfonso ( February 2019 , Bioinformatics)

   Network inference algorithms aim to uncover key regulatory interactions governing cellular decision-making, disease progression and therapeutic interventions. Having an accurate blueprint of this regulation is essential for understanding and controlling cell behavior. However, the utility and impact of these approaches are limited because the ways in which various factors shape inference outcomes remain largely unknown.

   We identify and systematically evaluate determinants of performance—including network properties, experimental design choices and data processing—by developing new metrics that quantify confidence across algorithms in comparable terms. We conducted a multifactorial analysis that demonstrates how stimulus target, regulatory kinetics, induction and resolution dynamics, and noise differentially impact widely used algorithms in significant and previously unrecognized ways. The results show how even if high-quality data are paired with high-performing algorithms, inferred models are sometimes susceptible to giving misleading conclusions. Lastly, we validate these findings and the utility of the confidence metrics using realistic in silico gene regulatory networks. This new characterization approach provides a way to more rigorously interpret how algorithms infer regulation from biological datasets.

   Code is available at http://github.com/bagherilab/networkinference/.

   Supplementary data are available at Bioinformatics online.

    

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3. NetTIME: a multitask and base-pair resolution framework for improved transcription factor binding site prediction

   https://doi.org/10.1093/bioinformatics/btac569  

   Yi, Ren ; Cho, Kyunghyun ; Bonneau, Richard ; Mathelier, ed., Anthony ( August 2022 , Bioinformatics)

   Machine learning models for predicting cell-type-specific transcription factor (TF) binding sites have become increasingly more accurate thanks to the increased availability of next-generation sequencing data and more standardized model evaluation criteria. However, knowledge transfer from data-rich to data-limited TFs and cell types remains crucial for improving TF binding prediction models because available binding labels are highly skewed towards a small collection of TFs and cell types. Transfer prediction of TF binding sites can potentially benefit from a multitask learning approach; however, existing methods typically use shallow single-task models to generate low-resolution predictions. Here, we propose NetTIME, a multitask learning framework for predicting cell-type-specific TF binding sites with base-pair resolution.

   We show that the multitask learning strategy for TF binding prediction is more efficient than the single-task approach due to the increased data availability. NetTIME trains high-dimensional embedding vectors to distinguish TF and cell-type identities. We show that this approach is critical for the success of the multitask learning strategy and allows our model to make accurate transfer predictions within and beyond the training panels of TFs and cell types. We additionally train a linear-chain conditional random field (CRF) to classify binding predictions and show that this CRF eliminates the need for setting a probability threshold and reduces classification noise. We compare our method’s predictive performance with two state-of-the-art methods, Catchitt and Leopard, and show that our method outperforms previous methods under both supervised and transfer learning settings.

   NetTIME is freely available at https://github.com/ryi06/NetTIME and the code is also archived at https://doi.org/10.5281/zenodo.6994897.

   Supplementary data are available at Bioinformatics online.

    

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4. Sequence2Vec: a novel embedding approach for modeling transcription factor binding affinity landscape

   https://doi.org/10.1093/bioinformatics/btx480  

   Dai, Hanjun ; Umarov, Ramzan ; Kuwahara, Hiroyuki ; Li, Yu ; Song, Le ; Gao, Xin ; Stegle, ed., Oliver ( July 2017 , Bioinformatics)

   An accurate characterization of transcription factor (TF)-DNA affinity landscape is crucial to a quantitative understanding of the molecular mechanisms underpinning endogenous gene regulation. While recent advances in biotechnology have brought the opportunity for building binding affinity prediction methods, the accurate characterization of TF-DNA binding affinity landscape still remains a challenging problem.

   Here we propose a novel sequence embedding approach for modeling the transcription factor binding affinity landscape. Our method represents DNA binding sequences as a hidden Markov model which captures both position specific information and long-range dependency in the sequence. A cornerstone of our method is a novel message passing-like embedding algorithm, called Sequence2Vec, which maps these hidden Markov models into a common nonlinear feature space and uses these embedded features to build a predictive model. Our method is a novel combination of the strength of probabilistic graphical models, feature space embedding and deep learning. We conducted comprehensive experiments on over 90 large-scale TF-DNA datasets which were measured by different high-throughput experimental technologies. Sequence2Vec outperforms alternative machine learning methods as well as the state-of-the-art binding affinity prediction methods.

   Our program is freely available at https://github.com/ramzan1990/sequence2vec.

   Supplementary data are available at Bioinformatics online.

    

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5. Genome wide predictions of miRNA regulation by transcription factors

   https://doi.org/10.1093/bioinformatics/btw452  

   Ruffalo, Matthew ; Bar-Joseph, Ziv ( August 2016 , Bioinformatics)

   Reconstructing regulatory networks from expression and interaction data is a major goal of systems biology. While much work has focused on trying to experimentally and computationally determine the set of transcription-factors (TFs) and microRNAs (miRNAs) that regulate genes in these networks, relatively little work has focused on inferring the regulation of miRNAs by TFs. Such regulation can play an important role in several biological processes including development and disease. The main challenge for predicting such interactions is the very small positive training set currently available. Another challenge is the fact that a large fraction of miRNAs are encoded within genes making it hard to determine the specific way in which they are regulated.

   To enable genome wide predictions of TF–miRNA interactions, we extended semi-supervised machine-learning approaches to integrate a large set of different types of data including sequence, expression, ChIP-seq and epigenetic data. As we show, the methods we develop achieve good performance on both a labeled test set, and when analyzing general co-expression networks. We next analyze mRNA and miRNA cancer expression data, demonstrating the advantage of using the predicted set of interactions for identifying more coherent and relevant modules, genes, and miRNAs. The complete set of predictions is available on the supporting website and can be used by any method that combines miRNAs, genes, and TFs.

   Code and full set of predictions are available from the supporting website: http://cs.cmu.edu/~mruffalo/tf-mirna/.

   zivbj@cs.cmu.edu

   Supplementary data are available at Bioinformatics online.

    

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