skip to main content


Search for: All records

Creators/Authors contains: "Kelso, ed., Janet"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Abstract Motivation

    Displaying proportional data across many spatially resolved coordinates is a challenging but important data visualization task, particularly for spatially resolved transcriptomics data. Scatter pie plots are one type of commonly used data visualization for such data but present perceptual challenges that may lead to difficulties in interpretation. Increasing the visual saliency of such data visualizations can help viewers more accurately identify proportional trends and compare proportional differences across spatial locations.

    Results

    We developed scatterbar, an open-source R package that extends ggplot2, to visualize proportional data across many spatially resolved coordinates using scatter stacked bar plots. We apply scatterbar to visualize deconvolved cell-type proportions from a spatial transcriptomics dataset of the adult mouse brain to demonstrate how scatter stacked bar plots can enhance the distinguishability of proportional distributions compared to scatter pie plots.

    Availability and implementation

    scatterbar is available on CRAN https://cran.r-project.org/package=scatterbar with additional documentation and tutorials at https://jef.works/scatterbar/.

     
    more » « less
  2. Abstract Motivation

    Recent advancements in natural language processing have highlighted the effectiveness of global contextualized representations from protein language models (pLMs) in numerous downstream tasks. Nonetheless, strategies to encode the site-of-interest leveraging pLMs for per-residue prediction tasks, such as crotonylation (Kcr) prediction, remain largely uncharted.

    Results

    Herein, we adopt a range of approaches for utilizing pLMs by experimenting with different input sequence types (full-length protein sequence versus window sequence), assessing the implications of utilizing per-residue embedding of the site-of-interest as well as embeddings of window residues centered around it. Building upon these insights, we developed a novel residual ConvBiLSTM network designed to process window-level embeddings of the site-of-interest generated by the ProtT5-XL-UniRef50 pLM using full-length sequences as input. This model, termed T5ResConvBiLSTM, surpasses existing state-of-the-art Kcr predictors in performance across three diverse datasets. To validate our approach of utilizing full sequence-based window-level embeddings, we also delved into the interpretability of ProtT5-derived embedding tensors in two ways: firstly, by scrutinizing the attention weights obtained from the transformer’s encoder block; and secondly, by computing SHAP values for these tensors, providing a model-agnostic interpretation of the prediction results. Additionally, we enhance the latent representation of ProtT5 by incorporating two additional local representations, one derived from amino acid properties and the other from supervised embedding layer, through an intermediate fusion stacked generalization approach, using an n-mer window sequence (or, peptide/fragment). The resultant stacked model, dubbed LMCrot, exhibits a more pronounced improvement in predictive performance across the tested datasets.

    Availability and implementation

    LMCrot is publicly available at https://github.com/KCLabMTU/LMCrot.

     
    more » « less
  3. Abstract Motivation

    Genome-scale metabolic network reconstructions (GENREs) are valuable for understanding cellular metabolism in silico. Several tools exist for automatic GENRE generation. However, these tools frequently (i) do not readily integrate with some of the widely-used suites of packaged methods available for network analysis, (ii) lack effective network curation tools, (iii) are not sufficiently user-friendly, and (iv) often produce low-quality draft reconstructions.

    Results

    Here, we present Reconstructor, a user-friendly, COBRApy-compatible tool that produces high-quality draft reconstructions with reaction and metabolite naming conventions that are consistent with the ModelSEED biochemistry database and includes a gap-filling technique based on the principles of parsimony. Reconstructor can generate SBML GENREs from three input types: annotated protein .fasta sequences (Type 1 input), a BLASTp output (Type 2), or an existing SBML GENRE that can be further gap-filled (Type 3). While Reconstructor can be used to create GENREs of any species, we demonstrate the utility of Reconstructor with bacterial reconstructions. We demonstrate how Reconstructor readily generates high-quality GENRES that capture strain, species, and higher taxonomic differences in functional metabolism of bacteria and are useful for further biological discovery.

    Availability and implementation

    The Reconstructor Python package is freely available for download. Complete installation and usage instructions and benchmarking data are available at http://github.com/emmamglass/reconstructor.

     
    more » « less
  4. Abstract Motivation

    Driven by technological advances, the throughput and cost of mass spectrometry (MS) proteomics experiments have improved by orders of magnitude in recent decades. Spectral library searching is a common approach to annotating experimental mass spectra by matching them against large libraries of reference spectra corresponding to known peptides. An important disadvantage, however, is that only peptides included in the spectral library can be found, whereas novel peptides, such as those with unexpected post-translational modifications (PTMs), will remain unknown. Open modification searching (OMS) is an increasingly popular approach to annotate modified peptides based on partial matches against their unmodified counterparts. Unfortunately, this leads to very large search spaces and excessive runtimes, which is especially problematic considering the continuously increasing sizes of MS proteomics datasets.

    Results

    We propose an OMS algorithm, called HOMS-TC, that fully exploits parallelism in the entire pipeline of spectral library searching. We designed a new highly parallel encoding method based on the principle of hyperdimensional computing to encode mass spectral data to hypervectors while minimizing information loss. This process can be easily parallelized since each dimension is calculated independently. HOMS-TC processes two stages of existing cascade search in parallel and selects the most similar spectra while considering PTMs. We accelerate HOMS-TC on NVIDIA’s tensor core units, which is emerging and readily available in the recent graphics processing unit (GPU). Our evaluation shows that HOMS-TC is 31× faster on average than alternative search engines and provides comparable accuracy to competing search tools.

    Availability and implementation

    HOMS-TC is freely available under the Apache 2.0 license as an open-source software project at https://github.com/tycheyoung/homs-tc.

     
    more » « less
  5. Abstract Motivation

    DNA-based data storage is a quickly growing field that hopes to harness the massive theoretical information density of DNA molecules to produce a competitive next-generation storage medium suitable for archival data. In recent years, many DNA-based storage system designs have been proposed. Given that no common infrastructure exists for simulating these storage systems, comparing many different designs along with many different error models is increasingly difficult. To address this challenge, we introduce FrameD, a simulation infrastructure for DNA storage systems that leverages the underlying modularity of DNA storage system designs to provide a framework to express different designs while being able to reuse common components.

    Results

    We demonstrate the utility of FrameD and the need for a common simulation platform using a case study. Our case study compares designs that utilize strand copies differently, some that align strand copies using multiple sequence alignment algorithms and others that do not. We found that the choice to include multiple sequence alignment in the pipeline is dependent on the error rate and the type of errors being injected and is not always beneficial. In addition to supporting a wide range of designs, FrameD provides the user with transparent parallelism to deal with a large number of reads from sequencing and the need for many fault injection iterations. We believe that FrameD fills a void in the tools publicly available to the DNA storage community by providing a modular and extensible framework with support for massive parallelism. As a result, it will help accelerate the design process of future DNA-based storage systems.

    Availability and implementation

    The source code for FrameD along with the data generated during the demonstration of FrameD is available in a public Github repository at https://github.com/dna-storage/framed, (https://dx.doi.org/10.5281/zenodo.7757762).

     
    more » « less
  6. Abstract Motivation

    Advances in sequencing technologies have led to a surge in genomic data, although the functions of many gene products coded by these genes remain unknown. While in-depth, targeted experiments that determine the functions of these gene products are crucial and routinely performed, they fail to keep up with the inflow of novel genomic data. In an attempt to address this gap, high-throughput experiments are being conducted in which a large number of genes are investigated in a single study. The annotations generated as a result of these experiments are generally biased towards a small subset of less informative Gene Ontology (GO) terms. Identifying and removing biases from protein function annotation databases is important since biases impact our understanding of protein function by providing a poor picture of the annotation landscape. Additionally, as machine learning methods for predicting protein function are becoming increasingly prevalent, it is essential that they are trained on unbiased datasets. Therefore, it is not only crucial to be aware of biases, but also to judiciously remove them from annotation datasets.

    Results

    We introduce GOThresher, a Python tool that identifies and removes biases in function annotations from protein function annotation databases.

    Availability and implementation

    GOThresher is written in Python and released via PyPI https://pypi.org/project/gothresher/ and on the Bioconda Anaconda channel https://anaconda.org/bioconda/gothresher. The source code is hosted on GitHub https://github.com/FriedbergLab/GOThresher and distributed under the GPL 3.0 license.

    Supplementary information

    Supplementary data are available at Bioinformatics online.

     
    more » « less
  7. Abstract Motivation

    Computational systems biology analyses typically make use of multiple software and their dependencies, which are often run across heterogeneous compute environments. This can introduce differences in performance and reproducibility. Capturing metadata (e.g. package versions, GPU model) currently requires repetitious code and is difficult to store centrally for analysis. Even where virtual environments and containers are used, updates over time mean that versioning metadata should still be captured within analysis pipelines to guarantee reproducibility.

    Results

    Microbench is a simple and extensible Python package to automate metadata capture to a file or Redis database. Captured metadata can include execution time, software package versions, environment variables, hardware information, Python version and more, with plugins. We present three case studies demonstrating Microbench usage to benchmark code execution and examine environment metadata for reproducibility purposes.

    Availability and implementation

    Install from the Python Package Index using pip install microbench. Source code is available from https://github.com/alubbock/microbench.

    Supplementary information

    Supplementary data are available at Bioinformatics online.

     
    more » « less
  8. Abstract Motivation

    Gene regulatory networks (GRNs) of the same organism can be different under different conditions, although the overall network structure may be similar. Understanding the difference in GRNs under different conditions is important to understand condition-specific gene regulation. When gene expression and other relevant data under two different conditions are available, they can be used by an existing network inference algorithm to estimate two GRNs separately, and then to identify the difference between the two GRNs. However, such an approach does not exploit the similarity in two GRNs, and may sacrifice inference accuracy.

    Results

    In this paper, we model GRNs with the structural equation model (SEM) that can integrate gene expression and genetic perturbation data, and develop an algorithm named fused sparse SEM (FSSEM), to jointly infer GRNs under two conditions, and then to identify difference of the two GRNs. Computer simulations demonstrate that the FSSEM algorithm outperforms the approaches that estimate two GRNs separately. Analysis of a dataset of lung cancer and another dataset of gastric cancer with FSSEM inferred differential GRNs in cancer versus normal tissues, whose genes with largest network degrees have been reported to be implicated in tumorigenesis. The FSSEM algorithm provides a valuable tool for joint inference of two GRNs and identification of the differential GRN under two conditions.

    Availability and implementation

    The R package fssemR implementing the FSSEM algorithm is available at https://github.com/Ivis4ml/fssemR.git. It is also available on CRAN.

    Supplementary information

    Supplementary data are available at Bioinformatics online.

     
    more » « less
  9. Abstract Summary

    RANGER-DTL 2.0 is a software program for inferring gene family evolution using Duplication-Transfer-Loss reconciliation. This new software is highly scalable and easy to use, and offers many new features not currently available in any other reconciliation program. RANGER-DTL 2.0 has a particular focus on reconciliation accuracy and can account for many sources of reconciliation uncertainty including uncertain gene tree rooting, gene tree topological uncertainty, multiple optimal reconciliations and alternative event cost assignments. RANGER-DTL 2.0 is open-source and written in C++ and Python.

    Availability and implementation

    Pre-compiled executables, source code (open-source under GNU GPL) and a detailed manual are freely available from http://compbio.engr.uconn.edu/software/RANGER-DTL/.

    Supplementary information

    Supplementary data are available at Bioinformatics online.

     
    more » « less
  10. Abstract Motivation

    Best performing named entity recognition (NER) methods for biomedical literature are based on hand-crafted features or task-specific rules, which are costly to produce and difficult to generalize to other corpora. End-to-end neural networks achieve state-of-the-art performance without hand-crafted features and task-specific knowledge in non-biomedical NER tasks. However, in the biomedical domain, using the same architecture does not yield competitive performance compared with conventional machine learning models.

    Results

    We propose a novel end-to-end deep learning approach for biomedical NER tasks that leverages the local contexts based on n-gram character and word embeddings via Convolutional Neural Network (CNN). We call this approach GRAM-CNN. To automatically label a word, this method uses the local information around a word. Therefore, the GRAM-CNN method does not require any specific knowledge or feature engineering and can be theoretically applied to a wide range of existing NER problems. The GRAM-CNN approach was evaluated on three well-known biomedical datasets containing different BioNER entities. It obtained an F1-score of 87.26% on the Biocreative II dataset, 87.26% on the NCBI dataset and 72.57% on the JNLPBA dataset. Those results put GRAM-CNN in the lead of the biological NER methods. To the best of our knowledge, we are the first to apply CNN based structures to BioNER problems.

    Availability and implementation

    The GRAM-CNN source code, datasets and pre-trained model are available online at: https://github.com/valdersoul/GRAM-CNN.

    Supplementary information

    Supplementary data are available at Bioinformatics online.

     
    more » « less