Biology subjects

Wiese, K. C.

Publications and source records attributed to Wiese, K. C..

4 recordsLinked to original sources

bioRxiv2025

Accurate hybrid plasmids assembly with HyPlAs

SummaryThe increasing availability of hybrid sequencing datasets comprising both short and long reads, is radically transforming microbial genomics, with the prospect of obtaining routinely near-complete bacterial genome assemblies. However, the complete assemblies of mobile genetic elements, especially plasmids, still remain challenging. We introduce HyPlAs, an assembly pipeline specifically designed to assemble plasmids from hybrid bacterial sequencing datasets. HyPlAs main novelty is to incorporate the use of a prior classification of short-read contigs as chromosomal or plasmidic. We evaluate HyPlAs on a large set of bacterial samples and demonstrate that it outperforms its competitor Plassembler. AvailabilityHyPlAs is freely available at https://github.com/cchauve/HyPlAs.

bioinformatics↗

bioRxiv2025

ChromeCRISPR - A High Efficacy Hybrid Machine Learning Model for CRISPR/Cas On-Target Predictions

Genome editing has the potential to treat genetic disorders at the source. This can be achieved by modifying the defective DNA through the intentional insertion, deletion, or substitution of genomic content. Among all genome editing technologies, CRISPR/Cas (Clustered Regularly Interspaced Short Palindromic Repeats and CRISPR-associated protein) is considered the gold standard. CRISPR/Cas uses a single guide RNA (sgRNA) to direct the Cas nuclease to a target DNA region. Due to the ease at creating small RNA molecules, it is possible to have the CRISPR/Cas complex target any arbitrary DNA sequence, thus making it a versatile tool. The efficacy of the complex is dependent on the ability of the sgRNA to bind to a complementary DNA sequence, which varies based on the sequence. Thus, a major challenge is finding sgRNA sequences that have good efficacy. This is where computational models can aid scientists: by predicting the activity of sgRNAs to help narrow the search space of finding the optimal sgRNA. We have used a large new dataset to build and compare the ability of several different machine learning architectures ability to predict on-target CRISPR/Cas activity. Additionally, we explored how adding GC content affects our sgRNA activity predictions. Our novel hybrid model, ChromeCRISPR, combines the strengths of Convolutional Neural Networks (CNN) with Recurrent Neural Network (RNN) models, has outperformed state-of-the-art models, including DeepHF and AttCrispr, establishing a new benchmark for predictive accuracy in CRISPR/Cas9 efficacy predictions.

bioinformatics↗

bioRxiv2025

Pan-cell type continuous chromatin state annotation of all IHEC epigenomes

The International Human Epigenome Consortium has generated thousands of epigenomic datasets that mea-sure various biochemical activities in the genome, including transcription factor binding, histone modification, and DNA accessibility. Currently, the predominant methods for integrating these datasets to annotate regu-latory elements are segmentation and genome annotation (SAGA) algorithms. The majority of annotations by these methods are cell type-specific. However, as the number of profiled cell types has grown into the thousands, using thousands of cell type-specific chromatin state annotations proves undesirable for many applications. Here, we present a pan-cell type annotation that summarizes all IHEC epigenomes using the recently-developed method, epigenome-ssm.

genomics↗

bioRxiv2021

SigTools: Exploratory Visualization for Genomic Signals

MotivationWith the advancement of sequencing technologies, genomic data sets are constantly being expanded by high volumes of different data types. One recently introduced data type in genomic science is genomic signals, which are usually short-read coverage measurements over the genome. An example of genomic signals is Epigenomic marks which are utilized to locate functional and nonfunctional elements in genome annotation studies. To understand and evaluate the results of such studies, one needs to understand and analyze the characteristics of the input data. ResultsSigTools is an R-based genomic signals visualization package developed with two objectives: 1) to facilitate genomic signals exploration in order to uncover insights for later model training, refinement, and development by including distribution and autocorrelation plots. 2) to enable genomic signals interpretation by including correlation, and aggregation plots. Moreover, Sigtools also provides text-based descriptive statistics of the given signals which can be practical when developing and evaluating learning models. We also include results from 2 case studies. The first examines several previously studied genomic signals called histone modifications. This use case demonstrates how SigTools can be beneficial for satisfying scientists curiosity in exploring and establishing recognized datasets. The second use case examines a dataset of novel chromatin state features which are novel genomic signals generated by a learning model. This use case demonstrates how SigTools can assist in exploring the characteristics and behavior of novel signals towards their interpretation. In addition, our corresponding web application, SigTools-Shiny, extends the accessibility scope of these modules to people who are more comfortable working with graphical user interfaces instead of command-line tools. AvailabilitySigTools source code, installation guide, and manual is available on http://github.com/shohre73. Contactshohre_masoumi@sfu.ca

bioinformatics↗