Article
Oncology
Li-Han Lin, Chung-Hsien Chou, Hui-Wen Cheng, Kuo-Wei Chang, Chung-Ji Liu
Summary: Understanding genomic alterations in oral carcinogenesis is crucial for the diagnosis and treatment of OSCC. The study used WES to uncover mutational spectrum in OSCC samples, revealing associations between tumor mutation burden and clinical parameters. Several high frequency false positive mutation genes were identified, along with known and novel genes frequently mutated in OSCC. Pathway analysis showed associations with OSCC prognosis, and a catalog of targetable genomic alterations was defined, showing potential for targeted therapies in OSCC patients. Analysis also revealed molecular subgroups in OSCC correlated with etiology and prognosis, providing valuable information for clinical trial design and patient stratification.
FRONTIERS IN ONCOLOGY
(2021)
Review
Biochemistry & Molecular Biology
Elisabetta Grillo, Cosetta Ravelli, Michela Corsini, Luca Zammataro, Stefania Mitola
Summary: Computational approaches have been developed to prioritize cancer mutations based on their biological and clinical significance, with protein domain-based methods allowing the identification of functionally relevant low frequency variants. Prioritizing mutations based on clustered specific residues of protein domains could aid in choosing patient-specific targeted drugs and improving cancer patient management.
BIOCHIMICA ET BIOPHYSICA ACTA-REVIEWS ON CANCER
(2021)
Article
Biochemical Research Methods
Jing Zhao, Bowen Zhao, Xiaotong Song, Chujun Lyu, Weizhi Chen, Yi Xiong, Dong-Qing Wei
Summary: The Subtype-DCC method, which integrates multi-omics data, is proposed for cancer subtyping and demonstrates superior performance compared to existing clustering methods. It has potential applications in cancer diagnosis, prognosis, and treatment.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Ying Yang, Sha Tian, Yushan Qiu, Pu Zhao, Quan Zou
Summary: Each type of cancer usually has multiple subtypes, and discovering and predicting these subtypes is crucial for disease diagnosis and treatment. Using single-omics data for prediction is challenging due to dysregulated genomes and complex molecular mechanisms. However, using multi-omics data is a promising approach, although it presents integration challenges. This study proposes a novel method, MDICC, for integrating multi-omics data to identify cancer subtypes, which outperforms current state-of-the-art clustering methods.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Xingze Wang, Guoxian Yu, Jun Wang, Azlan Mohd Zain, Wei Guo
Summary: Diagnosing lung cancer subtypes accurately is crucial for precise treatment. This study introduces an interpretable and flexible solution called LungDWM, which utilizes weakly paired multiomics data to diagnose lung cancer subtypes. By extracting important diagnostic features, imputing missing data, and fusing information from different omics, LungDWM outperforms other competitive methods in terms of performance, authenticity, and interpretability.
Article
Genetics & Heredity
Qihan Long, Yangyang Yuan, Miaoxin Li
Summary: The RNA-SSNV framework allows for the accurate identification of expressed somatic mutations and enables a more insightful analysis of cancer driver genes and carcinogenic mechanisms.
FRONTIERS IN GENETICS
(2022)
Article
Multidisciplinary Sciences
Tiantian Liu, Zhong Chen, Wanqiu Chen, Xin Chen, Maryam Hosseini, Zhaowei Yang, Jing Li, Diana Ho, David Turay, Ciprian P. Gheorghe, Wendell Jones, Charles Wang
Summary: The research compared seven different SARS-CoV-2 WGS library protocols and found significant differences in mappability, genome coverage, sensitivity, reproducibility, and precision, with certain protocols requiring important trimming steps for accurate variant calling. The study results provide guidance for choosing appropriate WGS protocols to study SARS-CoV-2 and its evolution.
Article
Biochemical Research Methods
Zhi-Kai Yang, Lingyu Pan, Yanming Zhang, Hao Luo, Feng Gao
Summary: The study analyzed the genomic sequences of hundreds of thousands of SARS-CoV-2 isolates, successfully identifying 303 subpopulations and revealing a gradual decrease in GC content in the viral genome, indicating stability in mutations. The research results not only provide a more accurate classification than existing clades but also shed light on the evolutionary trajectory of SARS-CoV-2.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Oncology
R. Tyler McLaughlin, Maansi Asthana, Marc Di Meo, Michele Ceccarelli, Howard J. Jacob, David L. Masica
Summary: Accurately identifying somatic mutations is crucial for precision oncology and calculating tumor-mutational burden (TMB), which predicts response to immunotherapy. This study applies machine learning models to classify somatic vs germline mutations in tumor-only solid tumor samples, achieving state-of-the-art performance. The addition of a machine-learning classifier improves the concordance of TMB estimates and eliminates racial bias in tumor-only variant calling.
NPJ PRECISION ONCOLOGY
(2023)
Article
Biochemical Research Methods
Zexian Zeng, Chengsheng Mao, Andy Vo, Xiaoyu Li, Janna Ore Nugent, Seema A. Khan, Susan E. Clare, Yuan Luo
Summary: DeepCues is a deep learning model that utilizes convolutional neural networks to derive features unbiasedly from raw cancer DNA sequencing data for disease classification and relevant gene discovery. By amalgamating germline variants and somatic mutations, including insertions and deletions, DeepCues showed significant improvement in cancer type prediction and successfully identified new cancer relevant genes.
BMC BIOINFORMATICS
(2021)
Article
Biochemical Research Methods
Cheng Liang, Mingchao Shang, Jiawei Luo
Summary: Our proposed method, Consensus Guided Graph Autoencoder (CGGA), effectively identifies cancer subtypes and outperforms other approaches in both general and cancer-specific datasets, demonstrating its superiority in leveraging multi-omics data for cancer subtype identification.
Review
Biochemistry & Molecular Biology
Lacramioara Ionela Butnariu, Eusebiu Vlad Gorduza, Laura Florea, Elena Tarca, Stefana Maria Moisa, Laura Mihaela Trandafir, Simona Stoleriu, Minerva Codruta Badescu, Alina-Costina Luca, Setalia Popa, Iulian Radu, Elena Cojocaru
Summary: This review presents the latest data on the genetic factors involved in the etiology of vascular anomalies (VAs) and possible directions for future research. The results indicate that the phenotypic variability of VAs is correlated with genetic heterogeneity. The identification of new genetic factors and molecular mechanisms will contribute to the development of personalized therapies.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Review
Genetics & Heredity
Isidro Cortes-Ciriano, Doga C. Gulhan, Jake June-Koo Lee, Giorgio E. M. Melloni, Peter J. Park
Summary: This review provides an overview of key algorithmic developments, popular tools, and emerging technologies used in the bioinformatic analysis of cancer genomes. It also describes how such analysis can identify point mutations, copy number alterations, structural variations, and mutational signatures in cancer genomes.
NATURE REVIEWS GENETICS
(2022)
Article
Biochemical Research Methods
Wenlan Chen, Hong Wang, Cheng Liang
Summary: Cancer heterogeneity presents challenges for precise therapeutic strategies. This paper proposes a self-supervised learning model called DMCL for cancer subtype identification. DMCL incorporates multiple losses into a unified framework, encoding discriminative information and preserving cluster structures. Experimental results on multiple datasets demonstrate the superior performance of DMCL. A case study on liver cancer highlights different responses to chemotherapeutic drugs among subtypes.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Oncology
Rong Bu, Abdul K. Siraj, Tariq Masoodi, Sandeep Kumar Parvathareddy, Kaleem Iqbal, Maha Al-Rasheed, Wael Haqawi, Mark Diaz, Ingrid G. Victoria, Saud M. Aldughaither, Saif S. Al-Sobhi, Fouad Al-Dayel, Khawla S. Al-Kuraya
Summary: This study evaluated the prevalence of MAP2K1 mutations in PTC and CRC in the Middle Eastern population, finding rates of 1.1% and 0.9%, respectively, in MAPK wildtype cases. The mutually exclusive nature of MAP2K1 and MAPK mutations suggests that they may function as initiating mutations in tumorigenesis.
FRONTIERS IN ONCOLOGY
(2021)
Article
Genetics & Heredity
Maud Fagny, Marieke Lydia Kuijjer, Maike Stam, Johann Joets, Olivier Turc, Julien Roziere, Stephanie Pateyron, Anthony Venon, Clementine Vitte
Summary: Enhancers play a key role in coordinating gene expression during important developmental processes. This study investigated the enhancer-driven regulatory network in maize leaves and husks at different growth stages, revealing tissue-specific regulatory modules and potential new TF binding sites. The findings shed light on the complexity of enhancer-mediated gene regulation in plants.
FRONTIERS IN GENETICS
(2021)
Article
Multidisciplinary Sciences
Iwona Grad, Robert Hanes, Pilar Ayuda-Duran, Marieke Lydia Kuijjer, Jorrit M. Enserink, Leonardo A. Meza-Zepeda, Ola Myklebost
Summary: Drug testing on liposarcoma cells has identified six potential anti-cancer drugs that target different mechanisms and have low toxicity to normal cells. These drugs show promise for the treatment of liposarcoma.
Article
Computer Science, Information Systems
Matthew Almeida, Yong Zhuang, Wei Ding, Scott E. Crouter, Ping Chen
Summary: The study addresses the issue of bias and variance in models when faced with inaccurate and uncertain training data labels. By estimating the uncertainty of labels and adjusting sample weights accordingly, the method reduces both bias and variance, improving model performance in a real-world case study.
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
(2021)
Review
Genetics & Heredity
Genis Calderer, Marieke L. Kuijjer
Summary: Networks are useful tools in biology for representing and analyzing interactions, with many biological networks being bipartite. Community structure detection can help identify clusters of nodes in these networks, aiding in understanding specific biological processes.
FRONTIERS IN GENETICS
(2021)
Article
Biochemical Research Methods
Daniel Osorio, Marieke L. Kuijjer, James J. Cai
Summary: This study introduces rPanglaoDB, an R package for downloading and merging uniformly processed scRNA-seq data to collect rare cell types by integrating multiple public datasets. By characterizing a set of 157 fibrocytes, the study demonstrates the potential and utility of rPanglaoDB in enabling the collection of rare cell types for transcriptomic profiling.
Article
Oncology
Camila M. Lopes-Ramos, Tatiana Belova, Tess H. Brunner, Marouen Ben Guebila, Daniel Osorio, John Quackenbush, Marieke L. Kuijjer
Summary: Analysis identified seven pathways associated with survival in glioblastoma patients, with dysregulation of PD1 signaling correlating with poor prognosis. This suggests a new approach to predict patient survival and highlights potential therapeutic interventions based on gene regulatory network analysis.
Article
Biochemistry & Molecular Biology
Marouen Ben Guebila, Camila M. Lopes-Ramos, Deborah Weighill, Abhijeet Rajendra Sonawane, Rebekka Burkholz, Behrouz Shamsaei, John Platig, Kimberly Glass, Marieke L. Kuijjer, John Quackenbush
Summary: Gene regulation networks play a crucial role in tissue identity, disease development, and therapeutic response. The GRAND database provides computationally-inferred gene regulatory network models and targeting scores for predicting drug effects on network structures and matching potential therapeutic drugs to disease states.
NUCLEIC ACIDS RESEARCH
(2022)
Letter
Biochemical Research Methods
Marouen Ben Guebila, Deborah Weighill, Camila M. Lopes-Ramos, Rebekka Burkholz, Romana T. Pop, Kalyan Palepu, Mia Shapoval, Maud Fagny, Daniel Schlauch, Kimberly Glass, Michael Altenbuchinger, Marieke L. Kuijjer, John Platig, John Quackenbush
Editorial Material
Genetics & Heredity
Maud Fagny, Kimberly Glass, Marieke L. Kuijjer
FRONTIERS IN GENETICS
(2022)
Article
Oncology
Sofia Birkealv, Mark Harland, Larissa Satiko Alcantara Sekimoto Matsuyama, Mamun Rashid, Ishan Mehta, Jonathan P. Laye, Kerstin Haase, Tracey Mell, Vivek Iyer, Carla Daniela Robles-Espinoza, Ultan McDermott, Peter van Loo, Marieke L. Kuijjer, Patricia A. Possik, Silvya Stuchi Maria Engler, D. Timothy Bishop, Julia Newton-Bishop, David J. Adams
Summary: This study conducted sequence profiling of 524 American Joint Committee on Cancer Stage I-III primary tumors, revealing recurrent driver mutations, mutually exclusive genetic interactions, and an absence of co-occurring genetic events. By intersecting copy number calls with CRISPR screening data, the transcription factor IRF4 was identified as a melanoma-associated dependency.
JOURNAL OF PATHOLOGY
(2023)
Article
Biotechnology & Applied Microbiology
Marouen Ben Guebila, Tian Wang, Camila M. M. Lopes-Ramos, Viola Fanfani, Des Weighill, Rebekka Burkholz, Daniel Schlauch, Joseph N. N. Paulson, Michael Altenbuchinger, Katherine H. H. Shutta, Abhijeet R. R. Sonawane, James Lim, Genis Calderer, David G. P. van IJzendoorn, Daniel Morgan, Alessandro Marin, Cho-Yi Chen, Qi Song, Enakshi Saha, Dawn L. L. DeMeo, Megha Padi, John Platig, Marieke L. L. Kuijjer, Kimberly Glass, John Quackenbush
Summary: This article introduces an open-source software called NetZoo, which is used for inference and analysis of gene regulatory networks. NetZoo integrates multi-omic data from various sources and harmonizes the implementations of network methods in different computing languages and between methods to allow better integration of these tools into analytical pipelines.
Article
Health Care Sciences & Services
Eric A. A. Stahlberg, Mohamed Abdel-Rahman, Boris Aguilar, Alireza Asadpoure, Robert A. A. Beckman, Lynn L. L. Borkon, Jeffrey N. N. Bryan, Colleen M. M. Cebulla, Young Hwan Chang, Ansu Chatterjee, Jun Deng, Sepideh Dolatshahi, Olivier Gevaert, Emily J. J. Greenspan, Wenrui Hao, Tina Hernandez-Boussard, Pamela R. R. Jackson, Marieke Kuijjer, Adrian Lee, Paul Macklin, Subha Madhavan, Matthew D. D. McCoy, Navid Mohammad Mirzaei, Talayeh Razzaghi, Heber L. L. Rocha, Leili Shahriyari, Ilya Shmulevich, Daniel G. G. Stover, Yi Sun, Tanveer Syeda-Mahmood, Jinhua Wang, Qi Wang, Ioannis Zervantonakis
Summary: We are approaching a future where cancer patient digital twins can accurately predict and assist in the prevention, diagnosis, and treatment of cancer. This is made possible through advancements in high performance computing, computational modeling, and the availability of diverse observational data. The US National Cancer Institute and the US Department of Energy have launched collaborative projects to develop and implement predictive Cancer Patient Digital Twins, aiming to explore different approaches, such as deep phenotyping, personalized treatment planning, and treatment monitoring. These efforts have provided valuable insights into the opportunities and challenges of cancer patient digital twin approaches and shaped the future research direction.
FRONTIERS IN DIGITAL HEALTH
(2022)
Article
Biochemical Research Methods
Cedric R. Weber, Teresa Rubio, Longlong Wang, Wei Zhang, Philippe A. Robert, Rahmad Akbar, Igor Snapkov, Jinghua Wu, Marieke L. Kuijjer, Sonia Tarazona, Ana Conesa, Geir K. Sandve, Xiao Liu, Sai T. Reddy, Victor Greiff
Summary: The similarity of immune repertoires can represent an individual's immune history. However, current understanding might be incorrect as certain immune states show highly similar immune repertoires in both healthy and diseased individuals, suggesting that immune perturbations do not cause significant changes in repertoires.
CELL REPORTS METHODS
(2022)
Article
Genetics & Heredity
Marouen Ben Guebila, Daniel C. Morgan, Kimberly Glass, Marieke L. Kuijjer, Dawn L. DeMeo, John Quackenbush
Summary: Gene regulatory network inference allows for modeling genome-scale regulatory processes. Researchers have developed a collection of tools to model various regulatory processes and improve their performance through GPU-accelerated calculations.
NAR GENOMICS AND BIOINFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Milena Pavlovic, Lonneke Scheffer, Keshav Motwani, Chakravarthi Kanduri, Radmila Kompova, Nikolay Vazov, Knut Waagan, Fabian L. M. Bernal, Alexandre Almeida Costa, Brian Corrie, Rahmad Akbar, Ghadi S. Al Hajj, Gabriel Balaban, Todd M. Brusko, Maria Chernigovskaya, Scott Christley, Lindsay G. Cowell, Robert Frank, Ivar Grytten, Sveinung Gundersen, Ingrid Hobaek Haff, Eivind Hovig, Ping-Han Hsieh, Gunter Klambauer, Marieke L. Kuijjer, Christin Lund-Andersen, Antonio Martini, Thomas Minotto, Johan Pensar, Knut Rand, Enrico Riccardi, Philippe A. Robert, Artur Rocha, Andrei Slabodkin, Igor Snapkov, Ludvig M. Sollid, Dmytro Titov, Cedric R. Weber, Michael Widrich, Gur Yaari, Victor Greiff, Geir Kjetil Sandve
Summary: immuneML is an AIRR-based machine learning tool that addresses reproducibility, transparency, and interoperability issues in the field of AIRR ML through an extensible, open-source software ecosystem. Users can utilize immuneML through a command-line tool or a Galaxy web interface, with extensive workflow documentation provided to facilitate widespread adoption.
NATURE MACHINE INTELLIGENCE
(2021)