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Biochemistry & Molecular Biology
Rokas Lukosevicius, Simonas Juzenas, Violeta Salteniene, Ugne Kulokiene, Justina Arstikyte, Georg Hemmrich-Stanisak, Andre Franke, Alexander Link, Paulius Ruzgys, Saulius Satkauskas, Henrikas Pauzas, Tadas Latkauskas, Gediminas Kiudelis, Francesc Balaguer, Juozas Kupcinskas, Jurgita Skieceviciene
Summary: This study explored regulatory changes in early colorectal cancer development and identified differentially expressed miRNAs using small RNA-seq profiling of colon biopsy samples. Functional experiments revealed the oncogenic effect of hsa-miR-1246 and its involvement in regulating the expression of tumor suppressors AXIN2 and CFTR.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Article
Biochemical Research Methods
Yifu Lu, Zhuohan Yu, Yunhe Wang, Zhiqiang Ma, Ka-Chun Wong, Xiangtao Li
Summary: A novel Graph-based Multiple Hierarchical Consensus Clustering (GMHCC) method is developed in this study to handle clustering of various biomolecular data, showing high effectiveness. Experiments validate the method's efficiency and provide new insights into cell developmental lineages and characterization mechanisms.
Article
Computer Science, Information Systems
Yunhe Wang, Ka-Chun Wong, Xiangtao Li
Summary: A multiobjective robust continuous clustering algorithm (MORCC) is proposed to discriminate different cell types in a single-cell RNA-seq dataset. By applying dimensionality reduction and optimizing connectivity weights, MORCC demonstrates superior clustering ability.
INFORMATION SCIENCES
(2022)
Article
Chemistry, Analytical
Erkan Bostanci, Engin Kocak, Metehan Unal, Mehmet Serdar Guzel, Koray Acici, Tunc Asuroglu
Summary: Data from omics studies are used for disease prediction and classification in biomedical and bioinformatics research. Machine Learning algorithms have been applied in healthcare systems for disease prediction. In this study, RNA-seq data of extracellular vesicles from colon cancer patients are analyzed to develop models for prediction and classification. Both canonical ML classifiers and DL models are used, and the best accuracy is achieved with different models for cancer prediction and stage classification.
Article
Biochemical Research Methods
Rick Gelhausen, Sarah L. Svensson, Kathrin Froschauer, Florian Heyl, Lydia Hadjeras, Cynthia M. Sharma, Florian Eggenhofer, Rolf Backofen
Summary: HRIBO is a workflow designed for reproducible and high-throughput analysis of bacterial Ribo-seq data, facilitating the rapid and high-confidence discovery of novel ORFs and their prioritization for functional characterization.
Article
Biotechnology & Applied Microbiology
Kazi Ferdous Mahin, Md Robiuddin, Mujahidul Islam, Shayed Ashraf, Farjana Yeasmin, Swakkhar Shatabda
Summary: The paper proposes a method called PanClassif for cancer detection using RNA-seq data and improving the performance of various machine learning classifiers. The method outperforms existing methods and shows consistent performance across different datasets.
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
Md Tauhidul Islam, Jen-Yeu Wang, Hongyi Ren, Xiaomeng Li, Masoud Badiei Khuzani, Shengtian Sang, Lequan Yu, Liyue Shen, Wei Zhao, Lei Xing
Summary: Single cell RNA sequencing is a promising technique for determining cell states and classifying cell subtypes, but there are issues with omitting low expression genes in current data analysis. Researchers have proposed a data-driven framework to recover missing expression values by learning data distribution and enforcing self-consistency, improving gene imputation accuracy.
NATURE COMMUNICATIONS
(2022)
Article
Chemistry, Analytical
Sylwia A. Stopka, Ellen A. Wood, Rikkita Khattar, Beverly J. Agtuca, Walid M. Abdelmoula, Nathalie Y. R. Agar, Gary Stacey, Akos Vertes
Summary: This study utilized mass spectrometry imaging (MSI) to analyze tissue-embedded single cells in high throughput, achieving accurate single-cell sampling through automated image analysis and discovering cellular phenotypes characterized by differing metabolite levels.
ANALYTICAL CHEMISTRY
(2021)
Article
Biochemical Research Methods
Xuan Liu, Sara J. C. Gosline, Lance T. Pflieger, Pierre Wallet, Archana Iyer, Justin Guinney, Andrea H. Bild, Jeffrey T. Chang
Summary: Single-cell RNA sequencing (scRNA-Seq) is a promising strategy for characterizing immune cell populations, but manually classifying immune cells from transcriptional profiles remains a challenge. ImmClassifier, a machine learning classifier, has shown to be effective in distinguishing fine-grained cell types in scRNA-Seq experiments, outperforming other tools in precision and recall.
BRIEFINGS IN BIOINFORMATICS
(2021)
Review
Immunology
Jessica M. Zielinski, Jason J. Luke, Silvia Guglietta, Carsten Krieg
Summary: High throughput single cell multi-omics platforms have enabled unprecedented insights into biological and clinical questions, creating whole atlases of cell types and interaction networks. Combining different -omic workflows on a single cell level can help examine cellular phenotypes and immune effector functions, as well as accelerate cellular biomarker discovery and definition of druggable target pathways. Challenges in the field include sharing disruptive technologies to scientific communities while incorporating new approaches like genomic cytometry and single cell metabolomics.
FRONTIERS IN IMMUNOLOGY
(2021)
Article
Biochemistry & Molecular Biology
James D. D. Beck, Jessica M. M. Roberts, Joey M. M. Kitzhaber, Ashlyn Trapp, Edoardo Serra, Francesca Spezzano, Eric J. J. Hayden
Summary: This study used machine learning approaches to analyze data from variants of the CPEB3 self-cleaving ribozyme and found that the trained models could predict active sequences at higher mutational distances, although the correlation decreased with increasing mutational distance. The research also suggests that a wide distribution of ribozyme activity may be crucial for accurate predictions.
FRONTIERS IN MOLECULAR BIOSCIENCES
(2022)
Review
Biochemical Research Methods
Erik Christensen, Ping Luo, Andrei Turinsky, Mia Husic, Alaina Mahalanabis, Alaine Naidas, Juan Javier Diaz-Mejia, Michael Brudno, Trevor Pugh, Arun Ramani, Parisa Shooshtari
Summary: Single-cell RNA sequencing (scRNA-seq) clustering and labelling methods are used to determine cellular composition. This study assesses different scRNA-seq labelling algorithms using cancer datasets. Results show that cell-based methods generally outperform cluster-based methods. The study also provides suggestions for algorithm selection.
BRIEFINGS IN BIOINFORMATICS
(2023)
Review
Biochemical Research Methods
Erik Christensen, Ping Luo, Andrei Turinsky, Mia Husic, Alaina Mahalanabis, Alaine Naidas, Juan Javier Diaz-Mejia, Michael Brudno, Trevor Pugh, Arun Ramani, Parisa Shooshtari
Summary: This study evaluates 17 cell-based and 9 cluster-based scRNA-seq labelling algorithms using 8 cancer datasets, providing a comprehensive assessment of these methods in a cancer-specific context. The results show that cell-based methods generally outperform cluster-based methods in terms of performance and speed. Additionally, larger cell numbers in training data have a positive impact on prediction scores for cell-based methods. The best performing algorithms are scPred and SVM, with suggestions for algorithm selection provided.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Huajun Liao, Qianqian Wang, Nan Zhang, Yuying Fu, Gang Wu, Xueqiang Ren, Bingjie Xue, Xiyu Liu, Zhihong Xu, Chongchong Yan
Summary: The study conducted high throughput sequencing to reveal the profiling of miRNA and mRNA in response to low-temperature stress in potato. A large number of differentially expressed microRNAs and genes were identified between Treatment and Control groups, with the phenylpropanoid biosynthesis pathway identified as a common KEGG pathway in differentially expressed miRNA and mRNA.
PLANT MOLECULAR BIOLOGY REPORTER
(2021)
