Article
Automation & Control Systems
Ziqian Zheng, Wei Zhao, Brock Hable, Yutao Gong, Xuan Wang, Robert W. Shannon, Kaibo Liu
Summary: This paper proposes a transfer learning-based independent component analysis (ICA) method to address the issue of degraded component extraction accuracy with limited available data. By transferring component distribution from a source domain, accurate component extraction results can be achieved in the target domain. Numerical simulations and a case study demonstrate the effectiveness of the proposed method in transferring knowledge and reducing negative transfer.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2022)
Article
Biochemistry & Molecular Biology
Ashley Mae Conard, Nathaniel Goodman, Yanhui Hu, Norbert Perrimon, Ritambhara Singh, Charles Lawrence, Erica Larschan
Summary: TIMEOR is the first web-based and adaptive time-series multi-omics pipeline method that infers the relationship between gene regulatory events across time, addressing the critical need for determining causal regulatory mechanism networks. It integrates time-series RNA-seq, motif analysis, protein-DNA binding data, and protein-protein interaction networks.
NUCLEIC ACIDS RESEARCH
(2021)
Article
Neurosciences
Shile Qi, Rogers F. Silva, Daoqiang Zhang, Sergey M. Plis, Robyn Miller, Victor M. Vergara, Rongtao Jiang, Dongmei Zhi, Jing Sui, Vince D. Calhoun
Summary: This study introduces a novel three-way parallel group independent component analysis (pGICA) fusion method that effectively incorporates temporal information in multimodal data fusion, demonstrating high accuracy and comparability in estimating cross-modality links. Experimental results suggest the potential of this method in investigating brain disorders.
HUMAN BRAIN MAPPING
(2022)
Article
Biochemical Research Methods
Takayuki Osabe, Kentaro Shimizu, Koji Kadota
Summary: The study demonstrated that a model-based clustering algorithm, MBCdeg, can be used for DE analysis. MBCdeg outperformed other methods when P-DEG was less than 50%, but showed less consistency in DEG identification. Using DEGES normalization with MBCdeg provided greater stability compared to the default normalization algorithm.
BMC BIOINFORMATICS
(2021)
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
Biochemical Research Methods
Xiunan Fang, Joshua W. K. Ho
Summary: FlowGrid is an open source python package that integrates into the Scanpy workflow for clustering very large scRNA-seq datasets. It implements a fast density-based clustering algorithm and introduces a new automated parameter tuning procedure to achieve comparable clustering accuracy with reduced run time. For example, FlowGrid can complete a one-hour clustering task for one million cells in about five min.
Article
Statistics & Probability
David K. Lim, Naim U. Rashid, Joseph G. Ibrahim
Summary: Clustering is an unsupervised learning approach to uncover latent groups within data based on feature similarity. FSCseq is a model-based clustering algorithm that adjusts for global normalization factors, selects discriminatory genes, and handles confounding variables, enabling subtype prediction in new patients through posterior probabilities.
ANNALS OF APPLIED STATISTICS
(2021)
Article
Biochemical Research Methods
Marta Nazzari, Duncan Hauser, Marcel van Herwijnen, Mirian Romitti, Daniel J. Carvalho, Anna M. Kip, Florian Caiment
Summary: In this paper, the authors introduce CODA, a workflow specifically developed for the processing of Combo-Seq data. The research shows that using CODA, more successfully trimmed reads are recovered compared with exceRpt, and the difference is more dramatic with short sequencing reads. Combo-Seq libraries can identify as many genes as the standard libraries, but fewer miRNAs, and conventional small RNA libraries perform better for miRNA validation.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Marta Nazzari, Duncan Hauser, Marcel van Herwijnen, Mirian Romitti, Daniel J. Carvalho, Anna M. Kip, Florian Caiment
Summary: Combo-Seq is a method for preparing combined mRNA-miRNA libraries from total RNA samples, but there is currently no dedicated bioinformatics method for processing Combo-Seq data. In this study, we developed the CODA workflow, which uses existing free tools to process Combo-Seq data and compared it with the exceRpt pipeline suggested by the kit manufacturer. The results show that CODA can successfully trim more reads compared to exceRpt, especially with short sequencing reads. Combo-Seq libraries identify the same number of genes but fewer miRNAs compared to standard libraries, and traditional small RNA libraries have an advantage in miRNA validation.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Mothe Rajesh, Sheshikala Martha
Summary: Single-cell RNA sequencing technology is utilized to analyze the transcriptomes of individual cells and identify rare cell populations. Traditional methods struggle to analyze transcriptomic profiles on a single-cell level, thus machine learning techniques have become crucial. In this study, we analyzed single-cell RNA sequencing data using linear dimensional reduction, identification of highly variable features, cell clustering, nonlinear dimensional reduction, and identification of gene markers. This analysis is important for identifying transcriptomic challenges and heterogeneity in cellular characteristics. Our research assists researchers in the field of bioinformatics and computational biology studying single-cell RNA sequencing data.
Review
Biochemistry & Molecular Biology
Juliana Costa-Silva, Mariangela Hungria, Douglas S. Domingues, David Menotti, Fabricio Martins Lopes
Summary: This paper provides a comprehensive review of the computational analysis pipeline for differential gene expression analysis from RNA-seq data. It introduces the objectives, methods, and properties of each step, presents a timeline of the computational methods, and discusses the relationships between important tools. The paper serves as a tutorial for beginners and helps established users update their analysis pipelines.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2023)
Review
Biochemistry & Molecular Biology
Juliana Costa-Silva, Douglas S. Domingues, David Menotti, Mariangela Hungria, Fabricio Martins Lopes
Summary: This paper provides a review of the pipeline for differential expression analysis, discussing the steps, methods, challenges, and tutorial aspects. It aims to guide new entrants and assist established users in updating their analysis pipelines.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2023)
Article
Multidisciplinary Sciences
Kiya W. Govek, Patrick Nicodemus, Yuxuan Lin, Jake Crawford, Artur B. Saturnino, Hannah Cui, Kristi Zoga, Michael P. Hart, Pablo G. Camara
Summary: The authors present a computational approach for cell morphometry and multi-modal analysis, which is based on concepts from metric geometry. They demonstrate the utility of this approach in integrating cell morphology data into single-cell omics analyses. The approach involves building cell morphology latent spaces using metric geometry, which facilitate the integration of single-cell morphological data and inference of relations with other data.
NATURE COMMUNICATIONS
(2023)
Article
Biochemical Research Methods
Yanglan Gan, Yuhan Chen, Guangwei Xu, Wenjing Guo, Guobing Zou
Summary: scRNA-seq is a technique that measures genome-wide gene expression at the single-cell level. In this study, a novel deep enhanced constraint clustering algorithm named scDECL is proposed for scRNA-seq data analysis, which combines contrastive learning and pairwise constraints. Experimental results demonstrate the superior performance of the scDECL algorithm on six real scRNA-seq datasets.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Zhixu Qiu, Siyuan Chen, Yuhong Qi, Chunni Liu, Jingjing Zhai, Shang Xie, Chuang Ma
Summary: deepTS is a user-friendly web-based tool that allows comprehensive and flexible analysis of transcriptional switch events in large-scale RNA-Seq datasets from pairwise, temporal, and population experiments. It offers rich functionality for both model and non-model organisms, with or without a reference transcriptome, streamlining the TS analysis process. Its potential for transcriptome-wide analysis of RNA-Seq data is demonstrated in presented case studies.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Agronomy
Antonio Carlos Da Silva Junior, Isabela Castro Sant'Anna, Gabi Nunes Silva, Cosme Damiao Cruz, Moyses Nascimento, Leonardo Bhering Lopes, Plinio Cesar Soares
Summary: This study quantified the best approaches for predicting the importance of characteristics in flood-irrigated rice using regression, artificial intelligence, and machine learning. Computational intelligence and machine learning were found to be effective in extracting nonlinear information and determining the relative importance of variables. The results indicated that flowering, number of grains filled by panicles, and length of panicles were important characteristics for decision making.
ACTA SCIENTIARUM-AGRONOMY
(2023)
Article
Agriculture, Dairy & Animal Science
Leticia Fernanda de Oliveira, Paulo Savio Lopes, Layla Cristien Cassia Miranda Dias, Leandro Magno Dornelas e Silva, Hugo Teixeira Silva, Simone Eliza Facioni Guimaraes, Daniele Botelho Diniz Marques, Delvan Alves da Silva, Renata Veroneze
Summary: This study estimated genetic parameters, effective population size, inbreeding, and inbreeding depression in Piau pigs. The results showed low direct heritabilities for birth weight and weaning weight, while average pre-weaning daily weight gain showed moderate heritability. There were high genetic correlations between weight at birth and weight at weaning, as well as weight at weaning and average pre-weaning daily weight gain. Inbreeding increased over generations and led to a reduction in effective population size. Inbreeding had a significant effect on average pre-weaning daily weight gain, with a decrease of 0.005 g for every 1% increase in inbreeding coefficient. Increasing effective population size is necessary to control inbreeding and preserve genetic variability in the Piau pig population.
TROPICAL ANIMAL HEALTH AND PRODUCTION
(2023)
Article
Genetics & Heredity
Laura Maritza Saavedra, Eveline Teixeira Caixeta, Geleta Dugassa Barka, Aluizio Borem, Laercio Zambolim, Moyses Nascimento, Cosme Damiao Cruz, Antonio Carlos Baiao de Oliveira, Antonio Alves Pereira
Summary: Marker-assisted recurrent selection was used to pyramid resistance gene alleles against coffee leaf rust and coffee berry diseases in Coffea arabica. 144 genotypes from 12 hybrid populations were evaluated, and molecular data were used for cross-certification, diversity study, and resistance allele marker-assisted selection. The results showed that the strategy of pyramiding resistance genes using marker-assisted selection was efficient in selecting superior coffee hybrids and could be used as a source of resistance in various crosses.
Article
Agronomy
Vinicius Quintao Carneiro, Jussara Mencalha, Isabela de Castro Sant'Anna, Gabi Nunes Silva, Julio Augusto de Castro Miguel, Pedro Crescencio Souza Carneiro, Moyses Nascimento, Cosme Damiao Cruz
Summary: The genotype by environment interaction is the main factor affecting the response of evaluated genotypes in value for cultivation and use trials. Adaptability and stability analyses are crucial for understanding genotype performance in a specific growing region. The phenotypic adaptability method by fuzzy clustering is effective in identifying adaptability patterns of common bean genotypes, with higher discriminatory power compared to the centroid method.
ACTA SCIENTIARUM-AGRONOMY
(2023)
Article
Biochemistry & Molecular Biology
Renata de Fatima Bretanha Rocha, Arielly Oliveira Garcia, Pamela Itajara Otto, Marcos Vinicius Barbosa da Silva, Marta Fonseca Martins, Marco Antonio Machado, Joao Claudio do Carmo Panetto, Simone Eliza Facioni Guimaraes
Summary: The study aimed to verify the impact of ROH and inbreeding depression on total oocytes and embryos in Gir Indicine cattle, and to identify genes and enriched regions related to these traits. Generally, an increase in ROH was found to decrease the number of total oocytes and viable embryos. Additionally, multiple genes and genomic regions were identified as being associated with different breeding values for these traits.
Article
Forestry
Matheus Massariol Suela, Camila Ferreira Azevedo, Ana Carolina Campana Nascimento, Mehdi Momen, Antonio Carlos Baiao de Oliveira, Eveline Teixeira Caixeta, Gota Morota, Moyses Nascimento
Summary: The standard MTM-GWAS does not capture the interrelated dependencies between coffee yield-related traits. By applying SEM to GWAS, we discovered positive correlations between vegetative vigor and yield, as well as between vegetative vigor and the number of reproductive nodes. Additionally, we identified three genes directly affecting coffee yield.
TREE GENETICS & GENOMES
(2023)
Article
Biochemistry & Molecular Biology
Renata de Fatima Bretanha Rocha, Arielly Oliveira Garcia, Pamela Itajara Otto, Mateus Guimaraes dos Santos, Marcos Vinicius Barbosa da Silva, Marta Fonseca Martins, Marco Antonio Machado, Joao Claudio do Carmo Panetto, Simone Eliza Facioni Guimaraes
Summary: This study used GWAS to identify genomic regions, genes, and biological processes associated with the number of embryos and oocytes in Gir dairy cattle. Several protein-coding genes were found to be related to embryo development and cell functions.
Article
Agronomy
Filipe Ribeiro Formiga Teixeira, Paulo Roberto Cecon, Matheus Massariol Suela, Moyses Nascimento
Summary: Evaluating the growth of fruit width and length in peppers is crucial for decision-making in managing and harvesting the crops. The Nonlinear Mixed-Effect Models (NLME) method was used to model the growth curves and residuals of pepper and bell pepper genotypes. The Richards model showed the best fit for fruit length (R-adj.(2)=0.9960), while the Logistic model was the best fit for fruit width (R-adj.(2)=0.9957). The NLME adjustment allowed efficient prediction and characterization of the genotypes.
Article
Agriculture, Multidisciplinary
Antonio Carlos da Silva Junior, Waldenia de Melo Moura, Livia Gomes Torres, Iara Goncalves dos Santos, Michele Jorge da Silva, Camila Ferreira Azevedo, Cosme Damiao Cruz
Summary: Identifying Coffea arabica cultivars with better genetic potential for cultivation in low-nitrogen concentrations is important for reducing environmental and economic impacts. This study used a Bayesian multitrait model to estimate heritability and select high-performing cultivars. Results showed that the cultivars Icatu Precoce 3282, Icatu Vermelho IAC 4045, Acaia Cerrado MG 1474, Tupi IAC 1669-33, Catucai 785/15, Caturra Vermelho, and Obata IAC 1669/20 demonstrated greater potential for cultivation in low-nitrogen concentration.
Article
Agronomy
Cristiane Botelho Valadares, Moyses Nascimento, Mauricio de Oliveira Celeri, Ana Carolina Campana Nascimento, Lais Mayara Azevedo Barroso, Isabela de Castro Sant'Anna, Camila Ferreira Azevedo
Summary: Quantile Random Forest (QRF) is a non-parametric approach that combines Random Forest (RF) and Quantile Regression (QR) to explore non-linear functions and extract information from different quantiles. This study evaluated the performance of QRF in genomic prediction for complex traits and compared it with G-BLUP. Simulation results showed that QRF had equal or greater accuracies than other evaluated methodologies, making it an alternative tool for predicting genetic values in complex traits.
Article
Agriculture, Multidisciplinary
Camila Ferreira Azevedo, Cynthia Aparecida Valiati Barreto, Matheus Massariol Suela, Moyses Nascimento, Antonio Carlos da Silva Junior, Ana Carolina Campana Nascimento, Cosme Damiao Cruz, Plinio Cesar Soraes
Summary: This study evaluated the efficiency and applicability of multi-trait multi-environment models within a Bayesian framework using an informative prior distribution strategy based on previous data on rice. The results showed that the Bayesian approach with informative prior distributions provided more accurate estimates and could detect genetic correlations between traits.
Article
Agriculture, Dairy & Animal Science
Margareth Evangelista Botelho, Marcos Soares Lopes, Pramod K. Mathur, Egbert F. Knol, Daniele B. D. Marques, Paulo Savio Lopes, Fabyano Fonseca e Silva, Simone Eliza Facioni Guimaraes, Renata Veroneze
Summary: This study aimed to investigate the causal relationship and causal effects among boar taint compounds measured in pig adipose tissue from carcasses and biopsies. The results showed that boar taint compounds measured in biopsies have direct effects on the compounds measured in carcasses.
ANIMAL PRODUCTION SCIENCE
(2023)