Review
Genetics & Heredity
Paolo Abondio, Elisabetta Cilli, Donata Luiselli
Summary: Signatures of positive selection in the genome can reveal how populations adapt to environmental changes. Statistical methods can help detect these selection marks and identify genetic variants that affect gene regulation, expression, and protein function.
Article
Biochemical Research Methods
Anyi Yang, Jingqi Chen, Xing-Ming Zhao
Summary: The study introduces a new approach called network-enhanced MAGMA (nMAGMA) for gene-wise variant annotation from GWAS summary statistics. Compared to MAGMA and H-MAGMA, nMAGMA significantly expands the genes that can be annotated to SNPs by integrating various signals. When applied to schizophrenia, nMAGMA is able to detect more risk genes compared to MAGMA and H-MAGMA, with more validated results and uncovering disease-related functions not identified by the other methods. Furthermore, nMAGMA provides tissue-specific risk signals, enhancing the understanding of disorders with multiple tissue origins.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Biochemistry & Molecular Biology
Louxin Zhang, Niloufar Abhari, Caroline Colijn, Yufeng Wu
Summary: The reconstruction of phylogenetic networks is a challenging task due to the vastness of the network space. One approach is to solve the minimum phylogenetic network problem by inferring phylogenetic trees first and then computing the smallest network that displays all the trees. A new method, named ALTS, was developed to infer the minimum tree-child network by aligning lineage taxon strings in the phylogenetic trees, overcoming the limitations of existing programs. ALTS is fast enough to handle large-scale networks with reticulations for a set of up to 50 phylogenetic trees with 50 taxa in about 15 minutes on average.
Article
Evolutionary Biology
Barbara D. Bitarello, Debora Y. C. Brandt, Diogo Meyer, Aida M. Andres
Summary: Identifying genomic regions and genes under natural selection is important in evolutionary genetics. While methods for detecting positive selection have been established, methods for identifying balancing selection targets are still developing. Balancing selection is recognized as a key driver of diversity within populations, and understanding its role in evolution requires identifying its signatures in genomes.
GENOME BIOLOGY AND EVOLUTION
(2023)
Article
Biochemistry & Molecular Biology
Janaina Lima de Oliveira, Atahualpa Castillo Morales, Laurence D. Hurst, Araxi O. Urrutia, Christopher R. L. Thompson, Jason B. Wolf
Summary: Studies have found that synonymous codons are used at different frequencies due to the effects of neutral and selective forces. Selection in highly expressed genes remains to be extensively studied, with some evidence suggesting that certain factors do not impact codon preference.
MOLECULAR BIOLOGY AND EVOLUTION
(2021)
Article
Biochemical Research Methods
Md. Shamsuzzoha Bayzid
Summary: This article proposes a new dynamic programming algorithm for accurately inferring species trees when gene trees are unrooted. The algorithm can also solve constrained problems in polynomial time. The researchers have also proven important structural properties of the algorithm and presented a linear time algorithm for finding the optimal rooted version of unrooted gene trees.
JOURNAL OF COMPUTATIONAL BIOLOGY
(2023)
Review
Biochemical Research Methods
Yiliang Zhang, Youshu Cheng, Wei Jiang, Yixuan Ye, Qiongshi Lu, Hongyu Zhao
Summary: Genetic correlation is a useful metric for quantifying the overall genetic similarity between complex traits by measuring the correlation of phenotypic effects by genetic variants across the genome. Research in this area focuses on addressing the two main technical challenges in estimating genetic correlation based on summary statistics: marker dependency caused by linkage disequilibrium (LD) and sample overlap between different studies.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Neurosciences
Ruben Sanchez-Romero, Michael W. Cole
Summary: Cognition and behavior are influenced by interactions within brain networks, emphasizing the importance of causal interactions in studying brain function. Traditional bivariate methods for functional connectivity analysis lack consideration of confounders, leading to false positives. A new combined FC method (CombinedFC) was proposed to incorporate both simple bivariate and partial correlation measures, providing more valid causal inferences and improving upon existing methods.
JOURNAL OF COGNITIVE NEUROSCIENCE
(2021)
Article
Biochemistry & Molecular Biology
Muhammad Saqib Sohail, Raymond H. Y. Louie, Zhenchen Hong, John P. Barton, Matthew R. McKay
Summary: This article introduces a method for inferring epistasis and the fitness effects of individual mutations from observed evolutionary histories. Simulations show that this method can accurately infer pairwise epistatic interactions when there is sufficient genetic diversity in the data.
MOLECULAR BIOLOGY AND EVOLUTION
(2022)
Article
Biochemical Research Methods
Ville Karhunen, Ilkka Launonen, Marjo-Riitta Jarvelin, Sylvain Sebert, Mikko J. Sillanpaa
Summary: FiniMOM is a novel Bayesian fine-mapping method that aims to detect independent causal variants from genetic associations. It uses a nonlocal inverse-moment prior to model non-null effects and a beta-binomial prior to control for potential misspecifications in linkage disequilibrium reference.
Article
Biochemical Research Methods
Ting Wang, Haojie Lu, Ping Zeng
Summary: Pleiotropy is important for understanding the genetic connection between complex phenotypes and diseases. This study proposes a gene-based method called MAIUP for efficient pleiotropy identification, considering the high-dimensional composite null hypothesis. MAIUP takes into account the composite nature of pleiotropy test and provides well-calibrated P-values for controlling family-wise error rate and false discovery rate. It also effectively addresses the issue of overlapping subjects commonly encountered in association studies. Simulation studies demonstrate the superiority of MAIUP in maintaining correct type I error control and higher power compared to other methods. Application of MAIUP to psychiatric disorders discovers new pleiotropic genes and functional and enrichment analyses support their association with the disorders.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Multidisciplinary Sciences
Gorka Munoz-Gil, Giovanni Volpe, Miguel Angel Garcia-March, Erez Aghion, Aykut Argun, Chang Beom Hong, Tom Bland, Stefano Bo, J. Alberto Conejero, Nicolas Firbas, Oscar Orts, Alessia Gentili, Zihan Huang, Jae-Hyung Jeon, Helene Kabbech, Yeongjin Kim, Patrycja Kowalek, Diego Krapf, Hanna Loch-Olszewska, Michael A. Lomholt, Jean-Baptiste Masson, Philipp G. Meyer, Seongyu Park, Borja Requena, Ihor Smal, Taegeun Song, Janusz Szwabinski, Samudrajit Thapa, Hippolyte Verdier, Giorgio Volpe, Artur Widera, Maciej Lewenstein, Ralf Metzler, Carlo Manzo
Summary: Deviations from Brownian motion leading to anomalous diffusion are commonly found in transport dynamics, but challenging to characterize. An open competition comparing different approaches for single trajectory analysis showed that machine learning methods outperform classical approaches.
NATURE COMMUNICATIONS
(2021)
Article
Astronomy & Astrophysics
Amir Shahmoradi, Joshua Alexander Osborne, Fatemeh Bagheri
Summary: The knowledge of redshifts of Short-duration Gamma-Ray Bursts (SGRBs) is crucial for understanding cosmic rates and related phenomena. This study presents a generic data-driven probabilistic modeling framework to infer the unknown redshifts of SGRBs and provides insights on applying this technique to other astronomical surveys.
Article
Multidisciplinary Sciences
Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, Suttipong Thajchayapong
Summary: To solve poverty issues, it is important to assess the severity of the problem. The Multidimensional Poverty Index (MPI) is commonly used to measure the degree of poverty in a given area. We propose a framework to infer causal relations among binary variables in poverty surveys.
Article
Biotechnology & Applied Microbiology
Guangyi Chen, Zhi-Ping Liu
Summary: Gene regulatory network provides valuable information for demonstrating pathology, predicting clinical outcomes, and identifying drug targets. However, existing machine learning methods lack interpretability in inferring gene regulatory networks. This article introduces a method that combines grey theory with an adaptive sliding window technique to capture gene interactions and transform them into causal relationships.
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
(2022)
Article
Biochemistry & Molecular Biology
Noah Dukler, Gregory T. Booth, Yi-Fei Huang, Nathaniel Tippens, Colin T. Waters, Charles G. Danko, John T. Lis, Adam Siepel
Article
Genetics & Heredity
Brad Gulko, Adam Siepel
Correction
Genetics & Heredity
Brad Gulko, Adam Siepel
Article
Biochemistry & Molecular Biology
Yi-Fei Huang, Adam Siepel
Article
Genetics & Heredity
Melissa J. Hubisz, Amy L. Williams, Adam Siepel
Article
Biochemistry & Molecular Biology
Hussein A. Hejase, Ziyi Mo, Leonardo Campagna, Adam Siepel
Summary: A novel method called SIA was developed to detect and quantify positive selection using the ancestral recombination graph and deep learning. Extensive benchmarking showed its effectiveness in simulations and real data, revealing novel signals of selection in European populations and confirming selection signals in genes related to human phenotypes.
MOLECULAR BIOLOGY AND EVOLUTION
(2022)
Article
Plant Sciences
Yaoyao Wu, Lynn Johnson, Baoxing Song, Cinta Romay, Michelle Stitzer, Adam Siepel, Edward Buckler, Armin Scheben
Summary: The study developed the msa_pipeline workflow for practical and sensitive multiple alignment of diverged plant genomes. Different masking approaches and parameters of the LAST aligner were explored, and parameter tuning was found to improve alignment rate and conservation scores.
Article
Multidisciplinary Sciences
Noah Dukler, Mehreen R. Mughal, Ritika Ramani, Yi-Fei Huang, Adam Siepel
Summary: The new method ExtRaINSIGHT used in this study enables the measurement of strong purifying selection in noncoding and coding regions of the human genome. The results reveal abundant ultraselection in evolutionarily ancient miRNAs and neuronal protein-coding genes, as well as at splice sites, while much less ultraselection is found in other noncoding RNAs and transcription factor binding sites.
NATURE COMMUNICATIONS
(2022)
Article
Genetics & Heredity
Ziyi Mo, Adam Siepel
Summary: This study introduces a method for addressing the problem of simulation mis-specification by using domain adaptation techniques. By training machine learning models jointly with simulated and real data, the effects of simulation mis-specification can be substantially mitigated, improving the performance of population genetic methods.
Article
Cell Biology
Abderhman Abuhashem, Alexandra G. Chivu, Yixin Zhao, Edward J. Rice, Adam Siepel, Charles G. Danko, Anna-Katerina Hadjantonakis
Summary: This study reveals that RNA Pol II pausing plays a critical role in the pluripotency continuum of mouse embryos. The researchers show that in the absence of NELF, pluripotent stem cells fail to balance the levels of induced and repressed genes and enhancers. Additionally, they find an increase in chromatin-associated NELF during the transition from the naive to later pluripotent states.
GENES & DEVELOPMENT
(2022)
Article
Biotechnology & Applied Microbiology
Elizabeth R. Hutton, Christopher R. Vakoc, Adam Siepel
Summary: High-throughput CRISPR-Cas9 knockout screens are widely used in cancer research to evaluate gene essentiality. The probabilistic modeling framework ACE can predict absolute and differential essentiality, identify genotype-specific essentiality candidates, and provide a robust framework for identifying genes responsive to subtype-specific therapeutic targeting.
Article
Plant Sciences
Zoe Joly-Lopez, Adrian E. Platts, Brad Gulko, Jae Young Choi, Simon C. Groen, Xuehua Zhong, Adam Siepel, Michael D. Purugganan
Article
Biotechnology & Applied Microbiology
Adam Siepel
Article
Biochemical Research Methods
Yixin Zhao, Noah Dukler, Gilad Barshad, Shushan Toneyan, Charles G. Danko, Adam Siepel
Summary: The new computational method DENR provides a more accurate estimation of gene and isoform abundances, revealing the presence of multiple pre-RNA isoforms within the same gene and differences between cell types. Furthermore, evidence is presented that the majority of human isoform diversity comes from primary transcription rather than post-transcriptional processes.
