Genomic Duplication, Fractionation and the Origin of Regulatory Novelty

Genetics ◽  
2004 ◽  
Vol 166 (2) ◽  
pp. 935-945 ◽  
Author(s):  
Richard J Langham ◽  
Justine Walsh ◽  
Molly Dunn ◽  
Cynthia Ko ◽  
Stephen A Goff ◽  
...  
Keyword(s):  
Leucine Zipper ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Duplicate Gene ◽  
The Other ◽  
Duplicate Region ◽  
Basic Leucine Zipper ◽  
Cis Acting ◽  
Making Sense ◽  
Gene Space

Abstract Having diverged 50 MYA, rice remained diploid while the maize lineage became tetraploid and then fractionated by losing genes from one or the other duplicate region. We sequenced and annotated 13 maize genes (counting the duplicate gene as one gene) on one or the other of the pair of homeologous maize regions; 12 genes were present in one cluster in rice. Excellent maize-rice synteny was evident, but only after the fractionated maize regions were condensed onto a finished rice map. Excluding the gene we used to define homeologs, we found zero retention. Once retained, fractionation (loss of functioning DNA sequence) could occur within cis-acting gene space. We chose a retained duplicate basic leucine zipper transcription factor gene because it was well marked with big, exact phylogenetic footprints (CNSs). Detailed alignments of lg2 and retained duplicate lrs1 to their rice ortholog found that fractionation of conserved noncoding sequences (CNSs) was rare, as expected. Of 30 CNSs, 27 were conserved. The 3 unexpected, missing CNSs and a large insertion support subfunctionalization as a reflection of fractionation of cis-acting gene space and the recent evolution of lg2’s novel maize leaf and shoot developmental functions. In general, the principles of fractionation and consolidation work well in making sense of maize gene and genomic sequence data.

scholarly journals Machine Learning for Population Genetics: A New Paradigm

2017 ◽  
Author(s):  
Daniel R. Schrider ◽  
Andrew D. Kern
Keyword(s):  
Machine Learning ◽  
Population Genetics ◽  
Population Genomics ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Supervised Machine Learning ◽  
New Paradigm ◽  
Making Sense ◽  
Daunting Task ◽  
Population Genetic Inference

AbstractAs population genomic datasets grow in size, researchers are faced with the daunting task of making sense of a flood of information. To keep pace with this explosion of data, computational methodologies for population genetic inference are rapidly being developed to best utilize genomic sequence data. In this review we discuss a new paradigm that has emerged in computational population genomics: that of supervised machine learning. We review the fundamentals of machine learning, discuss recent applications of supervised machine learning to population genetics that outperform competing methods, and describe promising future directions in this area. Ultimately, we argue that supervised machine learning is an important and underutilized tool that has considerable potential for the world of evolutionary genomics.

scholarly journals The Prevalence of the Citrus tristeza virus Trifoliate Resistance Breaking Genotype Among Puerto Rican Isolates

Plant Disease ◽  
2013 ◽  
Vol 97 (9) ◽  
pp. 1227-1234 ◽  
Author(s):  
Avijit Roy ◽  
Nandlal Choudhary ◽  
John S. Hartung ◽  
R. H. Brlansky
Keyword(s):  
Puerto Rico ◽  
New Zealand ◽  
Puerto Rican ◽  
Citrus Tristeza Virus ◽  
Genomic Sequence ◽  
Sequence Data ◽  
The Other ◽  
Resistance Breaking ◽  
Tristeza Virus ◽  
Citrus Tristeza

Citrus tristeza virus (CTV) isolates have been grouped into six genotypes: T3, T30, T36, VT, B165, and resistance breaking (RB) based on symptoms, host range, and genomic sequence data. The RB genotype has recently been identified with the novel property of replicating in trifoliate orange trees, a resistant host for the other five genotypes. Puerto Rican CTV isolate B301 caused mild vein clearing symptoms in Mexican lime but did not induce seedling yellows or stem pitting reactions in appropriate indicator Citrus spp., which are typical host reactions of the isolate T30. The isolate B301 was not detected by the genotype specific primer (GSP), which identifies the CTV-T3, -T30, -T36, -VT, and B165 genotypes. A primer pair for reverse transcription polymerase chain reaction (RT-PCR) amplification of the CTV-RB genotype was designed from the heat shock protein (p65) region based on the complete genomic sequences of trifoliate RB isolates from New Zealand available in the GenBank databases. The amplicon sequence from isolate B301 was 98% identical to that of the other trifoliate RB isolates. In addition, B301 was successfully inoculated into ‘Carrizo citrange’ (a trifoliate hybrid) but did not induce any symptoms. Furthermore, the complete genome sequence of B301 followed by the phylogenetic analysis revealed that the isolate is part of the RB clade with other CTV-RB isolates from New Zealand and Hawaii. Additional CTV isolates obtained from Puerto Rico were tested with the RB-GSP and confirmed the presence of trifoliate RB isolates in mixed infection with known CTV genotypes. Although this is the first report of a CTV trifoliate RB genotype from Puerto Rico, this genotype was present there prior to 1992.

PoGB-Pred: Prediction of Antifreeze Proteins Sequences using Amino Acid Composition with Feature Selection followed by a Sequential based Ensemble Approach

2020 ◽  
Vol 15 ◽  
Author(s):  
Affan Alim ◽  
Abdul Rafay ◽  
Imran Naseem
Keyword(s):  
Amino Acid ◽  
Dimension Reduction ◽  
Protein Identification ◽  
Cold Water ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Antifreeze Proteins ◽  
Building Blocks ◽  
Gradient Boosting ◽  
Proposed Model

Background: Proteins contribute significantly in every task of cellular life. Their functions encompass the building and repairing of tissues in human bodies and other organisms. Hence they are the building blocks of bones, muscles, cartilage, skin, and blood. Similarly, antifreeze proteins are of prime significance for organisms that live in very cold areas. With the help of these proteins, the cold water organisms can survive below zero temperature and resist the water crystallization process which may cause the rupture in the internal cells and tissues. AFP’s have attracted attention and interest in food industries and cryopreservation. Objective: With the increase in the availability of genomic sequence data of protein, an automated and sophisticated tool for AFP recognition and identification is in dire need. The sequence and structures of AFP are highly distinct, therefore, most of the proposed methods fail to show promising results on different structures. A consolidated method is proposed to produce the competitive performance on highly distinct AFP structure. Methods: In this study, we propose to use machine learning-based algorithms Principal Component Analysis (PCA) followed by Gradient Boosting (GB) for antifreeze protein identification. To analyze the performance and validation of the proposed model, various combinations of two segments composition of amino acid and dipeptide are used. PCA, in particular, is proposed to dimension reduction and high variance retaining of data which is followed by an ensemble method named gradient boosting for modelling and classification. Results: The proposed method obtained the superfluous performance on PDB, Pfam and Uniprot dataset as compared with the RAFP-Pred method. In experiment-3, by utilizing only 150 PCA components a high accuracy of 89.63 was achieved which is superior to the 87.41 utilizing 300 significant features reported for the RAFP-Pred method. Experiment-2 is conducted using two different dataset such that non-AFP from the PISCES server and AFPs from Protein data bank. In this experiment-2, our proposed method attained high sensitivity of 79.16 which is 12.50 better than state-of-the-art the RAFP-pred method. Conclusion: AFPs have a common function with distinct structure. Therefore, the development of a single model for different sequences often fails to AFPs. A robust results have been shown by our proposed model on the diversity of training and testing dataset. The results of the proposed model outperformed compared to the previous AFPs prediction method such as RAFP-Pred. Our model consists of PCA for dimension reduction followed by gradient boosting for classification. Due to simplicity, scalability properties and high performance result our model can be easily extended for analyzing the proteomic and genomic dataset.

scholarly journals Massive and Microscopic Sensemaking During COVID-19 Times

2020 ◽  
pp. 107780042096247 ◽  
Author(s):  
Annette N. Markham ◽  
Anne Harris ◽  
Mary Elizabeth Luka
Keyword(s):  
Critical Pedagogy ◽  
Lived Experience ◽  
Large Scale ◽  
Ethic Of Care ◽  
Feminist Perspective ◽  
The Other ◽  
Self And Other ◽  
Planetary Scale ◽  
Making Sense ◽  
Ongoing Experiment

How does this pandemic moment help us to think about the relationships between self and other, or between humans and the planet? How are people making sense of COVID-19 in their everyday lives, both as a local and intimate occurrence with microscopic properties, and a planetary-scale event with potentially massive outcomes? In this paper we describe our approach to a large-scale, still-ongoing experiment involving more than 150 people from 26 countries. Grounded in autoethnography practice and critical pedagogy, we offered 21 days of self guided prompts to for us and the other participants to explore their own lived experience. Our project illustrates the power of applying a feminist perspective and an ethic of care to engage in open ended collaboration during times of globally-felt trauma.

scholarly journals Roles of the 14-3-3 gene family in cotton flowering

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Na Sang ◽  
Hui Liu ◽  
Bin Ma ◽  
Xianzhong Huang ◽  
Lu Zhuo ◽  
...  
Keyword(s):  
Gene Family ◽  
Protein Interactions ◽  
Leucine Zipper ◽  
Expression Patterns ◽  
Bimolecular Fluorescence Complementation ◽  
Structural Motif ◽  
Virus Induced Gene Silencing ◽  
Protein Protein Interactions ◽  
Basic Leucine Zipper ◽  
Genome Wide

Abstract Background In plants, 14-3-3 proteins, also called GENERAL REGULATORY FACTORs (GRFs), encoded by a large multigene family, are involved in protein–protein interactions and play crucial roles in various physiological processes. No genome-wide analysis of the GRF gene family has been performed in cotton, and their functions in flowering are largely unknown. Results In this study, 17, 17, 31, and 17 GRF genes were identified in Gossypium herbaceum, G. arboreum, G. hirsutum, and G. raimondii, respectively, by genome-wide analyses and were designated as GheGRFs, GaGRFs, GhGRFs, and GrGRFs, respectively. A phylogenetic analysis revealed that these proteins were divided into ε and non-ε groups. Gene structural, motif composition, synteny, and duplicated gene analyses of the identified GRF genes provided insights into the evolution of this family in cotton. GhGRF genes exhibited diverse expression patterns in different tissues. Yeast two-hybrid and bimolecular fluorescence complementation assays showed that the GhGRFs interacted with the cotton FLOWERING LOCUS T homologue GhFT in the cytoplasm and nucleus, while they interacted with the basic leucine zipper transcription factor GhFD only in the nucleus. Virus-induced gene silencing in G. hirsutum and transgenic studies in Arabidopsis demonstrated that GhGRF3/6/9/15 repressed flowering and that GhGRF14 promoted flowering. Conclusions Here, 82 GRF genes were identified in cotton, and their gene and protein features, classification, evolution, and expression patterns were comprehensively and systematically investigated. The GhGRF3/6/9/15 interacted with GhFT and GhFD to form florigen activation complexs that inhibited flowering. However, GhGRF14 interacted with GhFT and GhFD to form florigen activation complex that promoted flowering. The results provide a foundation for further studies on the regulatory mechanisms of flowering.

scholarly journals Special Issue: Genetic Basis of Phenotypic Variation in Drosophila and Other Insects

Genes ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1212
Author(s):  
J. Spencer Johnston ◽  
Carl E. Hjelmen
Keyword(s):  
Next Generation Sequencing ◽  
Genetic Basis ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Complete Genomic Sequence ◽  
Special Issue ◽  
Model Species ◽  
Road Map ◽  
Generation Sequencing ◽  
Complete Genomic

Next-generation sequencing provides a nearly complete genomic sequence for model and non-model species alike; however, this wealth of sequence data includes no road map [...]

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