Biological Sequence Classification: A Review on Data and General Methods

With the rapid development of biotechnology, the number of biological sequences has grown exponentially. The continuous expansion of biological sequence data promotes the application of machine learning in biological sequences to construct predictive models for mining biological sequence information. There are many branches of biological sequence classification research. In this review, we mainly focus on the function and modification classification of biological sequences based on machine learning. Sequence-based prediction and analysis are the basic tasks to understand the biological functions of DNA, RNA, proteins, and peptides. However, there are hundreds of classification models developed for biological sequences, and the quite varied specific methods seem dizzying at first glance. Here, we aim to establish a long-term support website (http://lab.malab.cn/~acy/BioseqData/home.html), which provides readers with detailed information on the classification method and download links to relevant datasets. We briefly introduce the steps to build an effective model framework for biological sequence data. In addition, a brief introduction to single-cell sequencing data analysis methods and applications in biology is also included. Finally, we discuss the current challenges and future perspectives of biological sequence classification research

Artificially Selected Grain Shape Gene Combinations in Guangdong Simiao Varieties of Rice (Oryza sativa L.)

Abstract Background Grain shape is a key trait in rice breeding. Although many QTLs and genes of grain shape have been identified, how different combinations of alleles of these genes affect grain shape is largely unknown. It is important to understand the effects of grain shape gene combinations for breeding by design. In the present study, we performed genetic dissection of the grain shapes in Guangdong Simiao varieties, a popular kind of rice in South China, to identify the effective alleles and their combination for breeding. Results We selected two hundred nineteen indica accessions with diverse grain shapes and fifty-two Guangdong Simiao varieties with long and slender grain shapes for genome-wide selection analysis. The results showed that four (GS3, GS5, GW5 and GL7) of the twenty grain shape genes fall into the regions selected for in Guangdong Simiao varieties. Allele analysis and frequency distribution of these four genes showed that GS3 allele3 and GW5 allele2 accounted for 96.2%, and GL7 allele2 and GS5 allele2 accounted for 76.9% and 74.5% of the Simiao varieties, respectively. Further analysis of the allelic combinations showed that 30 allelic combinations were identified in the whole panel, with 28 allelic combinations found in the international indica accessions and 6 allelic combinations found in Guangdong Simiao varieties. There were mainly three combinations (combinations 17, 18 and 19) in the Guangdong Simiao varieties, with combination 19 (GS3 allele3 + GW5 allele2 + GL7 allele2 + GS5 allele2) having the highest percentage (51.9%). All three combinations carried GS3 allele3 + GW5 allele2, while combinations 17 (GL7 allele1) and 19 (GL7 allele2) showed significant differences in both grain length and length/width ratio due to differences in GL7 alleles. Pedigree analysis of Guang8B, the maintainer of the first released Simiao male sterile line Guang8A, showed that the parent lines and Guang8B carried GS3 allele3 + GW5 allele2 + GS5 allele2, while the GL7 allele differed, resulting in significant differences in grain size. Conclusion The results suggest that specific alleles of GS3, GS5, GW5 and GL7 are the key grain shape genes used in the Guangdong Simiao varieties and selected for grain shape improvement. Combination 19 is the predominant allelic combination in the Guangdong Simiao varieties. Our current study is the first to dissect the genetics of grain shape in Guangdong Simiao varieties, and the results will facilitate molecular breeding of Guangdong Simiao varieties

Research Progress on the Function of Rice Grain Type Genes

Rice is one of the important cereal crops in the world, and its yield and quality have always been the focus of breeders, which are related to global food security and human health. Rice grain type, mainly includes grain length, grain width and grain thickness, is an important quantitative trait controlled by multiple genes, and it not only directly affects rice yield, but is also closely related to rice quality. A good understanding of the formation and regulation mechanism of grain type will help to further increase rice yield per plant and improve rice quality. The development of molecular biology and the study of genomics have brought new changes to the exploration of the internal regulation mechanism of rice. A large number of quantitative trait locus (QTL) of rice grain type have been successfully identified and analyzed, and the functions of genes related to them have been verified. So far, several pathways regulating grain type have been identified, such as ubiquitin-proteasome degradation pathway, G protein signaling pathway, mitogen-activated protein kinase (MAPK) pathway, transcription factor regulation pathway, plant hormone pathway and MiRNA-related pathways. However, the regulatory network of grain type is extremely complex, and the mechanisms of the upstream and downstream regulatory components of many genes are still unclear, and there are even some cross interactions among the pathways that affect grain type. This review discussed the research progress of genes related to different signaling pathways affecting rice grain type and the interaction between key grain type genes, summarized the application of grain type genes in breeding in recent years, and proposed to analyze the regulation mechanism of rice grain type with multi-omics, with an aim to better serve the molecular design and breeding and provide support for the development of new high-yield and high-quality rice breeding

Breeding and Application of High-quality and High-yield Simiao Type Male Sterile Line Guang 8A

Guang 8A is a wild abortion Simiao type male sterile line of indica rice with high quality and high yield, which was developed by crossing the female parent Zengcheng Simiao-8 (a high-quality indica rice possessing relationship with Guangdong wild rice) with the maintainer line 1325B, and then backcrossing with 325A after test-crossing. It was selected by the Rice Research Institute of Guangdong Academy of Agricultural Sciences, with the characteristics of good quality, strong combining ability, disease resistance and excellent comprehensive agronomic characters. Since it passed the technical identification of Guangdong Province in 2010, 44 new rice varieties have been approved or authorized by the state and provincial governments in China, including Guangdong, Guangxi, Fujian, Yunnan and Sichuan Province (Region). All of these varieties showed the characteristics of high quality, high and stable yield and strong adaptability, fully demonstrating the role of Guang 8A as the "Core Rice of Guangdong". In addition, it was selected as test material and widely applied in the basic theory researches of cultivation and physiology. Among them, Guang 8 you 165, Guang 8 you Jinzhan and Guang 8 you 2168 showed high coordination of abundance, resistance and quality in provincial regional tests and have been selected as the leading agricultural varieties in Guangdong Province for four consecutive years. From 2017 to date, the accumulative promotion area in Guangdong has reached more than 0.26 million hm2, which has extremely important production value in South China and the middle and lower reaches of the Yangtze River

Magnetostratigraphy of the Xiaolongtan Formation bearing Lufengpithecus keiyuanensis in Yunnan, southwestern China : Constraint on the initiation time of the southern segment of the Xianshuihe–Xiaojiang fault

The late Cenozoic extensional basins in Yunnan Province (southwestern China), which are kinematically linked with the regional strike-slip faults, can provide meaningful constraints on the fault activity history and tectonic evolution of the southeast margin of the Tibetan Plateau (SEMTP), and further on the geodynamic evolution of the Tibetan Plateau. However, this has been severely impeded by the lack of precise age constraints on the timing of fault activity. To better constrain the timing of fault activity and the tectonic rotation of SEMTP, we undertook a high-resolution magnetostratigraphic study on the Xiaolongtan Formation in the Xiaolongtan Basin, which is located at the southern tip of the Xianshuihe–Xiaojiang fault and is well-known by the presence of hominoid Lufengpithecus keiyuanensis. Rock magnetic experiments indicate that magnetite is the main remanence carrier. Correlation to the geomagnetic polarity timescale was achieved by combining magnetostratigraphic and biostratigraphic data. Our correlation suggests that the Xiaolongtan Formation sedimentary sequence spans from Chron C5Ar.1r to Chron C5n.2n, which indicates that the age of the Xiaolongtan Formation ranges from ~ 10 Ma to 12.7 Ma, and that the ages of the two sedimentary layers possibly bearing the hominoid L. keiyuanensis are ~ 11.6 Ma or ~ 12.5 Ma. The basal age of the sediments is 12.7 Ma, which indicates that the activation of the southern Xianshuihe–Xiaojiang fault was initiated at this time. The overall mean paleomagnetic direction (D = 353.2°, I = 34.2°, α95 = 2.1°, n = 166) documents a counter-clockwise vertical axis rotation of − 8 ± 3° with respect to Eurasia, which is the response to the activity of the left-lateral Xianshuihe–Xiaojiang Fault

The Seventh Visual Object Tracking VOT2019 Challenge Results

The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative. Results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis as well as the standard VOT methodology for long-term tracking analysis. The VOT2019 challenge was composed of five challenges focusing on different tracking domains: (i) VOT-ST2019 challenge focused on short-term tracking in RGB, (ii) VOT-RT2019 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2019 focused on long-term tracking namely coping with target disappearance and reappearance. Two new challenges have been introduced: (iv) VOT-RGBT2019 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2019 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2019, VOT-RT2019 and VOT-LT2019 datasets were refreshed while new datasets were introduced for VOT-RGBT2019 and VOT-RGBD2019. The VOT toolkit has been updated to support both standard short-term, long-term tracking and tracking with multi-channel imagery. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website(1).Funding Agencies|Slovenian research agencySlovenian Research Agency - Slovenia [J2-8175, P2-0214, P2-0094]; Czech Science Foundation Project GACR [P103/12/G084]; MURI project - MoD/DstlMURI; EPSRCEngineering &amp; Physical Sciences Research Council (EPSRC) [EP/N019415/1]; WASP; VR (ELLIIT, LAST, and NCNN); SSF (SymbiCloud); AIT Strategic Research Programme; Faculty of Computer Science, University of Ljubljana, Slovenia</p