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To understand nuclear mRNA polyadenylation mechanisms in the model alga Chlamydomonas reinhardtii, we generated a data set of 16,952 in silico-verified poly(A) sites from EST sequencing traces based on Chlamydomonas Genome Assembly v.3.1. Analysis of this data set revealed a unique and complex polyadenylation signal profile that is setting Chlamydomonas apart from other organisms. In contrast to the high-AU con tent in the 3'-UTRs of other organisms, Chlamydomonas shows a high-guanylate content that transits to high-cytidylate around the poly(A) site. The average length of the 3'-UTR is 595 nucleotides (nt), significantly longer than that of Arabidopsis and rice. The dominant poly(A) signal, UGUAA, was found in 52% of the near-upstream elements, and its occurrence may be positively correlated with higher gene expression levels. The UGUAA signal also exists in Arabidopsis and in some mammalian genes but mainly in the far-upstream elements, suggesting a shift in function. The C-rich region after poly(A) sites with unique signal elements is a characteristic downstream element that is lacking in higher plants. We also found a high level of alternative polyadenylation in the Chlamydomonas genome, with a range of up to 33% of the 4057 genes analyzed having at least two unique poly(A) sites and ~1% of these genes having poly(A) sites residing in predicted coding sequences, introns, and 5'-UTRs. These potentially contribute to transcriptome diversity and gene expression regulation.

Many of Chlamydomonas reinhardtii expressed sequence tags (ESTs) in GenBank dbEST and community EST assemblies were either over- of undertrimmed in terms of their cDNA termini, which are defined as the diagnostic sequence elements that delineate 3'/5' ends of mRNA transcripts. Overtrimming represents a loss of directional, positional, and structural information of transcript ends whereas undertrimming causes unclean spurious sequences retained in ESTs that exert deleterious impacts on downstream EST-based applications. We examined 309,278 raw EST sequencing trace files of C. reinhardtii and found that only 57% had cDNA termini that matched the expected structures specified in their cDNA library constructions while satisfying our minimum length requirement for their final clean sequences. Using GMAP, 156,963 individual ESTs were mapped to the genome successfully, with their in silico-verified cDNA termini anchored to the genome. Our data analysis suggested strong macro- and microheterogeneity of 3'/5' end positions of individual transcripts derived from the same genes in C. reinhardtii. This work annotating differential ends of individual transcripts in the draft genome presents the research community with a new stream of data that will facilitate accurate determination of gene structures, genome annotation, and exploration of the transcriptome and mRNA metabolism in C. reinhardtii.

LiDAR SLAM technology is an important method for the accurate navigation of automatic vehicles and is a prerequisite for the safe driving of automatic vehicles in the unstructured road environment of complex parks. This paper proposes a LiDAR fast point cloud registration algorithm that can realize fast and accurate localization and mapping of automatic vehicle point clouds through a combination of a normal distribution transform (NDT) and point-to-line iterative closest point (PLICP). First, the NDT point cloud registration algorithm is applied for the rough registration of point clouds between adjacent frames to achieve a rough estimate of the pose of automatic vehicles. Then, the PLICP point cloud registration algorithm is adopted to correct the rough registration result of the point cloud. This step completes the precise registration of the point cloud and achieves an accurate estimate of the pose of the automatic vehicle. Finally, cloud registration is accumulated over time, and the point cloud information is continuously updated to construct the point cloud map. Through numerous experiments, we compared the proposed algorithm with PLICP. The average number of iterations of the point cloud registration between adjacent frames was reduced by 6.046. The average running time of the point cloud registration between adjacent frames decreased by 43.05156 ms. The efficiency of the point cloud registration calculation increased by approximately 51.7%. By applying the KITTI dataset, the computational efficiency of NDT-ICP was approximately 60% higher than that of LeGO-LOAM. The proposed method realizes the accurate localization and mapping of automatic vehicles relying on vehicle LiDAR in a complex park environment and was applied to a Small Cyclone automatic vehicle. The results indicate that the proposed algorithm is reliable and effective. [ABSTRACT FROM AUTHOR]

In recent years, synthesizing drugs powered by artificial intelligence has brought great convenience to society. Since retrosynthetic analysis occupies an essential position in synthetic chemistry, it has received broad attention from researchers. In this review, we comprehensively summarize the development process of retrosynthesis in the context of deep learning. This review covers all aspects of retrosynthesis, including datasets, models and tools. Specifically, we report representative models from academia, in addition to a detailed description of the available and stable platforms in the industry. We also discuss the disadvantages of the existing models and provide potential future trends, so that more abecedarians will quickly understand and participate in the family of retrosynthesis planning. [ABSTRACT FROM AUTHOR]

Abstract Background With the advent of low-cost, high-throughput sequencing, the amount of public domain Expressed Sequence Tag (EST) sequence data available for both model and non-model organism is growing exponentially. While these data are widely used for characterizing various genomes, they also present a serious challenge for data quality control and validation due to their inherent deficiencies, particularly for species without genome sequences. Description ConiferEST is an integrated system for data reprocessing, visualization and mining of conifer ESTs. In its current release, Build 1.0, it houses 172,229 loblolly pine EST sequence reads, which were obtained from reprocessing raw DNA sequencer traces using our software – WebTraceMiner. The trace files were downloaded from NCBI Trace Archive. ConiferEST provides biologists unique, easy-to-use data visualization and mining tools for a variety of putative sequence features including cloning vector segments, adapter sequences, restriction endonuclease recognition sites, polyA and polyT runs, and their corresponding Phred quality values. Based on these putative features, verified sequence features such as 3' and/or 5' termini of cDNA inserts in either sense or non-sense strand have been identified in-silico. Interestingly, only 30.03% of the designated 3' ESTs were found to have an authenticated 5' terminus in the non-sense strand (i.e., polyT tails), while fewer than 5.34% of the designated 5' ESTs had a verified 5' terminus in the sense strand. Such previously ignored features provide valuable insight for data quality control and validation of error-prone ESTs, as well as the ability to identify novel functional motifs embedded in large EST datasets. We found that "double-termini adapters" were effective indicators of potential EST chimeras. For all sequences with in-silico verified termini/terminus, we used InterProScan to assign protein domain signatures, results of which are available for in-depth exploration using our biologist-friendly web interfaces. Conclusion ConiferEST represents a unique and complementary public resource for EST data integration and mining in conifers by reprocessing raw DNA traces, identifying putative sequence features and determining and annotating in-silico verified features. Seamlessly integrated with other public resources, ConiferEST provides biologists powerful tools to verify data, visualize abnormalities, including EST chimeras, and explore large EST datasets.