Comparative and Phylogenetic Analysis of Complete Plastomes among Aristidoideae Species (Poaceae)

Aristidoideae is a subfamily in the PACMAD clade of family Poaceae, including three genera, Aristida, Stipagrostis, and Sartidia. In this study, the plastomes of Aristida adscensionis and Stipagrostis pennata were newly sequenced, and a total of 16 Aristidoideae plastomes were compared. All plastomes were conservative in genome size, gene number, structure, and IR boundary. Repeat sequence analysis showed that forward and palindrome repeats were the most common repeat types. The number of SSRs ranged from 30 (Sartidia isaloensis) to 54 (Aristida purpurea). Codon usage analysis showed that plastome genes preferred to use codons ending with A/T. A total of 12 highly variable regions were screened, including four protein coding sequences (matK, ndhF, infA, and rpl32) and eight non-coding sequences (rpl16-1-rpl16-2, ccsA-ndhD, trnY-GUA-trnD-GUC, ndhF-rpl32, petN-trnC-GCA, trnT-GGU-trnE-UUC, trnG-GCC-trnfM-CAU, and rpl32-trnL-UAG). Furthermore, the phylogenetic position of this subfamily and their intergeneric relationships need to be illuminated. All Maximum Likelihood and Bayesian Inference trees strongly support the monophyly of Aristidoideae and each of three genera, and the clade of Aristidoideae and Panicoideae was a sister to other subfamilies in the PACMAD clade. Within Aristidoideae, Aristida is a sister to the clade composed of Stipagrostis and Sartidia. The divergence between C4 Stipagrostis and C3 Sartidia was estimated at 11.04 Ma, which may be associated with the drought event in the Miocene period. Finally, the differences in carbon fixation patterns, geographical distributions, and ploidy may be related to the difference of species numbers among these three genera. This study provides insights into the phylogeny and evolution of the subfamily Aristidoideae.

Baltica: integrated splice junction usage analysis

Alternative splicing is a tightly regulated co- and post-transcriptional process contributing to the transcriptome diversity observed in eukaryotes. Several methods for detecting differential junction usage (DJU) from RNA sequencing (RNA-seq) datasets exist. Yet, efforts to integrate the results from DJU methods are lacking. Here, we present Baltica, a framework that provides workflows for quality control, de novo transcriptome assembly with StringTie2, and currently 4 DJU methods: rMATS, JunctionSeq, Majiq, and LeafCutter. Baltica puts the results from different DJU methods into context by integrating the results at the junction level. We present Baltica using 2 datasets, one containing known artificial transcripts (SIRVs) and the second dataset of paired Illumina and Oxford Nanopore Technologies RNA-seq. The data integration allows the user to compare the performance of the tools and reveals that JunctionSeq outperforms the other methods, in terms of F1 score, for both datasets. Finally, we demonstrate for the first time that meta-classifiers trained on scores of multiple methods outperform classifiers trained on scores of a single method, emphasizing the application of our data integration approach for differential splicing identification. Baltica is available at https://github.com/dieterich-lab/Baltica under MIT license.

Host Adaptation of Codon Usage in SARS-CoV-2 From Mammals Indicate Natural Selection

The outbreak of COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections, spread across hosts from humans to animals, transmitting particularly effectively in mink. How SARS-CoV-2 selects and evolves in the host, and the differences in the evolution of different animals are still unclear. To analysis the mutation and codon usage bias of SARS-CoV-2 in infected humans and animals. The SARS-CoV-2 sequence in mink (Mink-SARS2) and binding energy with receptor were calculated compared with human. The relative synonymous codon usage of viral encoded gene was analyzed to characterize the differences and the evolutionary characteristics. A synonymous codon usage analysis showed that SARS-CoV-2 is optimized to adapt in the animals in which it is currently reported, and all of the animals showed decreased adaptability relative to that of humans, except for mink. The neutrality plot showed that the effect of natural selection on different SARS-CoV-2 sequences is stronger than mutation pressure. A binding affinity analysis indicated that the spike protein of the SARS-CoV-2 variant in mink showed a greater preference for binding with the mink receptor ACE2 than with the human receptor, especially as the mutation Y453F and N501T in Mink-SARS2 lead to improvement of binding affinity for mink receptor. In summary, mutations Y453F and N501T in Mink-SARS2 lead to improvement of binding affinity with mink receptor, indicating possible natural selection and current host adaptation. Monitoring the variation and codon bias of SARS-CoV-2 provides a theoretical basis for tracing the epidemic, evolution and cross-species spread of SARS-CoV-2.

Comparative analysis of transcriptomic data shows the effects of multiple evolutionary selection processes on codon usage in Marsupenaeus japonicus and Marsupenaeus pulchricaudatus

Background Kuruma shrimp, a major commercial shrimp species in the world, has two cryptic or sibling species, Marsupenaeus japonicus and Marsupenaeus pulchricaudatus. Codon usage analysis would contribute to our understanding of the genetic and evolutionary characteristics of the two Marsupenaeus species. In this study, we analyzed codon usage and related indices using coding sequences (CDSs) from RNA-seq data. Results Using CodonW 1.4.2 software, we performed the codon bias analysis of transcriptomes obtained from hepatopancreas tissues, which indicated weak codon bias. Almost all parameters had similar correlations for both species. The gene expression level (FPKM) was negatively correlated with A/T3s. We determined 12 and 14 optimal codons for M. japonicus and M. pulchricaudatus, respectively, and all optimal codons have a C/G-ending. The two Marsupenaeus species had different usage frequencies of codon pairs, which contributed to further analysis of transcriptional differences between them. Orthologous genes that underwent positive selection (ω > 1) had a higher correlation coefficient than that of experienced purifying selection (ω < 1). Parity Rule 2 (PR2) and effective number of codons (ENc) plot analysis showed that the codon usage patterns of both species were influenced by both mutations and selection. Moreover, the average observed ENc value was lower than the expected value for both species, suggesting that factors other than GC may play roles in these phenomena. The results of multispecies clustering based on codon preference were consistent with traditional classification. Conclusions This study provides a relatively comprehensive understanding of the correlations among codon usage bias, gene expression, and selection pressures of CDSs for M. japonicus and M. pulchricaudatus. The genetic evolution was driven by mutations and selection pressure. Moreover, the results point out new insights into the specificities and evolutionary characteristics of the two Marsupenaeus species.

Usage analysis of SVD, DWT and JPEG compression methods for image compression

Through image compression, can save bandwidth usage on telecommunication networks, accelerate image file sending time and can save memory in image file storage. Technique to reduce image size through compression techniques is needed. Image compression is one of the image processing techniques performed on digital images with the aim of reducing the redundancy of the data contained in the image so that it can be stored or transmitted efficiently. This research analyzed the results of image compression and measure the error level of the image compression results. The analysis to be carried out is in the form of an analysis of JPEG compression techniques with various types of images. The method of measuring the compression results uses the MSE and PSNR methods. Meanwhile, to determine the percentage level of compression using the compression ratio calculation. The average ratio for JPEG compression was 0.08605, the compression rate was 91.39%. The average compression ratio for the DWT method was 0.133090833, the compression rate was 86.69%. The average compression ratio of the SVD method was 0.101938833 and the compression rate was 89.80%.

A pan-cancer analysis of tumor-infiltrating B cell repertoires

Tumor-infiltrating B cells can play an important role in anti-tumor responses but their presence is not well understood. In this study, we extracted the B cell receptor repertoires from 9522 tumor and adjacent non-tumor samples across 28 tumor types in the Cancer Genome Atlas (TCGA) project and performed diversity and network analysis. We identified differences in diversity and network statistics across tumor types and subtypes and we observed a trend towards increased clonality in primary tumor samples compared to adjacent non-tumor tissues. We also found significant associations between the repertoire features and mutation load, tumor stage, and age. Our V-gene usage analysis identified similar V-gene usage patterns in colorectal and endometrial cancers. Lastly, we evaluated the prognostic value of the repertoire features and identified significant associations with survival in seven tumor types. This study warrants further research into better understanding the role of tumor-infiltrating B cells across a wide range of tumor types.

Differential transcript usage analysis of bulk and single-cell RNA-seq data with DTUrtle

Motivation Each year, the number of published bulk and single-cell RNA-seq data sets is growing exponentially. Studies analyzing such data are commonly looking at gene-level differences, while the collected RNA-seq data inherently represents reads of transcript isoform sequences. Utilizing transcriptomic quantifiers, RNA-seq reads can be attributed to specific isoforms, allowing for analysis of transcript-level differences. A differential transcript usage (DTU) analysis is testing for proportional differences in a gene’s transcript composition, and has been of rising interest for many research questions, such as analysis of differential splicing or cell type identification. Results We present the R package DTUrtle, the first DTU analysis workflow for both bulk and single-cell RNA-seq data sets, and the first package to conduct a ‘classical’ DTU analysis in a single-cell context. DTUrtle extends established statistical frameworks, offers various result aggregation and visualization options and a novel detection probability score for tagged-end data. It has been successfully applied to bulk and single-cell RNA-seq data of human and mouse, confirming and extending key results. Additionally, we present novel potential DTU applications like the identification of cell type specific transcript isoforms as biomarkers. Availability The R package DTUrtle is available at https://github.com/TobiTekath/DTUrtle with extensive vignettes and documentation at https://tobitekath.github.io/DTUrtle/. Supplementary information Supplementary data are available at Bioinformatics online.

615 Evening Emergency Theatre Usage Analysis Pre And Post COVID-19: Are We Making the Cut?

Introduction Emergency theatre is a 24/7 service. We aim to assess a large UK hospital’s utilisation of theatre time in the evening. Method Electronic records of all evening (6pm-9pm) operations between January-February 2020 (pre COVID-19) and July-August 2020 (post COVID-19) assessed for start and finish times, send for times and booking times. Existence of pending cases during downtime noted as well as any delay reasons. Results Time between out of room and send for times (when cases pending) average 59 minutes pre COVID-19 vs. 78 minutes post COVID-19 (median 35 minutes vs. 59 minutes, respectively). Time between surgical end and send for times (when cases pending) average 83 minutes pre COVID-19 vs. 96 minutes post COVID-19 (median 64 minutes vs. 81 minutes, respectively). Time between out of room and anaesthetic start average 84 minutes pre COVID-19 vs. 101 minutes post COVID-19 (median 58 minutes vs. 80 minutes, respectively). Send for times post booking for new evening bookings average 80mins pre COVID-19 vs. 57 minutes post COVID-19 (63 minutes vs. 38 minutes, respectively). 20.45% of cases did not have a computer booking for surgery pre COVID-19 vs. 25.92% post COVID-19. Pre COVID-19, theatre overrunning contributed to delays, whereas post COVID-19, delays also due to repeat COVID-19 swabs, repeat group & saves and delays in bed availability. Conclusions Lingering effects of COVID-19 have caused further delays to a service already in need of implementation of changes to improve utilisation. Surgical, anaesthetic, nursing and executive teams must work together to improve efficiency.