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Mutation density patterns reveal unique biological properties of specific genomic regions and shed light on the mechanisms of carcinogenesis. Although previous studies reported insightful mutation density patterns associated with certain genomic regions such as transcription start sites and DNA replication origins, a tool that can systematically investigate mutational spatial patterns is still lacking. Thus, we developed MutDens, a bioinformatic tool for comprehensive analysis of mutation density patterns around genomic features, namely, genomic positions, in humans and model species. By scanning the bidirectional vicinity regions of given positions, MutDens systematically characterizes the mutation density for single-base substitution mutational classes after adjusting for total mutation burden and local nucleotide proportion. Analysis results using MutDens not only verified the previously reported transcriptional strand bias around transcription start sites and replicative strand bias around DNA replication origins, but also identified novel mutation density patterns around other genomics features, such as enhancers and retrotransposon insertion polymorphism sites. To our knowledge, MutDens is the first tool that systematically calculates, examines, and compares mutation density patterns, thus providing a valuable avenue for investigating the mutational landscapes associated with important genomic features., (© 2022 Yu et al.; Published by Cold Spring Harbor Laboratory Press.)

Simple Summary: Using the covariate-adjusted tensor classification in the high-dimension (CATCH) model, we integrated adjusted radiomics-based CT images into RNA immune genomic expression data to achieve the accurate classification of recurrent CRC. The correlation between radiomic features and immune gene expression identifies potential therapeutic targets in CRC. We provide individualized cancer therapeutic strategies based on adjusted radiomic features in recurrent stage III CRC. To evaluate whether adjusted computed tomography (CT) scan image-based radiomics combined with immune genomic expression can achieve accurate stratification of cancer recurrence and identify potential therapeutic targets in stage III colorectal cancer (CRC), this cohort study enrolled 71 patients with postoperative stage III CRC. Based on preoperative CT scans, radiomic features were extracted and selected to build pixel image data using covariate-adjusted tensor classification in the high-dimension (CATCH) model. The differentially expressed RNA genes, as radiomic covariates, were identified by cancer recurrence. Predictive models were built using the pixel image and immune genomic expression factors, and the area under the curve (AUC) and F1 score were used to evaluate their performance. Significantly adjusted radiomic features were selected to predict recurrence. The association between the significantly adjusted radiomic features and immune gene expression was also investigated. Overall, 1037 radiomic features were converted into 33 × 32-pixel image data. Thirty differentially expressed genes were identified. We performed 100 iterations of 3-fold cross-validation to evaluate the performance of the CATCH model, which showed a high sensitivity of 0.66 and an F1 score of 0.69. The area under the curve (AUC) was 0.56. Overall, ten adjusted radiomic features were significantly associated with cancer recurrence in the CATCH model. All of these methods are texture-associated radiomics. Compared with non-adjusted radiomics, 7 out of 10 adjusted radiomic features influenced recurrence-free survival. The adjusted radiomic features were positively associated with PECAM1, PRDM1, AIF1, IL10, ISG20, and TLR8 expression. We provide individualized cancer therapeutic strategies based on adjusted radiomic features in recurrent stage III CRC. Adjusted CT scan image-based radiomics with immune genomic expression covariates using the CATCH model can efficiently predict cancer recurrence. The correlation between adjusted radiomic features and immune genomic expression can provide biological relevance and individualized therapeutic targets. [ABSTRACT FROM AUTHOR]

Background: The Orchid family is the largest families of the monocotyledons and an economically important ornamental plant worldwide. Given the pivotal role of this plant to humans, botanical researchers and breeding communities should have access to valuable genomic and transcriptomic information of this plant. Previously, we established OrchidBase, which contains expressed sequence tags (ESTs) from different tissues and developmental stages of Phalaenopsis as well as biotic and abiotic stress-treated Phalaenopsis. The database includes floral transcriptomic sequences from 10 orchid species across all the five subfamilies of Orchidaceae. Description: Recently, the whole-genome sequences of Apostasia shenzhenica, Dendrobium catenatum, and Phalaenopsis equestris were de novo assembled and analyzed. These datasets were used to develop OrchidBase 4.0, including genomic and transcriptomic data for these three orchid species. OrchidBase 4.0 offers information for gene annotation, gene expression with fragments per kilobase of transcript per millions mapped reads (FPKM), KEGG pathways and BLAST search. In addition, assembled genome sequences and location of genes and miRNAs could be visualized by the genome browser. The online resources in OrchidBase 4.0 can be accessed by browsing or using BLAST. Users can also download the assembled scaffold sequences and the predicted gene and protein sequences of these three orchid species. Conclusions: OrchidBase 4.0 is the first database that contain the whole-genome sequences and annotations of multiple orchid species. OrchidBase 4.0 is available at http://orchidbase.itps.ncku.edu.tw/ [ABSTRACT FROM AUTHOR]

Motivation: Exome capture kits have capture efficiencies that range from 40 to 60%. A significant amount of off-target reads are from the mitochondrial genome. These unintentionally sequenced mitochondrial reads provide unique opportunities to study the mitochondria genome.Results: MitoSeek is an open-source software tool that can reliably and easily extract mitochondrial genome information from exome and whole genome sequencing data. MitoSeek evaluates mitochondrial genome alignment quality, estimates relative mitochondrial copy numbers and detects heteroplasmy, somatic mutation and structural variants of the mitochondrial genome. MitoSeek can be set up to run in parallel or serial on large exome sequencing datasets.Availability: https://github.com/riverlee/MitoSeekContact: yan.guo@vanderbilt.eduSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Traditionally, somatic mutations are detected by examining DNA sequence. The maturity of sequencing technology has allowed researchers to screen for somatic mutations in the whole genome. Increasingly, researchers have become interested in identifying somatic mutations through RNAseq data. With this motivation, we evaluated the practicability of detecting somatic mutations from RNAseq data. Current somatic mutation calling tools were designed for DNA sequencing data. To increase performance on RNAseq data, we developed a somatic mutation caller GLMVC based on bias reduced generalized linear model for both DNA and RNA sequencing data. Through comparison with MuTect and Varscan we showed that GLMVC performed better for somatic mutation detection using exome sequencing or RNAseq data. GLMVC is freely available for download at the following website: https://github.com/shengqh/GLMVC/wiki . [ABSTRACT FROM AUTHOR]