Temporal convolutional network for a Fast DNA mutation detection in breast cancer data.

Early detection of breast cancer can be achieved through mutation detection in DNA sequences, which can be acquired through patient blood samples. Mutation detection can be performed using alignment and machine learning techniques. However, alignment techniques require reference sequences, and machine learning techniques still cannot predict index mutation and require supporting tools. Therefore, in this research, a Temporal Convolutional Network (TCN) model was proposed to detect the type and index mutation faster and without reference sequences and supporting tools. The architecture of the proposed TCN model is specifically designed for sequential labeling tasks on DNA sequence data. This allows for the detection of the mutation type of each nucleotide in the sequence, and if the nucleotide has a mutation, the index mutation can be obtained. The proposed model also uses 2-mers and 3-mers mapping techniques to improve detection performance. Based on the tests that have been carried out, the proposed TCN model can achieve the highest F1-score of 0.9443 for COSMIC dataset and 0.9629 for RSCM dataset, Additionally, the proposed TCN model can detect index mutation six times faster than BiLSTM model. Furthermore, the proposed model can detect type and index mutations based on the patient's DNA sequence, without the need for reference sequences or other additional tools.

Gene expression profiles of oil palm leaves from different oil yields and genetic population: Transcriptomic dataset.

Oil palm (Elaeis guineensis Jacq.) is one of the most important oil-producing crops in the world. However, the demand for oil from this crop is expected to increase in the future. A comparative gene expression profile of the oil palm leaves was needed in order to understand the key factors that influence the oil production. Here, we reported an RNA-seq dataset from three different oil yields and three different genetic populations of oil palm. All raw sequencing reads were obtained from an Illumina NextSeq 500 platform. We also provide a list of the genes and their expression levels resulting from the RNA-sequencing. This transcriptomic dataset will provide a valuable resource for increasing oil yield.

1H NMR metabolomics analysis of oil palm stem tissue infected by Ganoderma boninense based on field severity Indices.

Basal stem rot disease (BSR) caused by G. boninense affects most oil palm plants in Southeast Asia. This disease can be fatal to palm oil production. BSR shows no signs on the tree in the early stages of infection. Therefore, it is essential to find an approach that can detect BSR disease in oil palm, especially at any level of disease severity in the field. This study aims to identify biomarkers of BSR disease in oil palm stem tissue based on various disease severity indices in the field using 1H NMR-based metabolomics analysis. The crude extract of oil palm stem tissue with four disease severity indices was analyzed by 1H NMR metabolomics. Approximately 90 metabolites from oil palm stem tissue were identified.Twenty of these were identified as metabolites that significantly differentiated the four disease severity indices. These metabolites include the organic acid group, the carbohydrate group, the organoheterocyclic compound group, and the benzoid group. In addition, different tentative biomarkers for different disease severity indices were also identified. These tentative biomarkers consist of groups of organic acids, carbohydrates, organoheterocyclic compounds, nitrogenous organic compounds, and benzene. There are five pathways in oil palm that are potentially affected by BSR disease.

Revealing the role of Plant Growth Promoting Rhizobacteria in suppressive soils against Fusarium oxysporum f.sp. cubense based on metagenomic analysis.

Fusarium oxysporum f.sp. cubense (Foc) is a soil-borne pathogen causing fusarium wilt banana disease. Management of soil-borne disease generally required the application of toxic pesticides or fungicides strongly affect the soil microbiomes ecosystem. Suppressive soil is a promising method for controlling soil-borne pathogens in which soil microbiomes may affect the suppressiveness. The comparative analysis of microbial diversity was conducted from suppressive and conducive soils by analyzing whole shotgun metagenomic DNA data. Two suppressive soil samples and two conducive soil samples were collected from a banana plantation in Sukabumi, West Java, Indonesia. Each soil sample was prepared by mixing the soil samples collected from three points sampling sites with 20 cm depth. Analysis of microbial abundance, diversity, co-occurrence network using Metagenome Analyzer 6 (MEGAN6) and functional analysis using Kyoto Encyclopedia of Genes and Genomes (KEGG) was performed. Data showed the abundance of Actinobacteria, Betaproteobacteria, Rhizobiales, Burkholderiales, Bradyrhizobiaceae, Methylobacteriaceae, Rhodopseudomonas palustris, and Methylobacterium nodulans were higher in the suppressive than conducive soils. Interestingly, those bacteria groups are known functionally as members of Plant Growth Promoting Rhizobacteria (PGPR). The co-occurrence analysis showed Pseudomonas, Burkholderia, and Streptomyces were present in the suppressive soils, while Bacillus and more Streptomyces were found in the conducive soils. Furthermore, the relative abundance of Pseudomonas, Burkholderia, Bacillus, and Streptomyces was performed. The analysis showed that the relative abundance of Pseudomonas and Burkholderia was higher in the suppressive than conducive soils. Therefore, it assumed Pseudomonas and Burkholderia play a role in suppressing Foc based on co-occurrence and abundance analysis. Functional analysis of Pseudomonas and Burkholderia showed that the zinc/manganese transport system was higher in the suppressive than conducive soils. In contrast, the phosphate transport system was not found in conducive soils. Both functions are may be responsible for the synthesis of a siderophore and phosphate solubilization. In conclusion, this study provides information that PGPR may be contributing to Foc growth suppressing by releasing secondary metabolites.

Production of polyhydroxyalkanoate (PHA) by Ralstonia eutropha JMP 134 with volatile fatty acids from palm oil mill effluent as precursors.

The highest volatile fatty acids (VFAs) concentration from palm oil mill effluent (POME) treated by anaerobic fermentation was achieved for a 1-day process when the main acids used were acetic, propionic and butyric acids. Polyhydroxyalkanoate (PHA) production with VFAs from POME as precursors in the fed-batch mode has advantages over batch mode, both in terms of its productivity and 3HV (3-hydroxyvalerate) composition in the produced polymer. With the fed batch, the productivity increased to 343% and contained more 3HV than those of the batch. The structures of the PHA were identified by different methods and they supported each other; the resulting products consisted of functional groups of 3HB (3-hydroxybutyrate) and 3HV.