Flexiplex: a versatile demultiplexer and search tool for omics data.

MOTIVATION: The process of analyzing high throughput sequencing data often requires the identification and extraction of specific target sequences. This could include tasks, such as identifying cellular barcodes and UMIs in single-cell data, and specific genetic variants for genotyping. However, existing tools, which perform these functions are often task-specific, such as only demultiplexing barcodes for a dedicated type of experiment, or are not tolerant to noise in the sequencing data. RESULTS: To overcome these limitations, we developed Flexiplex, a versatile and fast sequence searching and demultiplexing tool for omics data, which is based on the Levenshtein distance and thus allows imperfect matches. We demonstrate Flexiplex's application on three use cases, identifying cell-line-specific sequences in Illumina short-read single-cell data, and discovering and demultiplexing cellular barcodes from noisy long-read single-cell RNA-seq data. We show that Flexiplex achieves an excellent balance of accuracy and computational efficiency compared to leading task-specific tools. AVAILABILITY AND IMPLEMENTATION: Flexiplex is available at https://davidsongroup.github.io/flexiplex/.

FexRNA: Exploratory Data Analysis and Feature Selection of Non-Coding RNA.

Non-coding RNA (ncRNA) is involved in many biological processes and diseases in all species. Many ncRNA datasets exist that provide ncRNA data in FASTA format which is well suited for biomedical purposes. However, for ncRNA analysis and classification, statistical learning methods require hidden numerical features from the data. Furthermore, in the literature, a wealth of sequence intrinsic features has been proposed for ncRNA identification. The extraction of hidden features, their analysis, and usage of a suitable set of features is crucial for the performance of any statistical learning method. To alleviate the posed challenges, we generated 96 feature datasets from ncRNA widely used features. The feature datasets are based on RNACentral and consist of species, ncRNA types, and expert databases that are available on the FexRNA platform. Additionally, the feature datasets are explored and analysed to provide statistical information, univariate, and bivariate analysis. We sought to determine which of these 17 features would be most appropriate to use in developing ncRNA classification approaches. For feature selection (FS), a two-phase hierarchical FS framework based on correlation and majority voting is proposed and evaluated on 5 species. The FexRNA platform provides information about ncRNA feature analysis and selection.

Accumulation of heavy metals in edible parts of vegetables irrigated with waste water and their daily intake to adults and children, District Mardan, Pakistan.

Green vegetable crops irrigated with wastewater are highly contaminated with heavy metals and are the main source of human exposure to the contaminants. In this study accumulation of eight heavy metals (Cu, Ni, Zn, Cr, Fe, Mn, Co and Pb) in green vegetables like Allium cepa, Allium sativum, Solanum lycopersicum and Solanum melongena, irrigated with wastewater in Mardan are studied using Atomic Absorption spectrophotometer. The studied metals in vegetable grown on wastewater irrigated soil were significantly higher than those of tube well water irrigated soil and WHO/FAO permissible limits (P<0.05). The most heavily contaminated vegetable was wastewater irrigated A. cepa, where the accumulation of Mn (28.05 mg kg(-1)) in the edible parts was 50-fold greater than A. cepa irrigated with tube well water irrigated soil. It may be concluded that both adults and children consuming these vegetables grown in wastewater irrigated soil ingest significant amount of these metals and thus can cause serious health problems.