Disentangling potential genotypes for macro and micro nutrients and polymorphic markers in Chickpea

Background- The present investigation was conducted to assess the nutritional diverseness and identify novel genetic resources to be utilized in chickpea breeding for macro and micro nutrients. Methods-The plants were grown in randomized block design. Nutritional and phytochemical properties of nine chickpea genotypes were estimated. The EST sequences from NCBI database were downloaded in FASTA format, clustered into contigs using CAP3, mined for novel SSRs using TROLL analysis and primer pairs were designed using Primer 3 software. Jaccard’s similarity coefficients were used to compare the nutritional and molecular indexes followed by dendrograms construction employing UPGMA approach. Results- The genotypes PUSA-1103, K-850, PUSA-1108, PUSA-1053 and the EST-SSR markers ICCeM012, ICCeM0049, ICCeM0070, ICCeM0078, SVP55, SVP95, SVP96, SVP146, SVP213 & SVP217 were found as potential donor / marker resources for the macro-micro nutrients. The genotypes differed (p<0.05) for nutritional properties. Amongst newly designed primers, 6 were found polymorphic with median PIC (0.46). The alleles per primer ranged 1 to 8. Cluster analysis based on nutritional and molecular diversities partially matched to each other in principle. Conclusion-The identified novel genetic resources may be used to widen the germplasm base, prepare maintainable catalogue and identify systematic blueprints for future chickpea breeding strategies targeting macro-micro nutrients.

viromeBrowser: A Shiny App for Browsing Virome Sequencing Analysis Results

Experiments in which complex virome sequencing data is generated remain difficult to explore and unpack for scientists without a background in data science. The processing of raw sequencing data by high throughput sequencing workflows usually results in contigs in FASTA format coupled to an annotation file linking the contigs to a reference sequence or taxonomic identifier. The next step is to compare the virome of different samples based on the metadata of the experimental setup and extract sequences of interest that can be used in subsequent analyses. The viromeBrowser is an application written in the opensource R shiny framework that was developed in collaboration with end-users and is focused on three common data analysis steps. First, the application allows interactive filtering of annotations by default or custom quality thresholds. Next, multiple samples can be visualized to facilitate comparison of contig annotations based on sample specific metadata values. Last, the application makes it easy for users to extract sequences of interest in FASTA format. With the interactive features in the viromeBrowser we aim to enable scientists without a data science background to compare and extract annotation data and sequences from virome sequencing analysis results.

Qiime Artifact eXtractor (qax): A Fast and Versatile Tool to Interact with Qiime2 Archives

Qiime2 is one of the most popular software tools used for analysis of output from metabarcoding experiments (e.g., sequencing of 16S, 18S, or ITS amplicons). Qiime2 introduced a novel and innovative data exchange format: the ‘Qiime2 artifact’. Qiime2 artifacts are structured compressed archives containing a dataset and its associated metadata. Examples of datasets are FASTQ reads, representative sequences in FASTA format, a phylogenetic tree in Newick format, while examples of metadata are the command that generated the artifact, information on the execution environment, citations on the used software, and all the metadata of the artifacts used to produce it. While artifacts can improve the shareability and reproducibility of Qiime2 workflows, they are less easily integrated with general bioinformatics pipelines. Accessing metadata in the artifacts also requires full Qiime2 installation. Qiime Artifact eXtractor (qax) allows users to easily interface with Qiime2 artifacts from the command line, without needing the full Qiime2 environment installed (or activated).

sangeranalyseR: simple and Interactive Processing of Sanger Sequencing Data in R

sangeranalyseR is feature-rich, free, and open-source R package for processing Sanger sequencing data. It allows users to go from loading reads to saving aligned contigs in a few lines of R code by using sensible defaults for most actions. It also provides complete flexibility for determining how individual reads and contigs are processed, both at the command-line in R and via interactive Shiny applications. sangeranalyseR provides a wide range of options for all steps in Sanger processing pipelines including trimming reads, detecting secondary peaks, viewing chromatograms, detecting indels and stop codons, aligning contigs, estimating phylogenetic trees, and more. Input data can be in either ABIF or FASTA format. sangeranalyseR comes with extensive online documentation and outputs aligned and unaligned reads and contigs in FASTA format, along with detailed interactive HTML reports. sangeranalyseR supports the use of colourblind-friendly palettes for viewing alignments and chromatograms. It is released under an MIT licence and available for all platforms on Bioconductor (https://bioconductor.org/packages/sangeranalyseR) and on Github (https://github.com/roblanf/sangeranalyseR).

Modeling and validation of L-asparaginase enzyme, an anticancer agent using the tools of computational biology

Background: The L-Asparaginase is a medically important drug. The L-Asparaginase enzyme, an anticancer agent produced by microorganisms is used for the treatment of patients suffering from lymphoma and leukemia. The L-Asparaginase is economical and its administration is easy when compared to other commercial drugs available in market. Many microbes have been reported to produce the L-Asparaginase.Methods: In the present work the sequence of L-Asparaginase enzyme protein was obtained from the Universal Protein Resource (UNIPROT) server. The sequence of L-Asparaginase was used to generate 3-D model of L-Asparaginase in SWISS MODEL server. The constructed L-Asparaginase model was verified using Ramachandran Plot in PROCHECK server.Results: The FASTA format of L-Asparaginase enzyme of Bacillus subtilis strain 168 was retrieved from UNIPROT server. The FASTA format of L-Asparaginase was submitted to SWISS MODEL and its three-dimensional structural model was developed based on relevant template model. The model structure of L-Asparaginase was validated in PROCHECK server using Ramachandran Plot. The Ramachandran Plot of L-Asparaginase model inferred the reliability of L-Asparaginase structure model developed in SWISS MODEL server. Conclusions: In the present study computational tools were exploited to develop and validate a potent anticancer drug, L-Asparaginase. Further the modeled L-Asparaginase enzyme protein can be improved using advanced bioinformatics tools and the same improved enzyme can be produced by improving the L-Asparaginase producing microbial strains by site-directed mutagenesis in the corresponding gene.

Jasmine: a Java pipeline for isomiR characterization in miRNA-Seq data

Motivation The existence of complex subpopulations of miRNA isoforms, or isomiRs, is well established. While many tools exist for investigating isomiR populations, they differ in how they characterize an isomiR, making it difficult to compare results across different tools. Thus, there is a need for a more comprehensive and systematic standard for defining isomiRs. Such a standard would allow investigation of isomiR population structure in progressively more refined sub-populations, permitting the identification of more subtle changes between conditions and leading to an improved understanding of the processes that generate these differences. Results We developed Jasmine, a software tool that incorporates a hierarchal framework for characterizing isomiR populations. Jasmine is a Java application that can process raw read data in fastq/fasta format, or mapped reads in SAM format to produce a detailed characterization of isomiR populations. Thus, Jasmine can reveal structure not apparent in a standard miRNA-Seq analysis pipeline. Availability and implementation Jasmine is implemented in Java and R and freely available at bitbucket https://bitbucket.org/bipous/jasmine/src/master/. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.