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Azim, M. K.

Publications and source records attributed to Azim, M. K..

2 recordsLinked to original sources

A Versatile Tool for Precise Variant Calling in Mycobacterium tuberculosis Genetic Polymorphisms

BackgroundWhole genome sequencing (WGS) facilitates the diagnosis of multidrug-resistant MDR-TB through the interpretation of sequence variations (SV) in Mycobacterium tuberculosis (MTB) genes. Information on phenotypic and genotypic resistance associations continues to evolve, it is important to identify SV within genes of interest. We developed an MTB-VCF variant calling pipeline that can compare against the reference genome for any gene of interest. We demonstrate its utility for calling SV in genes associated with Rifampicin (RIF), Isoniazid (INH), Ethambutol (EM), and Streptomycin (SM) resistance. MethodsMTB-VCF is a Python-based command line Variant Calling pipeline designed to streamline batch processing from raw reads (FastQ) files. SV called by MTB-VCF were compared with those identified by TBProfiler, KVARQ, CASTB, Mykrobe Predictor and Phy-ResSE pipelines. The sensitivity and Specificity of MTB-VCF SV calling were calculated against the drug susceptibility testing (DST) phenotype. ResultsMTB-VCF identified 868 SV present in 200 phenotypically resistant MDR-TB isolates. These were across rpsl, rrs, rpoB, inhA, katG, ahpC, gidB and embCAB genes. Of these, 684 SV were known to be associated with a resistance genotype, leading to a specificity of 97.75%. The SV called by the MTB-VCF was compared separately to resistance genotypes called by TB-Profiler, KvarQ, CASTB, Mykrobe Predictor, and PhyRes-SE pipelines, demonstrating a sensitivity of 99.5%. ConclusionThe MTB-VCF pipeline offers a rapid and accurate solution for identifying SV in target genes for interpretation later. It can be run in large batches, proving flexible computing that allows for the customization of core bioinformatic pipelines, enabling the analysis of WGS data from different technologies.

bioinformatics↗

Comparative Transcriptomics of Mango (Mangifera indica L.) Cultivars Provide Insights of Biochemical Pathways Involved in Flavor and Color

Mango is an economically important fruit crop of many tropical and subtropical countries. Recently, leaf and fruit transcriptomes of mango cultivars grown in different geographical regions have characterized. Here, we presented comparative transcriptome analysis of four mango cultivars i.e. cv. Langra, cv. Zill, cv. Shelly and cv. Kent from Pakistan, China, Israel and Mexico respectively. De-novo sequence assembly generated 30,953-85,036 unigenes from RNASeq datasets of mango cultivars. KEGG pathway mapping of mango unigenes identified terpenoids, flavonoids and carotenoids biosynthetic pathways involved in flavor and color. The analysis revealed linalool as major monoterpenoid found in all cultivars studied whereas, monoterpene -terpineol was specifically found in cv. Shelly. Ditepene gibberellin biosynthesis pathway was found in all cultivars whereas, homoterpene synthase involved in biosynthesis of 4,8,12-trimethyltrideca-1,3,7,11-tetraene (TMTT; an insect induced diterpene) was found in cv. Kent. Among sesquiterpenes and triterpenes, biosynthetic pathway of Germacrene-D, an antibacterial and anti-insecticidal metabolite was found in cv. Zill and cv. Shelly. Two bioactive triterpenes, lupeol and {beta}-amyrin were found in cv. Langra and cv. Zill. Unigenes involved in biosynthesis of carotenoids, {beta}-carotene and lycopene, were found in cultivars studied. Many unigenes involved in flavonoid biosynthesis were also found. Comparative transcriptomics revealed naringenin (an anti-inflammatory and antioxidant metabolite) as central flavanone responsible for biosynthesis of an array of flavonoids. The present study provided insights on genetic resources responsible for flavor and color of mango fruit.

bioinformatics↗