Metagenomic sequence of saline desert microbiota from wild ass sanctuary, Little Rann of Kutch, Gujarat, India

Abstract

We report Metagenome from the saline desert soil sample of Little Rann of Kutch, Gujarat State, India. Metagenome consisted of 633,760 sequences with size 141,307,202 bp and 56% G + C content. Metagenome sequence data are available at EBI under EBI Metagenomics database with accession no. ERP005612. Community metagenomics revealed total 1802 species belonged to 43 different phyla with dominating Marinobacter (48.7%) and Halobacterium (4.6%) genus in bacterial and archaeal domain respectively. Remarkably, 18.2% sequences in a poorly characterized group and 4% gene for various stress responses along with versatile presence of commercial enzyme were evident in a functional metagenome analysis.

Cultivation-independent comprehensive survey of bacterial diversity in Tulsi Shyam Hot Springs, India

“…Furthermore, high-throughput community sequencing allows quick and inexpensive analysis of microbial diversity in a single run. This leads to revolutionizing microbial ecology studies of extreme environments including saline desert [6] , Soda Lake [7] and sulfidic hot springs [8] .…”

Deciphering the microbiota of Tuwa hot spring, India using shotgun metagenomic sequencing approach

“…Culturability of microorganisms in extreme habitats is usually very poor, which creates the need to explore the culture-independent techniques to evaluate the taxonomic and functional status of the microbes. Currently, either 16S rRNAs amplicon based metagenome sequencing or shotgun metagenome sequencing is largely used for the assessment of the microbial diversity [1] , [2] . Such culture-independent techniques have been successfully used for comprehensive analysis of microbial populations in hot spring [3] .…”

Shotgun metagenomic sequencing based microbial diversity assessment of Lasundra hot spring, India

“…From this 183,408 predicted protein features, 66.7% of features assigned an annotation using M5NR database whereas, 33.3% of features have no significant similarities to the protein database. A total of 104,110 features were assigned to functional categories with COG approach and interestingly 45.4% was metabolism connected and 19.6% falls in poorly characterized group that is higher than the earlier metagenome study of saline desert [9] indicating the possibilities of gaining novel gene from the metagenome. Similarly, in subsystem based annotation approach 14.0% was carbohydrates, 7.0% was protein metabolism and 3.0% genes were belonged to stress responses i.e.…”