Department of Biotechnology, Junagadh Agricultural University, Junagadh
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This bioinformatics cell contains computing resources for genome sequencing, assembly, annotation and analysis. It includes a high powered workstation and two servers for processing sequencing data from plant, fungal and bacterial genomes, as well as transcriptomes of various crops. The cell has submitted several plant and fungal genomes and bacterial sequencing data to NCBI. It has also generated barcode sequences from fish and submitted 16S and 18S rRNA sequences from bacteria and fungi respectively. The cell conducts transcriptome annotation and mapping studies on various crops to understand biological processes and molecular functions.
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This document contains information about Benben Miao's omics research interests and experience. It lists their contact information and links to their Github and website at the top. The rest of the document outlines different omics technologies that Benben works with, including genomics, transcriptomics, microbiome analysis, proteomics, metabolomics, and epigenomics. For each type of omics, it provides examples of relevant experimental techniques and analysis workflows/software used. It also includes links to relevant databases and analysis tools at the bottom. In summary, this document profiles Benben Miao's background in multi-omics data analysis and the technologies and approaches they apply in their research.
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The document discusses next generation sequencing (NGS) data analysis workflows and RNA sequencing data analysis. It provides an overview of primary, secondary, and tertiary analysis steps in NGS data analysis including quality control, mapping, assembly, and differential expression analysis. It also describes common file formats, tools for mapping, counting, and identifying differentially expressed genes from RNA-Seq data using either a reference genome or de novo assembly. Finally, it lists several pathways identified from comparative temporal analysis of differentially expressed genes.
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This document discusses various molecular marker techniques used in genetics, including their discovery and applications. It covers:
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The most prominent of growth-retarded in black tiger shrimp (Penaeus monodon) is associated with an infection of Laem-Singh virus (LSNV) that has been involved the reduction of annual production volume. To more accurately, rapidly and easily determine its effect on shrimp industries for further control disease, a portable Real-Amp Turbidimeter with real-time detection method was evaluated for LSNV detection.The device exploited the turbidity that produced by-product of magnesium pyrophosphate and displayed turbidity values in real-time on the provided software screen. The results of quantitative can be accomplished by reaction time measuring. The sensitivity of a turbidity detection was comparable to that of the nested RT-PCR, while it was 1000-time more sensitive than RT-PCR. With the use of a portable Real-Amp Turbidimeter. The application of RT-LAMP using a portable Real-Amp Turbidimeter offers a simple and rapid detection platform for LSNV detection in the field assessment. So, This 's awesome technic not only for detection of LSNV but also for many pathogen.
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Sustainable Land Management - Climate Smart Agriculture
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Pests of Storage_Identification_Dr.UPR.pdf
InIndia-post-harvestlosses-unscientificstorage,insects,rodents,micro-organismsetc.,accountforabout10percentoftotalfoodgrains
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Embracing Deep Variability For Reproducibility and Replicability
Embracing Deep Variability For Reproducibility and ReplicabilityUniversity of Rennes, INSA Rennes, Inria/IRISA, CNRS
Embracing Deep Variability For Reproducibility and Replicability
Abstract: Reproducibility (aka determinism in some cases) constitutes a fundamental aspect in various fields of computer science, such as floating-point computations in numerical analysis and simulation, concurrency models in parallelism, reproducible builds for third parties integration and packaging, and containerization for execution environments. These concepts, while pervasive across diverse concerns, often exhibit intricate inter-dependencies, making it challenging to achieve a comprehensive understanding. In this short and vision paper we delve into the application of software engineering techniques, specifically variability management, to systematically identify and explicit points of variability that may give rise to reproducibility issues (eg language, libraries, compiler, virtual machine, OS, environment variables, etc). The primary objectives are: i) gaining insights into the variability layers and their possible interactions, ii) capturing and documenting configurations for the sake of reproducibility, and iii) exploring diverse configurations to replicate, and hence validate and ensure the robustness of results. By adopting these methodologies, we aim to address the complexities associated with reproducibility and replicability in modern software systems and environments, facilitating a more comprehensive and nuanced perspective on these critical aspects.
https://hal.science/hal-04582287
Signatures of wave erosion in Titan’s coasts
The shorelines of Titan’s hydrocarbon seas trace flooded erosional landforms such as river valleys; however, it isunclear whether coastal erosion has subsequently altered these shorelines. Spacecraft observations and theo-retical models suggest that wind may cause waves to form on Titan’s seas, potentially driving coastal erosion,but the observational evidence of waves is indirect, and the processes affecting shoreline evolution on Titanremain unknown. No widely accepted framework exists for using shoreline morphology to quantitatively dis-cern coastal erosion mechanisms, even on Earth, where the dominant mechanisms are known. We combinelandscape evolution models with measurements of shoreline shape on Earth to characterize how differentcoastal erosion mechanisms affect shoreline morphology. Applying this framework to Titan, we find that theshorelines of Titan’s seas are most consistent with flooded landscapes that subsequently have been eroded bywaves, rather than a uniform erosional process or no coastal erosion, particularly if wave growth saturates atfetch lengths of tens of kilometers.
MICROBIAL INTERACTION PPT/ MICROBIAL INTERACTION AND THEIR TYPES // PLANT MIC...
MICROBIAL INTERACTION PPT/ MICROBIAL INTERACTION AND THEIR TYPES // PLANT MIC...ABHISHEK SONI NIMT INSTITUTE OF MEDICAL AND PARAMEDCIAL SCIENCES , GOVT PG COLLEGE NOIDA
Microbial interaction
Microorganisms interacts with each other and can be physically associated with another organisms in a variety of ways.
One organism can be located on the surface of another organism as an ectobiont or located within another organism as endobiont.
Microbial interaction may be positive such as mutualism, proto-cooperation, commensalism or may be negative such as parasitism, predation or competition
Types of microbial interaction
Positive interaction: mutualism, proto-cooperation, commensalism
Negative interaction: Ammensalism (antagonism), parasitism, predation, competition
I. Mutualism:
It is defined as the relationship in which each organism in interaction gets benefits from association. It is an obligatory relationship in which mutualist and host are metabolically dependent on each other.
Mutualistic relationship is very specific where one member of association cannot be replaced by another species.
Mutualism require close physical contact between interacting organisms.
Relationship of mutualism allows organisms to exist in habitat that could not occupied by either species alone.
Mutualistic relationship between organisms allows them to act as a single organism.
Examples of mutualism:
i. Lichens:
Lichens are excellent example of mutualism.
They are the association of specific fungi and certain genus of algae. In lichen, fungal partner is called mycobiont and algal partner is called
II. Syntrophism:
It is an association in which the growth of one organism either depends on or improved by the substrate provided by another organism.
In syntrophism both organism in association gets benefits.
Compound A
Utilized by population 1
Compound B
Utilized by population 2
Compound C
utilized by both Population 1+2
Products
In this theoretical example of syntrophism, population 1 is able to utilize and metabolize compound A, forming compound B but cannot metabolize beyond compound B without co-operation of population 2. Population 2is unable to utilize compound A but it can metabolize compound B forming compound C. Then both population 1 and 2 are able to carry out metabolic reaction which leads to formation of end product that neither population could produce alone.
Examples of syntrophism:
i. Methanogenic ecosystem in sludge digester
Methane produced by methanogenic bacteria depends upon interspecies hydrogen transfer by other fermentative bacteria.
Anaerobic fermentative bacteria generate CO2 and H2 utilizing carbohydrates which is then utilized by methanogenic bacteria (Methanobacter) to produce methane.
ii. Lactobacillus arobinosus and Enterococcus faecalis:
In the minimal media, Lactobacillus arobinosus and Enterococcus faecalis are able to grow together but not alone.
The synergistic relationship between E. faecalis and L. arobinosus occurs in which E. faecalis require folic acid
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Department of Biotechnology, Junagadh Agricultural University, Junagadh
- 1. Bioinformatics Cell WORKSTATION Intel xeone 3.1 252GB RAM (4 Sub drive) 100TB Storage CentOS 5.4 with Lifescope SERVER-1 Intel xeone 3.1 64GB RAM 20TB Storage CentOS 7 with CLC Genomic workbench SERVER -2 Intel xeone 3.1 48GB RAM 12TB Storage CentOS 6 with Ion torrent suite 5 PC with windows and 1 with Linux 14.2 and 1 with Bio-Linux 8
- 2. Sequence submitted at NCBI Plant Genome Sesamum Indicum Fungal Genome Puccinia arachidis Macrophomina phaseolina Sclerotium rolfsii Bacterial Genome Enterobacter cloacae MR1 Enterobacter spp. MR2 Pseudomonas putida MR3 Providencia rettgeri MR4 Pantoea ananatis MR5 Transcriptome Cicer arietinum (4) Sesumum indicum (3) Pennisetum glaucum (4) Carica papaya (12) Ricinus communis (2) Arachis hypogaea (2) 55, Fish-BOLD Barcode(CO5P) Submitted >120, 16S r RNA sequences for Bacteria >50, 18S, 5S RNA sequences for Fungi
- 4. Sequence Mapping and Assembly MIRA CAP3 SOAP ABYSS Valvet CLC Genome Assembler Ion torrent suite Life scope
- 7. SOFTWARE FACILITIES Quality control of NGS DATA FAST QC Tool kit Printseq Ion torrent suit Life scope
- 8. RAST MG-RAST Genome annotator GeneSAS Blast2Go NCBI Pipelines MAKER PASA Annotation Pseudomonas putida MR3
- 9. Comparative analysis VISTA CLC MeGe RAST Airtimis ACT
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- 13. Transcriptome Mapping of PRSV
- 14. Transcriptome annotation of samples od papaya infected with PRSV
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- 16. Sequence submitted at NCBI Plant Genome Sesamum Indicum Fungal Genome Puccinia arachidis Macrophomina phaseolina Sclerotium rolfsii Bacterial Genome Enterobacter cloacae MR1 Enterobacter spp. MR2 Pseudomonas putida MR3 Providencia rettgeri MR4 Pantoea ananatis MR5 Transcriptome Cicer arietinum (4) Sesumum indicum (3) Pennisetum glaucum (4) Carica papaya (12) Ricinus communis (2) 55 Fish-BOLD Barcode(CO5P) generated for CO5P >120, 16S r RNA sequences for Bacteria >50, 18S, 5S RNA sequences for Fungi
- 17. Transcriptome annotation of Sesamum Biological Process
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- 20. Transcriptome annotation of Castor
- 21. Transcriptome Mapping of PRSV
- 22. Pearl millet
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- 26. Perl millet
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- 28. Sequence submitted at NCBI Plant Genome Sesamum Indicum Fungal Genome Puccinia arachidis Macrophomina phaseolina Sclerotium rolfsii Bacterial Genome Enterobacter cloacae MR1 Enterobacter spp. MR2 Pseudomonas putida MR3 Providencia rettgeri MR4 Pantoea ananatis MR5 Transcriptome Cicer arietinum (4) Sesumum indicum (3) Pennisetum glaucum (4) Carica papaya (12) Ricinus communis (2) 55 Fish-BOLD Barcode(CO5P) Submitted >120, 16S r RNA sequences for Bacteria >50, 18S, 5S RNA sequences for Fungi
- 29. FISH cytochrome oxidase subunit I gene submitted to Barcode of Life Database