Mrigaya Mehra- Ph.D
- Group Leader at Persistent Systems (India)
Mrigaya Mehra
- Ph.D
- Group Leader at Persistent Systems (India)
About
15
Publications
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Introduction
I am a firm believer of the fact there is a solution to every problem which can be achieved through hard-work, dedication, perseverance and an open mind. I have worked on the regulatory aspects involved in miRNA biogenesis and currently I investigated the roles of different repetitive elements in genome dynamics. I hope to establish precise relationships between these two different realms.
Current institution
Additional affiliations
December 2016 - February 2018
November 2010 - February 2016
November 2012 - present
Education
July 2008 - August 2010
Publications
Publications (15)
miRNAs are small non-coding RNAs with average length of ~21 bp. miRNA formation seems to be dependent upon multiple factors besides Drosha and Dicer, in a tissue/stage-specific manner, with interplay of several specific binding factors. In the present study, we have investigated transcription factor binding sites in and around the genomic sequences...
Non-coding elements such as miRNAs play key regulatory roles in living systems. These ultra-short, ∼21 bp long, RNA molecules are derived from their hairpin precursors and usually participate in negative gene regulation by binding the target mRNAs. Discovering miRNA candidate regions across the genome has been a challenging problem. Most of the exi...
Repetitive elements have lately emerged as key components of genome, performing varieties of roles. It has now become necessary to have an account of repeats for every genome to understand its dynamics and state. Recently, genomes of two major Solanaceae species, Solanum tuberosum and Solanum lycopersicum, were sequenced. These species are importan...
Hepatocellular carcinoma (HCC) is a major malignancy in the liver and has emerged as one of the main cancers in the world with a high mortality rate. However, the molecular mechanisms of HCC are still poorly understood. Long noncoding RNAs (lncRNAs) have recently come to the forefront as functional non–protein-coding RNAs that are involved in a var...
Total sequences used in training and testing datasets of miR-BAG for different species and benchmarking of various tools.
(DOC)
Implementation details for parallelism in miR-BAG.
(DOC)
The top 15 feature scores along with the feature names and type of the features obtained after feature selection for every species.
(DOC)
Sequence data for novel miRNAs identified in
Bos taurus
genome, using the NGS module of miR-BAG.
(DOC)
The statistics of all conventional features used by previously existing tools.
(XLS)
Comparative ROC plots for different precursor identification tools.
(TIFF)
Web-server and standalone implementation details for miR-BAG.
(PDF)
Energy and Structural profile matrix score plots drawn between the positive and negative instances for all target species, depicting the distribution patterns for positive and negative instances.
(DOC)
Questions
Questions (13)
I am working with cancer genes and I wish to find out the COSMIC fraction of such genes but I am not sure what it represents or how is it calculated. Can anyone help?
I want to understand what kind of variations occur on long ncRNA. Like for example missense mutations, point mutations, frameshift mutations etc.
Similarly, for pseudogenes what are the variant types that exist for them?
Can anybody suggest some good reviews/publications where I can get this information?
Hi, I have a vcf with genomic variants and I want to fetch their information from TCGA which includes the following:
1. The gene ID, transcript ID, Protein ID, variant type and genomic consequence
2. The Biomarkers associated with the variants
3. The phenotype associated with the variants
4. Any drug/therapies associated with the variants
5. Any expression data available
6. Population densities for these variants
Can anyone suggest how I can get this information?
Is there some toolkit available for creating APIs? If so, please mention in comments. Thanks
I have RNA seq data for pseudomonas and other bacterial sps with different phenotypes and I want to identify the different pathways differentially regulated. But I am unable to find pathway enrichment tools. Can anyone list me some good tools which can be used in linux for pathway enrichment analysis in bacterial RNA-seq data
Hi,
I am just starting working with DESeq. I have a question regarding the basic biological interpretation of DESeq based DE gene expression. There are two situations I have listed below and I would like to know which one is more biologically relevant
I have two treatment groups: treatment 1 and treatment2 and I am comparing them with a control group all with three replicates. I devised my study as
1. I created a dataframe containing counts of all 9 count files and from this dataframe, I am creating comparisons as: T1 vs Control, T2 vs Control and T2 vs T1.
2. I create a dataframe everytime I create a comparison like when I am comparing T1 vs Control, then I am creating a dataframe with 6 count files. Again when I am comparing T2 vs Control I am creating another dataframe with 6 count files.
I want to know which of these two design strategies will give me a more accurate result as to what effects T1 and T2 are causing when compared with control and how are T1 and T2 different as well as similar?
I wish to assemble a metagenomic data and require the following
1) It should be operatable in a linux environment
2) It should be free
3) It should be able to achieve an appreciable accuracy
4) It should include tools for creation of OTUs and annotation of the genes as well
I wish to do research in future. But I do not know whether I will be associated with any research body or not. So can I do research as freelance. And will my work be eligible for publication.
