Ashwani Jha

Ashwani Jha
Persistent Systems Ltd

PhD. Bioinformatics

About

30
Publications
8,983
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221
Citations
Introduction
I am currently trying to understand the rgulatory networks which include ncRNAs, mRNA and other proteins.
Additional affiliations
August 2015 - present
University of British Columbia - Vancouver
Position
  • PostDoc Position
August 2012 - April 2013
Central University of Himachal Pradesh
Position
  • Guest faculty
August 2010 - July 2015
CSIR - Institute of Himalayan Bioresource Technology
Position
  • PhD Student
Education
September 2008 - May 2012
Karunya University
Field of study
  • Bioinformatics

Publications

Publications (30)
Article
Full-text available
Tea [Camellia sinensis (L.) O. Kuntze] is a perennial tree which undergoes winter dormancy and unlike deciduous trees, the species does not shed its leaves during winters. The present work dissected the molecular processes operating in the leaves during the period of active growth and winter dormancy through transcriptome analysis to understand a l...
Article
Full-text available
Post ENCODE, regulatory sRNAs (rsRNAs) like miRNAs have established their status as one of the core regulatory elements of cell systems. However, large number of rsRNAs are compromised due to traditional approaches to identify miRNAs, limiting the otherwise vast world of rsRNAs mainly to hair-pin loop bred typical miRNAs. The present study has anal...
Article
Full-text available
Background Sinopodophyllum hexandrum is an endangered medicinal herb, which is commonly present in elevations ranging between 2,400–4,500 m and is sensitive to temperature. Medicinal property of the species is attributed to the presence of podophyllotoxin in the rhizome tissue. The present work analyzed transcriptome of rhizome tissue of S. hexandr...
Article
Full-text available
Tea [Camellia sinensis (L.) O. Kuntze] is a perennial tree which undergoes winter dormancy and unlike deciduous trees, the species does not shed its leaves during winters. The present work dissected the molecular processes operating in the leaves during the period of active growth and winter dormancy through transcriptome analysis to understand a l...
Article
Full-text available
DNA methylation is a type of epigenetic modification where a methyl group is added to the cytosine or adenine residue of a given DNA sequence. It has been observed that DNA methylation is achieved by some collaborative agglomeration of certain proteins and non-coding RNAs. The assembly of IDN2 and its homologous proteins with siRNAs recruits the en...
Article
Full-text available
Along with computational approaches, NGS led technologies have caused a major impact upon the discoveries made in the area of miRNA biology, including novel miRNAs identification. However, to this date all microRNA discovery tools compulsorily depend upon the availability of reference or genomic sequences. Here, for the first time a novel approach,...
Data
miReader application instructions. (DOC)
Data
Known miRNA distribution with respect to sequenced genomes. (DOC)
Data
Identified miRNAs in Miscanthus , along with their expression values. (DOC)
Data
Known homologous miRNAs found in Miscanthus . (DOC)
Data
Identified targets for novel miRNAs in Miscanthus. (DOC)
Article
Full-text available
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...
Data
Total sequences used in training and testing datasets of miR-BAG for different species and benchmarking of various tools. (DOC)
Data
Implementation details for parallelism in miR-BAG. (DOC)
Data
The top 15 feature scores along with the feature names and type of the features obtained after feature selection for every species. (DOC)
Data
Sequence data for novel miRNAs identified in Bos taurus genome, using the NGS module of miR-BAG. (DOC)
Data
The statistics of all conventional features used by previously existing tools. (XLS)
Data
Comparative ROC plots for different precursor identification tools. (TIFF)
Data
Web-server and standalone implementation details for miR-BAG. (PDF)
Data
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)
Data
Expression correlation between miRNAs and targets. The file contains details about the miRNA targets found in Rice transcriptome, along with expression correlation values between the target and targeting miRNA.
Data
Full-text available
Performance tests and benchmarking related details. This additional file contains the details about the performance benchmarking and tests done for p-TAREF. In overall six different major tests were done for performance benchmarking.
Data
miRNA target predictions made on rice transcriptome. This file contains result data on Rice transcriptome specific miRNA targets, with corresponding targeting miRNA, encoded interaction pattern differences and SVR score details.
Data
Full-text available
miRNA groups and their corresponding functional category enrichments with p-values. miRNA targets in Rice transcriptome were grouped according to the miRNA targeting them and their associated GO functional categories for Molecular function and Biological processes.
Article
Full-text available
miRNAs are ~21 nucleotide long small noncoding RNA molecules, formed endogenously in most of the eukaryotes, which mainly control their target genes post transcriptionally by interacting and silencing them. While a lot of tools has been developed for animal miRNA target system, plant miRNA target identification system has witnessed limited developm...
Article
Full-text available
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...
Conference Paper
Full-text available
The main objective of our work is to align multiple sequences together on the basis of statistical approach in lieu of heuristics approach. Here we are proposing a novel idea for aligning multiple sequences in which we will be considering the DNA sequences as lines not as strings where each character represents a point in the line. DNA sequences ar...

Questions

Questions (2)
Question
Hi,
I am analyzing a data where I have 10 samples (5 control and 5 disease) and each sample has 2 replicate. The data for RNA-seq is generated for 0 and 12 hours for Brain and Lungs. I used DESeq to identify differentially expressed genes in Brain vs Lung for 0 and 12 hours. But I also want to identify DE genes at 0 and 12h in brain and 0 and 12h in Lungs.
I was thinking of using ANOVA as my matrix looks like something like this for each gene and for each replicate.
              0          12
Brain    EB0      EB12   
Lung    EL0       EL12 
ANOVA will give me p-values within sample and across sample for each gene.
Another problem is that I have more variables control and treatment. Ultimately I want to find DE genes between control and treatment also.
Its a messed up complex data and any help or referral to paper is highly appreciated.
Thanks for consideration.

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Projects

Project (1)
Project
This project is aimed to identify common modifier genes of neurodegenerative disease like Alzheimer's, Parkinson's, ALS etc.