Rashmi Tripathi

Next-generation sequencing revolution through big data analytics

ABSTRACT Next-generation sequencing (NGS) technology has led to an unrivaled explosion in the amount of genomic data and this escalation has collaterally raised the challenges of sharing, archiving, integrating and analyzing these data. The scale and efficiency of NGS have posed a challenge for analysis of these vast genomic data, gene interactions, annotations and expression studies. However, this limitation of NGS can be safely overcome by tools and algorithms using big data framework. Based on this framework, here we have reviewed the current state of knowledge of big data algorithms for NGS to reveal hidden patterns in sequencing, analysis and annotation, and so on. The APACHE-based Hadoop framework gives an on-interest and adaptable environment for substantial scale data analysis. It has several components for partitioning of large-scale data onto clusters of commodity hardware, in a fault-tolerant manner. Packages like MapReduce, Cloudburst, Crossbow, Myrna, Eoulsan, DistMap, Seal and Contrail perform various NGS applications, such as adapter trimming, quality checking, read mapping, de novo assembly, quantification, expression analysis, variant analysis, and annotation. This review paper deals with the current applications of the Hadoop technology with their usage and limitations in perspective of NGS.

Digital Forgeries: Problems and Challenges

we are leaving in digital era, over the past decade, digital Technology has matured to become predominant technology for creating, processing, transmitting and storing a information, a form of knowledge and intellectual assets. Multidimensional knowledge and intellectual assets are produced and represented in various forms such as audio, video, text , image , all together we can call it as a multimedia forms, finally all forms are stored as a digital bits and byte forms ie digital content . The recent advances in software developments, plug and play(run) tools to capture, process, access and transmission of digitizes information, it has never so easy to alter the information without leaving any visual clues of tempering of digital data The digital forgery is new research domain with many threats and opportunity with complexity in the problem; in this paper we discussed the seriousness of the problem, its impact and challenges ahead for future researchers

Systemic Review on Chronic Myeloid Leukemia: Therapeutic Targets, Pathways and Inhibitors

Chronic Myeloid Leukemia (CML) is a stem cell disorder, characterized by the translocation of 9th chromosome of Abelson (ABL) gene to the 22th chromosome of breakpoint cluster region (BCR) gene. Consequently, translocation results into the chimeric oncogene BCR-ABL which encodes the BCR-ABL oncoprotein. CML is mainly a disease of adults but it can occur in any stage of life and it accounts around 15% of the all the types of leukemia. Various methods have been used to combat this disease like Chemotherapy, Radiation therapy; tyrosine kinase inhibitors etc., Imatinib as a tyrosine kinase inhibitor has dramatically improved the survival rate of CML patients, hence can be referred as first generation drug against the CML. Later on, recurrence of the disease in some treated patients has also been seen probably due to mutation in oncogenes. Researchers have started to find out more efficient tyrosine kinase inhibitors which can work on mutated oncoprotein and which can be referred as second or third generation drugs. In this review, special emphasis have been given to the carcinogenic mechanism of abnormal fusion of the BCR-ABL genes, current therapeutic options to prevent this disease, and Systems Biology approach to explore the CML associated biochemical pathways. Various advantages and disadvantages of the all therapeutic options to combat CML have also been discussed.

In Silico Identification of Novel Glucagon Receptor Antagonist for theTreatment of Type 2 Diabetes Mellitus

Type 2 diabetes mellitus is caused mainly due to an imbalance in the relationship between glucagon and insulin levels in plasma. To counteract the actions of insulin and maintain normoglycemia during the fasting state by inducing hepatic glucose production are the major biological action of glucagon. Glucagon exerts its action through activation of the glucagon receptor (GCGR). These observations have prompted interest in blockade of GCGR activity for the control of over production of hepatic glucose or the treatment of type 2 diabetes mellitus. In the present study, a large virtual library of compounds was screened against the crystal structure of GCGR to identify a favorable therapeutic choice of GCGR antagonist. The interactions of lead compound with the active site of GCGR were analyzed and molecular dynamics study was also performed to check its stability in the receptor pocket. The proposed lead compound was also compared with some already reported GCGR antagonists for their binding affinity and other pharmacological properties. As a conclusion of this study, we have identified a compound STOCK1N82694 as potent GCGR antagonist for the treatment of type 2 diabetes mellitus.

Integrated analysis of dysregulated lncRNA expression in breast cancer cell identified by RNA-seq study

Among all the sequencing techniques, RNA sequencing (RNA-seq) has galloped with pace adopting the profiling of transcriptomic data in almost every biological analytics area like gene regulation study, development biology and clinical research. Recently the discovery of differentially expressed genes across different conditions has outshone the barrier of genetic & epigenetic regulations. The present work identified and analyzed differentially expressed novel long non-coding RNAs (lncRNAs) for breast cancer. A complex computational pipeline was adopted for the study which includes analysis of 18498 differentially expressed genes with 4114 up-regulated and 3475 down-regulated transcripts. The overexpression of lnc-MTAP (CDKN2B-AS1), lnc-PCP4 (DSCAM-S1), and lnc-FAM (H19) in breast cells suggests that these lncRNAs may have significant role to play in breast cancer. These results validated the relevance of the dysregulation pattern in cancer cells due to the presence of lncRNAs. The study further opens a new scope for experimental analysis to confirm the aberrant expression pattern of these lncRNAs which may act as potential bio-markers for the diagnosis and early detection of breast cancer.

Patent classification of the new invention using PLSA

In the current scenario of the world for Research and Development leading to patenting, content classification in accordance with the subject areas to which it belongs to is a challenging task. This is because todays R&D draws its novelty/newness not in one technical area but a unique combination of different technical areas. For example, a Typical ICT patent may be a composite effect for advancing the knowledge in some combination of Control Engg, Electronic Components, Databases Technology, Information retrieval methodology, Internet and Wireless technology, Speech, Signal, and Image Processing etc. In this paper, the work has been reported for the content classification for a newly drafted patent document using Probabilistic Latent Semantic Analysis technique. The probabilistic latent semantic analysis (PLSA) is used for automated indexing of the document by creating an indexer which tokenizes the documents and creates a proper generative model. Herein a singular value decomposition model is used for compacting the size of term document matrix and their co-occurrences in the matrix. The objective is to take up the large document corpora generated from the past patent document to categorize documents based on the concept generated model. The approach is illustrated and has been tested for by an example classification of the content for two typical US Patent Classes, and has been found to work well for them.

An HARQ Based Optimized Error Correction Technique

Errors during data communication are inevitable. Noise in the channel leads to bit error. The paper proposes a matrix based novel bits encoding technique, aim to achieve error correction capability with optimize redundancy. Furthermore an efficient software based decoding algorithm to detect and correct transmission errors is introduced. Here errors include single bit error, multiple bits error and burst errors. The proposed technique maintains high code rate, provides multiple bit error correction capability and can best be implemented as hybrid automatic repeat request (HARQ).

Unraveling long non-coding RNAs through analysis of high-throughput RNA-sequencing data

Extensive genome-wide transcriptome study mediated by high throughput sequencing technique has revolutionized the study of genetics and epigenetic at unprecedented resolution. The research has revealed that besides protein-coding RNAs, large proportions of mammalian transcriptome includes a heap of regulatory non protein-coding RNAs, the number encoded within human genome is enigmatic. Many taboos developed in the past categorized these non-coding RNAs as ‘‘dark matter” and “junks”. Breaking the myth, RNA-seq-- a recently developed experimental technique is widely being used for studying non-coding RNAs which has acquired the limelight due to their physiological and pathological significance. The longest member of the ncRNA family-- long non-coding RNAs, acts as stable and functional part of a genome, guiding towards the important clues about the varied biological events like cellular-, structural- processes governing the complexity of an organism. Here, we review the most recent and influential computational approach developed to identify and quantify the long non-coding RNAs serving as an assistant for the users to choose appropriate tools for their specific research.

Finding similar patents through semantic expansion

Semantic information retrieval technique is widely applied in various research areas. However the key consideration is about selecting the most extensive external source which can be used for augmenting the query. In this research paper we have employed WordNet and Wiktionary as two external sources for expanding the query and finding the effect of it over the similarity models. We found that a combination of two external sources gives much improved result in comparison to single source expansion. We have further selected wu-palmer model to measure the efficiency of these combined models of expansion to find the effect on similarity in comparison to the tradition similarity cosine model.

Count-based transcriptome analysis to identify differentially expressed genes for breast cancer

Sequencing the coding regions or the whole cancer transcriptome can provide valuable information about the differential expression patterns of the genes. Previous researches centered on ~2% of coding human genome, assuming that the non-coding sequences were “junk” lacking significant functional information. Recent medical research show that a major percentage of the human genome (~70-90%) are non-coding, stored in the cell in the form of non-coding RNA (ncRNA) which overshadows the coding information limited only to a small percentage. These ncRNAs are composed of mostly ultraconserved elements, lacking protein-coding potential and regulating gene expression acting as enhancers whose aberrant expression may be involved in pathological process such as cancer. Here, we have described RNA-seq data analysis for the profiling of transcriptome of Breast cells and provided a generic outline of the whole pipeline from next-generation sequencing (NGS) output for quantification of differential gene expression across different conditions (e.g., control vs test). We have used tool Cufflinks-Cuffdiff to estimate transcript-level expression for gene discovery extracted from high-throughput RNA-seq data across distinct conditions that represent candidate biomarkers for future research. This study provides the survey of coding transcripts associated genes expression within a cancer system.