Fig 5 - uploaded by Achuthsankar S Nair
Content may be subject to copyright.
Source publication
Similar publications
New high-dimensional, single-cell technologies offer unprecedented resolution in the analysis of heterogeneous tissues. However, because these technologies can measure dozens of parameters simultaneously in individual cells, data interpretation can be challenging. Here we present viSNE, a tool that allows one to map high-dimensional cytometry data...
Background
Recent technological advances have resulted in an unprecedented increase in publicly available biomedical data, yet the reuse of the data is often precluded by experimental bias and a lack of annotation depth and consistency. Missing annotations makes it impossible for researchers to find datasets specific to their needs.
Findings
Here,...
The pandemic Novel coronavirus disease (COVID-19) was aggressively
expanding throughout the world, and no effective vaccines and drugs are available.
Giant pharmaceutical industries and researchers use computer intelligence coupled
with bioinformatics knowledge to accelerate the development process of designing
an effective vaccine against SARS-CoV...
Historical records indicate that extensive cultural, commercial and technological interaction occurred between European and Asian populations. What have been the biological consequences of these contacts in terms of gene flow? We systematically estimated gene flow between Eurasian groups using genome-wide polymorphisms from 34 populations represent...
![Figure 4. Computerized system in DNA[5]](profile/Nagham-Aljamali/publication/357714546/figure/fig1/AS:1110727284797441@1641829429285/Computerized-system-in-DNA5_Q320.jpg)
The review accurately describes the biological sciences, the chemical sciences, and their computer applications, using the latest techniques in applied mathematics, informatics, statistics, and computer science to solve vital biological problems. Major research efforts in this field include sequence alignment, gene finding, the Human Genome Project...
Citations
... Computational biology is nowadays focused on the design and development of computer models to represent and simulate biological systems and solve related problems. In recent years, these disciplines have attracted increasing interest in the medical field [1]. ...
In this short communication, we focused on the biological role of Polydatin through two computerized analyzes (phamacophore study by PharmMapper server and Molecular Docking by Autodock Vina with the Pyrx program). According to the best normalized fit scores calculated by the PharmMapper database, Polydatin may be attractive for the following potential targets: Triggering receptor expressed on myeloid cells 1; Ephrin type-B receptor; Tyrosine protein kinase (HCK); ADP-ribose pyrophosphatase; mitochondrial Putative ATP-dependent Clp protease proteolytic subunit; Seprase and Caspase-3, ranked by normalized fit score in descending order (0.9921 to 09812). All these potential targets calculated by PharmMapper server are used to evaluate their role by the Docking method through Autodock Vina with Polydatin. From the results of the binding energies of our docking approach, only three potential targets play a key role with Polydatin, which are: the Trigger receptor expressed on myelod cells, mitochondrial ADP-ribose pyrophospatase, and Seprase (fibroblast activation protein). However, further computational methods and in vitro and in vivo assays are required to confirm our preliminary results.
... 16,17 Computational biology or bioinformatics is a science which refers to employment of computer or computational methods for analyses of biological data, prediction of molecular structures, modeling and simulations. 18 Nowadays, importance of bioinformatics is undeniable in different aspects of biological studies and has become increasingly important in drug discovery and design processes. 19 Therefore, in this study we aimed at designing a new recombinant CT composed of the last 30 amino acids at the c-terminal of the CPE and the StxA subunit of the Shiga toxin. ...
Cancer is one of the main reasons of mortality all over the world. Over the time, the major ways for cancer-therapy were based on radiotherapy, chemotherapy and surgery. These methods are not specific enough for that purpose, therefore, new ideas for design of new drugs with higher specificity are considered. Chimeric protein toxins are hybrid proteins consisting of a targeting portion and a toxic one which specifically bind and kill the target cancer cells. The main purpose of this study was designing a recombinant chimeric toxin with biding capability to one of the most key receptors namely claudin-4 which is over-expressed in almost all cancer cells. To design it, we utilized the last 30 C-terminal amino acids of Clostridium perfringens enterotoxin (CPE) as a binding module for claudin-4 and the toxic module which is the A-domain of Shiga toxin from Shigella dysenteriae. Using molecular modeling and docking methods, appropriate binding affinity of the recombinant chimeric toxin to its specific receptor was demonstrated. In the next step, the stability of this interaction was investigated by molecular dynamics simulation. Although partial instability was detected at some time points, however, sufficient stable situation of hydrogens bonds and high binding affinity between the chimeric toxin and receptor were observed in the in silico studies which in turn suggested that this complex could be formed successfully.
... In Bioinformatics, we used computational techniques, information technology, and mathematics to solve problems in biology and medicine. To develop novel biological complications information, it uses signal processing, image processing, artificial intelligence, data mining, modeling, algorithms, and databases (Nair, 2007). ...
Almost 285 million individuals suffer from vision impairment worldwide in which 90% live in underdeveloped countries. A total of 150 mutations and 60 genes have been associated to Retinitis Pigmentosa. In the current study, two families with Retinitis Pigmentosa were identified in Kohat, Khyber Pakhtunkhwa, Pakistan. The pedigree analysis indicated an autosomal recessive pattern of Retinitis Pigmentosa inheritance. The non-syndromic nature of Retinitis Pigmentosa was confirmed by clinical evaluation of affected members by an eye specialist. Homozygosity mapping was used to analyze the linkage of two families, RP1 and RP2. The microsatellite markers D1S2816, D1S2840, D1S1183, D1S1660, D1S158, D1S422, D1S412, D1S413, D8S532, D8S260, and D8S509 were chosen for Nat. Volatiles & Essent. Oils, 2022; 9(2): 310-338 311 linkage analysis in the RP1 and RP2 families. Affected members of the RP1 family (III.1, III.2, III.3, and III.4) demonstrated homozygosity at the D8S260 marker. Individuals from the RP2 family that were impaired (III.3 and III.4) showed homozygosity at marker D1S1660. The CRB1 locus was used to confirm family linkage in RP1, while the RP1 locus was used to confirm family linkage in RP2. Haplotyping confirmed the allelic pattern in RP1 and RP2 families. Protein-protein interactions were investigated using the Stitch 5 database, and the closest functional partner was discovered. The one closest to CRB1 has a maximum interaction score of 0. 978.The interaction of a mutant protein with other proteins and how it affects the pathway can be predicted by applying this research study. This research could aid in prenatal diagnostics and gene therapy for linked diseases in the future.
... The protein production process takes place through the single strand of RNA, which is part of the double helix of DNA. The tRNA strand is responsible for the transcription of the nucleic acids to the ribosome, after which it binds the ribosome RNA (rRNA) to make the protein [16]. Figure 2 shows the process of replication, transcription and protein production. ...
In bioinformatics systems, the study of genetics is a popular research discipline. These systems depend on the amount of similarity between the biological data. These data are based on DNA sequences or raw sequencing reads. In the preprocessing stage, there are several methods for measuring similarity between sequences. The most popular of these methods is the alignment method and alignment-free method, which are applied to determine the amount of functional matching between sequences of nucleotides DNA, ribosome RNA, or proteins. Alignment-based methods pose a great challenge in terms of computational complexity, In addition to delaying the time to search for a match, especially if the data is heterogeneous and its size is huge, and thus the classification accuracy decreases in the post-processing stage. Alignment-free methods have overcome the challenges of alignment-based methods for measuring the distance between sequences, The size of the data used is 1000 genomes uploaded from National Center for Biotechnology Information (NCBI), after eliminating the missing and irrelevant values, it becomes 860 genomes, ready to be segmented into words by the k-mer analysis, after which the frequency of each word is counted for each query. The size of a word depends on a value of k. In this paper we used a value of k =3. .. .8, for each iteration will count times of frequencies words.
... In complementary base pairing, adenine (A) always pairs with thymine (T) and guanine (G) with cytosine (C). The arrangement of these bases verifies the information available for developing and sustaining an organism [1]. Ribonucleic acid (RNA) is a single-stranded nucleic acid with bases: adenine (A) pairs with uracil (U) and guanine (G) with cytosine (C). ...
Complementary DNA (cDNA) microarray technology is used to study the gene expressions at different times in organisms , whose DNA sequence is completely known. The methodology provides a systematic and comprehensive way to monitor thousands of gene mutations within a pair of monochrome images obtained in a single experiment. Each image contains subgrids of orderly arranged gene probes called as spots. Microarray image processing mainly comprises of the steps namely Gridding, Spot Segmentation, Spot quality assessment, and Intensity Extraction. These types of images show entirely different characteristics from those of typical natural images. Hence, the processing of such images is highly challenging. Images are of low contrast and suffer from background noise, spot location rotation, spot shape deviation, and other artifacts. An automatic gridding technique to find an optimal threshold for spot area and background is implemented in this paper. This is a modified form of Maximum Between Class Variance method also known as OTSU method. Preprocessing operations are proposed here for tilt correction, denoising and contrast enhancement for effective gridding in these images. The performance of the gridding technique is tested using ISREC, ApoA1 and MicroZip image datasets. Bounding boxes around each spot are also calculated using the horizontal and vertical grid points. Experimental results show that the proposed method performs better than the other competitive methods available in literature in terms of accuracy in finding the spot regions.
... Bioinformatics is a swiftly unfolding multifaceted field where different techniques of computer science are applied to solve compute intensive biological problems [1]. Due to technological augmentation, volume of molecular data is increasing expeditiously [2]. ...
Basic Local Alignment Search Tool (BLAST) is one of the most frequently used algorithms for bioinformatics applications. In this paper, an accelerated implementation of protein BLAST, i.e., CLUS_GPU-BLASTP for multiple query sequence processing in parallel, on graphical processing unit (GPU)-enabled high-performance cluster is proposed. The experimental setup consisted of a high-performance GPU-enabled cluster. Each compute node of the cluster consisted of two hex-core Intel, Xeon 2.93 GHz processors with 50 GB RAM and 12 MB cache. Each compute node was also equipped with a NVIDIA M2050 GPU. In comparison with the famous GPU-BLAST, our BLAST implementation is 2.1 times faster on single compute node. On a cluster of 12 compute nodes, our implementation gave a speedup of 13.2X. In comparison with standard single-threaded NCBI-BLAST, our implementation achieves a speedup ranging from 7.4X to 8.2X.
... Since the 1980s, computational methods have been used for comparative analysis of genome data [1], leading to the Bioinformatics field expansion, where various branches such as Genomics, Proteomics, Computer-Aided Drug Design, Bio Data Bases & Data Mining, Molecular Phylogenetics, Microarray Informatics or System Biology emerged [2]. With the arrival of high-throughput 'omics' data, a new dimension was added to data-driven comparative research [1], where manual analysis became unsuitable, requiring the use of computational methods. ...
In this paper we present two case studies of Proteomics applications development using the AIBench framework, a Java desktop application framework mainly focused in scientific software development. The applications presented in this work are Decision Peptide- Driven, for rapid and accurate protein quantification, and Bacterial Identification, for Tuberculosis biomarker search and diagnosis. Both tools work with mass spectrometry data, specifically with MALDI-TOF spectra, minimizing the time required to process and analyze the experimental data.
... Biological Data is often characterized by huge size. There are four important data generated and collected at biological points 10 The large, huge and complex amount of biological data needed to be stored, accessed and manipulated in an efficient and powerful manner. So it was the need to build Bioinformatics databases which are classified into sequence databases, microarray databases, genome databases, protein structures databases and many more 2 . ...
Bioinformatics is a new science that is glowing out in the recent years. It is a multidisciplinary science that is made out of different kinds of other scientific fields like biology, computer science, chemistry, statistics, mathematics and others. It was a big challenge for researchers to describe this new field in a systematic scientific way and bring out the attention of its applications and services; one of these important services that Bioinformatics can be applied in, is the cancer studies, research and therapies for many beneficial reasons. This paper will give a clear glance overview of bioinformatics, its definition, aims, applications, technologies, the large amount of data produced in the biological field and how bioinformatics can organize, analyze and store them, discuss some algorithms that can be implemented over bioinformatics data, and how to apply bioinformatics to discover and diagnose diseases like cancer.
Key Words: Bioinformatics, Computational Biology, Cancer
... The number of features varies from 37 to 49152.these datasets covers range of application domains such as text, image microarray data [2] [12]. Following Table 1 shows the different dataset used in paper. ...
... Alongside implications for more effective drug design [2] , the main motivation for structural genomics concerns the elucidation of each protein's function, given that the structure of a protein almost completely governs its function [3] . Currently, structural genomics is supported through a synergetic gambit of processes and applications on both the experimentation and prediction side, including (respectively) "wet lab" procedures such as X-ray crystallography [4] and Nuclear Magnetic Resonance (NMR) spectroscopy [5] , and bioinformatics algorithms which include homologymodeling, threading, and de novo modeling [6] . Wet lab procedures drive the process of structural genomics such that "target" proteins are selected and their structures explicitly determined through accurate albeit extremely expensive and time consuming processes. ...
Protein sequence motifs extraction is an important field of bioinformatics since its relevance to the structural analysis. Two major problems are related to this field: (1) searching the motifs within the same protein family; and (2) assuming a window size for the motifs search. This work proposes the Hierarchically Clustered Hidden Markov Model (HC-HMM) approach, which represents the behavior and structure of proteins in terms of a Hidden Markov Model chain and hierarchically clusters each chain by minimizing distance between two given chains' structure and behavior. It is well known that HMM can be utilized for clustering, however, methods for clustering on Hidden Markov Models themselves are rarely studied. In this paper, we developed a hierarchical clustering based algorithm for HMMs to discover protein sequence motifs that transcend family boundaries with no assumption on the length of the motif. This paper carefully examines the effectiveness of this approach for motif extraction on 2593 proteins that share no more than 25% sequence identity. Many interesting motifs are generated. Three example motifs generated by the HC-HMM approach are analyzed and visualized with their tertiary structure. We believe the proposed method provides a unique protein sequence motif extraction strategy. The related data mining fields using Hidden Markova Model may also benefit from this clustering on HMM themselves approach.
