REPA: Applying Pathway Analysis to Genome-Wide Transcription Factor Binding Data

Patra, Pranjal; Izawa, T.; Peña‐Castillo, Lourdes

doi:10.1109/tcbb.2015.2453948

Cited by 4 publications

(3 citation statements)

References 142 publications

(125 reference statements)

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“…Patra et al explained a novel Regulatory Enrichment Pathway Analysis (REPA) approach, which assists in the application of gene set analysis to genome-wide transcription factor binding data. The model is highly scalable and can be used for the analysis of ribosome, alcoholism, cancer pathways, bacterial invasion of epithelial cells, etc ., with 83% accuracy, thereby making it useful for a wide variety of applications [ 70 ]. An interesting piece of research was done by Waldvogel et al , where evolutionary computational models were used for the estimation of species responses to global climate change.…”

Section: Genome Sequence Processing Modelsmentioning

confidence: 99%

Heuristic Analysis of Genomic Sequence Processing Models for High Efficiency Prediction: A Statistical Perspective

Shrimankar

Durge

Sawarkar

2022

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Genome sequences indicate the wide variety of characteristics which include species & sub-species type, genotype, diseases, growth indicators, yield quality, etc. To analyze and study these characteristics of the genome sequences across different species, various deep learning models have been proposed by researchers such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Multilayer Perceptron’s (MLPs), etc. which vary in terms of evaluation performance, area of application and species that are processed. Due to a wide differentiation between the algorithmic implementations, it becomes difficult for research programmer to select the best possible genome processing model for their application. In order to facilitate this selection, the paper reviews a wide variety of such models and compares their performance in terms of accuracy, area of application, computational complexity, processing delay, precision and recall. Thus, in the present review, various deep learning and machine learning models have different accuracies for different applications. For multiple genomic data, Repeated Incremental Pruning to Produce Error Reduction with Support Vector Machine (Ripper SVM) outputs 99.7% of accuracy and for cancer genomic data it gives 99.27% of accuracy using CNN Bayesian method. Whereas for Covid genome analysis Bidirectional Long Short-Term Memory with CNN (BiLSTM CNN) is having highest accuracy of 99.95%. The similar analysis of precision and recall of different models have been reviewed. Finally, this paper review concludes with some interesting observations about the genomic processing models and recommends applications for their efficient use.

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Section: Genome Sequence Processing Modelsmentioning

confidence: 99%

Heuristic Analysis of Genomic Sequence Processing Models for High Efficiency Prediction: A Statistical Perspective

Shrimankar

Durge

Sawarkar

2022

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“…We implemented a previously reported competitive method to apply gene set association analysis to transcription factor binding data sets to test whether an RBP preferentially targets to genes in a given gene set (Patra et al 2015). This method finds the common genes between given RBP targets and genes in a given gene set and compares the scores of common genes to the scores of randomly permutated genes from RBP targets by a competitive test where the Mann-Whitney U test is used to test whether the median score of the common genes is significantly higher than that of randomly permutated genes (Mann and Whitney 1947).…”

Section: Gene Set Association Analysismentioning

confidence: 99%

Seten: a tool for systematic identification and comparison of processes, phenotypes, and diseases associated with RNA-binding proteins from condition-specific CLIP-seq profiles

Budak

Srivastava

Janga

2017

RNA

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RNA-binding proteins (RBPs) control the regulation of gene expression in eukaryotic genomes at post-transcriptional level by binding to their cognate RNAs. Although several variants of CLIP (crosslinking and immunoprecipitation) protocols are currently available to study the global protein-RNA interaction landscape at single-nucleotide resolution in a cell, currently there are very few tools that can facilitate understanding and dissecting the functional associations of RBPs from the resulting binding maps. Here, we present Seten, a web-based and command line tool, which can identify and compare processes, phenotypes, and diseases associated with RBPs from condition-specific CLIP-seq profiles. Seten uses BED files resulting from most peak calling algorithms, which include scores reflecting the extent of binding of an RBP on the target transcript, to provide both traditional functional enrichment as well as gene set enrichment results for a number of gene set collections including BioCarta, KEGG, Reactome, Gene Ontology (GO), Human Phenotype Ontology (HPO), and MalaCards Disease Ontology for several organisms including fruit fly, human, mouse, rat, worm, and yeast. It also provides an option to dynamically compare the associated gene sets across data sets as bubble charts, to facilitate comparative analysis. Benchmarking of Seten using eCLIP data for IGF2BP1, SRSF7, and PTBP1 against their corresponding CRISPR RNA-seq in K562 cells as well as randomized negative controls, demonstrated that its gene set enrichment method outperforms functional enrichment, with scores significantly contributing to the discovery of true annotations. Comparative performance analysis using these CRISPR control data sets revealed significantly higher precision and comparable recall to that observed using ChIP-Enrich. Seten's web interface currently provides precomputed results for about 200 CLIP-seq data sets and both command line as well as web interfaces can be used to analyze CLIP-seq data sets. We highlight several examples to show the utility of Seten for rapid profiling of various CLIP-seq data sets. Seten is available on http://www.iupui.edu/∼sysbio/seten/.

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“…Transcription [2] is the mechanism of copying genetic material data from one strand of DNA into the RNA strand. The principle of complementary governs the process, except to the fact that the A or adenine forms the base pair bond with U or uracil instead of T or thymine.…”

Section: Introductionmentioning

confidence: 99%

Correlation-Based Analysis of COVID-19 Virus Genome Versus Other Fatal Virus Genomes

et al. 2021

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Virus attacks have had devastating effects on mankind. The prominent viruses such as Ebola virus ( 2012), SARS-CoV or Severe acute respiratory syndrome, Middle East respiratory syndrome-related coronavirus called as the MERS (EMC/2012), Spanish flu (H1N1 virus-1918) and the most recent COVID-19(SARS-CoV-2) are the ones that have created a difficult situation for the survival of the human race. Currently, throughout the world, a global pandemic situation has put economy, livelihood and human existence in a very pathetic situation. Most of the above-mentioned viruses exhibit some similar characteristics and genetic pattern. Analysing such characteristics and genetic pattern can help the researchers to get a deeper insight into the viruses and helps in finding appropriate medicine or cure. To address these issues, this paper proposes an experimental analysis of the above-mentioned viruses data using correlation methods. The virus data considered for the experimental analysis include the distribution of various amino acids, protein sequences, 3D modelling of viruses, pairwise alignment of proteins that comprise the DNA genome of the viruses. Furthermore, this comparative analysis can be used by the researchers and organizations like WHO(World Health Organization), computational biologists, genetic engineers to frame a layout for studying the DNA sequence distribution, percentage of GC (guanine-cytosine) protein which determines the heat stability of viruses. We have used the Biopython to illustrate the gene study of prominent viruses and have derived results and insights in the form of 3D modelling. The experimental results are more promising with an accuracy rate of 96% in overall virus relationship calculation.

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REPA: Applying Pathway Analysis to Genome-Wide Transcription Factor Binding Data

Cited by 4 publications

References 142 publications

Heuristic Analysis of Genomic Sequence Processing Models for High Efficiency Prediction: A Statistical Perspective

Heuristic Analysis of Genomic Sequence Processing Models for High Efficiency Prediction: A Statistical Perspective

Seten: a tool for systematic identification and comparison of processes, phenotypes, and diseases associated with RNA-binding proteins from condition-specific CLIP-seq profiles

Correlation-Based Analysis of COVID-19 Virus Genome Versus Other Fatal Virus Genomes

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