There are plenty specific types of data which are needed to compress for easy storage and to reduce overall retrieval times. Moreover, compressed sequence can be used to understand similarities between biological sequences. DNA data compression challenge has become a major task for many researchers for the last few years as a result of exponential increase of produced sequences in gene databases. In this research paper we have attempt to develop an algorithm by self-reference bases; namely Single Base Variable Repeat Length DNA Compression (SBVRLDNAComp). There are a number of reference based compression methods but they are not satisfactory for forthcoming new species. SBVRLDNAComp is an optimal solution of the result obtained from small to long, uniform identical and non-identical string of nucleotides checked in four different ways. Both exact repetitive and non-repetitive bases are compressed by SBVRLDNAComp.The sound part of it is without any reference database BVRLDNAComp achieves 1.70 to 1.73 compression ratio α after testing on ten benchmark DNA sequences. The compressed file can be further compressed with standard tools (such as WinZip or WinRar) but even without this SBVRLDNAComp outperforms many standard DNA compression algorithms.
De novo transcriptome assembly of solid sequencing data in cucumis melobioejjournal
As sequencing technologies progress, focus shifts towards solving bioinformatic challenges, of which sequence read assembly is the first task. In the present study, we have carried out a comparison of two assemblers (SeqMan and CLC) for transcriptome assembly, using a new dataset from Cucumis melo. Between two assemblers SeqMan generated an excess of small, redundant contigs where as CLC generated the least redundant assembly. Since different assemblers use different algorithms to build contigs, wefollowed the merging of assemblies by CAP3 and found that the merged assembly is better than individual assemblies and more consistent in the number and size of contigs. Combining the assemblies from different programs gave a more credible final product, and therefore this approach is recommended for quantitative
output.
Examining gene expression and methylation with next gen sequencingStephen Turner
Slides on RNA-seq and methylation studies using next-gen sequencing given at the University of Miami Hussman Institute for Human Genomics "Genetic Analysis of Complex Human Diseases" course in 2012 (http://hihg.med.miami.edu/educational-programs/analysis-of-complex-human-diseases/genetic-analysis-of-complex-human-diseases/)
This session will follow up from transcript quantification of RNAseq data and discusses statistical means of identifying differentially regulated transcripts, and isoforms and contrasts these against microarray analysis approaches.
Bioo Scientific - Reduced Bias Small RNA Library Prep with Gel-Free or Low-In...Bioo Scientific
microRNAs (miRNAs) may provide useful markers for the development of disease diagnostic and prognostic assays. NGS brings sensitivity, specificity, and the ability to maximize data acquisition and minimize costs of miRNA sequencing by using multiplex strategies to allow many samples to be sequenced simultaneously with small RNA analysis. However, small RNA sequencing has typically suffered from three major drawbacks: severe bias, such that sequencing data does not reflect original miRNA abundances, the need to gel purify final libraries, and lack of low-input protocols. The NEXTflex™ Small RNA-Seq Kit v3 addresses these drawbacks by using two strategies: randomized adapters to reduce ligation-associated bias, and a dual approach to adapter-dimer reduction, thereby allowing gel-free or low-input small RNA library preparation.
Abstract: The focus in this session will be put on the differences between standard DNA mapping and RNAseq-specific transcript mapping: identifying splice variants and isoforms. The issue of transcript quantification and genomic variants that can be identified from RNAseq data will be discussed.
Bioo Scientific - Absolute Quantitation for RNA-SeqBioo Scientific
PCR bias during RNA-seq library preparation can incorrectly amplify some transcripts over others, making accurate quantification of transcript numbers difficult. Molecular indexing uses unique molecular identifier adapters to label each transcript, correcting for PCR bias and improving RNA quantification. Published research shows molecular indexing returns over 15% of reads to libraries and improves accuracy, especially for highly expressed genes, by accounting for distinct fragments with identical start/stop sites eliminated by typical unique molecular identifier methods.
Bioo Scientific - Simplify and Reduce Cost of mtDNA Isolation and Library PrepBioo Scientific
Mutations in mitochondrial DNA (mtDNA) have been implicated in various human disorders and in aging, making NGS analysis of mtDNA a priority for a number of labs. However, accurately determining the diversity of mtDNA has been difficult for a number of reasons. The standard methods for mitochondrial DNA extraction have a number of limitations making them inferior solutions for NGS library preparation. Bioo Scientific has commercialized a kit which overcomes these limitations of mtDNA isolation by selectively digesting linear nuclear DNA (nDNA) while leaving circular mtDNA intact. This technology has been incorporated into the NEXTflex mtDNA-Seq Kit which includes optimized reagents for the isolation of mtDNA and for the construction of Illumina mtDNA libraries. Libraries constructed using the NEXTflex mtDNA-Seq Kit are ideal for many NGS applications including heteroplasmy analysis.
The document discusses various gene editing technologies. It begins by introducing genome/gene editing as a type of genetic engineering that uses engineered nucleases to precisely modify genomes by creating DNA insertions, deletions, or replacements at specific DNA sequences. It then describes three main gene editing systems - zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and the CRISPR/Cas9 system. For each system, it provides details on the nuclease domains, methods for engineering DNA binding specificity, and mechanisms for creating DNA double strand breaks to facilitate gene modifications.
De novo transcriptome assembly of solid sequencing data in cucumis melobioejjournal
As sequencing technologies progress, focus shifts towards solving bioinformatic challenges, of which sequence read assembly is the first task. In the present study, we have carried out a comparison of two assemblers (SeqMan and CLC) for transcriptome assembly, using a new dataset from Cucumis melo. Between two assemblers SeqMan generated an excess of small, redundant contigs where as CLC generated the least redundant assembly. Since different assemblers use different algorithms to build contigs, wefollowed the merging of assemblies by CAP3 and found that the merged assembly is better than individual assemblies and more consistent in the number and size of contigs. Combining the assemblies from different programs gave a more credible final product, and therefore this approach is recommended for quantitative
output.
Examining gene expression and methylation with next gen sequencingStephen Turner
Slides on RNA-seq and methylation studies using next-gen sequencing given at the University of Miami Hussman Institute for Human Genomics "Genetic Analysis of Complex Human Diseases" course in 2012 (http://hihg.med.miami.edu/educational-programs/analysis-of-complex-human-diseases/genetic-analysis-of-complex-human-diseases/)
This session will follow up from transcript quantification of RNAseq data and discusses statistical means of identifying differentially regulated transcripts, and isoforms and contrasts these against microarray analysis approaches.
Bioo Scientific - Reduced Bias Small RNA Library Prep with Gel-Free or Low-In...Bioo Scientific
microRNAs (miRNAs) may provide useful markers for the development of disease diagnostic and prognostic assays. NGS brings sensitivity, specificity, and the ability to maximize data acquisition and minimize costs of miRNA sequencing by using multiplex strategies to allow many samples to be sequenced simultaneously with small RNA analysis. However, small RNA sequencing has typically suffered from three major drawbacks: severe bias, such that sequencing data does not reflect original miRNA abundances, the need to gel purify final libraries, and lack of low-input protocols. The NEXTflex™ Small RNA-Seq Kit v3 addresses these drawbacks by using two strategies: randomized adapters to reduce ligation-associated bias, and a dual approach to adapter-dimer reduction, thereby allowing gel-free or low-input small RNA library preparation.
Abstract: The focus in this session will be put on the differences between standard DNA mapping and RNAseq-specific transcript mapping: identifying splice variants and isoforms. The issue of transcript quantification and genomic variants that can be identified from RNAseq data will be discussed.
Bioo Scientific - Absolute Quantitation for RNA-SeqBioo Scientific
PCR bias during RNA-seq library preparation can incorrectly amplify some transcripts over others, making accurate quantification of transcript numbers difficult. Molecular indexing uses unique molecular identifier adapters to label each transcript, correcting for PCR bias and improving RNA quantification. Published research shows molecular indexing returns over 15% of reads to libraries and improves accuracy, especially for highly expressed genes, by accounting for distinct fragments with identical start/stop sites eliminated by typical unique molecular identifier methods.
Bioo Scientific - Simplify and Reduce Cost of mtDNA Isolation and Library PrepBioo Scientific
Mutations in mitochondrial DNA (mtDNA) have been implicated in various human disorders and in aging, making NGS analysis of mtDNA a priority for a number of labs. However, accurately determining the diversity of mtDNA has been difficult for a number of reasons. The standard methods for mitochondrial DNA extraction have a number of limitations making them inferior solutions for NGS library preparation. Bioo Scientific has commercialized a kit which overcomes these limitations of mtDNA isolation by selectively digesting linear nuclear DNA (nDNA) while leaving circular mtDNA intact. This technology has been incorporated into the NEXTflex mtDNA-Seq Kit which includes optimized reagents for the isolation of mtDNA and for the construction of Illumina mtDNA libraries. Libraries constructed using the NEXTflex mtDNA-Seq Kit are ideal for many NGS applications including heteroplasmy analysis.
The document discusses various gene editing technologies. It begins by introducing genome/gene editing as a type of genetic engineering that uses engineered nucleases to precisely modify genomes by creating DNA insertions, deletions, or replacements at specific DNA sequences. It then describes three main gene editing systems - zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and the CRISPR/Cas9 system. For each system, it provides details on the nuclease domains, methods for engineering DNA binding specificity, and mechanisms for creating DNA double strand breaks to facilitate gene modifications.
WGS data for bacterial typing
This document discusses using whole genome sequencing (WGS) data for bacterial strain typing and phylogenetic analysis. It covers:
1) Bacterial genomes consist of DNA made up of 4 nucleotides (A, C, T, G) that can be sequenced. Genes encode proteins and make up most of bacterial genomes.
2) Mutations like single nucleotide changes can be used to differentiate bacterial strains. Molecular methods like MLST, MLVA, and core genome MLST analyze categorical or continuous differences in bacterial sequences.
3) As sequencing technology advanced, it became possible to generate and analyze whole bacterial genomes, allowing highly discriminatory strain typing and reconstruction of bacterial phylogenies based on single nucleotide polymorph
CRISPR-Cas9 Review: A potential tool for genome editingDavient Bala
The document discusses CRISPR-Cas9 as a potential tool for genome editing. It describes how CRISPR was originally discovered in bacteria and archaea as a mechanism for adaptive immunity against viruses. The CRISPR-Cas9 system uses guide RNA to direct an endonuclease called Cas9 to introduce targeted double-strand breaks in DNA, which can then be repaired through non-homologous end joining or homology directed repair for genome editing. Applications discussed include using CRISPR-Cas9 for disease modeling in animals and cell lines more efficiently compared to previous methods, as well as for drug development by generating gene knockouts and mutations for target validation.
Part 1 of RNA-seq for DE analysis: Defining the goalJoachim Jacob
First part of the training session 'RNA-seq for Differential expression' analysis. We explain how we can detect differential expression based on RNA-seq data. Interested in following this session? Please contact http://www.jakonix.be/contact.html
Gene editing application for cancer therapeuticsNur Farrah Dini
The application of TALENs as one of the gene editing tools in order to modify a specific targeted sites on a genome. This method shows a tremendous benefits especially in cancer research.
This document outlines an RNA-Seq differential expression analysis workflow to identify differentially expressed genes between breast tumor and normal tissue samples. The proposed pipeline includes quality control checks, mapping reads to the human genome, counting reads per gene, normalization methods to account for sequencing depth differences, and four statistical analysis methods (DESeq, DESeq2, edgeR, voom-Limma) to identify differentially expressed genes while controlling the false discovery rate. Visualization of sample distances and principal components analysis are used for quality control. The results are compared across methods to determine overlapping significant genes. Further biological insights from these gene lists are suggested.
A new revisited compression technique through innovative partition group binaryIAEME Publication
This document discusses a new compression technique called Partition Group Binary Compression (PGBC) for compressing DNA sequences. It begins with background on DNA sequencing and challenges with compressing large DNA databases. It then reviews existing compression algorithms like Huffbit Compress and Genbit Compress that use 2-bit encoding but do not perform well on sequences with few repeats. The proposed PGBC technique aims to achieve better compression ratios than existing methods even for sequences with little repetition. The paper is organized into sections on general compression algorithms, related existing algorithms, a description of how PGBC analysis improves on them, a comparative study on sample sequences, and conclusions.
Genome Editing Comes of Age; CRISPR, rAAV and the new landscape of molecular ...Candy Smellie
Information is no longer a bottleneck, emphasis is shifting to the ‘what does it all mean’
In a translational context we hope that by answering that question we will be able to is to characterise the genetics that drive disease, and indeed develop drugs and diagnostics that are personalised to patients.
Genome editing provides the link between the information here, and this outcome here, by allowing scientists to recapitulate specific genetic alterations in any gene in any living tissue to probe function, develop disease models and identify therapeutic strategies. So, not only do we now have unparalleled access to genetic information, but we now have the tools to most accuartely understand what this genetic information – with genome editing allowing us to explore the genetic drivers of disease in physiological models.
AAV is a single-stranded, linear DNA virus with a a 4.7 kb genome which for the purpose of genome editing is replaced almost in entirety with the targeting vector sequence (except for the iTRs)
It is in effect a highly effective DNA delivery mechanism
After entry of the vector into the cell, target-specific homologous DNA is believed to activate and recruit HR-dependent repair factors can induce HR at rates approximately 1,000 times greater than plasmid based double stranded DNA vectors, but the mechanism by which it achieves this is still largely unknown
By including a selection cassette can select for cells that have integrated the targeting vector, and then screen for clones which have undergone targeted insetion rather than random integration, which will generally be around 1%.
RNA-seq is a revolutionary tool for transcriptomics that has advantages over previous methods like microarrays. It allows for single-base resolution expression profiling, detection of splicing variants and gene fusions, and can detect a wider dynamic range of expression levels. RNA-seq is being used to improve genome annotations by characterizing alternative splicing events and verifying gene boundaries. It is also useful for generating genetic resources for non-model species by performing de novo transcriptome sequencing and annotation. Additionally, RNA-seq can help advance proteomics by providing a reference database to match peptide spectra. Studies are using RNA-seq to examine spatial and temporal transcriptome landscapes in various plants.
Satrupa Das discusses using programmable nucleases like TALENs and CRISPR-Cas9 for genome editing to treat diseases. She describes a study where exon 44 was knocked back into the dystrophin gene of an iPSC line from a Duchenne muscular dystrophy patient using TALENs or Cas9. Over 90% of clones showed targeted integration of the donor template, and sequencing confirmed restoration of the reading frame without other mutations. Differentiated muscle cells from corrected clones expressed dystrophin, demonstrating therapeutic potential of genome editing for Duchenne muscular dystrophy.
The Efficiency and Ethics of the CRISPR System in Human EmbryosStephen Cranwell
This document summarizes research on the CRISPR/Cas9 system for genome editing in human embryos. It discusses efforts to understand DNA repair mechanisms after inducing double-strand breaks, reduce off-target mutations, and improve the specificity and efficiency of editing. While the technology shows promise, significant issues around off-target effects, mosaicism, and ethical concerns must still be addressed before any clinical applications. The document concludes that further basic research is needed to advance the field while also having open discussions on societal implications.
The document discusses genome assembly and finishing processes. It begins by outlining typical project goals of completely restoring the genome and producing a high-quality consensus sequence. It then describes the evolution of sequencing technologies from Sanger to newer platforms and their impact on draft assemblies. Key steps in the assembly and finishing process include library preparation, assembly, identifying gaps, and improving consensus quality.
This document summarizes an RNA-seq analysis that compared gene expression between wild type and fibrosis samples from mouse muscle tissue. The analysis used the DESeq2 package to normalize counts, perform quality control, differential expression analysis, and clustering. It identified around 6,000 genes with a log fold change not equal to 0 and subset those genes with a Benjamini-Hochberg adjusted p-value less than 0.05 as significantly differentially expressed between the two conditions. Next steps mentioned are further analyzing these significant genes for systems biology networks, gene ontology terms, and pathway enrichment.
RNASeq DE methods review Applied Bioinformatics Journal ClubJennifer Shelton
This document summarizes a journal club discussion on comparing commonly used differential expression software packages using two benchmark datasets. It describes the focus of comparing normalization methods, sensitivity and specificity of differential expression detection, and the impact of sequencing depth and replication. The document then provides details on the normalization and statistical modeling approaches used by different packages, including DESeq, edgeR, Cuffdiff, baySeq, PoissonSeq and limma. It concludes by outlining the results presented on normalization performance, differential expression analysis, and how factors like replication and sequencing depth influence detection of differentially expressed genes.
Dr. Chris Lowe presented on Horizon Discovery's precision genome editing platform called GENESISTM. The presentation discussed optimizing GENESISTM by combining CRISPR and rAAV technologies to improve gene targeting efficiency. Custom cell line development services are offered to modify genes of interest in various cell lines for applications such as generating disease models and studying drug sensitivity. Key considerations for successful gene editing experiments include factors like gene/cell line selection, gRNA design/activity, donor design, screening/validation approaches. Case studies demonstrated applications of engineered cell lines.
The document describes a presentation given by Gunnar Rätsch on tools for RNA-seq analysis and isoform characterization. It discusses the increasing amounts of biological data and challenges in developing accurate analysis algorithms. The presentation covers multiple tools developed by Rätsch's group for analyzing RNA-seq data, including tools for transcript quantification, multiple read mapping, alternative splicing analysis and detection of novel isoforms. The tools aim to improve RNA-seq analysis for large datasets and characterization of transcript isoforms and splicing.
Genome engineering using CRISPR/Cas9 has several advantages over traditional gene targeting methods: it is faster, more precise, applicable to many species, and less expensive. CRISPR/Cas9 uses the Cas9 nuclease guided by a single guide RNA to introduce double-strand breaks at targeted genomic loci. This can generate gene knockouts through error-prone non-homologous end joining or allow for targeted insertions and modifications through homology-directed repair. While CRISPR/Cas9 has great potential, careful design of guide RNAs and donor templates is needed to minimize off-target effects.
CRISPR/Cas9 gene editing is based on a microbial restriction system, that has been harnessed for genome targeting using only a short sequence of RNA as a guide.
The beauty of the system is that unlike protein binding based technologies such as Zinc Fingers and TALENs which require complex protein engineering, the design rules are very simple, and it is this fact that is allowing CRISPR to take genome engineering from a relatively niche persuit to the mainstream scientific community.
The principle of the system is that a short guide RNA, homologous to the target site recruits a nuclease – Cas9
This then cuts the dsDNA, triggering repair by either the low fidelity NHEJ pathway, or by HDR in the presence of an exogenous donor sequence.
High Efficiencies for both knockouts and knock-ins have been reported and whilst there are understandable concerns about specificity, new methodologies to address these are now being developed
The system itself is comprised of three key components
the Cas9 protein, which cuts/cleaves the DNA and
Two RNAs - a crispr RNA contains the sequence homologous to the target site and a trans-activating crisprRNA (or TracrRNA) which recruits the nuclease/crispr complex
For genome editing, the crisperRNA and TraceRNA are generally now constructed together into a single guideRNA or sgRNA
Genome editing is elicited through hybridization of the sgRNA with its matching genomic sequence, and the recruitment of the Cas9, which cleaves at the target site.
An approach to decrease dimentions of logicalijcsa
In this paper we consider manufacturing logical elements with function AND-NOT based on bipolar transistors.Based on recently considered approach to decrease dimensions of solid state electronic devices with the same time increasing of their performance we introduce an approach to decrease dimensions of transistors and p-n-junctions, which became a part of the logical element. Framework the approach a heterostructure
with required configuration should be manufactured. After the manufacture required areas of the heterostructures should be doped by diffusion or ion implantation. The doping should be finished by optimized annealing of dopant and/or radiation defects.
SUCCESSIVE LINEARIZATION SOLUTION OF A BOUNDARY LAYER CONVECTIVE HEAT TRANSFE...ijcsa
The purpose of this paper is to discuss the flow of forced convection over a flat plate. The governing partial
differential equations are transformed into ordinary differential equations using suitable transformations.
The resulting equations were solved using a recent semi-numerical scheme known as the successive
linearization method (SLM). A comparison between the obtained results with homotopy perturbation method and numerical method (NM) has been included to test the accuracy and convergence of the method.
Quite often in experimental work, many situations arise where some observations are lost or become
unavailable due to some accidents or cost constraints. When there are missing observations, some
desirable design properties like orthogonality,rotatability and optimality can be adversely affected. Some
attention has been given, in literature, to investigating the prediction capability of response surface
designs; however, little or no effort has been devoted to investigating same for such designs when some
observations are missing. This work therefore investigates the impact of a single missing observation of the
various design points: factorial, axial and center points, on the estimation and predictive capability of
Central Composite Designs (CCDs). It was observed that for each of the designs considered, precision of
model parameter estimates and the design prediction properties were adversely affected by the missing
observations and that the largest loss in precision of parameters corresponds to a missing factorial point.
WGS data for bacterial typing
This document discusses using whole genome sequencing (WGS) data for bacterial strain typing and phylogenetic analysis. It covers:
1) Bacterial genomes consist of DNA made up of 4 nucleotides (A, C, T, G) that can be sequenced. Genes encode proteins and make up most of bacterial genomes.
2) Mutations like single nucleotide changes can be used to differentiate bacterial strains. Molecular methods like MLST, MLVA, and core genome MLST analyze categorical or continuous differences in bacterial sequences.
3) As sequencing technology advanced, it became possible to generate and analyze whole bacterial genomes, allowing highly discriminatory strain typing and reconstruction of bacterial phylogenies based on single nucleotide polymorph
CRISPR-Cas9 Review: A potential tool for genome editingDavient Bala
The document discusses CRISPR-Cas9 as a potential tool for genome editing. It describes how CRISPR was originally discovered in bacteria and archaea as a mechanism for adaptive immunity against viruses. The CRISPR-Cas9 system uses guide RNA to direct an endonuclease called Cas9 to introduce targeted double-strand breaks in DNA, which can then be repaired through non-homologous end joining or homology directed repair for genome editing. Applications discussed include using CRISPR-Cas9 for disease modeling in animals and cell lines more efficiently compared to previous methods, as well as for drug development by generating gene knockouts and mutations for target validation.
Part 1 of RNA-seq for DE analysis: Defining the goalJoachim Jacob
First part of the training session 'RNA-seq for Differential expression' analysis. We explain how we can detect differential expression based on RNA-seq data. Interested in following this session? Please contact http://www.jakonix.be/contact.html
Gene editing application for cancer therapeuticsNur Farrah Dini
The application of TALENs as one of the gene editing tools in order to modify a specific targeted sites on a genome. This method shows a tremendous benefits especially in cancer research.
This document outlines an RNA-Seq differential expression analysis workflow to identify differentially expressed genes between breast tumor and normal tissue samples. The proposed pipeline includes quality control checks, mapping reads to the human genome, counting reads per gene, normalization methods to account for sequencing depth differences, and four statistical analysis methods (DESeq, DESeq2, edgeR, voom-Limma) to identify differentially expressed genes while controlling the false discovery rate. Visualization of sample distances and principal components analysis are used for quality control. The results are compared across methods to determine overlapping significant genes. Further biological insights from these gene lists are suggested.
A new revisited compression technique through innovative partition group binaryIAEME Publication
This document discusses a new compression technique called Partition Group Binary Compression (PGBC) for compressing DNA sequences. It begins with background on DNA sequencing and challenges with compressing large DNA databases. It then reviews existing compression algorithms like Huffbit Compress and Genbit Compress that use 2-bit encoding but do not perform well on sequences with few repeats. The proposed PGBC technique aims to achieve better compression ratios than existing methods even for sequences with little repetition. The paper is organized into sections on general compression algorithms, related existing algorithms, a description of how PGBC analysis improves on them, a comparative study on sample sequences, and conclusions.
Genome Editing Comes of Age; CRISPR, rAAV and the new landscape of molecular ...Candy Smellie
Information is no longer a bottleneck, emphasis is shifting to the ‘what does it all mean’
In a translational context we hope that by answering that question we will be able to is to characterise the genetics that drive disease, and indeed develop drugs and diagnostics that are personalised to patients.
Genome editing provides the link between the information here, and this outcome here, by allowing scientists to recapitulate specific genetic alterations in any gene in any living tissue to probe function, develop disease models and identify therapeutic strategies. So, not only do we now have unparalleled access to genetic information, but we now have the tools to most accuartely understand what this genetic information – with genome editing allowing us to explore the genetic drivers of disease in physiological models.
AAV is a single-stranded, linear DNA virus with a a 4.7 kb genome which for the purpose of genome editing is replaced almost in entirety with the targeting vector sequence (except for the iTRs)
It is in effect a highly effective DNA delivery mechanism
After entry of the vector into the cell, target-specific homologous DNA is believed to activate and recruit HR-dependent repair factors can induce HR at rates approximately 1,000 times greater than plasmid based double stranded DNA vectors, but the mechanism by which it achieves this is still largely unknown
By including a selection cassette can select for cells that have integrated the targeting vector, and then screen for clones which have undergone targeted insetion rather than random integration, which will generally be around 1%.
RNA-seq is a revolutionary tool for transcriptomics that has advantages over previous methods like microarrays. It allows for single-base resolution expression profiling, detection of splicing variants and gene fusions, and can detect a wider dynamic range of expression levels. RNA-seq is being used to improve genome annotations by characterizing alternative splicing events and verifying gene boundaries. It is also useful for generating genetic resources for non-model species by performing de novo transcriptome sequencing and annotation. Additionally, RNA-seq can help advance proteomics by providing a reference database to match peptide spectra. Studies are using RNA-seq to examine spatial and temporal transcriptome landscapes in various plants.
Satrupa Das discusses using programmable nucleases like TALENs and CRISPR-Cas9 for genome editing to treat diseases. She describes a study where exon 44 was knocked back into the dystrophin gene of an iPSC line from a Duchenne muscular dystrophy patient using TALENs or Cas9. Over 90% of clones showed targeted integration of the donor template, and sequencing confirmed restoration of the reading frame without other mutations. Differentiated muscle cells from corrected clones expressed dystrophin, demonstrating therapeutic potential of genome editing for Duchenne muscular dystrophy.
The Efficiency and Ethics of the CRISPR System in Human EmbryosStephen Cranwell
This document summarizes research on the CRISPR/Cas9 system for genome editing in human embryos. It discusses efforts to understand DNA repair mechanisms after inducing double-strand breaks, reduce off-target mutations, and improve the specificity and efficiency of editing. While the technology shows promise, significant issues around off-target effects, mosaicism, and ethical concerns must still be addressed before any clinical applications. The document concludes that further basic research is needed to advance the field while also having open discussions on societal implications.
The document discusses genome assembly and finishing processes. It begins by outlining typical project goals of completely restoring the genome and producing a high-quality consensus sequence. It then describes the evolution of sequencing technologies from Sanger to newer platforms and their impact on draft assemblies. Key steps in the assembly and finishing process include library preparation, assembly, identifying gaps, and improving consensus quality.
This document summarizes an RNA-seq analysis that compared gene expression between wild type and fibrosis samples from mouse muscle tissue. The analysis used the DESeq2 package to normalize counts, perform quality control, differential expression analysis, and clustering. It identified around 6,000 genes with a log fold change not equal to 0 and subset those genes with a Benjamini-Hochberg adjusted p-value less than 0.05 as significantly differentially expressed between the two conditions. Next steps mentioned are further analyzing these significant genes for systems biology networks, gene ontology terms, and pathway enrichment.
RNASeq DE methods review Applied Bioinformatics Journal ClubJennifer Shelton
This document summarizes a journal club discussion on comparing commonly used differential expression software packages using two benchmark datasets. It describes the focus of comparing normalization methods, sensitivity and specificity of differential expression detection, and the impact of sequencing depth and replication. The document then provides details on the normalization and statistical modeling approaches used by different packages, including DESeq, edgeR, Cuffdiff, baySeq, PoissonSeq and limma. It concludes by outlining the results presented on normalization performance, differential expression analysis, and how factors like replication and sequencing depth influence detection of differentially expressed genes.
Dr. Chris Lowe presented on Horizon Discovery's precision genome editing platform called GENESISTM. The presentation discussed optimizing GENESISTM by combining CRISPR and rAAV technologies to improve gene targeting efficiency. Custom cell line development services are offered to modify genes of interest in various cell lines for applications such as generating disease models and studying drug sensitivity. Key considerations for successful gene editing experiments include factors like gene/cell line selection, gRNA design/activity, donor design, screening/validation approaches. Case studies demonstrated applications of engineered cell lines.
The document describes a presentation given by Gunnar Rätsch on tools for RNA-seq analysis and isoform characterization. It discusses the increasing amounts of biological data and challenges in developing accurate analysis algorithms. The presentation covers multiple tools developed by Rätsch's group for analyzing RNA-seq data, including tools for transcript quantification, multiple read mapping, alternative splicing analysis and detection of novel isoforms. The tools aim to improve RNA-seq analysis for large datasets and characterization of transcript isoforms and splicing.
Genome engineering using CRISPR/Cas9 has several advantages over traditional gene targeting methods: it is faster, more precise, applicable to many species, and less expensive. CRISPR/Cas9 uses the Cas9 nuclease guided by a single guide RNA to introduce double-strand breaks at targeted genomic loci. This can generate gene knockouts through error-prone non-homologous end joining or allow for targeted insertions and modifications through homology-directed repair. While CRISPR/Cas9 has great potential, careful design of guide RNAs and donor templates is needed to minimize off-target effects.
CRISPR/Cas9 gene editing is based on a microbial restriction system, that has been harnessed for genome targeting using only a short sequence of RNA as a guide.
The beauty of the system is that unlike protein binding based technologies such as Zinc Fingers and TALENs which require complex protein engineering, the design rules are very simple, and it is this fact that is allowing CRISPR to take genome engineering from a relatively niche persuit to the mainstream scientific community.
The principle of the system is that a short guide RNA, homologous to the target site recruits a nuclease – Cas9
This then cuts the dsDNA, triggering repair by either the low fidelity NHEJ pathway, or by HDR in the presence of an exogenous donor sequence.
High Efficiencies for both knockouts and knock-ins have been reported and whilst there are understandable concerns about specificity, new methodologies to address these are now being developed
The system itself is comprised of three key components
the Cas9 protein, which cuts/cleaves the DNA and
Two RNAs - a crispr RNA contains the sequence homologous to the target site and a trans-activating crisprRNA (or TracrRNA) which recruits the nuclease/crispr complex
For genome editing, the crisperRNA and TraceRNA are generally now constructed together into a single guideRNA or sgRNA
Genome editing is elicited through hybridization of the sgRNA with its matching genomic sequence, and the recruitment of the Cas9, which cleaves at the target site.
An approach to decrease dimentions of logicalijcsa
In this paper we consider manufacturing logical elements with function AND-NOT based on bipolar transistors.Based on recently considered approach to decrease dimensions of solid state electronic devices with the same time increasing of their performance we introduce an approach to decrease dimensions of transistors and p-n-junctions, which became a part of the logical element. Framework the approach a heterostructure
with required configuration should be manufactured. After the manufacture required areas of the heterostructures should be doped by diffusion or ion implantation. The doping should be finished by optimized annealing of dopant and/or radiation defects.
SUCCESSIVE LINEARIZATION SOLUTION OF A BOUNDARY LAYER CONVECTIVE HEAT TRANSFE...ijcsa
The purpose of this paper is to discuss the flow of forced convection over a flat plate. The governing partial
differential equations are transformed into ordinary differential equations using suitable transformations.
The resulting equations were solved using a recent semi-numerical scheme known as the successive
linearization method (SLM). A comparison between the obtained results with homotopy perturbation method and numerical method (NM) has been included to test the accuracy and convergence of the method.
Quite often in experimental work, many situations arise where some observations are lost or become
unavailable due to some accidents or cost constraints. When there are missing observations, some
desirable design properties like orthogonality,rotatability and optimality can be adversely affected. Some
attention has been given, in literature, to investigating the prediction capability of response surface
designs; however, little or no effort has been devoted to investigating same for such designs when some
observations are missing. This work therefore investigates the impact of a single missing observation of the
various design points: factorial, axial and center points, on the estimation and predictive capability of
Central Composite Designs (CCDs). It was observed that for each of the designs considered, precision of
model parameter estimates and the design prediction properties were adversely affected by the missing
observations and that the largest loss in precision of parameters corresponds to a missing factorial point.
COVERAGE OPTIMIZED AND TIME EFFICIENT LOCAL SEARCH BETWEENNESS ROUTING FOR HE...ijcsa
The document proposes a Local Search and Enhanced Betweenness Routing (LS-EBR) model for wireless sensor networks used for health monitoring. The LS-EBR model aims to improve routing efficiency by increasing sensor node coverage and minimizing routing time. It uses a local search algorithm based on greedy forwarding to route packets to neighboring nodes that are closest to the destination while also considering the reliability of sensor nodes. An enhanced betweenness routing algorithm is also used to measure energy consumption and select routes that consider both routing overhead and remaining energy of sensor nodes. Simulation results showed the LS-EBR model achieved higher coverage and improved routing efficiency compared to opportunistic routing.
The Boathouse Wine & Grill in Phuket, Thailand is located right on Kata Beach and offers both Thai and Western cuisine alongside an award-winning wine list with over 230 bottles. The restaurant aims to provide a casual yet sophisticated dining experience in an open-air setting overlooking the ocean. Both the food and wine selections cover a broad range of options from around the world to satisfy varied tastes.
This document outlines the steps involved in evaluating a company's services which includes introducing the evaluating company, learning about the evaluated company's information, advantages, strategic alliances, top users, current services and goals, then performing discovery, evaluation, and potential implementation if agreed upon by both parties.
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SBVRLDNACOMP:AN EFFECTIVE DNA SEQUENCE COMPRESSION ALGORITHM
1. International Journal on Computational Science & Applications (IJCSA) Vol.5, No.4, August 2015
DOI:10.5121/ijcsa.2015.5407 73
SBVRLDNACOMP:AN EFFECTIVE DNA
SEQUENCE COMPRESSION ALGORITHM
Subhankar Roy1
,Akash Bhagot2
,Kumari Annapurna Sharma2
and Sunirmal
Khatua3
1
Department of Computer Science and Engineering Academy of Technology, G. T. Road,
Aedconagar, Hooghly-712121, W.B., India
2
Master of Computer Application, Academy of Technology, G. T. Road, Aedconagar,
Hooghly-712121, W.B., India
3
Department of Computer Science and Engineer, University of Calcutta, 92 A.P.C. Road,
Kolkata-700009, India
ABSTRACT
There are plenty specific types of data which are needed to compress for easy storage and to reduce overall
retrieval times. Moreover, compressed sequence can be used to understand similarities between biological
sequences. DNA data compression challenge has become a major task for many researchers for the last
few years as a result of exponential increase of produced sequences in gene databases. In this research
paper we have attempt to develop an algorithm by self-reference bases; namely Single Base Variable
Repeat Length DNA Compression (SBVRLDNAComp). There are a number of reference based compression
methods but they are not satisfactory for forthcoming new species. SBVRLDNAComp is an optimal solution
of the result obtained from small to long, uniform identical and non-identical string of nucleotides checked
in four different ways. Both exact repetitive and non-repetitive bases are compressed by SBVRLDNAComp.
The sound part of it is without any reference database SBVRLDNAComp achieves 1.70 to 1.73 compression
ratio α after testing on ten benchmark DNA sequences. The compressed file can be further compressed with
standard tools (such as WinZip or WinRar) but even without this SBVRLDNAComp outperforms many
standard DNA compression algorithms.
KEYWORDS
DNA; Redundancy; Reference Base; Optimized Exact Repeat; Non-Repetition; LZ77; and Compression
Ratio.
1.INTRODUCTION
The size of the genome database is increasing annually with a great speed. Each day several
thousand nucleotides are sequenced in the labs. From 1982 to the present, the number of bases in
GenBank has doubled approximately every 18 months. It is found that in Dec 1982, the number
of bases and the number of sequence records were 680338 and 606 respectively for GenBank and
none for WGS (Whole Genome Shotgun) and with Release 129 in Apr 2002, the number of bases
2. International Journal on Computational Science & Applications (IJCSA) Vol.5, No.4, August 2015
74
and the number of sequence records were 19072679701 and 16769983 respectively for GenBank
and the WGS had 692266338 number of bases and 172768 number of sequences. Again in the
Release 206 in Feb 2015, the number of bases and the number of sequence records were
187893826750 and 181336445 respectively for GenBank and the WGS had 873281414087
numbers of bases and 205465046 numbers of sequences.
High-throughput sequencing technologies [1] make it possible to rapidly acquire large numbers of
individual genomes, which, for a given organism, vary slightly from one to another. Such
repetitive and large sequence collections are a unique challenge for compression. Compressed
data reduce the communication cost and storage cost. Furthermore, compressed sequence can be
used to get the similarities within sequences.
The highly repetitive DNA sequences own some motivating properties [2], [3] which can be
utilize to compress it. As DNA sequences consists of four nucleotides bases A, C, G and T called
exon (i.e. coding regions or protein synthesis) or introns (i.e. non-coding regions or no protein
synthesis) a, c, g and t in frequent cases, two bits is enough to store each base, in spite of this fact,
the standard compression algorithm like “COMPRESS”, “GZIP”, “BZIP2”, “WinZip” or
“WinRar” uses more than 2 bits per base [4]. Even both static and adaptive Huffman’s code fails
badly on DNA sequences because the probabilities of occurrence of these symbols are not very
different. In this paper we focus on compression of this particular data. DNA is a double stranded
molecule with neighboring strands connected through hydrogen bonding between the bases. This
hydrogen bonding is quite specific with Thymine (T/t) on one strand pairing with Adenine (A/a)
on the other strand and Guanine (G/g) on one strand pairing with Cytosine (C/c) on the other and
vice versa. All compression algorithms compress only one strand.
These behaviors are primary to substantial expansion in the size of DNA data sets, and are
providing opportunities for novel compression techniques that take advantage of the
characteristics of these new data. Our aspiration is to discover mechanisms for detecting this
redundancy and use it in compression by searching optimal level of similarity within individual’s
sequence.
The majority approach of compressing genomic data is to interpret the difference between the
newly data that should be compressed with a reference sequence and then find out the differences
[5], [6], [7]. This will be competent and possible when dealing with species that have a valid
reference but is less satisfactory when approaching new species data due to the lack of a standard
reference genome; for example, RNA sequencing data should be aligned against the entire
transcriptome and is not feasible to account each possible substitute transcript splicing.
DNA sequences in higher eukaryotes contain many repetitive nucleotides and have several copies
and also the Genes duplicate themselves for evolutionary purposes. To analyze, these sequences
have to be stored. So, these facts conclude that DNA sequences should be compressed. Human
DNA almost has 3 billion bases and among then more than 99% are the same in all human [8],
[9]. Data compression reveals certain theoretical ideas such as entropy, mutual information and
complexity between sequences of different genomes.
The most common and is very simple form of DNA compression is just binary encode the DNA
sequence bases using 2 bit for each nucleotides i.e. by replacing A/a, C/c, G/g and T/t with “00”,
“01”, “10” and “11” chosen abruptly. But in practically just by binary encoding the sequence, we
can cut the file size near about 75% with slide more than 2 bit per base [10]. The advantage of
3. International Journal on Computational Science & Applications (IJCSA) Vol.5, No.4, August 2015
75
this method is that the file can be easily parsed without needing complicated compression
algorithms, but it is not satisfactory because it does not use any property within a sequence.
Traditionally, DNA data compression methods usually compress based on the different properties
such as complementary string, complementary palindrome string or reverse complements, cross-
chromosomal similarity, approximate repeat, direct repeat etc. [11] of DNA sequence. However a
slight change in properties may give worst result. The preliminary summit of SBVRLDNAComp
based on repeat checked in four different ways, which have been applied to each DNA sequence.
SBVRLDNAComp encode both exact repetitive and non-repetitive parts without using exact
Lempel-Ziv based compression algorithms or order-2 arithmetic encoding for former and later
one respectively which are very common. So a sequence which is not so redundant also gives
good compression ratio. SBVRLDNAComp permits independent compression and
decompression of individual sequences.
The study is organized as follows: The description of a number of other specialized DNA
compression algorithms (Section 2), the SBVRLDNAComp algorithm (Section 3), and
experimental results (Section 4) are followed by concluding remarks of methods and their affect
on compression (Section 5).
2.RELATED WORK
All Genome compression algorithms utilize redundancy within the sequence, but vary greatly in
the way they do so. In general, compression algorithms can be classified into Naive Bit
Manipulation, Dictionary-based, Statistical and Referential Algorithms.
Two best known lossless compression algorithms are LZ77 [12] and PPM [13]. PPM predicts the
probability distribution of the next symbol based on all previously observed symbols. Both
approaches follow sequential processing to encode a string. They test to see if the current
substring has been seen formerly and, if so, encode it by reference to the earlier happening. A
sliding window is maintained for newly observed text. If the text exceeds this window size, it
cannot be used in compression. The application of LZ77 algorithm are gzip, 7-zip etc.
Compression algorithm using exact repeats are begin with BioCompress [14], BioCompress-2
[15], Cfact [16], Off-Line [17], DNASC [18] and B2DNR [19] use the common characteristic of
a sequence.
Initial DNA compression algorithm based on exact repeat proposed by S. Grumbach and F. Tahi
BioCompress search the regularities, such as the presence of palindromes. Although obtained
result is not satisfactory but better than the existing general purpose compression technique.
Extended version of BioCompress is BioCompress-2; later one is based on LZ77. It searches for
longest exact repeats or longest palindrome or reverse complement in already encoded sequences,
then encodes that repeats by repeat length l and the first position p of preceding repeat appeared
i.e. a pair of integers (l, p); when no repetition is found it uses order-2 arithmetic coding. The
difference between Biocompress and Biocompress-2 is the addition of order-2 arithmetic coding
in the later one.
4. International Journal on Computational Science & Applications (IJCSA) Vol.5, No.4, August 2015
76
Cfact is a two phase algorithm executes sequentially. The parsing phase obtains the longest
repeated factors using a suffix tree data structure. The encoding phase compresses first
occurrences of repetitive segments and all non repetitive segments using 2-bit method. The
repeated segment is replaced by a pointer in the form of (pos, len) tuples. But the suffix tree
formation for large data set is not possible in memory.
Off-Line compression algorithm approach is quite similar to Cfact. It uses a suffix tree to find
out the exact repeated substring. But unlike Cfact it use augmented suffix tree which reduces the
time and space complexities to O(n log2
n) and O(n log n) from both O(n2
), where n is the number
of bases in a sequence. It encodes most frequent non-overlapping substrings of a sequence. The
bpc of Off-line is 1.97, which is not better than any DNA specific compression algorithm but it is
a general purpose compression algorithm.
Most of the DNA compression techniques considered frequent occurred of bases i.e. A, C, G and
T. But DNASC have taken one of the infrequent occasion nucleotides N; which can be either A,
C, G or T with equal probability. It is used to compress both DNA and RNA the former one can
be converted to later just by replacing T with U. Bases are compressed by first horizontally and
then vertically. In vertical process it follows LZ style representation of nucleotides with window
size 128 bases i.e. 1024 bits and block size of 6 digits as a combination of 2 i.e. 21 bits using
extended LZ style. Compression of the next block with respect to the current block is done by one
of the 22 ways of redundancy.
Two algorithms B2 and B2DNR considering frequent and all infrequent bases. The rare
characters are {K, M, R, S, W, Y, B, D, H, V, N}. They have formed nucleic acid sequences
fragments of {A, C, G, T} and {K, M, R, S, W, Y, B, D, H, V, N} into 152
=255 combinations and
then converting them into 255 ASCII characters out of 256. For repeat count in the later method
they have used 9 characters from digit ‘1’ to ‘9’. If repeat is greater than 9; then it recounts the
repeats.
Algorithms using approximate repeat detection, start with GenCompress [20], followed by
DNACompress [21], DNAPack [22], GeNML [23] and DNAEcompress [24] show that even
improved results can be achieved by exploiting the approximate nature of the repeated regions in
DNA sequences.
GenCompress based on LZ77. GenCompress uses both approximate repeats and reverse
complements and also uses reverse complements that contain errors. It considers three standard
edit operations Replace, Insert and Delete for approximate matching. There are two versions of
GenCompress: GenCompress-1 uses the Hamming distance i.e. searches for approximate repeats
with replacement or substitution operations only, and GenCompress-2 uses edit distance that
searches for approximate repeats based on the operation insert and delete. This algorithm is able
to detect more properties in DNAsequences, such as mutation and crossover. In addition, they use
arithmetic coding of order 2 if no significant repetition is found i.e. for non-profit encoding by
these properties.
5. International Journal on Computational Science & Applications (IJCSA) Vol.5, No.4, August 2015
77
DNACompress algorithm compresses a sequence in two phases. In the first phase it finds all
approximate repeats with highest score including complemented palindromes using a software
tool called Pattern Hunter [25] and in the second it encode approximate repeat regions and non-
repeat regions. It encodes those approximate repeats that give profits on overall compression.
DNAPack compression algorithm compresses both the repeat segments and the non-repeat
segments. It detects the long approximate repeats and the approximate complementary
palindrome repeats using dynamic programming. Both GenCompress and DNACompress use the
greedy approach for selection of the repeat segments. DNAPack used Hamming distance, i.e. the
approximation is only done by substitution. The non-copied regions are encoded by the best
choice from an Order-2 Arithmetic Coding, Context Tree Weighting Coding (CTW) and naive 2
bits per symbol methods.
The GeNML algorithm split the sequence into fixed size blocks. It encodes the block by
maximum likelihood model. GeNML combines both substitution and statistical styles. An inexact
repeat is encoded using a pointer to an earlier instance of the subsequence followed by
substitution, insertion or deletion operation. In compare with the above three algorithms; it
produced better compression results than using approximate repeat.
DNAEcompress compression algorithm for DNA sequence uses three standard edit operations;
replacement, insertion and delete which are extended to five operations. These are
Complementary replace define by crep (pos), Insert string represented by inss (pos; str), Delete
string i.e. dels (poslen), Exchange expressed as exch (pos1; pos2) and Inversion define by inv
(poslen) respectively. The matched patterns both exact and inexact are encoded by LZ algorithm
and unmatched pattern are order-2 arithmetic encoding. So it is like GenCompress algorithm.
Sequentially lossless compression algorithm such as PPM and the other key family of this
category are the basis of the DNA compression algorithms CDNA [26], CTW+LZ [27], and XM
[28].
The first compression algorithms based on statistical method by detecting the approximate repeat
within DNA sequences is CDNA. It predicts the probability distribution of each nucleotide by
using partial matching of the current context to earlier seen substrings. To measure the inexact
similarity CDNA use Hamming distance.
CTW+LZ is a non-greedy algorithm which searches for exact and approximate repeats; exact and
approximate reverse complements or complementary palindrome using hash table and dynamic
programming. It follows time consuming greedy search to get the longer repeat. LZ77 algorithm
is used to compress long exact or approximate repeats. Short repeats are encoded by order-32
Context Tree Weighting (CTW) and edit operations are encoded by arithmetic coding. It uses
PPM a statistical compression algorithm to predict the probability of next symbol using preceding
symbols.
The best compression algorithm compared to other two recent above algorithms is expert model
(XM). It estimates the probability of recent bases with multiple “experts” but based on PPM. An
example of an expert is an order-k Markov model where k>0. Based on the k preceding symbols
it estimates the probability of recent nucleotides. Once the symbol’s probability distribution is
6. International Journal on Computational Science & Applications (IJCSA) Vol.5, No.4, August 2015
78
determined, it is compressed by using a primary compression algorithm such as arithmetic
coding. Another is a copy expert that gives a probability based on whether the next symbol is part
of a copied region from a particular offset.
There is one important feature that has not observed by all of the above algorithms based on
exact repeat. They have not checked all promising types of exact repeats from very small to
maximum possible and uniform of particular size. Our algorithm overcomes that.
In the following section we clarify SBVRLDNAComp algorithm in details and all the associated
components methods that SBVRLDNAComp invoke; and then experimental results. Comparison
with other algorithm is also enlightened in the result.
3.PROPOSED METHODS
The algorithm SBVRLDNAComp is designed for the compression of DNA. It can also used for
RNA sequence but not for proteins. It encodes a text of characters ∑ = {A, C, G, T or U}. This
algorithm is an optimal solution of four proposed methods compressed any sequence by searching
the repeats in four different ways. It is a sequential compression algorithm. After getting the bits
sequence form Method 1 and Method 2 it compares the bit length and chooses the optimal one
dynamically before going to the subsequent method. All methods allows for access to individual
sequences in the compressed data.
Therefore, Nopt.= Min(N',N'',N''',N''''), Where N', N'', N''' and N'''' are the number of bits by four
proposed method and Nopt., the optimal number of bits before mapping to character. The character
mapped intermediate compressed file is finally compressed by LZ77 [12]. The Fig. 1 shows the
general structure of the SBVRLDNAComp algorithm. Following sub-sections discussed the
components method of SBVRLDNAComp and general algorithm.
Sequence
N', N'', N''' & N''''
Opt. Method Nopt.
Compressed Compression
Sequence (S') Ratio (α)
Fig. 1. SBVRLDNAComp structure
Optimal Method
Searching
DNA Sequence
Apply M1 -> M2 ->
M3 -> M4 Sequentially
Compressed by Optimal Components
Method and LZ77
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3.1.Components methods of SBVRLDNAComp
The compression process by all methods is given below. Decompression methods are just reverse
of compression process. All methods will use a self dynamic reference variable R which stores
the current character i.e. A, C, G or T, a temporary variable T and scanning the sequence
horizontally from left to right and top to bottom. Each module search repeated regions from
different aspect then encode them to their individual logic, however, the non-repeated region and
R are coded just by straightforward 2bit coding rule assuming A/a = 00, C/c = 01, G/g = 10 and
T/t or U/u =11 respectively. Result of each method is a binary stream and the optimal one
mapped to ASCII character from fixed window of size 8. Method 1 (M1) gives profitable
encoding when the segments length is form 4 to 9. Whereas Method 2 (M2) for longest repetition.
Method 3 (M3) act well for 2 successive identical bases throughout the sequence. For uniform
repeats of segment size 3, Method 4 (M4) performed well. In the following sub-sections the
details of each method have been discussed.
3.1.1.Method 1
This method finds the repeated optimal segments length of 1 to 8 characters with respect to the
current reference base R. Control bit 0 i.e. denoted by B0 for repetitive R and bit 1 denoted by B1
for non-repetitive R to distinguish between two, followed by the 2-bits representation of R.
Repeated variable length segment of characters (2 =< length <= 9) are encodes by the relation:
{Sr = r, r = 1, 2, 3, 4, 5, 6, 7, 8}. Three bits coding rules form S1 to S8; for segments of length 2 to
9 in step of 1, are represented by {000, 001, 010, 011, 100, 101, 110 and 111}. The codeword is
B0RS1...r for repetitive segments and B1R for non-repetitive portion.
The total number of bits required to compressed any sequence by M1 is obtained by the following
equation,
N' = n * c0 + ݊′ * c1
Where:
c0 = Number of bits for repetitive bases = (1 + 1 * 2 + 3) = 6,
c1 = Number of bits for non-repetitive bases = (1 + 1 * 2) = 3,
n = Total number of repetition,
݊′= Total number of non-repetition.
Then, N' = 6 * n + 3 * ݊′ = 3(2n+ ݊′)
3.1.2 Method 2
For each repetition with respect to R the assigned bit is 1 and for end of repetitive base or bases
bit is 0. It will search for maximum possible repetition. It uses dynamic programming approach.
So if 4 consecutive bases appeared then the codeword is RB1B1B1B1B0 and for non-repetitive R it
is RB0, where R is encoded by 2-bit coding. Then the following equation determines the number
of bits desired by M2,
N'' = n * c0 + ݊′ * c1
Where:
c0 = (1 * 2 + ri + 1) = 3 + ri,
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80
ri = The number of base repetitions with respect to ith
reference base,
c1 = (1 * 2 + 1) = 3.
Therefore, ܰ′′
= ݊ ∗ 3 + ݎ
ୀଵ
+ ݊′ ∗ 3
= 3݊ + ݎ
ୀଵ
+ 3݊′
= 3݊ + ݎ
ୀଵ
+ 3݊′
= ݎ
ୀଵ
+ 3(݊ + ݊′)
3.1.3.Method 3
Here the assigned bit for each individual repetitive base is 0 and bit 1 for each non-repetitive
base. It follows greedy algorithm. The codeword for identical part is RB0 and RB1 for non-
identical characters. The bits length by M3 follows the equation below,
N''' = n * c0 + ݊′ * c1
Where:
c0 = (1 * 2 + 1) = 3,
c1 = (1 * 2 + 1) = 3,
Hence, N''' = 3n + 3n' = 3(n + n')
3.1.4.Method 4
Divide each sequence into disjoint segments size 3 using divide-and-conquer algorithm. Bit flag 0
for exact three repetitive bases and bit flag 1 for any unmatched base. The codeword of
indistinguishable segments is B0R and for distinguishable segments B1RRR. For the last segments
of length < 3; if any the coding is Rk, where 0 < k < 3. The total number of bits is obtained by the
equation given below,
N'''' = n * c0 + n' * c1+ Rk
Where:
Rk = n'' * 2
n'' = Number of bases (0 < Length < 3),
c0 = (1 * 2 + 1) = 3,
c1 = (3 * 2 + 1) = 7.
So, N'''' = n * c0 + n' * c1+ n'' * 2
= 3n + 7n' + 2n''
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81
3.2.Combined Method
The basic versions of the each method have been discussed in the previous section. The following
section will discuss the combined edition i.e. SBVRLDNAComp algorithm. The space
complexity is O(n), where n is the number of bases in a sequence. SBVRLDNAComp first
calculates the number of bits needed by each component methods sequentially; then compares
between the results obtained N', N'', N''' and N''''; chooses the optimal one and the corresponding
optimal method suited for a particular sequence. It uses substitutional method; form the optimal
generated bit pattern intermediate file. The number of repeated bases or segments r varies from
method to method. For first component r = 8, but second module r is unpredictable, third one
does not have r dependency and for last part r = 3 respectively. An outline is shown in Fig. 2.
Input:
1: A DNA sequence S
2: Three bits coding rules: {S1, S2, S3, S4, S5, S6, S7 andS8}
3: Flag variable v for repeat B0 and non-repeat B1 segments
4: Two bits coding rule (A – 00, C – 01, G -10 and T - 11)
5: Count bits c
Output:
1: Compressed sequence S'
2: ‘α’ bits/bases (bpb)
Algorithm:
1: Store the current character of remaining S into R
2: Check the next character relative to R, store it to T
3: c ← 0
4: while R != null do
5: if R = T then
6: v = B0
7: else
8: v = B1
9: end if
10: The compressed codes are (B0RS1…r) or (B1R); (RB1…rB0) or (RB0); (RB1) or (RB0); (B0R) or
(B1R1…r) or (R1… n'')
11: Update c according to coding Splice
12: end while
13: The total number of bits c and optimal method for a specific sequence is obtained
14: Compressed by optimal method
15: Use LZ77 for final stage compression
Fig. 2. Outline of the SBVRLDNAComp algorithm.
10. International Journal on Computational Science & Applications (IJCSA) Vol.5, No.4, August 2015
82
4. Results
This section evaluates the performance of the SBVRLDNAComp by applying on ten standards
DNA sequences [14], details of these sequences are summarized in Table 1. Although
SBVRLDNAComp performance has been test on 10 standard DNA sequence of small size; this
algorithm can be applied on any DNA or RNA sequence of any size.
In the following Table 2 other concerns of data compression such as compression ratio has been
discussed of different sequences. In best case it takes 1.7009 bpb. The definition of compression
ratio α is the number of characters after compression l divided by the same before compression n.
α = l / n
= l * 8 / n bpb
= (Nopt. / n) bpb
Where,
Nopt. = l * 8
The average bits per base of the proposed algorithm and other existing methods on DNA
sequences are illustrated in Fig. 3, it shows that the SBVRLDNAComp achieves the best
compression ratio among all other algorithms.
The algorithm is implemented by Java6, on a Core 2 Due processor with a 2 GB of RAM and OS
is Fedora 19. The compression ratio considered to be excellent to the best of author’s knowledge.
But it can vary slightly depends on the machine hardware and software.
Tab1e 1. Information of ten standard benchmark DNA sequences.
Sequences name Source File size(KB)
chmpxx Chloroplasts 118.1875
chntxx 152.1914
humdystrop Human 37.8613
humghcsa 64.9365
humhdabcd 57.4844
humhprtb 55.4072
mpomtcg Mitochondria 182.2344
mtpacg 97.9629
hehcmvcg Viruses 223.9785
vaccg 187.2431
Tab1e 2. The compression ratio (bpb) for the ten benchmark DNA sequences after compression.
Sequences
name
Number of
characters before
compression (N)
Number of
characters after
compression (L)
Nopt. Compression
ratio (α)
chmpxx 121024 25746 205971 1.7019
11. International Journal on Computational Science & Applications (IJCSA) Vol.5, No.4, August 2015
83
chntxx 155,844 33549 268395 1.7222
humdystrop 38,770 8277 66215 1.7079
humghcsa 66,495 14367 114937 1.7285
humhdabcd 58,864 12604 100828 1.7129
humhprtb 56,737 12063 96504 1.7009
mpomtcg 186,608 40221 321768 1.7243
mtpacg 100,314 21694 173553 1.7301
hehcmvcg 229,354 49036 392287 1.7104
vaccg 191,737 40993 327947 1.7104
Fig. 3. Average bits per base for DNA compression algorithms
5. Discussions
It is not likely that exactly one compression strategy will be optimal for diverse DNA sequence.
Different experimental results are going to show various bases distributions whereby one
compression strategy can be more efficient than another. We have proposed four new
compressions methods specialized on searching redundant substrings on highly repetitive
sequences. The first one is particularly significant for medium repeat value of r = 9; whereas the
second one is relevant for large r values r > 9, third one is applicable for small r value r = 2 and
the final one stands out on extremely uniform repetitive collections with the small segments of
size r = 3. Depending on the repetition of bases one of the four modules gives extremely good
quality result. So for any type of exact base repeat SBVRLDNABase surpass the other standard
techniques.
2.092
1.784
1.743 1.739 1.725 1.715 1.714 1.714
1.5
1.6
1.7
1.8
1.9
2
2.1
2.2
Average
Compression
Ratio Per…
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84
Acknowledgements
This work is supported in part by the Bioinformatics Centre of Bose Institute, Computer Science
and Engineering Department of University of Calcutta and Academy of Technology under
WBUT. We thank Dr. Zumur Ghosh and Arijita Sarkar.
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Authors
Subhankar Roy is currently an Assistant Professor in the Department of Computer
Science & Engineering, Academy of Technology, India. He has received his B. Tech and
M. Tech degree in Computer Science and Engineering both from University of Calcutta,
India in 2010 and 2012 respectively. His research interests are in the areas of
Bioinformatics and compression techniques. He is a member of IEEE.
Akash Bhagat is presently working as Faculty at Mahendra Educational Pvt. Ltd.,
Asansol, India. He has received his MCA degree from Academy of Technology, India in
2015. His research interests include Bioinformatics and DNA data compression
techniques.
Kumari Annapurna Sharma is presently working as Project Engineer at WIPRO-Project
SHELL, India. He has received his MCA degree from Academy of Technology, India in
2015. His research interests include Bioinformatics and DNA data compression
techniques.
Sunirmal Khatua is currently an Assistant Professor in the Department of Computer
Science and Engineering, University of Calcutta, India. He has received the M.E. degree in
Computer Science and Engineering from Jadavpur University, India in 2006. He is also
pursuing his PhD in Cloud Computing from Jadavpur University. His research interests
are in the areas of Distributed Computing, Cloud Computing, Bioinformatics, and Sensor
Networks. He is a member of IEEE.