2^nd World Summit on Transcriptomics September 26-27, 2022 Singapore City, Singapore

biochemistry and enzymology encompass many areas of biology including molecular biology, cell biology, pharmaceutical research and development, food science, plant biology, etc. Applications include protein purification for research, manufacturing for biopharmaceutical development, molecular cloning, agriculture, and X-ray crystallography, among others. Tools and supplies for protein biochemistry include instruments such as mass spectrometers and chromatography systems, cloning, purification and enrichment kits, western blot systems and consumables. The tools needed for many protein biochemistry applications can be made "from scratch" or kits and other useful thing products can be used. Economy and time available are important considerations in choosing the right type of products. Proteins also vary greatly in size and profusion. The tools used to manipulate a large, very abundant protein will be different from those needed for very small, rare proteins.

Epigenetics is the study of how cells control gene activity without altering DNA sequence. "Epi-" means over or above in Greek, and "epigenetics" describes factors beyond the genetic code. Epigenetic changes are DNA modifications that regulate the activation or deactivation of genes. These modifications are attached to the DNA and do not change the sequence of the building blocks of DNA. In a cell's complete set of DNA (genome), all changes that regulate the activity (expression) of genes are known as the epigenome.

Simply put, bioinformatics is the science of storing, retrieving, and analyzing large amounts of biological information. It is a highly multidisciplinary field involving many different types of specialists, including biologists, molecular life scientists, computer scientists, and mathematicians.

The term bioinformatics was coined by Paulien Hogeweg and Ben Hesper to describe "the study of trigonometric processes in anatomical systems" and it found early use when the first biological sequence data began to be shared. While the original analytical methods are still fundamental to many large-scale experiments in the molecular life sciences, today bioinformatics is seen as a much broader discipline, encompassing image modeling and analysis. in addition to the classical methods used for the comparison of linear sequences or three-dimensional structures.

Metabolomics is a collection of powerful tools for phenotype analysis, both by hypothesis generation and by hypothesis testing. Building on the strengths of “omics technologies that preceded it, proteomic uniquely includes analytical technologies that can provide characteristic models via fingerprinting, terrific dimension of targeted constructive metabolism via analysis of pool, the relative dimension of large portions of the metabolome using metabolite profiling, and the tracing of the biochemical fate of individual metabolites through a metabolic system via flux analysis. Each of these technologies is supported by the two most commonly used and powerful techniques currently available for biotransformation: mass chromatography and NMR.

Metabolomics techniques based on mass spectrometry are the most sensitive for the simultaneous analysis of a large number of compounds. Although limited in quantification capabilities without appropriate labeled standards, the amount of information available in a single LC-MS or GC-MS experiment can provide detailed insights into patterns of metabolite change throughout the metabolic network.

NMR metabolomics complements mass spectrometry. It is limited in terms of sensitivity, but is only able to elucidate molecular structure. An important additional characteristic of NMR is that it is quantitative, capable of providing absolute levels of detected compounds when appropriate techniques are used.

Interpretation and derivation of context from complex metabolomics datasets is very difficult and represents a major area of ​​research. Yet great strides are being made in integrating metabolome data with genomics and proteomics. Metabolomics offers the promise that in the future, biochemical analysis of the entire path from genotype to phenotype will be measured and explored for new insights in biology and medicine.

Next-generation sequencing (NGS) is a term that broadly encompasses several related technologies that enable massively parallel or deep sequencing coverage for a selected region or the entire genome of an organism. Essential in the discipline of genomics-based research, sequencing technologies have been around for decades. However, continued advances in NGS or massively parallel DNA and RNA sequencing technologies have provided researchers with increased coverage of genome-wide sequencing and data analysis tools while rapidly reducing costs. The applications of NGS extend beyond whole genome analysis, as they have important implications for recent advances in basic genomics and disease research.

Genome sequencing (GS) covers the entire genome, including non-coding regions. Compared to ES, GS is generally PCR-free in the library preparation step, therefore is more uniform in its coverage and is better able to detect larger deletions or duplications (up to about 2 kb). The read depth for GS is lower (30–50×), so it is less susceptible to mosaicism. GS is also able to detect structural variations as well as repeated tandem expansions, if the appropriate software is used. Generally, the diagnostic rate of GS over ES is around 10-15% (Palmer et al., 2021), mainly due to the detection of structural variants and larger deletions and duplications.

Due to the large amount of variation in DNA sequences detectable by GS, data analysis and data storage are more difficult. Non-coding regions are also generally more variable than exon regions, complicating the difficulty. For this reason, it is even more important to include parental samples as comparators in GS.

Transcriptome analysis experiments allow researchers to characterize transcriptional activity (coding and non-coding), focus on a subset of target genes and relevant substantiation, or profile thousands of genetic code at once to create an overall picture of cellular function. Gene expression analysis studies can provide actively expressed genes and transcripts under various conditions.

Next-generation sequencing (NGS) capabilities have shifted the scope of transcriptomics from interrogating a few genes at a time to profiling genome-wide gene expression levels in a single experiment. Learn how NGS-based RNA sequencing (RNA-Seq) compares to other common gene expression and transcript profiling methods, gene expression microarrays, and qRT-PCR. Learn how to analyze gene expression and identify novel transcripts using RNA-Seq.

Molecular and cellular technologies have evolved into the era of single-cell genomics, which allows the simultaneous measurement of thousands of genes in thousands of "single" cells from a single specimen. . Advances in microfluidic and molecular cloning technologies have revolutionized our understanding of complex biological processes by improving resolution at the single cell level. Single-cell sequencing technology has also evolved over time, from processing dozens of cells simultaneously to millions of cells. New approaches to well-established models are being explored at the single-cell level in medical science, and new rare cell types are being reported, one after another.

The Human Cell Atlas (HCA) project represents an organized international collaborative effort to develop a comprehensive reference dataset covering all cell types in the human body1. The Functional Annotation of the Mammalian Genome (FANTOM)2 and Genotype-Tissue Expression (GTEx)3 consortia represent earlier global efforts to profile the transcriptomes of various human cell types. These public transcriptome data on several major organs can be used as a reference in biological studies, as they provide single-cell genomic data for mice and humans. In particular, the HCA introduced the concept of modification and equity in data collection and analysis, thereby promoting single-cell genomics. Ando et al. discussed the introduction of single-cell genomic consortia that consider regional environments to develop the universal reference dataset of human cells4

Molecular and cellular technologies have evolved into the era of single-cell genomics, which allows the simultaneous measurement of thousands of genes in thousands of "single" cells from a single specimen. . Advances in microfluidic and molecular cloning technologies have revolutionized our understanding of complex biological processes by improving resolution at the single cell level. Single-cell sequencing technology has also evolved over time, from processing dozens of cells simultaneously to millions of cells. New approaches to well-established models are being explored at the single-cell level in medical science, and new rare cell types are being reported, one after another.

The Human Cell Atlas (HCA) project represents an organized international collaborative effort to develop a comprehensive reference dataset covering all cell types in the human body1. The Functional Annotation of the Mammalian Genome (FANTOM)2 and Genotype-Tissue Expression (GTEx)3 consortia represent earlier global efforts to profile the transcriptomes of various human cell types. These public transcriptome data on several major organs can be used as a reference in biological studies, as they provide single-cell genomic data for mice and humans. In particular, the HCA introduced the concept of modification and equity in data collection and analysis, thereby promoting single-cell genomics. Ando et al. discussed the introduction of single-cell genomic consortia that consider regional environments to develop the universal reference dataset of human cells4

Biostatistics and computational biology involve the development and application of data-analytical and theoretical methods, mathematical modeling techniques, and computer simulation for the study of biological, behavioral, and social systems. Biostatics and computational biology consist of expertise in the mathematical sciences applied to biology, including statistics, probability, biomathematics, and computer science. His interdisciplinary research provides expert advice on experimental design, analysis of large-scale datasets, data collection, data warehousing, data integration, causal inference, data design. clinical trials, longitudinal data analysis, modeling and analysis of data derived from biological and social networks.

A review of the transcriptome and gene expression review is the first and most fundamental of the points to address. While going in and out of the topic, it is important to understand the transcriptome as key players in gene expression. For this, we need to know the basics of learning how the focal doctrine works. This can be accomplished by increasing legitimate information about how mRNA, tRNA, and rRNA work. Investigative analyzes of quality expression can focus on a subset of relevant target qualities. The quality zone and the relative separations between qualities on a chromosome can be resolved through sequence mapping. Indeed, even without the reference genome, the transcriptome can be made using again the transcriptome joining technique. All around, a large number of universities and foundations transmit research on gene expression and investigation of the transcriptome. University of Leeds, Case Western Reserve University, Arizona State University, Tempe are among them. Foundations like The Genome Institute - St. Louis, Missouri and NIH - National Human Genome Research Institute are working hard towards a similar approach where scientists have a database of over 40,000 quality successions that they can use to this reason.

The DNA chip is one of the most recent advances used in cancer research; it provides assistance in the pharmacological approach to treat various diseases including oral lesions. Microarray helps to analyze a large amount of samples that have been recorded previously or new samples; it even helps to test the incidence of a particular marker in tumors. Until recently, the use of DNA chips in dentistry was very limited, but in the future, as the technology becomes affordable, its use may increase. Here we discuss the different techniques and applications of microarrays.

RNA-seq (RNA sequencing) is a technique to examine the amount and sequences of RNA in a sample using next-generation sequencing (NGS). It analyzes the transcriptome, indicating which of the genes encoded in our DNA are turned on or off and to what extent. Here we look at why RNA-seq is useful, how the technique works, and the basic protocol commonly used today.

Proteins are large molecules that can perform many different tasks. They can facilitate chemical reactions (eg enzymes), provide structural support (eg cytoskeleton), transmit signals from the cell surface (eg membrane receptors) and much more. But where do they come from?

The genes in our DNA are similar to the recipes used to make proteins. But since the recipes are coded using potassium-nitrate bases (ATCG), they must first be translated. Many proteins work together on this translation task. The strands of the DNA double helix must first give way for the targeted gene to be accessible. The proteins then produce an identical copy of the targeted DNA sequence: a messenger RNA.

This copy of the recipe, now transcribed as messenger RNA, is then sent outside the cell nucleus since proteins are made elsewhere in the cell. From there, the ribosomes, small particles present in large numbers around the nucleus, will serve as leaders by reading the recipe to make the protein. Amino acids are the basic ingredients that go into the protein recipe and ribosomes use the blueprint provided by messenger RNA to put amino acids in the correct order and form a long chain. But proteins in this linear form are not yet ready. To function, it must fold back on itself in an origami fashion. It is then that it changes from a single chain to a complex three-dimensional structure.