Search results “Methods for microarray analysis”
Gene expression analysis
This molecular genetics lecture explains about gene expression analysis techniques using DNA chip technology and microarrays. For more information, log on to- http://shomusbiology.weebly.com/ Download the study materials here- http://shomusbiology.weebly.com/bio-materials.html Question source - www.indiabix.com
Views: 95014 Shomu's Biology
Methods for analysis of gene expression Microarray 1
Subject:Zoology Paper: Molecular cell biology
Views: 482 Vidya-mitra
DNA Microarray Methodology
This animation demonstrates how DNA microarray experiments are performed. One common use of microarrays is to determine which genes are activated and which are repressed when two populations of cells are compared. Every gene is measured simultaneously. As an example, we’ll compare what happens to yeast genes when cells are grown in aerobic versus anaerobic conditions. Written by: A. Malcolm Campbell, Ph.D. Copyright © 2017 NC Community Colleges and BioNetwork
Views: 72655 BioNetwork
DNA Microarray synthesis
This DNA technology lecture explains the synthesis and application of DNA microarray or DNA chip technology For more information, log on to- http://shomusbiology.weebly.com/ Download the study materials here- http://shomusbiology.weebly.com/bio-materials.html Question source - www.indiabix.com
Views: 106311 Shomu's Biology
Hybridization (microarray) | Biomolecules | MCAT | Khan Academy
Visit us (http://www.khanacademy.org/science/healthcare-and-medicine) for health and medicine content or (http://www.khanacademy.org/test-prep/mcat) for MCAT related content. These videos do not provide medical advice and are for informational purposes only. The videos are not intended to be a substitute for professional medical advice, diagnosis or treatment. Always seek the advice of a qualified health provider with any questions you may have regarding a medical condition. Never disregard professional medical advice or delay in seeking it because of something you have read or seen in any Khan Academy video. Created by Ronald Sahyouni. Watch the next lesson: https://www.khanacademy.org/test-prep/mcat/biomolecules/dna-technology/v/expressing-cloned-genes?utm_source=YT&utm_medium=Desc&utm_campaign=mcat Missed the previous lesson? https://www.khanacademy.org/test-prep/mcat/biomolecules/dna-technology/v/dna-cloning-and-recombinant-dna?utm_source=YT&utm_medium=Desc&utm_campaign=mcat MCAT on Khan Academy: Go ahead and practice some passage-based questions! About Khan Academy: Khan Academy offers practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. We tackle math, science, computer programming, history, art history, economics, and more. Our math missions guide learners from kindergarten to calculus using state-of-the-art, adaptive technology that identifies strengths and learning gaps. We've also partnered with institutions like NASA, The Museum of Modern Art, The California Academy of Sciences, and MIT to offer specialized content. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to Khan Academy’s MCAT channel: https://www.youtube.com/channel/UCDkK5wqSuwDlJ3_nl3rgdiQ?sub_confirmation=1 Subscribe to Khan Academy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 229846 khanacademymedicine
Validating differential gene expression: Methods, Sarah Diermeier, Ph.D.
Dr. Diermeier, post-doctoral fellow, CSHL gives a lecture on "Validating RNA-Seq data by qRT-PCR"
Views: 5738 DNA Learning Center
SAGE podcast 2018
Serial Analysis of Gene Expression
Views: 5820 David Carroll
Gene Expression Data Analysis
Subject:Biophysics Paper: Bioinformatics
Views: 825 Vidya-mitra
DNA Microarray [HD Animation]
DNA Microarray Animation #Please → Like, comment, share and subscribe 👍🏻❤️
Views: 14163 McGraw-Hill Animations
DNA microarrays
A short film about DNA microarrays, and how they are used to show dynamic gene expression levels.
Views: 925062 Proneural
Microarrays vs RNA Sequencing
We are often asked the question: “Should I use RNA Sequencing or Microarrays for my gene expression study?” The answer is of course… “It depends”. Money will almost always be factor and hence you will need to strike a balance between cost and performance. The particular goals of your project will also heavily influence which platform makes more sense. In this video we’ll discuss in which situations it makes sense to use which method and why in some cases, both methods are appropriate and can even complement each other.
Views: 65699 LC Sciences
Gene Expression Data Microarray Lab Part 3
This is a three part lab tutorial exercise touching on microarray data analysis using SAM 3.05and R 2.8.1 on Microsoft Excel and a brief overview of GEPAS tools.
Views: 2888 USD Bioinformatics
MIT CompBio Lecture 06 - Gene Expression Analysis: Clustering and Classification
MIT Computational Biology: Genomes, Networks, Evolution, Health Prof. Manolis Kellis http://compbio.mit.edu/6.047/ Fall 2018 Lecture 6- Gene expression analysis: Clustering and Classification 1. Introduction to gene expression analysis - Technology: microarrays vs. RNAseq. Resulting data matrices - Supervised (Clustering) vs. unsupervised (classification) learning 2. K-means clustering (clustering by partitioning) - Algorithmic formulation: Update rule, optimality criterion. Fuzzy k-means. - Machine learning formulation: Generative models, Expectation Maximization. 3. Hierarchical Clustering (clustering by agglomeration) - Basic algorithm, Distance measures. Evaluating clustering results 4. Naïve Bayes classification (generative approach to classification) - Discriminant function: class priors, and class-conditional distributions - Training and testing, Combine mult features, Classification in practice 5. (optional) Support Vector Machines (discriminative approach) - SVM formulation, Margin maximization, Finding the support vectors - Non-linear discrimination, Kernel functions, SVMs in practice Slides for Lecture 6: https://stellar.mit.edu/S/course/6/fa18/6.047/courseMaterial/topics/topic2/lectureNotes/Lecture06_ExpressionClust---ication4_thin.pptx/Lecture06_ExpressionClust---Classification_6up.pdf
Views: 1095 Manolis Kellis
Microarray Data Analysis : Part I
Microarray Data Analysis: Part I
Views: 2638 NOC16 July-Sep BT06
Microarray Analysis
Views: 0 thomas
DNA Microarray
Views: 84657 Faisal FrezeePrime
Podcast #6: What is microarray analysis?
In the sixth of eight podcasts, Dr. Jannine Cody of the Chromosome 18 Registry & Research Society explains microarray analysis.
Views: 8382 Chromosome18Registry
Zen and the Art of Microarray Analysis (2): Bioinformatics to Guide Analysis and Justify Methods
This is part of a lecture I gave for a 10-day intensive, hands-on workshop in the summer of 2010 designed to provide a comprehensive view of proteomics, genomics, and bioinformatics to researchers from across the country. The topic of this talk deals with using the biological 'connectivity' in a given list of genes to identify key signaling pathways and interesting relationships in your study . This workshop was supported by the NHLBI T15 HL086386 educational grant.
Views: 414 Michael Edwards
Methods for analysis of gene expression Microarray 2
Subject:Zoology Paper: Molecular cell biology
Views: 158 Vidya-mitra
StatQuest: A gentle introduction to RNA-seq
RNA-seq may sound mysterious, but it's not. Here's go over the main ideas behind how it's done and how the data is analyzed. NOTE: If you want to learn about ChIP-seq, check out the StatQuest: https://youtu.be/nkWGmaYRues For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider a StatQuest t-shirt or sweatshirt... https://teespring.com/stores/statquest ...or buying one or two of my songs (or go large and get a whole album!) https://joshuastarmer.bandcamp.com/ ...or just donating to StatQuest! https://www.paypal.me/statquest
Hillary reviews chromosome microarray analysis. This is a genetic test often recommended for children with global developmental delay, birth defects, or multiple unexplained serious medical issues.
important clustering methods used in microarray data analysis
Subscribe today and give the gift of knowledge to yourself or a friend important clustering methods used in microarray data analysis Important clustering methods used in microarray data analysis. Steve Horvath Human Genetics and Biostatistics UCLA. Contents. Multidimensional scaling plots Related to principal component analysis k-means clustering hierarchical clustering. Introduction to clustering. Slideshow 2976735 by lynn show1 : Important clustering methods used in microarray data analysis show2 : Important clustering methods used in microarray data analysis show3 : Contents show4 : Contents show5 : Introduction to clustering show6 : Introduction to clustering show7 : Mds plot of clusters show8 : Mds plot of clusters show9 : Mds plot of clusters1 show10 : Mds plot of clusters1 show11 : 2 references for clustering show12 : 2 references for clustering show13 : Introduction to clustering1 show14 : Introduction to clustering1 show15 : Proximity matrices are the input to most clustering algorithms show16 : Proximity matrices are the input to most clustering algorithms show17 : Different intergroup dissimilarities show18 : Different intergroup dissimilarities show19 : Agglomerative clustering hierarchical clustering and dendrograms show20 : Agglomerative clustering hierarchical clustering and dendrograms show21 : Hierarchical clustering plot show22 : Hierarchical clustering plot show23 : Agglomerative clustering show24 : Agglomerative clustering show25 : Comparing different linkage methods show26 : Comparing different linkage methods show27 : Dendrogram show28 : Dendrogram show29 : Comments on dendrograms show30 : Comments on dendrograms show31 : Figure 1
Views: 42 Magalyn Melgarejo
analysis of microarray data
My MS defence
Views: 216 Sayan Mukhopadhyay
Microarray Method for Genetic Testing
Microarray Method for Genetic Testing
Views: 136734 bentripp
Novel Methods for Glycan Analysis - Seq It Out #18
Learn more at http://www.thermofisher.com/glycanassure. Did you know that analyzing glycan profiles is crucial for any scientist working with glycoproteins, but that the process can get really frustrating when analyzing large numbers of samples? While some good methods for glycan analysis have been used routinely over the years, thanks to new methods, we have better solutions available now to analyze glycans. Liquid chromatography is most commonly used to separate and quantify glycans. It provides high resolution of glycan separation and great data. However, to obtain great data for 96 samples, the overall process from sample prep to analysis takes about 2-3 days. Capillary electrophoresis, also known as CE, is another technique that can give high data quality and resolution; similar to liquid chromatography. But currently used CE methods are not robust and the instruments tend to have frail capillaries. Current CE methods can take 2-3 days to prepare, process and analyze 96 samples and therefore don’t solve the low throughput problem presented by liquid chromatography. Microchip based systems have solved the low throughput problem, but only at the expense of data quality and resolution. Because of a short separation window, microchip based methods cannot provide high resolution of separation. This means that you have to choose multiple platforms in order to get both high throughput AND high resolution. Think you need to choose between high throughput and high resolution? Well let’s talk about a new solution in the market that might offer you the best of both worlds. Thermo Fisher’s Applied Biosystems recently introduced GlycanAssure, an end-to-end solution for glycan analysis and quantitation. With this CE based GlycanAssure system, you can prepare, process and analyze 96 samples in one workday. Moreover, it is the only system that gives you both, high throughput and high resolution. Its magnetic bead-based sample prep method uses highly sensitive dyes that enable use of very small amounts of sample. The 3500 Genetic Analyzer, which is the CE instrument in the GlycanAssure system, has been used for years in forensic and clinical research labs for genetic analysis. This speaks volumes about the instrument’s robustness. Contrary to running one sample at a time sequentially, its capillary array consists of 8 and 24 capillaries enabling all samples to run parallel to each other AT THE SAME TIME. These capillaries enable consistent results over long periods of time and across multiple capillaries. So run-to-run, day-to-day, and capillary-to-capillary, the system is designed to produce reliable data. The ability to analyze glycans in parallel offers a system that combines very high throughput and high data quality with FAST and accurate results. GlycanAssure has simple and intuitive software that provides data trends and the ability to analyze data for 96 samples within one hour. The data acquisition dashboard provides easy access to instrument status and setup. Using the step-by-step workflow, you can create your favorite experiments and directly report or export your data GlycanAssure minimizes the need to use several platforms to address high throughput and high data quality by giving you the option of doing both with ONE system. If you want to learn more about GlycanAssure, please visit our website at thermofisher.com/glycanassure. And remember, when in doubt, just Seq It Out
Zen and the Art of Microarray Analysis (1): What You Need to Consider Before Running an Analysis
This is part of a lecture I gave for a 10-day intensive, hands-on workshop in the summer of 2010 designed to provide a comprehensive view of proteomics, genomics, and bioinformatics to investigators from across the country. The topic of this talk concerns techniques to assure the quality of microarray data and to determine if any biological differences exist. This workshop was supported by the NHLBI T15 HL086386 educational grant.
Views: 665 Michael Edwards
Retrieve and analyze a gene expression data set from NCBI GEO in R
R script is available at: https://github.com/hongqin/RCompBio/blob/master/ncbigeo/ncbiGEO2012Nov14-demo-youtube.R SBIO386, Spelman College, Fall 2012
Views: 25238 Hong Qin
microRNA Discovery & Profiling
microRNA Discovery & Analysis MicroRNAs are a class of small non-coding RNAs that are negative regulators of gene expression in eukaryotic organisms. Some common characteristics of microRNAs include: short length (17-25nt), predictable stem and loop structure of precursors, highly conserved nature, existence in high copy numbers, and their expression is tissue (and developmental stage) specific. Additionally, their mechanism of action is far reaching and complex. Each individual microRNA may control many genes and it is estimated that microRNAs regulate the expression of up to 1/3 of all human genes. The study of microRNA is growing rapidly as researchers discover new microRNAs and uncover the importance of these small regulatory elements linked to a wide range of biological functions. Contributing to the rapid rate of new discoveries is the development of several new advanced technologies such as high-throughput sequencing and custom microfluidic arrays. The increasing availability of these technologies makes the discovery of new sequences in lesser understood organisms now routinely possible. The miRBase sequence database is the primary public repository for newly discovered microRNAs and the number of miRBase entries has grown rapidly to almost 30,000 in the latest version. LC Sciences offers a complete suite of microRNA analysis services giving you access to these latest technologies that are enabling discoveries in this exciting field of biology. Our services include: microRNA sequencing for discovery and digital expression profiling applications, microRNA detection microarrays for differential expression profiling and confirmation of the newly discovered microRNAs, and degradome sequencing for microRNA target identification in plants and some animal species. microRNA Sequencing Service http://www.lcsciences.com/applications/transcriptomics/mirna-profiling/mirna-sequencing/ LC Sciences provides a comprehensive sequencing service utilizing Illumina’s high-throughput sequencing technology which enables comprehensive, highly sensitive and specific discovery and profiling of all forms of small RNAs in your sample. The sequencing strategy provides a comprehensive view of the transcriptome because it is not dependent on any prior sequence knowledge. Expression profiling is possible for any species, even those with little known genome information and sequencing is the method of choice for discovery of novel microRNAs. Because each individual transcript in your sample is sequenced, the technology is well suited to detecting low abundance transcripts and differentiating biologically critical isoforms. Unlike microarrays, which measure probe intensities, sequencing quantifies discreet, digital read counts aligned to a particular sequence. Therefore digital transcript expression analysis is achievable. microRNA Profiling Microarray Service http://www.lcsciences.com/applications/transcriptomics/mirna-profiling/mirna/ For routine expression profiling, microarrays are a robust, reliable method proven over decades of use. For expression profiling of known microRNAs, LC Sciences comprehensive microarray service provides a high-throughput, fast, and cost-effective solution. Our microarrays are built on the innovative µParaflo® microfluidic technology platform. Optimized RNA hybridization probes ensure uniform hybridization with enhanced specificity and sensitivity. Flexible on-chip synthesis means we have standard microarrays available for every organism containing the latest probe content from the miRBase sequence database. We routinely synthesize custom arrays for profiling non-model species, validation of computational predictions, and sequencing data follow-up. Degradome Sequencing Service http://www.lcsciences.com/applications/transcriptomics/degradome-sequencing/ Degradome sequencing provides a comprehensive means of analyzing patterns of RNA degradation. Sequencing of the 5’ ends of degraded RNA products allows identification of over-represented 5’ ends. Because microRNAs can cause endonucleolytic cleavage of mRNA by extensive and often perfect complementarity to mRNAs, matching cleavage sites to known microRNA sequences links microRNAs to their targets. LC Sciences degradome sequencing service has confirmed many known and revealed novel plant microRNA targets.
Views: 16949 LC Sciences
Microarray based SNP genotyping
many SNP genotyping methods are available and this video explains how the SNP genotyping can be done using microarray platform.
Views: 63944 Genomics Lab
Next Generation Sequencing for Gene Expression Analysis - Seq It Out #11
Submit your question at http://www.thermofisher.com/ask Do you perform qPCR as a routine part of your research? Are you interested in gene expression, but haven’t yet looked into how next generation sequencing or NGS can help you out? Let me see if I can help answer some of those questions Next generation sequencing, also known as NGS, provides opportunities to profile gene expression in a wide range of settings. Whether you are looking to discover potentially novel genes or transcripts, or study how certain genes respond to stimuli, NGS can offer a great complement to your existing methodology. Let’s say you’re interested in studying a particular set of genes’ response to a certain stimulus, and currently you’re using a set of TaqMan probes to accomplish this. This works well enough, but you’re relying on a prior knowledge of this particular set of genes’ involvement in the affected pathway. Are there other genes or RNA transcripts that are playing a part in the response? Let’s think a bit differently and talk about targeted RNA sequencing. Using this approach, you can target a known set of genes or non-coding RNA’s and measure their expression levels, much like a qPCR/Taqman approach. This method can allow you to target a small number of custom-selected genes with custom Ion AmpliSeq RNA panels, or all the way up to over 20,000 genes in a single reaction when using Ion AmpliSeq Transcriptome for instance. These methods also allow for low input starting material and work well with degraded RNA, such as that sourced from Formalin Fixed paraffin embedded tissue also refered to as FFPE. When combined with the ability to multiplex many samples and sequence them together in a single run, this approach provides the ability to economically analyze a large number of targets within a large number of samples. For targeted RNA sequencing, analysis is much more straightforward. Data is analyzed using a provided software plugin available in Torrent Suite software, and gene expression values are provided as well as clustering of genes that exhibit different patterns of expression. The expression data can be used in a variety of standard software packages for further statistical analysis if desired. For many, this is the best of both worlds – being able to use low amounts of input material, having a comprehensive set of genes or transcripts to target economically, and have a simple analysis path to allow them to move right into the interpretation and understanding of their data. I hope this video was helpful regarding Gene Expression applications using next gen sequencing and I am sure you’ll have more questions. Submit your question at thermofisher.com/ask and subscribe to our channel to see more videos like this. And remember, when in doubt, just Seq It Out
DNA microarray
Animation Source: http://learn.genetics.utah.edu/content/labs/microarray/ All the credit goes to University of Utah, department of genetics. copyright, UTAH. For more information, log on to- http://shomusbiology.weebly.com/ Download the study materials here- http://shomusbiology.weebly.com/bio-materials.html A DNA microarray (also commonly known as DNA chip or biochip) is a collection of microscopic DNA spots attached to a solid surface. Scientists use DNA microarrays to measure the expression levels of large numbers of genes simultaneously or to genotype multiple regions of a genome. Each DNA spot contains picomoles (10−12 moles) of a specific DNA sequence, known as probes (or reporters or oligos). These can be a short section of a gene or other DNA element that are used to hybridize a cDNA or cRNA (also called anti-sense RNA) sample (called target) under high-stringency conditions. Probe-target hybridization is usually detected and quantified by detection of fluorophore-, silver-, or chemiluminescence-labeled targets to determine relative abundance of nucleic acid sequences in the target. Source of the article published in description is Wikipedia. I am sharing their material. © by original content developers of Wikipedia. Link- http://en.wikipedia.org/wiki/Main_Page
Views: 140571 Shomu's Biology
micro array techniques with clear expalnation
Views: 135 surengnani
6G - How SNP-typing works
6G.mp4 This is Lecture 6G of the free online course Useful Genetics Part 1. All of the lectures are on YouTube in the Useful Genetics library. Register for the full course here: https://www.edx.org/course/useful-genetics-part-1-how-genes-shape-ubcx-usegen-1x
Views: 34735 Useful Genetics
Microarray Gene Expression
An animated overview of the microarray process for gene expression profiling.
Views: 71577 uhnmac
RT-PCR for Gene Expression
RT-PCR for Gene Expression
Views: 48320 Matthew Bremgartner
Analyzing Quantitative PCR Data
Relative and absolute methods of qPCR analysis. Created for an assignment for BIOC3001: Molecular Biology at the University of Western Australia. ****SCRIPT**** [I know it's a bit fast] qPCR or quantitative real-time PCR… ….is simply classic PCR monitored using fluorescent dyes or probes. qPCR is accurate, reliable and extremely sensitive, it can even detect a SINGLE copy of a specific transcript. qPCR is commonly coupled to reverse transcription to measure gene expression. No wonder it is so important for molecular diagnostics, life sciences, agriculture, and medicine. Firstly, let's go over the NUTS and BOLTS of qPCR. For this you use a fluorescent dye which binds to the DNA. As qPCR progresses, the fluorescent signal increases. Ideally the signal should double with every cycle, which is then plotted. Because there are few template strands to start with, initially there’s a faint signal. Eventually, usually after 15 cycles, the signal rises above the background noise and can be detected. We call this the THRESHOLD CYCLE, Ct, the point from which all quantitative data analysis begins. But how do you analyse qPCR data? You can either use an absolute quantification method, with a standard curve, OR a relative method, using one or more reference genes to standardize and compare the differences in Ct values between two treatments. The absolute standard curve method determines ORIGINAL DNA concentration by comparing the Ct value of the sample of interest with a standard curve. To create the standard curve, you need to make DNA samples of different KNOWN concentrations. After doing PCR on these, you will see different PCR plots for each standard ….. and unsurprisingly they have different Ct values. The GREATER the concentration of the original DNA sample, the SMALLER the Ct value. So if you plot ORIGINAL DNA concentration against the Ct values. You will have a standard curve like this….. Now let’s say the PCR plot of your unknown DNA sample is somewhere here….. ...which corresponds to this Ct value on the standard curve here…. Using the standard curve you can figure out the log concentration of your DNA sample to be x. As this is in log scale, you can simply calculate your sample DNA concentration to be 10 to the power of x. Absolute analysis is suitable when you want to determine the ACTUAL transcript copy number, that is the level of gene expression. On the other hand, Relative quantification is used when you want to COMPARE the difference in gene expression BETWEEN two treatments, for example light or dark treated Arabadopsis thaliana. This is done using one or more reference genes, such as actin, which are expressed at the SAME level for both treatments. You then perform qPCR on both your samples and the reference genes, find out the DIFFERENCE between the two Cts values, delta Ct, in EACH treatment. Now the RATIO of the two delta Cts …[pause a bit] . tells you how much gene expression has changed. For instance, in the dark treatment, the Ct value of your reference gene is at THIS level, the Ct value of your target gene is THIS Level. So you have this delta Ct which is the difference in Cts in the first treatment. in the dark treatment, the Ct value of your reference gene is STILL at THIS level, but the Ct value of your target gene may become only this much. So the ratio of the two Ct values is.. delta Ct(dark treatment) divided by delta Ct(light treament) equals one third ….showing the delta Ct has DECREASED by a factor of 3, which means that gene expression of the target gene is GREATER in the dark treated sample. This is how relative quantification using a reference gene helps detect change in the expression of your target gene. In conclusion, there are two ways to quantify transcripts using qPCR: absolute quantification using a standard curve, and relative quantification using a reference gene. The method used depends on whether you want to determine the ACTUAL number of transcripts or the RELATIVE change in gene expression.
Views: 210037 TARDIStennant
See what you've been missing: Explore RNA-Seq for Gene Expression research
RNA-seq using NGS (next-generation) sequencing enables you to look beyond what you see with microarray research. Learn in this 3 minute video how the Transcriptome sequencing works and overcomes a number of limitations inherent in microarray research to sequence all transcripts in a sample, including interesting transcripts that may or may not be included in a fixed microarray design. You'll see how the Ion Proton System combined with Ambion RNA library construction & purification kits and our data analysis solution is ideally suited to Transcriptome sequencing.
Beyond Gene Lists: Biological Analysis Strategies for Gene Expression Data
Gene expression research is rapidly evolving beyond basic statistical or data-driven analysis, where the final result is a list of significantly changed genes. Researchers are now faced with an increased need for stronger biological interpretation of omics data in order to successfully publish and guide next steps in the lab. This webinar will demonstrate why a biologically based analysis approach is emerging as a crucial strategy for validated, mechanistically supported biological conclusions. Using published data, the webinar panelists will present biological analysis strategies and tools that can help you get a better understanding of your gene expression data, such as understanding the biological processes, molecular interactions, or pathways that may be causing observed effects. Examples will highlight ways in which researchers with microarray data reached a more complete biological understanding of their results and were able to identify interesting genes for followup, whether by qPCR validation or another method. You will see how this type of approach provides a rich biological context that allows the researcher to generate unique insights into molecular and chemical interactions, cellular phenotypes, chemical interactions, and fundamental disease processes of specific systems.
Views: 2596 GENNews
RNA sequencing
This lecture explains about the RNA sequencing process and the methods or RNA splicing is explained. It also states the use of RNA sequencing in molecular biology. RNA Sequencing, often known as entire Transcriptome Shotgun Sequencing, is a science that uses the capabilities of subsequent-iteration sequencing to disclose a photo of RNA presence and wide variety from a genome at a given second in time. RNA sequencing can give us a snapshot of RNA profile from a dynamic transcriptome. RNA sequencing is a very important tool in transcriptomics. For more information, log on to- http://www.shomusbiology.com/ Get Shomu's Biology DVD set here- http://www.shomusbiology.com/dvd-store/ Download the study materials here- http://shomusbiology.com/bio-materials.html Remember Shomu’s Biology is created to spread the knowledge of life science and biology by sharing all this free biology lectures video and animation presented by Suman Bhattacharjee in YouTube. All these tutorials are brought to you for free. Please subscribe to our channel so that we can grow together. You can check for any of the following services from Shomu’s Biology- Buy Shomu’s Biology lecture DVD set- www.shomusbiology.com/dvd-store Shomu’s Biology assignment services – www.shomusbiology.com/assignment -help Join Online coaching for CSIR NET exam – www.shomusbiology.com/net-coaching We are social. Find us on different sites here- Our Website – www.shomusbiology.com Facebook page- https://www.facebook.com/ShomusBiology/ Twitter - https://twitter.com/shomusbiology SlideShare- www.slideshare.net/shomusbiology Google plus- https://plus.google.com/113648584982732129198 LinkedIn - https://www.linkedin.com/in/suman-bhattacharjee-2a051661 Youtube- https://www.youtube.com/user/TheFunsuman Thank you for watching the video lecture on rna sequencing mechanism and the role of rna sequencing
Views: 57359 Shomu's Biology
Biclustering to analyze gene expression data - Video abstract: 32622
Video abstract of methodology paper "A novel biclustering approach with iterative optimization to analyze gene expression data" published in open access journal Advances and Applications in Bioinformatics and Chemistry by Sawannee Sutheeworapong, Motonori Ota, Hiroyuki Ohta, and Kengo Kinoshita. Objective: With the dramatic increase in microarray data, biclustering has become a promising tool for gene expression analysis. Biclustering has been proven to be superior over clustering in identifying multifunctional genes and searching for co-expressed genes under a few specific conditions; that is, a subgroup of all conditions. Biclustering based on a genetic algorithm (GA) has shown better performance than greedy algorithms, but the overlap state for biclusters must be treated more systematically. Results: We developed a new biclustering algorithm (binary-iterative genetic algorithm [BIGA]), based on an iterative GA, by introducing a novel, ternary-digit chromosome encoding function. BIGA searches for a set of biclusters by iterative binary divisions that allow the overlap state to be explicitly considered. In addition, the average of the Pearson's correlation coefficient was employed to measure the relationship of genes within a bicluster, instead of the mean square residual, the popular classical index. As compared to the six existing algorithms, BIGA found highly correlated biclusters, with large gene coverage and reasonable gene overlap. The gene ontology (GO) enrichment showed that most of the biclusters are significant, with at least one GO term over represented. Conclusion: BIGA is a powerful tool to analyze large amounts of gene expression data, and will facilitate the elucidation of the underlying functional mechanisms in living organisms. Read this methodology and sign up to receive Advances and Applications in Bioinformatics and Chemistry here: http://www.dovepress.com/articles.php?article_id=10949
Views: 1423 Dove Medical Press
Glycan Microarray Analysis using GenePix Pro
The following video describes how to use the GenePix Pro software to analyze glycan microarray data. To learn how to collect the glycan binding data, check out our previous video: https://youtu.be/tuVGhXW5XyY Please subscribe to our channel for more videos in the future. For the complete protocol please visit: https://ncfg.hms.harvard.edu/protocol... If you have any queries or concerns please contact us using the form at: https://ncfg.hms.harvard.edu/contact-us Credits: Experiment performed by: Richard H. Barnes II Narration by: Richard H. Barnes II Produced by: Akul Y. Mehta Produced at: National Center for Functional Glycomics, Beth Israel Deaconess Medical Center, Harvard Medical School.