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Transcriptome analysis_ Learn library preparation and data analysis from scratch_
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[Music] hello and welcome to explore bio rna c chord transcriptome sequencing is a powerful technique to characterize and quantify gene expression in my previous video i explained that transcriptome is the entire set of rna expressed at a specified time in a particular biological sample its importance in identifying and studying gene expression which is useful to study development disease in response to stresses i also mentioned about two major techniques of transcriptome analysis namely microarray and rna-seq and their applications if you are new to transcriptome and its analysis you should watch my introductory video the link is provided in the description below this is the second video in the transcription series in the first part of the video i will cover the basic steps involved in transcriptome library preparation for sequencing in the second part i will cover the basic workflow for transcriptome data analysis i hope the video will be useful for beginners who have little or no idea about transcriptome and willing to learn more about it it would also help the researchers who are planning or currently dealing with some kind of transcriptome work i request you to stay tuned and watch the complete series of videos on transcriptome at last i will mention some of the important things to remember and consider before you plan a transcript of experiments so let's begin with the basic steps involved in transcript on library preparation here i will focus on one of the popular illumina mrna enrichment library preparation there are separate protocols for other small rna and microorganic library preparations too the first and the foremost thing you need to start is a high quality of rna extracted from biological samples to be studied along with appropriate controls for comparison next you proceed for mrna enrichment if your target is protein coding rnas else this step can be omitted here poly a tail containing rna is captured using magnetic beads with oligodt attached to it next comes the cdna library preparation which involves series of steps the mrna is fragmented appropriately using chemical or heat treatment to shorter fragments of usually 100 to 300 base pairs that can be sequenced note that full length mrnas are not sequenced unless you are using oxford nanopore sequencing chemistry the fragmented rna is now reverse transcribed to double stranded cdna using reverse transcriptase after end repair and addition of adenine net 3 prime end the adapters which are short double stranded oligonucleotides are ligated at both the ends of cdna fragments these adapters serves as the site for primer binding to facilitate clonal amplification in pcr in the next step the adapter ligated at cdns is termed as cdna library which represents the complete set of rnas expressed in the sample and are ready to be sequenced multiple samples are ligated with different adapters so that they can be pulled together for sequencing in a single run on a machine this is known as multiplexing after the sequencing is over the data generated can be demultiplexed based on the different adapters used cdna libraries can be sequenced from one or both the ends which is termed as single end or pair end sequencing using suitable ngs platform the amount of sequence data generated in the form of short reads depend upon the sequencing platform and the need of experiment usually 10 to 30 million reads for each sample are appropriate for analysis coming on to the second part which is the basic workflow for transcriptome analysis once the sequencing run is complete you will get sequence data in the form of raw reads the read files are usually in fastq format which contain the information about the sequence and base quality qc or quality check of the sequence rate is the first step of transcriptome analysis generally done using tool like fast qc raw reads generated after transcriptome sequencing using next generation sequencing platform such as illumina or roche is processed to remove low quality reads adapter sequences used during transcription library preparation sometimes read and streaming is also required as the basis sequence at the end of sequencing rung may be of lower quality some of the commonly used tools for raw read filtering are ngs qc and fast p so next comes is the read alignment or mapping the short high quality reads are then aligned or mapped back to the reference genome or transcriptome if available this is known as reference based assembly if reference is not available for example in case of non-modal organisms de novo or fresh assembly is done in case of genome guided assembly spliced aligner tools and for transcriptome guided or deno assembly unspliced aligners are used examples of routinely used aligners are bowtie and top head short reads are meaningless to us unless they are assembled to larger and more complete sequence termed as transcripts or context that actually represents mrna from which they are derived the assembly is done based on sequence overlaps in the reads to form a must longer sequence in case of reference guided assembly reads are first aligned to the reference genome transcriptome and then the overlapping reads are assembled together in case of denom assembly the reads are assembled into transcripts without reference most popular tools for transcriptome assembly are trinity oss clc genomics workbench and curve link sometimes assembly is done with multiple tools before finding the best one transcripts or the contigs are further clustered using tools like cd heat est to reduce the redundancy once the assembly is done based on the alignment with the conserved orthologous genes in related lineage the completeness of the assembly may be checked example of one such tool is busco to quantify the expression of individual transcript the mapping file generated during read alignment is used as input gene level or transcript level abundance is determined using different tools such as rsm solvent or cufflink the abundance or expression level of transcripts is represented as normalized read counts that are mapped to the transcript major ways to represent normalized read counts are tpm fpkm rpk or cpm to compare the change in expression in treatment versus control samples differential expression analysis or dge is done various programs such as hr desec curvedif performs differential expression analysis between samples after normalizing the abundance data p-value and fdr tells how significant is the differential expression results and should be or should not be considered for further analysis later using real-time pcr the transcriptome expression is validated to learn more about it i highly recommend you to watch my video on real-time pcr and how it is done to predict the function of transcripts or contigs after assembly they are assigned functions based on the sequence homology against known protein in the databases such as nr swiss prod and tear using blast search i have made a separate tutorial on how to perform standalone ncbi blast on your computer you may watch it later subsequently geo classification and pathway analysis may be done so these are the major steps involved in the transcript of sequencing and analysis coming on to the last part of the video about the things to consider for planning a transcriptome experiment following question should be asked is the aim is to identify a quantified transcript sequence what are the biological samples controls and number of replicates you are going to take how much sequencing data needs to be generated what sequencing platform you will use will it be reference based or de novo this will determine the sequencing depth you will need is mrn enrichment required sequential should be single end or pair handed do you have budget for sequencing analysis and is access to high-end computing available so that's all for the today's video you can do a lot more things once you have the transcriptome data for example you can study gene enrichment pathway enrichment classify the genes based on their ontologies cag analysis identify orthologous groups coexpression analysis and generate a heat map develop protein protein interaction network to identify interacting partners and lot more to make most use of transcription data generated some of these may be the part of my subsequent videos in my next video i will be covering the important terms such as reads transcripts annotation blast e-value bit score read count dge n50 faster format rpk fp tpm cpm and others which are routinely used in transcriptome analysis if you like the information do share with your friends comment about what new you want to learn subscribe and check my playlist to stay tuned with other informative videos and finally thanks for watching [Music] you
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